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What Is an Observational Study? | Guide & Examples

Published on 5 April 2022 by Tegan George . Revised on 20 March 2023.

An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects, and no control and treatment groups .

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Observational studies are generally used in hard science, medical, and social science fields. This is often due to ethical or practical concerns that prevent the researcher from conducting a traditional experiment . However, the lack of control and treatment groups means that forming inferences is difficult, and there is a risk of confounding variables impacting your analysis.

Table of contents

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs experiment, frequently asked questions.

There are many types of observation, and it can be challenging to tell the difference between them. Here are some of the most common types to help you choose the best one for your observational study.

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There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies.

Cohort studies

Cohort studies are more longitudinal in nature, as they follow a group of participants over a period of time. Members of the cohort are selected because of a shared characteristic, such as smoking, and they are often observed over a period of years.

Case–control studies

Case–control studies bring together two groups, a case study group and a control group . The case study group has a particular attribute while the control group does not. The two groups are then compared, to see if the case group exhibits a particular characteristic more than the control group.

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyse a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analysing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for ethical or practical reasons, or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

• Are you determining what you want to observe beforehand, or going in open-minded?
• Is there another research method that would make sense in tandem with an observational study?
• If yes, make sure you conduct a covert observation.
• If not, think about whether observing from afar or actively participating in your observation is a better fit.
• How can you preempt confounding variables that could impact your analysis?
• You could observe the children playing at the playground in a naturalistic observation.
• You could spend a month at a day care in your town conducting participant observation, immersing yourself in the day-to-day life of the children.
• You could conduct covert observation behind a wall or glass, where the children can’t see you.

Overall, it is crucial to stay organised. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

• Plan ahead: If you’re interested in day cares, you’ll need to call a few in your area to plan a visit. They may not all allow observation, or consent from parents may be needed, so give yourself enough time to set everything up.
• Determine your note-taking method: Observational studies often rely on note-taking because other methods, like video or audio recording, run the risk of changing participant behavior.
• Get informed consent from your participants (or their parents) if you want to record:  Ultimately, even though it may make your analysis easier, the challenges posed by recording participants often make pen-and-paper a better choice.

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or ‘lurking’ variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyse your data

After you finish your observation, immediately record your initial thoughts and impressions, as well as follow-up questions or any issues you perceived during the observation. If you audio- or video-recorded your observations, you can transcribe them.

Your analysis can take an inductive or deductive approach :

• If you conducted your observations in a more open-ended way, an inductive approach allows your data to determine your themes.
• If you had specific hypotheses prior to conducting your observations, a deductive approach analyses whether your data confirm those themes or ideas you had previously.

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis.

Step 6: Discuss avenues for future research

Observational studies are generally exploratory in nature, and they often aren’t strong enough to yield standalone conclusions due to their very high susceptibility to observer bias and confounding variables. For this reason, observational studies can only show association, not causation .

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.

• Observational studies can provide information about difficult-to-analyse topics in a low-cost, efficient manner.
• They allow you to study subjects that cannot be randomised safely, efficiently, or ethically .
• They are often quite straightforward to conduct, since you just observe participant behavior as it happens or utilise preexisting data.
• They’re often invaluable in informing later, larger-scale clinical trials or experiments.

Disadvantages

• Observational studies struggle to stand on their own as a reliable research method. There is a high risk of observer bias and undetected confounding variables.
• They lack conclusive results, typically are not externally valid or generalisable, and can usually only form a basis for further research.
• They cannot make statements about the safety or efficacy of the intervention or treatment they study, only observe reactions to it. Therefore, they offer less satisfying results than other methods.

The key difference between observational studies and experiments is that a properly conducted observational study will never attempt to influence responses, while experimental designs by definition have some sort of treatment condition applied to a portion of participants.

However, there may be times when it’s impossible, dangerous, or impractical to influence the behavior of your participants. This can be the case in medical studies, where it is unethical or cruel to withhold potentially life-saving intervention, or in longitudinal analyses where you don’t have the ability to follow your group over the course of their lifetime.

An observational study may be the right fit for your research if random assignment of participants to control and treatment groups is impossible or highly difficult. However, the issues observational studies raise in terms of validity , confounding variables, and conclusiveness can mean that an experiment is more reliable.

If you’re able to randomise your participants safely and your research question is definitely causal in nature, consider using an experiment.

An observational study could be a good fit for your research if your research question is based on things you observe. If you have ethical, logistical, or practical concerns that make an experimental design challenging, consider an observational study. Remember that in an observational study, it is critical that there be no interference or manipulation of the research subjects. Since it’s not an experiment, there are no control or treatment groups either.

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

Exploratory research explores the main aspects of a new or barely researched question.

Explanatory research explains the causes and effects of an already widely researched question.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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The purpose of a field report in the social sciences is to describe the deliberate observation of people, places, and/or events and to analyze what has been observed in order to identify and categorize common themes in relation to the research problem underpinning the study. The content represents the researcher's interpretation of meaning found in data that has been gathered during one or more observational events.

Flick, Uwe. The SAGE Handbook of Qualitative Data Collection . London: SAGE Publications, 2018; Lofland, John, David Snow, Leon Anderson, and Lyn H. Lofland. Analyzing Social Settings: A Guide to Qualitative Observation and Analysis. Long Grove, IL: Waveland Press, 2022; Baker, Lynda. "Observation: A Complex Research Method." Library Trends 55 (Summer 2006): 171-189.; Kellehear, Allan. The Unobtrusive Researcher: A Guide to Methods . New York: Routledge, 2020.

How to Approach Writing a Field Report

How to Begin

Field reports are most often assigned in disciplines of the applied social sciences [e.g., social work, anthropology, gerontology, criminal justice, education, law, the health care services] where it is important to build a bridge of relevancy between the theoretical concepts learned in the classroom and the practice of actually doing the work you are being taught to do. Field reports are also common in certain science disciplines [e.g., geology] but these reports are organized differently and serve a different purpose than what is described below.

Professors will assign a field report with the intention of improving your understanding of key theoretical concepts by applying methods of careful and structured observation of, and reflection about, people, places, or phenomena existing in their natural settings. Field reports facilitate the development of data collection techniques and observation skills and they help you to understand how theory applies to real world situations. Field reports are also an opportunity to obtain evidence through methods of observing professional practice that contribute to or challenge existing theories.

We are all observers of people, their interactions, places, and events; however, your responsibility when writing a field report is to conduct research based on data generated by the act of designing a specific study, deliberate observation, synthesis of key findings, and interpretation of their meaning.

When writing a field report you need to:

• Systematically observe and accurately record the varying aspects of a situation . Always approach your field study with a detailed protocol about what you will observe, where you should conduct your observations, and the method by which you will collect and record your data.
• Continuously analyze your observations . Always look for the meaning underlying the actions you observe. Ask yourself: What's going on here? What does this observed activity mean? What else does this relate to? Note that this is an on-going process of reflection and analysis taking place for the duration of your field research.
• Keep the report’s aims in mind while you are observing . Recording what you observe should not be done randomly or haphazardly; you must be focused and pay attention to details. Enter the observation site [i.e., "field"] with a clear plan about what you are intending to observe and record in relation to the research problem while, at the same time, being prepared to adapt to changing circumstances as they may arise.
• Consciously observe, record, and analyze what you hear and see in the context of a theoretical framework . This is what separates data gatherings from reporting. The theoretical framework guiding your field research should determine what, when, and how you observe and act as the foundation from which you interpret your findings in relation to the underlying assumptions embedded in the theoretical framework .

Techniques to Record Your Observations Although there is no limit to the type of data gathering techniques you can use, these are the most frequently used methods:

Note Taking This is the most common and easiest method of recording your observations. Tips for taking notes include: organizing some shorthand symbols beforehand so that recording basic or repeated actions does not impede your ability to observe, using many small paragraphs, which reflect changes in activities, who is talking, etc., and, leaving space on the page so you can write down additional thoughts and ideas about what’s being observed, any theoretical insights, and notes to yourself that are set aside for further investigation. See drop-down tab for additional information about note-taking.

Photography With the advent of smart phones, an almost unlimited number of high quality photographs can be taken of the objects, events, and people observed during a field study. Photographs can help capture an important moment in time as well as document details about the space where your observation takes place. Taking a photograph can save you time in documenting the details of a space that would otherwise require extensive note taking. However, be aware that flash photography could undermine your ability to observe unobtrusively so assess the lighting in your observation space; if it's too dark, you may need to rely on taking notes. Also, you should reject the idea that photographs represent some sort of "window into the world" because this assumption creates the risk of over-interpreting what they show. As with any product of data gathering, you are the sole instrument of interpretation and meaning-making, not the object itself. Video and Audio Recordings Video or audio recording your observations has the positive effect of giving you an unfiltered record of the observation event. It also facilitates repeated analysis of your observations. This can be particularly helpful as you gather additional information or insights during your research. However, these techniques have the negative effect of increasing how intrusive you are as an observer and will often not be practical or even allowed under certain circumstances [e.g., interaction between a doctor and a patient] and in certain organizational settings [e.g., a courtroom]. Illustrations/Drawings This does not refer to an artistic endeavor but, rather, refers to the possible need, for example, to draw a map of the observation setting or illustrating objects in relation to people's behavior. This can also take the form of rough tables, charts, or graphs documenting the frequency and type of activities observed. These can be subsequently placed in a more readable format when you write your field report. To save time, draft a table [i.e., columns and rows] on a separate piece of paper before an observation if you know you will be entering data in that way.

NOTE:   You may consider using a laptop or other electronic device to record your notes as you observe, but keep in mind the possibility that the clicking of keys while you type or noises from your device can be obtrusive, whereas writing your notes on paper is relatively quiet and unobtrusive. Always assess your presence in the setting where you're gathering the data so as to minimize your impact on the subject or phenomenon being studied.

ANOTHER NOTE:   Techniques of deliberate observation and data gathering are not innate skills; they are skills that must be learned and practiced in order to achieve proficiency. Before your first observation, practice the technique you plan to use in a setting similar to your study site [e.g., take notes about how people choose to enter checkout lines at a grocery store if your research involves examining the choice patterns of unrelated people forced to queue in busy social settings]. When the act of data gathering counts, you'll be glad you practiced beforehand.

YET ANOTHER NOTE:   An issue rarely discussed in the literature about conducting field research is whether you should move around the study site while observing or remaining situated in one place. Moving around can be intrusive, but it facilitates observing people's behavior from multiple vectors. However, if you remain in one place throughout the observation [or during each observation], you will eventually blend into the background and diminish the chance of unintentionally influencing people's behavior. If the site has a complex set of interactions or interdependent activities [e.g., a play ground], consider moving around; if the study site is relatively fixed [e.g., a classroom], then consider staying in one place while observing.

Examples of Things to Document While Observing

• Physical setting . The characteristics of an occupied space and the human use of the place where the observation(s) are being conducted.
• Objects and material culture . This refers to the presence, placement, and arrangement of objects that impact the behavior or actions of those being observed. If applicable, describe the cultural artifacts representing the beliefs [i.e., the values, ideas, attitudes, and assumptions] of the individuals you are observing [e.g., the choice of particular types of clothing in the observation of family gatherings during culturally specific holidays].
• Use of language . Don't just observe but  listen to what is being said, how is it being said, and the tone of conversations among participants.
• Behavior cycles . This refers to documenting when and who performs what behavior or task and how often they occur. Record at which stage this behavior is occurring within the setting.
• The order in which events unfold . Note sequential patterns of behavior or the moment when actions or events take place and their significance. Also, be prepared to note moments that diverge from these sequential patterns of behavior or actions.
• Physical characteristics of subjects. If relevant, document personal characteristics of individuals being observed. Note that, unless this data can be verified in interviews or from documentary evidence, you should only focus on characteristics that can be clearly observed [e.g., clothing, physical appearance, body language].
• Expressive body movements . This would include things like body posture or facial expressions. Note that it may be relevant to also assess whether expressive body movements support or contradict the language used in conversation [e.g., detecting sarcasm].

Brief notes about all of these examples contextualize your observations; however, your observation notes will be guided primarily by your theoretical framework, keeping in mind that your observations will feed into and potentially modify or alter these frameworks.

Sampling Techniques

Sampling refers to the process used to select a portion of the population for study . Qualitative research, of which observation is one method of data gathering, is generally based on non-probability and purposive sampling rather than probability or random approaches characteristic of quantitatively-driven studies. Sampling in observational research is flexible and often continues until no new themes emerge from the data, a point referred to as data saturation.

All sampling decisions are made for the explicit purpose of obtaining the richest possible source of information to answer the research questions. Decisions about sampling assumes you know what you want to observe, what behaviors are important to record, and what research problem you are addressing before you begin the study. These questions determine what sampling technique you should use, so be sure you have adequately answered them before selecting a sampling method.

Ways to sample when conducting an observation include:

• Ad Libitum Sampling -- this approach is not that different from what people do at the zoo; they observe whatever seems interesting at the moment. There is no organized system of recording the observations; you just note whatever seems relevant at the time. The advantage of this method is that you are often able to observe relatively rare or unusual behaviors that might be missed by more deliberately designed sampling methods. This method is also useful for obtaining preliminary observations that can be used to develop your final field study. Problems using this method include the possibility of inherent bias toward conspicuous behaviors or individuals, thereby missing mundane or repeated patterns of behavior, and that you may miss brief interactions in social settings.
• Behavior Sampling -- this involves watching the entire group of subjects and recording each occurrence of a specific behavior of interest and with reference to which individuals were involved. The method is useful in recording rare behaviors missed by other sampling methods and is often used in conjunction with focal or scan methods [see below]. However, sampling can be biased towards particular conspicuous behaviors.
• Continuous Recording -- provides a faithful record of behavior including frequencies, durations, and latencies [the time that elapses between a stimulus and the response to it]. This is a very demanding method because you are trying to record everything within the setting and, thus, measuring reliability may be sacrificed. In addition, durations and latencies are only reliable if subjects remain present throughout the collection of data. However, this method facilitates analyzing sequences of behaviors and ensures obtaining a wealth of data about the observation site and the people within it. The use of audio or video recording is most useful with this type of sampling.
• Focal Sampling -- this involves observing one individual for a specified amount of time and recording all instances of that individual's behavior. Usually you have a set of predetermined categories or types of behaviors that you are interested in observing [e.g., when a teacher walks around the classroom] and you keep track of the duration of those behaviors. This approach doesn't tend to bias one behavior over another and provides significant detail about a individual's behavior. However, with this method, you likely have to conduct a lot of focal samples before you have a good idea about how group members interact. It can also be difficult within certain settings to keep one individual in sight for the entire period of the observation without being intrusive.
• Instantaneous Sampling -- this is where observation sessions are divided into short intervals divided by sample points. At each sample point the observer records if predetermined behaviors of interest are taking place. This method is not effective for recording discrete events of short duration and, frequently, observers will want to record novel behaviors that occur slightly before or after the point of sampling, creating a sampling error. Though not exact, this method does give you an idea of durations and is relatively easy to do. It is also good for recording behavior patterns occurring at a specific instant, such as, movement or body positions.
• One-Zero Sampling -- this is very similar to instantaneous sampling, only the observer records if the behaviors of interest have occurred at any time during an interval instead of at the instant of the sampling point. The method is useful for capturing data on behavior patterns that start and stop repeatedly and rapidly, but that last only for a brief period of time. The disadvantage of this approach is that you get a dimensionless score for an entire recording session, so you only get one one data point for each recording session.
• Scan Sampling -- this method involves taking a census of the entire observed group at predetermined time periods and recording what each individual is doing at that moment. This is useful for obtaining group behavioral data and allows for data that are evenly representative across individuals and periods of time. On the other hand, this method may be biased towards more conspicuous behaviors and you may miss a lot of what is going on between observations, especially rare or unusual behaviors. It is also difficult to record more than a few individuals in a group setting without missing what each individual is doing at each predetermined moment in time [e.g., children sitting at a table during lunch at school]. The use of audio or video recording is useful with this type of sampling.

Alderks, Peter. Data Collection. Psychology 330 Course Documents. Animal Behavior Lab. University of Washington; Emerson, Robert M. Contemporary Field Research: Perspectives and Formulations . 2nd ed. Prospect Heights, IL: Waveland Press, 2001; Emerson, Robert M. et al. “Participant Observation and Fieldnotes.” In Handbook of Ethnography . Paul Atkinson et al., eds. (Thousand Oaks, CA: Sage, 2001), 352-368; Emerson, Robert M. et al. Writing Ethnographic Fieldnotes . 2nd ed. Chicago, IL: University of Chicago Press, 2011; Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University; Hazel, Spencer. "The Paradox from Within: Research Participants Doing-Being-Observed." Qualitative Research 16 (August 2016): 446-457; Pace, Tonio. Writing Field Reports. Scribd Online Library; Presser, Jon and Dona Schwartz. “Photographs within the Sociological Research Process.” In Image-based Research: A Sourcebook for Qualitative Researchers . Jon Prosser, editor (London: Falmer Press, 1998), pp. 115-130; Pyrczak, Fred and Randall R. Bruce. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences . 5th ed. Glendale, CA: Pyrczak Publishing, 2005; Report Writing. UniLearning. University of Wollongong, Australia; Wolfinger, Nicholas H. "On Writing Fieldnotes: Collection Strategies and Background Expectancies.” Qualitative Research 2 (April 2002): 85-95; Writing Reports. Anonymous. The Higher Education Academy.

Structure and Writing Style

How you choose to format your field report is determined by the research problem, the theoretical framework that is driving your analysis, the observations that you make, and/or specific guidelines established by your professor. Since field reports do not have a standard format, it is worthwhile to determine from your professor what the preferred structure and organization should be before you begin to write. Note that field reports should be written in the past tense. With this in mind, most field reports in the social sciences include the following elements:

I.  Introduction The introduction should describe the research problem, the specific objectives of your research, and the important theories or concepts underpinning your field study. The introduction should describe the nature of the organization or setting where you are conducting the observation, what type of observations you have conducted, what your focus was, when you observed, and the methods you used for collecting the data. Collectively, this descriptive information should support reasons why you chose the observation site and the people or events within it. You should also include a review of pertinent literature related to the research problem, particularly if similar methods were used in prior studies. Conclude your introduction with a statement about how the rest of the paper is organized.

II.  Description of Activities

Your readers only knowledge and understanding of what happened will come from the description section of your report because they were not witnesses to the situation, people, or events that you are writing about. Given this, it is crucial that you provide sufficient details to place the analysis that will follow into proper context; don't make the mistake of providing a description without context. The description section of a field report is similar to a well written piece of journalism. Therefore, a useful approach to systematically describing the varying aspects of an observed situation is to answer the "Five W’s of Investigative Reporting." As Dubbels notes [p. 19], these are:

• What -- describe what you observed. Note the temporal, physical, and social boundaries you imposed to limit the observations you made. What were your general impressions of the situation you were observing. For example, as a student teacher, what is your impression of the application of iPads as a learning device in a history class; as a cultural anthropologist, what is your impression of women's participation in a Native American religious ritual?
• Where -- provide background information about the setting of your observation and, if necessary, note important material objects that are present that help contextualize the observation [e.g., arrangement of computers in relation to student engagement with the teacher].
• When -- record factual data about the day and the beginning and ending time of each observation. Note that it may also be necessary to include background information or key events which impact upon the situation you were observing [e.g., observing the ability of teachers to re-engage students after coming back from an unannounced fire drill].
• Who -- note background and demographic information about the individuals being observed e.g., age, gender, ethnicity, and/or any other variables relevant to your study]. Record who is doing what and saying what, as well as, who is not doing or saying what. If relevant, be sure to record who was missing from the observation.
• Why -- why were you doing this? Describe the reasons for selecting particular situations to observe. Note why something happened. Also note why you may have included or excluded certain information.

III.  Interpretation and Analysis

Always place the analysis and interpretations of your field observations within the larger context of the theoretical assumptions and issues you described in the introduction. Part of your responsibility in analyzing the data is to determine which observations are worthy of comment and interpretation, and which observations are more general in nature. It is your theoretical framework that allows you to make these decisions. You need to demonstrate to the reader that you are conducting the field work through the eyes of an informed viewer and from the perspective of a casual observer.

Here are some questions to ask yourself when analyzing your observations:

• What is the meaning of what you have observed?
• Why do you think what you observed happened? What evidence do you have for your reasoning?
• What events or behaviors were typical or widespread? If appropriate, what was unusual or out of the ordinary? How were they distributed among categories of people?
• Do you see any connections or patterns in what you observed?
• Why did the people you observed proceed with an action in the way that they did? What are the implications of this?
• Did the stated or implicit objectives of what you were observing match what was achieved?
• What were the relative merits of the behaviors you observed?
• What were the strengths and weaknesses of the observations you recorded?
• Do you see connections between what you observed and the findings of similar studies identified from your review of the literature?
• How do your observations fit into the larger context of professional practice? In what ways have your observations possibly changed or affirmed your perceptions of professional practice?
• Have you learned anything from what you observed?

NOTE:   Only base your interpretations on what you have actually observed. Do not speculate or manipulate your observational data to fit into your study's theoretical framework.

IV.  Conclusion and Recommendations

The conclusion should briefly recap of the entire study, reiterating the importance or significance of your observations. Avoid including any new information. You should also state any recommendations you may have based on the results of your study. Be sure to describe any unanticipated problems you encountered and note the limitations of your study. The conclusion should not be more than two or three paragraphs.

V.  Appendix

This is where you would place information that is not essential to explaining your findings, but that supports your analysis [especially repetitive or lengthy information], that validates your conclusions, or that contextualizes a related point that helps the reader understand the overall report. Examples of information that could be included in an appendix are figures/tables/charts/graphs of results, statistics, pictures, maps, drawings, or, if applicable, transcripts of interviews. There is no limit to what can be included in the appendix or its format [e.g., a DVD recording of the observation site], provided that it is relevant to the study's purpose and reference is made to it in the report. If information is placed in more than one appendix ["appendices"], the order in which they are organized is dictated by the order they were first mentioned in the text of the report.

VI.  References

List all sources that you consulted and obtained information from while writing your field report. Note that field reports generally do not include further readings or an extended bibliography. However, consult with your professor concerning what your list of sources should be included and be sure to write them in the preferred citation style of your discipline or is preferred by your professor [i.e., APA, Chicago, MLA, etc.].

Alderks, Peter. Data Collection. Psychology 330 Course Documents. Animal Behavior Lab. University of Washington; Dubbels, Brock R. Exploring the Cognitive, Social, Cultural, and Psychological Aspects of Gaming and Simulations . Hershey, PA: IGI Global, 2018; Emerson, Robert M. Contemporary Field Research: Perspectives and Formulations . 2nd ed. Prospect Heights, IL: Waveland Press, 2001; Emerson, Robert M. et al. “Participant Observation and Fieldnotes.” In Handbook of Ethnography . Paul Atkinson et al., eds. (Thousand Oaks, CA: Sage, 2001), 352-368; Emerson, Robert M. et al. Writing Ethnographic Fieldnotes . 2nd ed. Chicago, IL: University of Chicago Press, 2011; Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University; Pace, Tonio. Writing Field Reports. Scribd Online Library; Pyrczak, Fred and Randall R. Bruce. Writing Empirical Research Reports: A Basic Guide for Students of the Social and Behavioral Sciences . 5th ed. Glendale, CA: Pyrczak Publishing, 2005; Report Writing. UniLearning. University of Wollongong, Australia; Wolfinger, Nicholas H. "On Writing Fieldnotes: Collection Strategies and Background Expectancies.” Qualitative Research 2 (April 2002): 85-95; Writing Reports. Anonymous. The Higher Education Academy.

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Observational studies and their utility for practice

Julia fm gilmartin-thomas.

2 Research Department of Practice and Policy, University College London, School of Pharmacy, London

1 Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne

Ingrid Hopper

Randomised controlled clinical trials are the best source of evidence for assessing the efficacy of drugs. Observational studies provide critical descriptive data and information on long-term efficacy and safety that clinical trials cannot provide, at generally much less expense.

Observational studies include case reports and case series, ecological studies, cross-sectional studies, case-control studies and cohort studies. New and ongoing developments in data and analytical technology, such as data linkage and propensity score matching, offer a promising future for observational studies. However, no study design or statistical method can account for confounders and bias in the way that randomised controlled trials can.

Clinical registries are gaining importance as a method to monitor and improve the quality of care in Australia. Although registries are a form of cohort study, clinical trials can be incorporated into them to exploit the routine follow-up of patients to capture relevant outcomes.

Introduction

Observational studies involve the study of participants without any forced change to their circumstances, that is, without any intervention. 1 Although the participants’ behaviour may change under observation, the intent of observational studies is to investigate the ‘natural’ state of risk factors, diseases or outcomes. For drug therapy, a group of people taking the drug can be compared to people not taking the drug.

The main types of observational studies used in health research, their purpose and main strengths and limitations are shown in the Table . 2 - 8

Their purpose may be descriptive, analytical or both.

• Descriptive studies are primarily designed to describe the characteristics of a studied population.
• Analytical studies seek to address cause-and-effect questions.

Case reports and case series

Case reports and case series are strictly speaking not studies. However, they serve a useful role in describing new or notable events in detail. These events often warrant further formal investigation. Examples include reports of unexpected benefits or adverse events, such as a case report describing the use of high-dose quetiapine in treatment-resistant schizophrenia after intolerance to clozapine developed 9 and a case report of a medication error involving lookalike packaging. 10

Ecological studies

Ecological studies are based on analysis of aggregated data at group levels (for example populations), and do not involve data on individuals. These data can be analysed descriptively, but not definitively for causation. Typical examples include studies that examine patterns of drug use over time. One example is the comparison of the use of non-steroidal anti-inflammatory drugs and COX-2 inhibitors in Australia and Canada. 11 Sometimes ecological studies describe associations between drugs and outcomes, such as changes in the rates of upper gastrointestinal haemorrhage after the introduction of COX-2 inhibitors. 12 However, because individual-level data are not presented, causality is at best only implied in ecological studies. The 'ecological fallacy' refers to the error of assuming that associations observed in ecological studies are causal when they are not.

Cross-sectional studies

Cross-sectional studies collect data at a single point in time for each single individual, but the actual data collection may take place over a period of time or on more than one occasion. There is no longitudinal follow-up of individuals. Cross-sectional studies represent the archetypal descriptive study. 1 Typically, they provide a profile of a population of interest, which may be broad, like the Australian Health Survey undertaken intermittently by the Australian Bureau of Statistics, 13 or focused on specific populations, such as older Australians. 14

Case-control studies

Case-control studies focus on determining risk factors for an outcome of interest (such as a disease or a drug’s adverse effect) that has already occurred. 5

• those who already have the outcome (cases)
• those who do not have the outcome (controls), who are often matched to the cases to make them similar and reduce bias.

Second, data on previous exposure to selected risk factors are collected and compared to see if these risk factors are more (or less) common among cases versus controls. Case-control studies are useful for studying the risk factors of rare outcomes, as there is no need to wait for these to occur. Multiple risk factors can be studied, but each case-control study can involve only one outcome. 5 One example explored the relationship between the use of antiplatelet and anticoagulant drugs (risk factor) and the risk of hospitalisation for bleeding (outcome) in older people with a history of stroke. 15 Another case-control study explored the risk factors for the development of flucloxacillin-associated jaundice (outcome). 16

Cohort studies

Cohort studies compare outcomes between or among subgroups of participants defined on the basis of whether or not they are exposed to a particular risk or protective factor (defined as an exposure). They provide information on how these exposures are associated with changes in the risk of particular downstream outcomes. Compared to case-control studies, cohort studies take individuals with exposures and look for outcomes, rather than taking those with outcomes and looking for exposures. Cohort studies are longitudinal, that is they involve follow-up of a cohort of participants over time. This follow-up can be prospective or retrospective. Retrospective cohort studies are those for which follow-up has already occurred. They are typically used to estimate the incidence of outcomes of interest, including the adverse effects of drugs.

Cohort studies provide a higher level of evidence of causality than case-control studies because temporality (the explicit time relationship between exposures and outcomes) is preserved. They also have the advantage of not being limited to a single outcome of interest. Their main disadvantage, compared to case-control studies, has been that longitudinal data are more expensive and time-consuming to collect. However, with the availability of electronic data, it has become easier to collect longitudinal data.

One prospective cohort study explored the relationship between the continuous use of antipsychotic drugs (exposure) and mortality (outcome) and hospitalisation (outcome) in older people. 17 In another older cohort, a retrospective study was used to explore the relationship between long-term treatment adherence (exposure) and hospital readmission (outcome). 18

Observational studies versus randomised controlled trials

Compared to randomised controlled trials, observational studies are relatively quick, inexpensive and easy to undertake. Observational studies can be much larger than randomised controlled trials so they can explore a rare outcome. They can be undertaken when a randomised controlled trial would be unethical. However, observational studies cannot control for bias and confounding to the extent that clinical trials can. Randomisation in clinical trials remains the best way to control for confounding by ensuring that potential confounders (such as age, sex and comorbidities) are evenly matched between the groups being compared. In observational studies, adjustment for potential confounders can be undertaken, but only for a limited number of confounders, and only those that are known. Randomisation in clinical trials also minimises selection bias, while blinding (masking) controls for information bias. Hence, for questions regarding drug efficacy, randomised controlled trials provide the most robust evidence.

New and upcoming developments

New methods of analysis and advances in technology are changing the way observational studies are performed.

Clinical registries

Clinical registries are essentially cohort studies, and are gaining importance as a method to monitor and improve the quality of care. 19 These registries systematically collect a uniform longitudinal dataset to evaluate specific outcomes for a population that is identified by a specific disease, condition or exposure. This allows for the identification of variations in clinical practice 20 and benchmarking across practitioners or institutions. These data can then be used to develop initiatives to improve evidence-based care and patient outcomes. 21

An example of a clinical registry in Australia is the Australian Rheumatology Association Database, 22 which collects data on the biologic disease-modifying antirheumatic drugs used for inflammatory arthritis. Clinical data from treating specialists are combined with patient-reported quality of life data and linked to national databases such as Medicare and the National Death Index. This registry has provided insight into the safety and efficacy of drugs and their effect on quality of life. It was used by the Pharmaceutical Benefits Advisory Committee to assess cost-effectiveness of these drugs. 23

Another example is the Haemostasis Registry. It was used to determine the thromboembolic adverse effects of off-label use of recombinant factor VII. 24

Clinical registries can also be used to undertake clinical trials which are nested within the registry architecture. Patients within a registry are randomised to interventions and comparators of interest. Their outcome data are then collected as part of the routine operation of the registry. The key advantages are convenience, reduced costs and greater representativeness of registry populations as opposed to those of traditional clinical trials.

One of the first registry-based trials was nested within the SWEDEHEART registry. 25 This prospectively examined manual aspiration of thrombus at the time of percutaneous coronary intervention in over 7000 patients. 26 The primary endpoint of all-cause mortality was ascertained through linkage to another Swedish registry. The cost of the trial was estimated to be US$400 000, which was a fraction of the many millions that a randomised controlled trial would have cost.

Propensity score matching

Even without randomising people within cohorts, methods have emerged in recent years that allow for less biased comparisons of two or more subgroups. Propensity score matching is a way to assemble two or more groups for comparison so that they appear like they had been randomised to an intervention or a comparator. 27 In short, the method involves logistic regression analyses to determine the likelihood (propensity) of each person within a cohort being on the intervention, and then matching people who were on the intervention to those who were not on the basis of propensity scores. Outcomes are then compared between the groups. Propensity score analysis of a large cohort of patients with relapsing remitting multiple sclerosis found that natalizumab was superior to interferon beta and glatiramer acetate in terms of improved outcomes. 28

Data technology

Increasing sophistication in techniques for data collection will lead to ongoing improvements in the capacity to undertake observational studies (and also clinical trials). Data linkage already offers a convenient way to capture outcomes, including retrospectively. However, ethical considerations must be taken into account, such as the possibility that informed consent might be required before linking data. Machine learning will soon allow for easy analyses of unstructured text (such as free text entries in an electronic prescription). 29 Patient-reported outcome measures are important and in future will be greatly facilitated by standardised, secure hardware and software platforms that allow for their capture, processing and analyses.

While clinical trials remain the best source of evidence regarding the efficacy of drugs, observational studies provide critical descriptive data. Observational studies can also provide information on long-term efficacy and safety that is usually lacking in clinical trials. New and ongoing developments in data and analytical technology offer a promising future for observational studies in pharmaceutical research.

Conflict of interest: Julia Gilmartin-Thomas is a Dementia research development fellow with the National Health and Medical Research Council (NHMRC) - Australian Research Council (ARC). Ingrid Hopper is supported by an NHMRC Early Career Fellowship.

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Observational Research: What is, Types, Pros & Cons + Example

Researchers can gather customer data in a variety of ways, including surveys, interviews, and research. But not all data can be collected by asking questions because customers might not be conscious of their behaviors. 

It is when observational research comes in. This research is a way to learn about people by observing them in their natural environment. This kind of research helps researchers figure out how people act in different situations and what things in the environment affect their actions.

This blog will teach you about observational research, including types and observation methods. Let’s get started.

What is observational research?

Observational research is a broad term for various non-experimental studies in which behavior is carefully watched and recorded.

The goal of this research is to describe a variable or a set of variables. More broadly, the goal is to capture specific individual, group, or setting characteristics.

Since it is non-experimental and uncontrolled, we cannot draw causal research conclusions from it. The observational data collected in research studies is frequently qualitative observation , but it can also be quantitative or both (mixed methods).

Types of observational research

Conducting observational research can take many different forms. There are various types of this research. These types are classified below according to how much a researcher interferes with or controls the environment.

Naturalistic observation

Taking notes on what is seen is the simplest form of observational research. A researcher makes no interference in naturalistic observation. It’s just watching how people act in their natural environments. 

Importantly, there is no attempt to modify factors in naturalistic observation, as there would be when comparing data between a control group and an experimental group.

Case studiesCase studies

A case study is a sort of observational research that focuses on a single phenomenon. It is a naturalistic observation because it captures data in the field. But case studies focus on a specific point of reference, like a person or event, while other studies may have a wider scope and try to record everything that happens in the researcher’s eyes. 

For example, a case study of a single businessman might try to find out how that person deals with a certain disease’s ups and down or loss.

Participant observation

Participant observation is similar to naturalistic observation, except that the researcher is a part of the natural environment they are studying. In such research, the researcher is also interested in rituals or cultural practices that can only be evaluated by sharing experiences. 

For example, anyone can learn the basic rules of table Tennis by going to a game or following a team. Participant observation, on the other hand, lets people take part directly to learn more about how the team works and how the players relate to each other.

It usually includes the researcher joining a group to watch behavior they couldn’t see from afar. Participant observation can gather much information, from the interactions with the people being observed to the researchers’ thoughts.

Controlled observation

A more systematic structured observation entails recording the behaviors of research participants in a remote place. Case-control studies are more like experiments than other types of research, but they still use observational research methods. When researchers want to find out what caused a certain event, they might use a case-control study.

Longitudinal observation

This observational research is one of the most difficult and time-consuming because it requires watching people or events for a long time. Researchers should consider longitudinal observations when their research involves variables that can only be seen over time. 

After all, you can’t get a complete picture of things like learning to read or losing weight in a single observation. Longitudinal studies keep an eye on the same people or events over a long period of time and look for changes or patterns in behavior.

Observational research methods

When doing this research, there are a few observational methods to remember to ensure that the research is done correctly. Along with other research methods, let’s learn some key research methods of it:

Have a clear objective

For an observational study to be helpful, it needs to have a clear goal. It will help guide the observations and ensure they focus on the right things.

Get permission

Get permission from your participants. Getting explicit permission from the people you will be watching is essential. It means letting them know that they will be watched, the observation’s goal, and how their data will be used.

Unbiased observation

It is important to make sure the observations are fair and unbiased. It can be done by keeping detailed notes of what is seen and not putting any personal meaning on the data.

Hide your observers

In the observation method, keep your observers hidden. The participants should be unaware of the observers to avoid potential bias in their actions.

Documentation

It is important to document the observations clearly and straightforwardly. It will allow others to examine the information and confirm the observational research findings.

Data analysis

Data analysis is the last method. The researcher will analyze the collected data to draw conclusions or confirm a hypothesis.

Pros and cons of observational research

Observational studies are a great way to learn more about how your customers use different parts of your business. There are so many pros and cons of observational research. Let’s have a look at them.

• It provides a practical application for a hypothesis. In other words, it can help make research more complete.
• You can see people acting alone or in groups, such as customers. So, you can answer a number of questions about how people act as customers.
• There is a chance of researcher bias in observational research. Experts say that this can be a very big problem.
• Some human activities and behaviors can be difficult to understand. We are unable to see memories or attitudes. In other words, there are numerous situations in which observation alone is inadequate.

Example of observational research

The researcher observes customers buying products in a mall. Assuming the product is soap, the researcher will observe how long the customer takes to decide whether he likes the packaging or comes to the mall with his decision already made based on advertisements.

If the customer takes their time making a decision, the researcher will conclude that packaging and information on the package affect purchase behavior. If a customer makes a quick decision, the decision is likely predetermined. 

As a result, the researcher will recommend more and better advertisements in this case. All of these findings were obtained through simple observational research.

How to conduct observational research with QuestionPro?

QuestionPro can help with observational research by providing tools to collect and analyze data. It can help in the following ways:

Define the research goals and question types you want to answer with your observational study . Use QuestionPro’s customizable survey templates and questions to do a survey that fits your research goals and gets the necessary information. 

You can distribute the survey to your target audience using QuestionPro’s online platform or by sending a link to the survey. 

With QuestionPro’s real-time data analysis and reporting features, you can collect and look at the data as people fill out the survey. Use the advanced analytics tools in QuestionPro to see and understand the data and find insights and trends. 

If you need to, you can export the data from QuestionPro into the analysis tools you like to use. Draw conclusions from the collected and analyzed data and answer the research questions that were asked at the beginning of the research.

For a deeper understanding of human behaviors and decision-making processes, explore the realm of Behavioral Research .

To summarize, observational research is an effective strategy for collecting data and getting insights into real-world phenomena. When done right, this research can give helpful information and help people make decisions. 

QuestionPro is a valuable tool that can help with observational research by letting you create online surveys, analyze data in real time, make surveys your own, keep your data safe, and use advanced analytics tools.

To do this research with QuestionPro, researchers need to define their research goals, do a survey that matches their goals, send the survey to participants, collect and analyze the data, visualize and explain the results, export data if needed, and draw conclusions from the data collected.

By keeping in mind what has been said above, researchers can use QuestionPro to help with their observational research and gain valuable data. Try out QuestionPro today!

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Frequently Asked Questions (FAQ)

Observational research is a method in which researchers observe and systematically record behaviors, events, or phenomena without directly manipulating them.

There are three main types of observational research: naturalistic observation, participant observation, and structured observation.

Naturalistic observation involves observing subjects in their natural environment without any interference.

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Statistics By Jim

Making statistics intuitive

What is an Observational Study: Definition & Examples

By Jim Frost 10 Comments

What is an Observational Study?

An observational study uses sample data to find correlations in situations where the researchers do not control the treatment, or independent variable, that relates to the primary research question. The definition of an observational study hinges on the notion that the researchers only observe subjects and do not assign them to the control and treatment groups. That’s the key difference between an observational study vs experiment. These studies are also known as quasi-experiments and correlational studies .

True experiments assign subject to the experimental groups where the researchers can manipulate the conditions. Unfortunately, random assignment is not always possible. For these cases, you can conduct an observational study.

In this post, learn about the types of observational studies, why they are susceptible to confounding variables, and how they compare to experiments. I’ll close this post by reviewing a published observational study about vitamin supplement usage.

Observational Study Definition

In an observational study, the researchers only observe the subjects and do not interfere or try to influence the outcomes. In other words, the researchers do not control the treatments or assign subjects to experimental groups. Instead, they observe and measure variables of interest and look for relationships between them. Usually, researchers conduct observational studies when it is difficult, impossible, or unethical to assign study participants to the experimental groups randomly. If you can’t randomly assign subjects to the treatment and control groups, then you observe the subjects in their self-selected states.

Observational Study vs Experiment

Randomized experiments provide better results than observational studies. Consequently, you should always use a randomized experiment whenever possible. However, if randomization is not possible, science should not come to a halt. After all, we still want to learn things, discover relationships, and make discoveries. For these cases, observational studies are a good alternative to a true experiment. Let’s compare the differences between an observational study vs. an experiment.

Random assignment in an experiment reduces systematic differences between experimental groups at the beginning of the study, which increases your confidence that the treatments caused any differences between groups you observe at the end of the study. In contrast, an observational study uses self-formed groups that can have pre-existing differences, which introduces the problem of confounding variables. More on that later!

In a randomized experiment, randomization tends to equalize confounders between groups and, thereby, prevents problems. In my post about random assignment , I describe that process as an elegant solution for confounding variables. You don’t need to measure or even know which variables are confounders, and randomization will still mitigate their effects. Additionally, you can use control variables in an experiment to keep the conditions as consistent as possible. For more detail about the differences, read Observational Study vs. Experiment .

If you’re looking for a middle ground choice between observational studies vs experiments, consider using a quasi-experimental design. These methods don’t require you to randomly assign participants to the experimental groups and still allow you to draw better causal conclusions about an intervention than an observational study. Learn more about Quasi-Experimental Design Overview & Examples .

Related posts : Experimental Design: Definition and Examples , Randomized Controlled Trials (RCTs) , and Control Groups in Experiments

Observational Study Examples

Consider using an observational study when random assignment for an experiment is problematic. This approach allows us to proceed and draw conclusions about effects even though we can’t control the independent variables. The following observational study examples will help you understand when and why to use them.

For example, if you’re studying how depression affects performance of an activity, it’s impossible to assign subjects to the depression and control group randomly. However, you can have subjects with and without depression perform the activity and compare the results in an observational study.

Or imagine trying to assign subjects to cigarette smoking and non-smoking groups randomly?! However, you can observe people in both groups and assess the differences in health outcomes in an observational study.

Suppose you’re studying a treatment for a disease. Ideally, you recruit a group of patients who all have the disease, and then randomly assign them to the treatment and control group. However, it’s unethical to withhold the treatment, which rules out a control group. Instead, you can compare patients who voluntarily do not use the medicine to those who do use it.

In all these observational study examples, the researchers do not assign subjects to the experimental groups. Instead, they observe people who are already in these groups and compare the outcomes. Hence, the scientists must use an observational study vs. an experiment.

Types of Observational Studies

The observational study definition states that researchers only observe the outcomes and do not manipulate or control factors . Despite this limitation, there various types of observational studies.

The following experimental designs are three standard types of observational studies.

• Cohort Study : A longitudinal observational study that follows a group who share a defining characteristic. These studies frequently determine whether exposure to risk factor affects an outcome over time.
• Case-Control Study : A retrospective observational study that compares two existing groups—the case group with the condition and the control group without it. Researchers compare the groups looking for potential risk factors for the condition.
• Cross-Sectional Study : Takes a snapshot of a moment in time so researchers can understand the prevalence of outcomes and correlations between variables at that instant.

Qualitative research studies are usually observational in nature, but they collect non-numeric data and do not perform statistical analyses.

Retrospective studies must be observational.

Later in this post, we’ll closely examine a quantitative observational study example that assesses vitamin supplement consumption and how that affects the risk of death. It’s possible to use random assignment to place each subject in either the vitamin treatment group or the control group. However, the study assesses vitamin consumption in 40,000 participants over the course of two decades. It’s unrealistic to enforce the treatment and control protocols over such a long time for so many people!

Drawbacks of Observational Studies

While observational studies get around the inability to assign subjects randomly, this approach opens the door to the problem of confounding variables. A confounding variable, or confounder, correlates with both the experimental groups and the outcome variable. Because there is no random process that equalizes the experimental groups in an observational study, confounding variables can systematically differ between groups when the study begins. Consequently, confounders can be the actual cause for differences in outcome at the end of the study rather than the primary variable of interest. If an experiment does not account for confounding variables, confounders can bias the results and create spurious correlations .

Performing an observational study can decrease the internal validity of your study but increase the external validity. Learn more about internal and external validity .

Let’s see how this works. Imagine an observational study that compares people who take vitamin supplements to those who do not. People who use vitamin supplements voluntarily will tend to have other healthy habits that exist at the beginning of the study. These healthy habits are confounding variables. If there are differences in health outcomes at the end of the study, it’s possible that these healthy habits actually caused them rather than the vitamin consumption itself. In short, confounders confuse the results because they provide alternative explanations for the differences.

Despite the limitations, an observational study can be a valid approach. However, you must ensure that your research accounts for confounding variables. Fortunately, there are several methods for doing just that!

Learn more about Correlation vs. Causation: Understanding the Differences .

Accounting for Confounding Variables in an Observational Study

Because observational studies don’t use random assignment, confounders can be distributed disproportionately between conditions. Consequently, experimenters need to know which variables are confounders, measure them, and then use a method to account for them. It involves more work, and the additional measurements can increase the costs. And there’s always a chance that researchers will fail to identify a confounder, not account for it, and produce biased results. However, if randomization isn’t an option, then you probably need to consider an observational study.

Trait matching and statistically controlling confounders using multivariate procedures are two standard approaches for incorporating confounding variables.

Related post : Causation versus Correlation in Statistics

Matching in Observational Studies

Matching is a technique that involves selecting study participants with similar characteristics outside the variable of interest or treatment. Rather than using random assignment to equalize the experimental groups, the experimenters do it by matching observable characteristics. For every participant in the treatment group, the researchers find a participant with comparable traits to include in the control group. Matching subjects facilitates valid comparisons between those groups. The researchers use subject-area knowledge to identify characteristics that are critical to match.

For example, a vitamin supplement study using matching will select subjects who have similar health-related habits and attributes. The goal is that vitamin consumption will be the primary difference between the groups, which helps you attribute differences in health outcomes to vitamin consumption. However, the researchers are still observing participants who decide whether they consume supplements.

Matching has some drawbacks. The experimenters might not be aware of all the relevant characteristics they need to match. In other words, the groups might be different in an essential aspect that the researchers don’t recognize. For example, in the hypothetical vitamin study, there might be a healthy habit or attribute that affects the outcome that the researchers don’t measure and match. These unmatched characteristics might cause the observed differences in outcomes rather than vitamin consumption.

Learn more about Matched Pairs Design: Uses & Examples .

Using Multiple Regression in Observational Studies

Random assignment and matching use different methods to equalize the experimental groups in an observational study. However, statistical techniques, such as multiple regression analysis , don’t try to equalize the groups but instead use a model that accounts for confounding variables. These studies statistically control for confounding variables.

In multiple regression analysis, including a variable in the model holds it constant while you vary the variable/treatment of interest. For information about this property, read my post When Should I Use Regression Analysis?

As with matching, the challenge is to identify, measure, and include all confounders in the regression model. Failure to include a confounding variable in a regression model can cause omitted variable bias to distort your results.

Next, we’ll look at a published observational study that uses multiple regression to account for confounding variables.

Related post : Independent and Dependent Variables in a Regression Model

Vitamin Supplement Observational Study Example

Murso et al. (2011)* use a longitudinal observational study that ran 22 years to assess differences in death rates for subjects who used vitamin supplements regularly compared to those who did not use them. This study used surveys to record the characteristics of approximately 40,000 participants. The surveys asked questions about potential confounding variables such as demographic information, food intake, health details, physical activity, and, of course, supplement intake.

Because this is an observational study, the subjects decided for themselves whether they were taking vitamin supplements. Consequently, it’s safe to assume that supplement users and non-users might be different in other ways. From their article, the researchers found the following pre-existing differences between the two groups:

Supplement users had a lower prevalence of diabetes mellitus, high blood pressure, and smoking status; a lower BMI and waist to hip ratio, and were less likely to live on a farm. Supplement users had a higher educational level, were more physically active and were more likely to use estrogen replacement therapy. Also, supplement users were more likely to have a lower intake of energy, total fat, and monounsaturated fatty acids, saturated fatty acids and to have a higher intake of protein, carbohydrates, polyunsaturated fatty acids, alcohol, whole grain products, fruits, and vegetables.

Whew! That’s a long list of differences! Supplement users were different from non-users in a multitude of ways that are likely to affect their risk of dying. The researchers must account for these confounding variables when they compare supplement users to non-users. If they do not, their results can be biased.

This example illustrates a key difference between an observational study vs experiment. In a randomized experiment, the randomization would have equalized the characteristics of those the researchers assigned to the treatment and control groups. Instead, the study works with self-sorted groups that have numerous pre-existing differences!

Using Multiple Regression to Statistically Control for Confounders

To account for these initial differences in the vitamin supplement observational study, the researchers use regression analysis and include the confounding variables in the model.

The researchers present three regression models. The simplest model accounts only for age and caloric intake. Next, are two models that include additional confounding variables beyond age and calories. The first model adds various demographic information and seven health measures. The second model includes everything in the previous model and adds several more specific dietary intake measures. Using statistical significance as a guide for specifying the correct regression model , the researchers present the model with the most variables as the basis for their final results.

It’s instructive to compare the raw results and the final regression results.

Raw results

The raw differences in death risks for consumers of folic acid, vitamin B6, magnesium, zinc, copper, and multivitamins are NOT statistically significant. However, the raw results show a significant reduction in the death risk for users of B complex, C, calcium, D, and E.

However, those are the raw results for the observational study, and they do not control for the long list of differences between the groups that exist at the beginning of the study. After using the regression model to control for the confounding variables statistically, the results change dramatically.

Adjusted results

Of the 15 supplements that the study tracked in the observational study, researchers found consuming seven of these supplements were linked to a statistically significant INCREASE in death risk ( p-value < 0.05): multivitamins (increase in death risk 2.4%), vitamin B6 (4.1%), iron (3.9%), folic acid (5.9%), zinc (3.0%), magnesium (3.6%), and copper (18.0%). Only calcium was associated with a statistically significant reduction in death risk of 3.8%.

In short, the raw results suggest that those who consume supplements either have the same or lower death risks than non-consumers. However, these results do not account for the multitude of healthier habits and attributes in the group that uses supplements.

In fact, these confounders seem to produce most of the apparent benefits in the raw results because, after you statistically control the effects of these confounding variables, the results worsen for those who consume vitamin supplements. The adjusted results indicate that most vitamin supplements actually increase your death risk!

This research illustrates the differences between an observational study vs experiment. Namely how the pre-existing differences between the groups allow confounders to bias the raw results, making the vitamin consumption outcomes look better than they really are.

In conclusion, if you can’t randomly assign subjects to the experimental groups, an observational study might be right for you. However, be aware that you’ll need to identify, measure, and account for confounding variables in your experimental design.

Jaakko Mursu, PhD; Kim Robien, PhD; Lisa J. Harnack, DrPH, MPH; Kyong Park, PhD; David R. Jacobs Jr, PhD; Dietary Supplements and Mortality Rate in Older Women: The Iowa Women’s Health Study ; Arch Intern Med . 2011;171(18):1625-1633.

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Reader Interactions

December 30, 2023 at 5:05 am

I see, but our professor required us to indicate what year it was put into the article. May you tell me what year was this published originally? <3

December 30, 2023 at 3:40 pm

December 29, 2023 at 10:46 am

Hi, may I use your article as a citation for my thesis paper? If so, may I know the exact date you published this article? Thank you!

December 29, 2023 at 2:13 pm

Definitely feel free to cite this article! 🙂

When citing online resources, you typically use an “Accessed” date rather than a publication date because online content can change over time. For more information, read Purdue University’s Citing Electronic Resources .

November 18, 2021 at 10:09 pm

Love your content and has been very helpful!

Can you please advise the question below using an observational data set:

I have three years of observational GPS data collected on athletes (2019/2020/2021). Approximately 14-15 athletes per game and 8 games per year. The GPS software outputs 50+ variables for each athlete in each game, which we have narrowed down to 16 variables of interest from previous research.

2 factors 1) Period (first half, second half, and whole game), 2) Position (two groups with three subgroups in each – forwards (group 1, group 2, group 3) and backs (group 1, group 2, group 3))

16 variables of interest – all numerical and scale variables. Some of these are correlated, but not all.

My understanding is that I can use a oneway ANOVA for each year on it’s own, using one factor at a time (period or position) with post hoc analysis. This is fine, if data meets assumptions and is normally distributed. This tells me any significant interactions between variables of interest with chosen factor. For example, with position factor, do forwards in group 1 cover more total running distance than forwards in group 2 or backs in group 3.

However, I want to go deeper with my analysis. If I want to see if forwards in group 1 cover more total running distance in period 1 than backs in group 3 in the same period, I need an additional factor and the oneway ANOVA does not suit. Therefore I can use a twoway ANOVA instead of 2 oneway ANOVA’s and that solves the issue, correct?

This is complicated further by looking to compare 2019 to 2020 or 2019 to 2021 to identify changes over time, which would introduce a third independent variable.

I believe this would require a threeway ANOVA for this observational data set. 3 factors – Position, Period, and Year?

Are there any issues or concerns you see at first glance?

I appreciate your time and consideration.

April 12, 2021 at 2:02 pm

Could an observational study use a correlational design.

e.g. measuring effects of two variables on happiness, if you’re not intervening.

April 13, 2021 at 12:14 am

Typically, with observational studies, you’d want to include potential confounders, etc. Consequently, I’ve seen regression analysis used more frequently for observational studies to be able to control for other things because you’re not using randomization. You could use correlation to observe the relationship. However, you wouldn’t be controlling for potential confounding variables. Just something to consider.

April 11, 2021 at 1:28 pm

Hi, If I am to administer moderate doses of coffee for a hypothetical experiment, does it raise ethical concerns? Can I use random assignment for it?

April 11, 2021 at 4:06 pm

I don’t see any inherent ethical problems here as long as you describe the participant’s experience in the experiment including the coffee consumption. They key with human subjects is “informed consent.” They’re agreeing to participate based on a full and accurate understanding of what participation involves. Additionally, you as a researcher, understand the process well enough to be able to ensure their safety.

In your study, as long as subject know they’ll be drinking coffee and agree to that, I don’t see a problem. It’s a proven safe substance for the vast majority of people. If potential subjects are aware of the need to consume coffee, they can determine whether they are ok with that before agreeing to participate.

June 17, 2019 at 4:51 am

Really great article which explains observational and experimental study very well. It presents broad picture with the case study which helped a lot in understanding the core concepts. Thanks

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups

What is observational research?

Uses for observational research, observations in research, the different types of observational research, conducting observational studies, uses with other methods, challenges of observational studies.

• Case studies
• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Observational research

Observational research is a social research technique that involves the direct observation of phenomena in their natural setting.

An observational study is a non-experimental method to examine how research participants behave. Observational research is typically associated with qualitative methods , where the data ultimately require some reorganization and analysis .

Contemporary research is often associated with controlled experiments or randomized controlled trials, which involve testing or developing a theory in a controlled setting. Such an approach is appropriate for many physical and material sciences that rely on objective concepts such as the melting point of substances or the mass of objects. On the other hand, observational studies help capture socially constructed or subjective phenomena whose fundamental essence might change when taken out of their natural setting.

What is an example of observational research?

For example, imagine a study where you want to understand the actions and behaviors of single parents taking care of children. A controlled experiment might prove challenging, given the possibility that the behaviors of parents and their children will change if you isolate them in a lab or an otherwise unfamiliar context.

Instead, researchers pursuing such inquiries can observe participants in their natural environment, collecting data on what people do, say, and behave in interaction with others. Non-experimental research methods like observation are less about testing theories than learning something new to contribute to theories.

The goal of the observational study is to collect data about what people do and say. Observational data is helpful in several fields:

• market research
• health services research
• educational research
• user research

Observational studies are valuable in any domain where researchers want to learn about people's actions and behaviors in a natural setting. For example, observational studies in market research might seek out information about the target market of a product or service by identifying the needs or problems of prospective consumers. In medical contexts, observers might be interested in how patients cope with a particular medical treatment or interact with doctors and nurses under certain conditions.

Researchers may still be hung up on science being all about experiments to the point where they may overlook the empirical contribution that observations bring to research and theory. With that in mind, let's look at the strengths and weaknesses of observations in research .

Strengths of observational research

Observational research, especially those conducted in natural settings, can generate more insightful knowledge about social processes or rituals that one cannot fully understand by reading a plain-text description in a book or an online resource. Think about a cookbook with recipes, then think about a series of videos showing a cook making the same recipes. Both are informative, but the videos are often easier to understand as the cook can describe the recipe and show how to follow the steps at the same time. When you can observe what is happening, you can emulate the process for yourself.

Observing also allows researchers to create rich data about phenomena that cannot be explained through numbers. The quality of a theatrical performance, for example, cannot easily be reduced to a set of numbers. Qualitatively, a researcher can analyze aspects gleaned from observing that performance and create a working theory about the quality of that performance. Through data analysis, the researcher can identify patterns related to the aesthetics and creativity of the performance to provide a framework to judge the quality of other performances.

Weaknesses of observational research

Science is about organizing knowledge for the purposes of identifying the aspects of a concept or of determining cause-and-effect relationships between different phenomena. Experiments look to empirically accomplish these tasks by controlling certain variables to determine how other variables change under changing conditions. Those conducting observational research, on the other hand, exert no such control, which makes replication by other researchers difficult or even impossible when observing dynamic environments.

Observational studies take on various forms. There are various types of observational research, each of which has strengths and weaknesses. These types are organized below by the extent to which an experimenter intrudes upon or controls the environment.

Naturalistic observation

Naturalistic observation refers to a method where researchers study participants in their natural environment without manipulating variables or intervening in any way. It provides a realistic snapshot of behavior as it occurs in real-life settings, thereby enhancing ecological validity.

Examples of naturalistic observation include people-watching in public places, observing animal behaviors in the wild, and longitudinally studying children's social development at school. This method can reveal insights about behavior and relationships that might not surface in experimental designs, such as patterns of social interaction, routines, or responses to environmental changes.

Participant observation

Participant observation is similar to naturalistic observation, except that the researcher is part of the natural environment they are observing. In such studies, the researcher is also interested in rituals or cultural practices where they can only determine their value by actually experiencing them firsthand. For example, any individual can understand the basic rules of baseball by watching a game or following a team. Participant observation, on the other hand, allows for direct participation to develop a better sense of team dynamics and relationships among fellow players.

Most commonly, this process involves the researcher inserting themselves into a group to observe behavior that otherwise would not be accessible by observing from afar. Participant observation can capture rich data from the interactions with those who are observed to the reflections of the researchers themselves.

Controlled observation

A more structured observation involves capturing the behaviors of research participants in an isolated environment. Case-control studies have a greater resemblance to experimental research while still relying on observational research methods. Researchers may utilize a case-control study when they want to establish the causation of a particular phenomenon.

For example, a researcher may want to establish a structured observation of a control group and an experimental group, each with randomly assigned research participants, to observe the effects of variables such as distractions on people completing a particular task. By subjecting the experimental group to distractions such as noise and lights, researchers can observe the time it takes participants to complete a task and determine causation accordingly.

Longitudinal study

Among the different types of observational research, this observational method is quite arduous and time-consuming as it requires observation of people or events over extended periods. Researchers should consider longitudinal observations when their inquiry involves variables that can only be observed over time. After all, variables such as literacy development or weight loss cannot be fully captured in any particular moment of observation. Longitudinal studies keep track of the same research participants or events through multiple observations to document changes to or patterns in behavior.

A cohort study is a specific type of longitudinal study where researchers observe participants with similar traits (e.g., a similar risk factor or biological characteristic). Cohort studies aim to observe multiple participants over time to identify a relationship between observed phenomena and a common characteristic.

All forms of observational or field research benefit extensively from the special capabilities of qualitative research tools like ATLAS.ti . Our software can accommodate the major forms of data , such as text, audio, video, and images . The ATLAS.ti platform can help you organize all your observations , whatever method you employ.

Whatever your research, make it happen with ATLAS.ti.

Powerful analytical tools at your fingertips. Try for free by clicking here.

Like any other study design, observational studies begin by posing research questions . Inquiries common when employing observational methods include the study of different cultures, interactions between people from different communities, or people in particular circumstances warranting further study (e.g., people coping with a rare disease).

Generally, a research question that seeks to learn more about a relatively unfamiliar phenomenon would be best suited for observational research. On the other hand, quantitative methods or experimental research methods may be more suitable for inquiries where the theory about a social phenomenon is fairly established.

Study design

Study design for observational research involves thinking about who to observe, where they should be observed, and what the researcher should look for during observation. Many events can occur in a natural, dynamic environment in a short period, so it is challenging to document everything. If the researcher knows what they want to observe, they can pursue a structured observation which involves taking notes on a limited set of phenomena.

The actual data collection for an observational study can take several forms. Note-taking is common in observational research, where the researcher writes down what they see during the course of their observation. The goal of this method is to provide a record of the events that are observed to determine patterns and themes useful for theoretical development.

Observation can also involve taking pictures or recording audio for a richer understanding of social phenomena. Video recorded from observations can also provide data that the researcher can use to document the facial expressions, gestures, and other body language of research participants.

Note that there are ethical considerations when conducting observational research. Researchers should respect the privacy and confidentiality of their research participants to ensure they are not adversely affected by the research. Researchers should obtain informed consent from participants before any observation where possible.

Observational studies can be supplemented with other methods to further contextualize the research inquiry. Researchers can conduct interviews or focus groups with research participants to gather data about what they recall about their actions and behaviors in a natural setting. Focus groups, in particular, provide further opportunities to observe participants interacting with each other. In both cases, these research methods are ideal where the researcher needs to follow up with research participants about the evidence they've collected regarding their behaviors or actions.

As with many other methods in qualitative research , conducting an observational study is time-consuming. While experimental methods can quickly generate data , observational research relies on documenting events and interactions in detail that can be analyzed for theoretical development.

Unstructured data

One common critique of observational research is that it lacks the structure inherent to experimental research, which has concepts such as selection bias and interrater reliability to ensure research quality. On the other hand, qualitative research relies on the assumption that the study and its data are presented transparently and honestly . Under this principle, researchers are responsible for convincing their audiences that the assertions they make are connected empirically to the observations they have made and the data they have collected.

Researcher bias

In most qualitative research, but especially in observational research, the most important data collection instrument is the researcher themselves. This raises issues of bias and subjectivity influencing the collection and interpretation of the data.

Later in this guide, there will be discussion of reflexivity , a concept where the researcher comprehensively accounts for their place in the research relative to others in the environment. For now, it's important to know that social science researchers can and do adequately address critiques of researcher bias to maintain the empirical nature of their observational research.

Conduct your observational study with ATLAS.ti

From the inception of your study to disseminating to your research audience, get it all done with our powerful software platform. Start with a free trial.

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Home » Observational Research – Methods and Guide

Observational Research – Methods and Guide

Table of Contents

Observational Research

Definition:

Observational research is a type of research method where the researcher observes and records the behavior of individuals or groups in their natural environment. In other words, the researcher does not intervene or manipulate any variables but simply observes and describes what is happening.

Observation

Observation is the process of collecting and recording data by observing and noting events, behaviors, or phenomena in a systematic and objective manner. It is a fundamental method used in research, scientific inquiry, and everyday life to gain an understanding of the world around us.

Types of Observational Research

Observational research can be categorized into different types based on the level of control and the degree of involvement of the researcher in the study. Some of the common types of observational research are:

Naturalistic Observation

In naturalistic observation, the researcher observes and records the behavior of individuals or groups in their natural environment without any interference or manipulation of variables.

Controlled Observation

In controlled observation, the researcher controls the environment in which the observation is taking place. This type of observation is often used in laboratory settings.

Participant Observation

In participant observation, the researcher becomes an active participant in the group or situation being observed. The researcher may interact with the individuals being observed and gather data on their behavior, attitudes, and experiences.

Structured Observation

In structured observation, the researcher defines a set of behaviors or events to be observed and records their occurrence.

Unstructured Observation

In unstructured observation, the researcher observes and records any behaviors or events that occur without predetermined categories.

Cross-Sectional Observation

In cross-sectional observation, the researcher observes and records the behavior of different individuals or groups at a single point in time.

Longitudinal Observation

In longitudinal observation, the researcher observes and records the behavior of the same individuals or groups over an extended period of time.

Data Collection Methods

Observational research uses various data collection methods to gather information about the behaviors and experiences of individuals or groups being observed. Some common data collection methods used in observational research include:

Field Notes

This method involves recording detailed notes of the observed behavior, events, and interactions. These notes are usually written in real-time during the observation process.

Audio and Video Recordings

Audio and video recordings can be used to capture the observed behavior and interactions. These recordings can be later analyzed to extract relevant information.

Surveys and Questionnaires

Surveys and questionnaires can be used to gather additional information from the individuals or groups being observed. This method can be used to validate or supplement the observational data.

Time Sampling

This method involves taking a snapshot of the observed behavior at pre-determined time intervals. This method helps to identify the frequency and duration of the observed behavior.

Event Sampling

This method involves recording specific events or behaviors that are of interest to the researcher. This method helps to provide detailed information about specific behaviors or events.

Checklists and Rating Scales

Checklists and rating scales can be used to record the occurrence and frequency of specific behaviors or events. This method helps to simplify and standardize the data collection process.

Observational Data Analysis Methods

Observational Data Analysis Methods are:

Descriptive Statistics

This method involves using statistical techniques such as frequency distributions, means, and standard deviations to summarize the observed behaviors, events, or interactions.

Qualitative Analysis

Qualitative analysis involves identifying patterns and themes in the observed behaviors or interactions. This analysis can be done manually or with the help of software tools.

Content Analysis

Content analysis involves categorizing and counting the occurrences of specific behaviors or events. This analysis can be done manually or with the help of software tools.

Time-series Analysis

Time-series analysis involves analyzing the changes in behavior or interactions over time. This analysis can help identify trends and patterns in the observed data.

Inter-observer Reliability Analysis

Inter-observer reliability analysis involves comparing the observations made by multiple observers to ensure the consistency and reliability of the data.

Multivariate Analysis

Multivariate analysis involves analyzing multiple variables simultaneously to identify the relationships between the observed behaviors, events, or interactions.

Event Coding

This method involves coding observed behaviors or events into specific categories and then analyzing the frequency and duration of each category.

Cluster Analysis

Cluster analysis involves grouping similar behaviors or events into clusters based on their characteristics or patterns.

Latent Class Analysis

Latent class analysis involves identifying subgroups of individuals or groups based on their observed behaviors or interactions.

Social network Analysis

Social network analysis involves mapping the social relationships and interactions between individuals or groups based on their observed behaviors.

The choice of data analysis method depends on the research question, the type of data collected, and the available resources. Researchers should choose the appropriate method that best fits their research question and objectives. It is also important to ensure the validity and reliability of the data analysis by using appropriate statistical tests and measures.

Applications of Observational Research

Observational research is a versatile research method that can be used in a variety of fields to explore and understand human behavior, attitudes, and preferences. Here are some common applications of observational research:

• Psychology : Observational research is commonly used in psychology to study human behavior in natural settings. This can include observing children at play to understand their social development or observing people’s reactions to stress to better understand how stress affects behavior.
• Marketing : Observational research is used in marketing to understand consumer behavior and preferences. This can include observing shoppers in stores to understand how they make purchase decisions or observing how people interact with advertisements to determine their effectiveness.
• Education : Observational research is used in education to study teaching and learning in natural settings. This can include observing classrooms to understand how teachers interact with students or observing students to understand how they learn.
• Anthropology : Observational research is commonly used in anthropology to understand cultural practices and beliefs. This can include observing people’s daily routines to understand their culture or observing rituals and ceremonies to better understand their significance.
• Healthcare : Observational research is used in healthcare to understand patient behavior and preferences. This can include observing patients in hospitals to understand how they interact with healthcare professionals or observing patients with chronic illnesses to better understand their daily routines and needs.
• Sociology : Observational research is used in sociology to understand social interactions and relationships. This can include observing people in public spaces to understand how they interact with others or observing groups to understand how they function.
• Ecology : Observational research is used in ecology to understand the behavior and interactions of animals and plants in their natural habitats. This can include observing animal behavior to understand their social structures or observing plant growth to understand their response to environmental factors.
• Criminology : Observational research is used in criminology to understand criminal behavior and the factors that contribute to it. This can include observing criminal activity in a particular area to identify patterns or observing the behavior of inmates to understand their experience in the criminal justice system.

Observational Research Examples

Here are some real-time observational research examples:

• A researcher observes and records the behaviors of a group of children on a playground to study their social interactions and play patterns.
• A researcher observes the buying behaviors of customers in a retail store to study the impact of store layout and product placement on purchase decisions.
• A researcher observes the behavior of drivers at a busy intersection to study the effectiveness of traffic signs and signals.
• A researcher observes the behavior of patients in a hospital to study the impact of staff communication and interaction on patient satisfaction and recovery.
• A researcher observes the behavior of employees in a workplace to study the impact of the work environment on productivity and job satisfaction.
• A researcher observes the behavior of shoppers in a mall to study the impact of music and lighting on consumer behavior.
• A researcher observes the behavior of animals in their natural habitat to study their social and feeding behaviors.
• A researcher observes the behavior of students in a classroom to study the effectiveness of teaching methods and student engagement.
• A researcher observes the behavior of pedestrians and cyclists on a city street to study the impact of infrastructure and traffic regulations on safety.

How to Conduct Observational Research

Here are some general steps for conducting Observational Research:

• Define the Research Question: Determine the research question and objectives to guide the observational research study. The research question should be specific, clear, and relevant to the area of study.
• Choose the appropriate observational method: Choose the appropriate observational method based on the research question, the type of data required, and the available resources.
• Plan the observation: Plan the observation by selecting the observation location, duration, and sampling technique. Identify the population or sample to be observed and the characteristics to be recorded.
• Train observers: Train the observers on the observational method, data collection tools, and techniques. Ensure that the observers understand the research question and objectives and can accurately record the observed behaviors or events.
• Conduct the observation : Conduct the observation by recording the observed behaviors or events using the data collection tools and techniques. Ensure that the observation is conducted in a consistent and unbiased manner.
• Analyze the data: Analyze the observed data using appropriate data analysis methods such as descriptive statistics, qualitative analysis, or content analysis. Validate the data by checking the inter-observer reliability and conducting statistical tests.
• Interpret the results: Interpret the results by answering the research question and objectives. Identify the patterns, trends, or relationships in the observed data and draw conclusions based on the analysis.
• Report the findings: Report the findings in a clear and concise manner, using appropriate visual aids and tables. Discuss the implications of the results and the limitations of the study.

When to use Observational Research

Here are some situations where observational research can be useful:

• Exploratory Research: Observational research can be used in exploratory studies to gain insights into new phenomena or areas of interest.
• Hypothesis Generation: Observational research can be used to generate hypotheses about the relationships between variables, which can be tested using experimental research.
• Naturalistic Settings: Observational research is useful in naturalistic settings where it is difficult or unethical to manipulate the environment or variables.
• Human Behavior: Observational research is useful in studying human behavior, such as social interactions, decision-making, and communication patterns.
• Animal Behavior: Observational research is useful in studying animal behavior in their natural habitats, such as social and feeding behaviors.
• Longitudinal Studies: Observational research can be used in longitudinal studies to observe changes in behavior over time.
• Ethical Considerations: Observational research can be used in situations where manipulating the environment or variables would be unethical or impractical.

Purpose of Observational Research

Observational research is a method of collecting and analyzing data by observing individuals or phenomena in their natural settings, without manipulating them in any way. The purpose of observational research is to gain insights into human behavior, attitudes, and preferences, as well as to identify patterns, trends, and relationships that may exist between variables.

The primary purpose of observational research is to generate hypotheses that can be tested through more rigorous experimental methods. By observing behavior and identifying patterns, researchers can develop a better understanding of the factors that influence human behavior, and use this knowledge to design experiments that test specific hypotheses.

Observational research is also used to generate descriptive data about a population or phenomenon. For example, an observational study of shoppers in a grocery store might reveal that women are more likely than men to buy organic produce. This type of information can be useful for marketers or policy-makers who want to understand consumer preferences and behavior.

In addition, observational research can be used to monitor changes over time. By observing behavior at different points in time, researchers can identify trends and changes that may be indicative of broader social or cultural shifts.

Overall, the purpose of observational research is to provide insights into human behavior and to generate hypotheses that can be tested through further research.

Advantages of Observational Research

There are several advantages to using observational research in different fields, including:

• Naturalistic observation: Observational research allows researchers to observe behavior in a naturalistic setting, which means that people are observed in their natural environment without the constraints of a laboratory. This helps to ensure that the behavior observed is more representative of the real-world situation.
• Unobtrusive : Observational research is often unobtrusive, which means that the researcher does not interfere with the behavior being observed. This can reduce the likelihood of the research being affected by the observer’s presence or the Hawthorne effect, where people modify their behavior when they know they are being observed.
• Cost-effective : Observational research can be less expensive than other research methods, such as experiments or surveys. Researchers do not need to recruit participants or pay for expensive equipment, making it a more cost-effective research method.
• Flexibility: Observational research is a flexible research method that can be used in a variety of settings and for a range of research questions. Observational research can be used to generate hypotheses, to collect data on behavior, or to monitor changes over time.
• Rich data : Observational research provides rich data that can be analyzed to identify patterns and relationships between variables. It can also provide context for behaviors, helping to explain why people behave in a certain way.
• Validity : Observational research can provide high levels of validity, meaning that the results accurately reflect the behavior being studied. This is because the behavior is being observed in a natural setting without interference from the researcher.

Disadvantages of Observational Research

While observational research has many advantages, it also has some limitations and disadvantages. Here are some of the disadvantages of observational research:

• Observer bias: Observational research is prone to observer bias, which is when the observer’s own beliefs and assumptions affect the way they interpret and record behavior. This can lead to inaccurate or unreliable data.
• Limited generalizability: The behavior observed in a specific setting may not be representative of the behavior in other settings. This can limit the generalizability of the findings from observational research.
• Difficulty in establishing causality: Observational research is often correlational, which means that it identifies relationships between variables but does not establish causality. This can make it difficult to determine if a particular behavior is causing an outcome or if the relationship is due to other factors.
• Ethical concerns: Observational research can raise ethical concerns if the participants being observed are unaware that they are being observed or if the observations invade their privacy.
• Time-consuming: Observational research can be time-consuming, especially if the behavior being observed is infrequent or occurs over a long period of time. This can make it difficult to collect enough data to draw valid conclusions.
• Difficulty in measuring internal processes: Observational research may not be effective in measuring internal processes, such as thoughts, feelings, and attitudes. This can limit the ability to understand the reasons behind behavior.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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6.5 Observational Research

Learning objectives.

• List the various types of observational research methods and distinguish between each
• Describe the strengths and weakness of each observational research method. 

What Is Observational Research?

The term observational research is used to refer to several different types of non-experimental studies in which behavior is systematically observed and recorded. The goal of observational research is to describe a variable or set of variables. More generally, the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting. As described previously, observational research is non-experimental because nothing is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. The data that are collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). There are several different types of observational research designs that will be described below.

Naturalistic Observation

Naturalistic observation  is an observational method that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr.  Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation  could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied. Such an approach is called disguised naturalistic observation.  Ethically, this method is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated. 

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct  undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is  reactivity. Reactivity  refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are, flirting, having sex, wearing next to nothing, screaming at each other, and at times acting like complete fools in front of the entire nation.

Participant Observation

Another approach to data collection in observational research is participant observation. In  participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that is collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation, the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers. In contrast with undisguised participant observation,  the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation.  First no informed consent can be obtained and second passive deception is being used. The researcher is passively deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further,  disguised participant observation is less prone to reactivity than undisguised participant observation. 

Rosenhan’s study (1973) [1]   of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins (published in  Social Psychology Quarterly ) on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [2] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

One of the primary benefits of participant observation is that the researcher is in a much better position to understand the viewpoint and experiences of the people they are studying when they are apart of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation when researchers because active members of the social group they are studying, additional concerns arise that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Structured Observation

Another observational method is structured observation. Here the investigator makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic and participant observation. Often the setting in which the observations are made is not the natural setting, rather the researcher may observe people in the laboratory environment. Alternatively, the researcher may observe people in a natural setting (like a classroom setting) that they have structured some way, for instance by introducing some specific task participants are to engage in or by introducing a specific social situation or manipulation. Structured observation is very similar to naturalistic observation and participant observation in that in all cases researchers are observing naturally occurring behavior, however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic and participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Structured observation is very similar to naturalistic observation and participant observation in that in all cases researchers are observing naturally occurring behavior, however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic and participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999) [3] . One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation  takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider. Levine and Norenzayan described their sampling process as follows:

“Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities.” (p. 186).  Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.  In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance.

As another example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979) [4] . But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

When the observations require a judgment on the part of the observers—as in Kraut and Johnston’s study—this process is often described as  coding . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that different observers code them in the same way. This difficulty with coding is the issue of interrater reliability, as mentioned in Chapter 4. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, often times the environment is structured to encourage the behaviors of interested which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

Case Studies

A  case study  is an in-depth examination of an individual. Sometimes case studies are also completed on social units (e.g., a cult) and events (e.g., a natural disaster). Most commonly in psychology, however, case studies provide a detailed description and analysis of an individual. Often the individual has a rare or unusual condition or disorder or has damage to a specific region of the brain.

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individuals’ depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

HM is one of the most notorious case studies in psychology. HM suffered from intractable and very severe epilepsy. A surgeon localized HM’s epilepsy to his medial temporal lobe and in 1953 he removed large sections of his hippocampus in an attempt to stop the seizures. The treatment was a success, in that it resolved his epilepsy and his IQ and personality were unaffected. However, the doctors soon realized that HM exhibited a strange form of amnesia, called anterograde amnesia. HM was able to carry out a conversation and he could remember short strings of letters, digits, and words. Basically, his short term memory was preserved. However, HM could not commit new events to memory. He lost the ability to transfer information from his short-term memory to his long term memory, something memory researchers call consolidation. So while he could carry on a conversation with someone, he would completely forget the conversation after it ended. This was an extremely important case study for memory researchers because it suggested that there’s a dissociation between short-term memory and long-term memory, it suggested that these were two different abilities sub-served by different areas of the brain. It also suggested that the temporal lobes are particularly important for consolidating new information (i.e., for transferring information from short-term memory to long-term memory).

www.youtube.com/watch?v=KkaXNvzE4pk

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [5] , who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [6] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Figure 10.1 Anna O. “Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis. Source: http://en.wikipedia.org/wiki/File:Pappenheim_1882.jpg

Case studies are useful because they provide a level of detailed analysis not found in many other research methods and greater insights may be gained from this more detailed analysis. As a result of the case study, the researcher may gain a sharpened understanding of what might become important to look at more extensively in future more controlled research. Case studies are also often the only way to study rare conditions because it may be impossible to find a large enough sample to individuals with the condition to use quantitative methods. Although at first glance a case study of a rare individual might seem to tell us little about ourselves, they often do provide insights into normal behavior. The case of HM provided important insights into the role of the hippocampus in memory consolidation. However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods.

The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with internal and external validity. Case studies lack the proper controls that true experiments contain. As such they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (indeed questioning into the possibility of a separate brain lesion began after HM’s death and dissection of his brain) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So as with all observational methods case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically a very abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity, with case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research is the use of  archival research  which involves analyzing data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005) [7] . In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [8] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s  r  was +.25.

This method is an example of  content analysis —a family of systematic approaches to measurement using complex archival data. Just as structured observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Key Takeaways

• There are several different approaches to observational research including naturalistic observation, participant observation, structured observation, case studies, and archival research.
• Naturalistic observation is used to observe people in their natural setting, participant observation involves becoming an active member of the group being observed, structured observation involves coding a small number of behaviors in a quantitative manner, case studies are typically used to collect in-depth information on a single individual, and archival research involves analysing existing data.
• Describe one problem related to internal validity.
• Describe one problem related to external validity.
• Generate one hypothesis suggested by the case study that might be interesting to test in a systematic single-subject or group study.
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
• Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
• Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205. ↵
• Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553. ↵
• Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
• Freud, S. (1961).  Five lectures on psycho-analysis . New York, NY: Norton. ↵
• Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
• Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

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Lab Report Format: Step-by-Step Guide & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

In psychology, a lab report outlines a study’s objectives, methods, results, discussion, and conclusions, ensuring clarity and adherence to APA (or relevant) formatting guidelines.

A typical lab report would include the following sections: title, abstract, introduction, method, results, and discussion.

The title page, abstract, references, and appendices are started on separate pages (subsections from the main body of the report are not). Use double-line spacing of text, font size 12, and include page numbers.

The report should have a thread of arguments linking the prediction in the introduction to the content of the discussion.

This must indicate what the study is about. It must include the variables under investigation. It should not be written as a question.

Title pages should be formatted in APA style .

The abstract provides a concise and comprehensive summary of a research report. Your style should be brief but not use note form. Look at examples in journal articles . It should aim to explain very briefly (about 150 words) the following:

• Start with a one/two sentence summary, providing the aim and rationale for the study.
• Describe participants and setting: who, when, where, how many, and what groups?
• Describe the method: what design, what experimental treatment, what questionnaires, surveys, or tests were used.
• Describe the major findings, including a mention of the statistics used and the significance levels, or simply one sentence summing up the outcome.
• The final sentence(s) outline the study’s “contribution to knowledge” within the literature. What does it all mean? Mention the implications of your findings if appropriate.

The abstract comes at the beginning of your report but is written at the end (as it summarises information from all the other sections of the report).

Introduction

The purpose of the introduction is to explain where your hypothesis comes from (i.e., it should provide a rationale for your research study).

Ideally, the introduction should have a funnel structure: Start broad and then become more specific. The aims should not appear out of thin air; the preceding review of psychological literature should lead logically into the aims and hypotheses.

• Start with general theory, briefly introducing the topic. Define the important key terms.
• Explain the theoretical framework.
• Summarise and synthesize previous studies – What was the purpose? Who were the participants? What did they do? What did they find? What do these results mean? How do the results relate to the theoretical framework?
• Rationale: How does the current study address a gap in the literature? Perhaps it overcomes a limitation of previous research.
• Aims and hypothesis. Write a paragraph explaining what you plan to investigate and make a clear and concise prediction regarding the results you expect to find.

There should be a logical progression of ideas that aids the flow of the report. This means the studies outlined should lead logically to your aims and hypotheses.

Do be concise and selective, and avoid the temptation to include anything in case it is relevant (i.e., don’t write a shopping list of studies).

USE THE FOLLOWING SUBHEADINGS:

Participants

• How many participants were recruited?
• Say how you obtained your sample (e.g., opportunity sample).
• Give relevant demographic details (e.g., gender, ethnicity, age range, mean age, and standard deviation).
• State the experimental design .
• What were the independent and dependent variables ? Make sure the independent variable is labeled and name the different conditions/levels.
• For example, if gender is the independent variable label, then male and female are the levels/conditions/groups.
• How were the IV and DV operationalized?
• Identify any controls used, e.g., counterbalancing and control of extraneous variables.
• List all the materials and measures (e.g., what was the title of the questionnaire? Was it adapted from a study?).
• You do not need to include wholesale replication of materials – instead, include a ‘sensible’ (illustrate) level of detail. For example, give examples of questionnaire items.
• Include the reliability (e.g., alpha values) for the measure(s).
• Describe the precise procedure you followed when conducting your research, i.e., exactly what you did.
• Describe in sufficient detail to allow for replication of findings.
• Be concise in your description and omit extraneous/trivial details, e.g., you don’t need to include details regarding instructions, debrief, record sheets, etc.
• Assume the reader has no knowledge of what you did and ensure that he/she can replicate (i.e., copy) your study exactly by what you write in this section.
• Write in the past tense.
• Don’t justify or explain in the Method (e.g., why you chose a particular sampling method); just report what you did.
• Only give enough detail for someone to replicate the experiment – be concise in your writing.
• The results section of a paper usually presents descriptive statistics followed by inferential statistics.
• Report the means, standard deviations, and 95% confidence intervals (CIs) for each IV level. If you have four to 20 numbers to present, a well-presented table is best, APA style.
• Name the statistical test being used.
• Report appropriate statistics (e.g., t-scores, p values ).
• Report the magnitude (e.g., are the results significant or not?) as well as the direction of the results (e.g., which group performed better?).
• It is optional to report the effect size (this does not appear on the SPSS output).
• Avoid interpreting the results (save this for the discussion).
• Make sure the results are presented clearly and concisely. A table can be used to display descriptive statistics if this makes the data easier to understand.
• DO NOT include any raw data.
• Follow APA style.

Use APA Style

• Numbers reported to 2 d.p. (incl. 0 before the decimal if 1.00, e.g., “0.51”). The exceptions to this rule: Numbers which can never exceed 1.0 (e.g., p -values, r-values): report to 3 d.p. and do not include 0 before the decimal place, e.g., “.001”.
• Percentages and degrees of freedom: report as whole numbers.
• Statistical symbols that are not Greek letters should be italicized (e.g., M , SD , t , X 2 , F , p , d ).
• Include spaces on either side of the equals sign.
• When reporting 95%, CIs (confidence intervals), upper and lower limits are given inside square brackets, e.g., “95% CI [73.37, 102.23]”
• Outline your findings in plain English (avoid statistical jargon) and relate your results to your hypothesis, e.g., is it supported or rejected?
• Compare your results to background materials from the introduction section. Are your results similar or different? Discuss why/why not.
• How confident can we be in the results? Acknowledge limitations, but only if they can explain the result obtained. If the study has found a reliable effect, be very careful suggesting limitations as you are doubting your results. Unless you can think of any c onfounding variable that can explain the results instead of the IV, it would be advisable to leave the section out.
• Suggest constructive ways to improve your study if appropriate.
• What are the implications of your findings? Say what your findings mean for how people behave in the real world.
• Suggest an idea for further research triggered by your study, something in the same area but not simply an improved version of yours. Perhaps you could base this on a limitation of your study.
• Concluding paragraph – Finish with a statement of your findings and the key points of the discussion (e.g., interpretation and implications) in no more than 3 or 4 sentences.

Reference Page

The reference section lists all the sources cited in the essay (alphabetically). It is not a bibliography (a list of the books you used).

In simple terms, every time you refer to a psychologist’s name (and date), you need to reference the original source of information.

If you have been using textbooks this is easy as the references are usually at the back of the book and you can just copy them down. If you have been using websites then you may have a problem as they might not provide a reference section for you to copy.

References need to be set out APA style :

Author, A. A. (year). Title of work . Location: Publisher.

Journal Articles

Author, A. A., Author, B. B., & Author, C. C. (year). Article title. Journal Title, volume number (issue number), page numbers

A simple way to write your reference section is to use Google scholar . Just type the name and date of the psychologist in the search box and click on the “cite” link.

Next, copy and paste the APA reference into the reference section of your essay.

Once again, remember that references need to be in alphabetical order according to surname.

Psychology Lab Report Example

Quantitative paper template.

Quantitative professional paper template: Adapted from “Fake News, Fast and Slow: Deliberation Reduces Belief in False (but Not True) News Headlines,” by B. Bago, D. G. Rand, and G. Pennycook, 2020,  Journal of Experimental Psychology: General ,  149 (8), pp. 1608–1613 ( https://doi.org/10.1037/xge0000729 ). Copyright 2020 by the American Psychological Association.

Qualitative paper template

Qualitative professional paper template: Adapted from “‘My Smartphone Is an Extension of Myself’: A Holistic Qualitative Exploration of the Impact of Using a Smartphone,” by L. J. Harkin and D. Kuss, 2020,  Psychology of Popular Media ,  10 (1), pp. 28–38 ( https://doi.org/10.1037/ppm0000278 ). Copyright 2020 by the American Psychological Association.

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Non-Experimental Research

32 Observational Research

Learning objectives.

• List the various types of observational research methods and distinguish between each.
• Describe the strengths and weakness of each observational research method. 

What Is Observational Research?

The term observational research is used to refer to several different types of non-experimental studies in which behavior is systematically observed and recorded. The goal of observational research is to describe a variable or set of variables. More generally, the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting. As described previously, observational research is non-experimental because nothing is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. The data that are collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). There are several different types of observational methods that will be described below.

Naturalistic Observation

Naturalistic observation  is an observational method that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr.  Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation  could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied. Such an approach is called disguised naturalistic observation .  Ethically, this method is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated. 

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct  undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is  reactivity. Reactivity refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. This type of reactivity is known as the Hawthorne effect . For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are flirting, having sex, wearing next to nothing, screaming at each other, and occasionally behaving in ways that are embarrassing.

Participant Observation

Another approach to data collection in observational research is participant observation. In  participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that are collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation , the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

In a famous example of disguised participant observation, Leon Festinger and his colleagues infiltrated a doomsday cult known as the Seekers, whose members believed that the apocalypse would occur on December 21, 1954. Interested in studying how members of the group would cope psychologically when the prophecy inevitably failed, they carefully recorded the events and reactions of the cult members in the days before and after the supposed end of the world. Unsurprisingly, the cult members did not give up their belief but instead convinced themselves that it was their faith and efforts that saved the world from destruction. Festinger and his colleagues later published a book about this experience, which they used to illustrate the theory of cognitive dissonance (Festinger, Riecken, & Schachter, 1956) [1] .

In contrast with undisguised participant observation ,  the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation.  First no informed consent can be obtained and second deception is being used. The researcher is deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further, disguised participant observation is less prone to reactivity than undisguised participant observation. 

Rosenhan’s study (1973) [2]   of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [3] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

One of the primary benefits of participant observation is that the researchers are in a much better position to understand the viewpoint and experiences of the people they are studying when they are a part of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation, additional concerns arise when researchers become active members of the social group they are studying because that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Case Studies

A  case study   is an in-depth examination of an individual. Sometimes case studies are also completed on social units (e.g., a cult) and events (e.g., a natural disaster). Most commonly in psychology, however, case studies provide a detailed description and analysis of an individual. Often the individual has a rare or unusual condition or disorder or has damage to a specific region of the brain.

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individual’s depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

HM is one of the most notorious case studies in psychology. HM suffered from intractable and very severe epilepsy. A surgeon localized HM’s epilepsy to his medial temporal lobe and in 1953 he removed large sections of his hippocampus in an attempt to stop the seizures. The treatment was a success, in that it resolved his epilepsy and his IQ and personality were unaffected. However, the doctors soon realized that HM exhibited a strange form of amnesia, called anterograde amnesia. HM was able to carry out a conversation and he could remember short strings of letters, digits, and words. Basically, his short term memory was preserved. However, HM could not commit new events to memory. He lost the ability to transfer information from his short-term memory to his long term memory, something memory researchers call consolidation. So while he could carry on a conversation with someone, he would completely forget the conversation after it ended. This was an extremely important case study for memory researchers because it suggested that there’s a dissociation between short-term memory and long-term memory, it suggested that these were two different abilities sub-served by different areas of the brain. It also suggested that the temporal lobes are particularly important for consolidating new information (i.e., for transferring information from short-term memory to long-term memory).

The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [4] , who allegedly learned to fear a white rat—along with other furry objects—when the researchers repeatedly made a loud noise every time the rat approached him.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [5] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, he believed that her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Case studies are useful because they provide a level of detailed analysis not found in many other research methods and greater insights may be gained from this more detailed analysis. As a result of the case study, the researcher may gain a sharpened understanding of what might become important to look at more extensively in future more controlled research. Case studies are also often the only way to study rare conditions because it may be impossible to find a large enough sample of individuals with the condition to use quantitative methods. Although at first glance a case study of a rare individual might seem to tell us little about ourselves, they often do provide insights into normal behavior. The case of HM provided important insights into the role of the hippocampus in memory consolidation.

However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods. The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with both internal and external validity. Case studies lack the proper controls that true experiments contain. As such, they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (a possibility suggested by the dissection of HM’s brain following his death) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So, as with all observational methods, case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically an abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity. With case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research involves analyzing archival data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005) [6] . In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [7] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s  r  was +.25.

This method is an example of  content analysis —a family of systematic approaches to measurement using complex archival data. Just as structured observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Media Attributions

• What happens when you remove the hippocampus? – Sam Kean by TED-Ed licensed under a standard YouTube License
• Pappenheim 1882  by unknown is in the  Public Domain .
• Festinger, L., Riecken, H., & Schachter, S. (1956). When prophecy fails: A social and psychological study of a modern group that predicted the destruction of the world. University of Minnesota Press. ↵
• Rosenhan, D. L. (1973). On being sane in insane places. Science, 179 , 250–258. ↵
• Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301. ↵
• Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3 , 1–14. ↵
• Freud, S. (1961).  Five lectures on psycho-analysis . New York, NY: Norton. ↵
• Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110. ↵
• Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27. ↵

Research that is non-experimental because it focuses on recording systemic observations of behavior in a natural or laboratory setting without manipulating anything.

An observational method that involves observing people’s behavior in the environment in which it typically occurs.

When researchers engage in naturalistic observation by making their observations as unobtrusively as possible so that participants are not aware that they are being studied.

Where the participants are made aware of the researcher presence and monitoring of their behavior.

Refers to when a measure changes participants’ behavior.

In the case of undisguised naturalistic observation, it is a type of reactivity when people know they are being observed and studied, they may act differently than they normally would.

Researchers become active participants in the group or situation they are studying.

Researchers pretend to be members of the social group they are observing and conceal their true identity as researchers.

Researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation.

An in-depth examination of an individual.

A family of systematic approaches to measurement using qualitative methods to analyze complex archival data.

Research Methods in Psychology Copyright © 2023 by William L. Kelemen, Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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23 Qualitative Observation Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Qualitative observation is a method of collecting data that involves describing the attributes and properties of a phenomenon or subject through the senses, rather than using numerical measurements.

This type of observation is most beneficial for studies designed to provide in-depth, detailed, nuanced, and contextualized case studies. The qualitative data generated can help to explain complex concepts.

However, unlike quantitative observation (such as measuring lengths, instances, and intervals), this data cannot be generalized beyond the scope of the research project itself, limiting its applicability to a wide variety of situations.

Qualitative Observation Examples

1. participant observation.

Participant observation is a type of qualitative observation where the researcher not only observes the researched group or individuals, but also actively engages in the activities of the group or individuals [ 1 ] .

This immersion in the activity and culture of the group allows for a deeper understanding, usually over a longer period. It allows the researcher to fully participate in the day-to-day lives and rituals of the participants.

The goal is to gain deeper insights into their social and cultural practices, which can then be explained through thick description.

Participant Observation Example : An anthropologist might live within a remote tribe for a period to understand their lifestyle, beliefs, and customs. They would participate in daily activities, mimic behaviors, and even learn the local language to gather empirical and comprehensive data.

2. Non-Participant Observation

In non-participant observation, the researcher observes the participants without getting involved in their actions or activities [ 2 ] . In other words, they remain “invisible” to the research subjects, who are unaware of being observed.

This reduces potential observer-effects, such as participants modifying their behavior because they know they are being watched. However, it also places some distance between the observer and the observed, which may limit the depth of understanding.

Example: A researcher studying interactions in a workplace might install surveillance cameras and watch the footage to observe how employees interact when they think they are not being observed. By not involving themselves directly, they aim to capture the most accurate and natural interactions.

3. Naturalistic Observation

Naturalistic observation is a method where the researcher observes behavior in a natural setting, without manipulating or controlling the situation [ 3 ] .

It allows the researcher to study things in their natural environment, providing a real-world context. This observational method provides the opportunity to collect data that is not artificial or manipulated while providing deep insights into a phenomenon.

Naturalistic Observation Example : A psychologist studying children’s play behavior might observe a group of children in a playground during their recess. By leaving the children in their natural and uncontrolled play environment, the psychologist can see the real-life application of the children’s social skills, the play schemes they adopt, and how they use their imagination and negotiation skills.

4. Focus Groups

Focus groups involve a group of individuals who participate in a guided discussion about a particular topic. The researchers take notes and discern themes based on these focus group discussions [ 4 , 5 ] .

The group setting encourages participants to share diverse perspectives, generating rich, detailed data. However, the dynamic can be influenced by more dominant individuals, potentially biasing the group’s collective voice.

Focus Group Example : A market researcher seeking feedback on a new product might conduct a focus group with potential consumers. In the discussion, they would ask about the product’s appeal, perceived usefulness, and areas for improvement, gaining insights into consumer preferences and acceptance.

5. Thick Description

Thick description an approach to qualitative observation used specifically in ethnographic research . It involves taking detailed accounts of people’s behavior in their social and cultural context.

This concept was created by Clifford Geertz [ 6 ] , an anthropologist seeking a better method for the analysis of natural phenomena.

Thick descriptions not only detail the behavior but also its context, as well as the intentions and emotions of the actors. Though rich in detail, interpretation of thick description depends on the cultural competence and analytical skills of the researcher.

Thick Description Example : An anthropologist studying an indigenous tribe might use thick description to provide in-depth accounts of their rituals. They would describe not only the actions of the rituals but also the emotions experienced, the social and cultural context, the symbolism involved, the participants’ understanding and interpretation of the ritual, essentially providing a nuanced, multilayered understanding of the tribe’s practices.

6. Structured Observation

Structured observation is a systematic method in observational research where the researcher explicitly decides where, when, and how the observation will take place.

The researcher typically uses previously defined criteria and may have a specific list of behaviors to look for [ 7 , 8 ] . It often involves the use of standardized scales or coding systems to categorize observed behaviors, and it aims at statistical reliability.

Example: A behavioral psychologist studying aggressive behaviors in children after playing violent video games might conduct structured observations by having the children play the games in a set environment (controlled setting, same gaming platform, same game) and then observing for specific aggressive behaviors (like hitting, shouting, throwing objects) based on a previously prepared checklist.

7. Unstructured Observation

Unstructured observation, unlike structured observation, does not involve pre-determined criteria or an exact plan for the observation.

Instead, the observer merely watches the participants in a situation and records whatever they think is relevant or significant [ 8 ] . This approach allows for the discovery of unexpected phenomena but can be more time consuming and may elicit subjective interpretations.

Example: A sociologist studying homeless people’s daily routines might follow several homeless individuals around throughout their day without a specific plan or list of behaviors to observe. They take note of any behavior they believe is significant to understanding the lifestyle and hardships of a homeless individual.

8. Direct Observation

Direct observation involves a researcher being physically present to observe and record behavior as it occurs. The researcher may or may not be visible to the participants [ 9 ] .

Direct observation allows the researcher to collect firsthand data and reduces the chances of missing any relevant information. However, it can also introduce bias as the researcher’s interpretation and presence can affect the situation.

Example: An educator studying classroom dynamics might sit at the back of the classroom to directly observe student behavior, interactions, and engagement. They could observe how different teaching styles affect student attentiveness, participation, and overall classroom behavior.

9. Indirect Observation

Indirect observation is the collection of data through some means other than direct observation; the researcher does not have to be present at the site of observation [ 9 ] .

The researcher can use techniques such as video recordings, photographs, or existing records. This approach lessens potential bias from the researcher’s presence but might miss contexts or non-verbal cues.

Example: A city planner studying traffic patterns might use footage from surveillance cameras placed at various intersections around the city, rather than observing every intersection in person. This method allows them to analyze traffic flow, most frequent peak hours, and violations at different times and days.

10. Continuous Monitoring

Continuous monitoring involves observing a subject or a phenomenon over an uninterrupted period, recording all actions and events as they occur [ 10 ] .

This type of observation provides comprehensive data about a subject’s behavior across different times and situations. However, it can be time-consuming and requires extensive resources.

Example: A wildlife biologist studying the behavior of a certain animal species might set up a live camera feed on the animals’ habitat to continuously monitor their activities. They would track their feeding habits, social behaviors, mating rituals, sleep patterns, etc., to gain a holistic understanding of the species’ lifestyle.

See Also: Types of Qualitative Research

11. Interval Recording

Interval recording is a method where the observation period is divided into smaller intervals, and the researcher records whether or not a specific behavior occurs within each interval [ 10 ] .

This method provides a snapshot of behavior during certain times and is not as time-consuming as continuous monitoring. Still, it might miss occurrences that happen between intervals.

Example: A clinical psychologist studying an individual with Tourette Syndrome might record every 15 seconds whether or not a tic occurs. They might do this over a series of therapy sessions to understand the frequency of the tics and the effect of the therapy.

12. Time Sampling

Time sampling is an observational method where the researcher records behaviors at pre-determined intervals of time, regardless of the event type [ 10 ] .

The researcher chooses specific time periods and determines whether or not the behavior of interest is happening during those times. Time sampling can simplify long observations and reduce the workload, but it might miss events that happen outside the selected time periods.

Example: An educator studying student engagement during a lecture might conduct a time sampling by noting what the students are doing (listening attentively, talking to fellow students, using their phones) at 10-minute intervals throughout the lecture.

13. Narrative Observation

Narrative observation involves recording detailed, descriptive notes about what is being observed. It attempts to capture the full context and detail of events, rather than just specific behaviors [ 9 ] .

This method is unstructured and allows for capturing unforeseen information, as it doesn’t focus only on specific behaviors or events. However, it can also be time-consuming and data may be difficult to analyze systematically.

Example: A social worker trying to understand the dynamics of a dysfunctional family might record detailed narrative observations of family interactions. This could involve noting down not just what is said and done, but also the context, the tones of voice, facial expressions, and any other significant details such as time and physical setting.

14. Anecdotal Records

Anecdotal records provides a detailed description of a significant behavior or event that the observer finds interesting or important [ 10 ] .

The observer might not necessarily be looking for this specific behavior or event in advance, but upon noticing it, they write it down in as much detail as possible, hence creating an “anecdote” about it. This form of observation captures information that might not have been sought, but could be very significant.

Anecdote Example : A teacher noticing a child displaying advanced reading skills might write an anecdotal record about it, describing how the child was reading, which book they chose, and ways they interacted with the material. These records can then be used to tailor the child’s education plan to their advanced skill level.

15. Checklist Observation

A checklist observation is a form of structured observation where the observer has a specific list of behaviors, characteristics, or events that they are looking to observe [ 11 ] .

It allows for systematic collection of data and can provide a quick and efficient way to record and compare information across multiple observations or participants. However, it might miss other relevant information that is not on the checklist.

Example: A human resource manager observing employee performance might have a checklist that includes factors like punctuality, completion of tasks, cooperation with team members, and ability to meet deadlines. The manager would use this to evaluate the employees and identify areas for improvement.

16. Rubrics

Rubrics are a type of observation tool that provides a detailed performance scoring guide. They include criteria that are specific, observable, and measurable [ 12 ] .

Each criterion is evaluated against a performance scale, allowing for consistency among different observers. While rubrics establish clear expectations and standards, developing effective rubrics can be time-consuming.

Example: A physical education teacher evaluating a student’s performance in gymnastics might use a rubric. Criteria might include the technique of different moves, the level of difficulty, the gracefulness and fluidity of performance, and the creativity of the routine.

17. Video Observation

Video observation refers to using video recording as a tool for data collection in observational research. It involves recording the behaviors or events of interest so they can be analyzed at a later time [ 12 ] .

It allows for capturing details that the observer might miss during real-time observation and for re-visiting the observation for further analysis. However, it may also hinder the naturalness of behaviors due to camera awareness.

Example: A sociolinguist studying nonverbal communication might use video observation capturing conversations between participants. Recording would allow the researcher to review and analyze subtle body language, facial expressions, or pauses that might have been missed during live observation.

18. Audio Observation

Audio observation is a method that uses audio recordings to capture information during a research study. Behaviors or interactions are recorded for subsequent analysis [ 12 ] .

This method assists in preserving the verbal aspects of an event but lacks visual data. It is often used in group discussions, interviews, or conversation analysis where auditory information is primary.

Example: A linguist studying dialect variations in a certain region might conduct audio observations by recording conversations between natives of that region. They could then analyze variations in pronunciation, vocabulary, and syntax.

19. Focused Observation

Focused observation is a method where observers focus on specific behaviors, interactions, or events rather than trying to capture everything that occurs [ 13 ] .

These observations often address specific research questions, allowing for a deeper understanding of the focused elements. However, other contextually significant behaviors or interactions might be overlooked.

Example: A researcher studying gender dynamics in office settings might conduct a focused observation on how men and women participate in team meetings—who initiates discussions more frequently, who gets interrupted more, who takes on leadership roles.

20. Longitudinal Observation

Longitudinal observation spans over a long period, allowing researchers to study changes and developments [ 14 ] .

This method is valuable for exploring long-term effects or trends, but it requires a significant commitment of time and resources. Additionally, maintaining the same observation conditions might be challenging over time.

Example: A developmental psychologist studying the effect of parental involvement on school performance might conduct longitudinal observation by regularly observing a group of students from kindergarten through high school, correlating their academic progression with different degrees of parental involvement.

See Also: Longitudinal Research Guide

21. Field Observation

Field observation , often used in ethnographic research, involves observing subjects in their natural environment, or “in the field” [ 1 ] .

Real world settings offer a wealth of contextual richness, allowing researchers to study individuals or groups in a more natural, less controlled manner, often resulting in more honest behaviors. However, uncontrolled environments can introduce uncontrolled variables, offering potential challenges to data interpretation.

Example: An anthropologist studying the subculture of graffiti artists might conduct field observations by spending time at locations where these artists typically work, observing their process, community interactions, and reactions of the general public.

22. Controlled Observation

Controlled observation takes place in a setting where variables can be manipulated or controlled by the observer [ 15 ] .

Often conducted in a lab setting, it allows the observer to establish cause-and-effect relationships by manipulating independent variables and observing their effect on dependent variables.

While this method can provide strong evidence, it may lack ecological validity, as outcomes observed in a controlled setting may not necessarily translate to real-world situations.

Example: A psychologist studying the effects of anxiety on test performance might conduct a controlled observation by administering a test to two groups: one group under normal conditions, and the other under conditions designed to induce anxiety, then observing for any differences in performance.

23. Case Study Observation

Case study observation involves an in-depth examination of a specific individual, group, or event in real-life context [ 16 ] .

It provides a holistic view of the subject and allows for understanding of complex issues. However, findings might not be generalizable due to specificity.

Example: A social scientist interested in understanding the impact of poverty on education might conduct case study observations on children from low-income families. They might observe the child at home, at school, or in other social settings over a period of time to ascertain how their socio-economic status affects their educational opportunities and performance.

See Also: Advantages and Disadvantages of Case Studies

Qualitative vs Quantitative Observation

Qualitative observation delves into understanding underlying meanings and human behavior through non-numerical data collection, while quantitative observation seeks to uncover patterns and establish facts through numerical data collection and analysis.

Qualitative observation is focused on understanding underlying meanings and concepts that govern behavior or situations. It seeks to explore the depth, richness, and complexity inherent in phenomena. On the other hand, quantitative observation aims at drawing conclusions based on numerical data. It relies on measurable data to formulate facts and uncover patterns in research.

The research methodologies employed in qualitative and quantitative observations significantly differ:

• Qualitative methods are often more flexible, allowing for a broader understanding of issues at hand, and are particularly useful in exploring new or complex issues. They provide a narrative or descriptive outcome, which can be instrumental in understanding the context of a particular situation.
• Quantitative methods are rigid but provide a clear-cut answer to a research question. They yield numerical data that can be analyzed using statistical methods to validate or refute hypotheses. The outcome of quantitative research often comes in the form of graphs, charts, or tables showcasing the relationships between different variables.

The choice between qualitative and quantitative observation often hinges on the nature of the research question and the stage of research.

Qualitative observation is generally more suited for exploratory or foundational research where not much is known about the problem. It helps in hypothesis generation by providing insights into the problem.

Quantitative observation, on the other hand, is ideal for confirmatory or validation studies where hypotheses are tested under controlled conditions. It helps in hypothesis testing and contributes to the establishment of facts or the discovery of general laws.

Both types of observation are crucial for a comprehensive understanding of research problems, and a mixed-methods approach, which combines both qualitative and quantitative methods, is often deemed most effective in tackling complex research questions.

Pros and Cons of Qualitative Observation

Qualitative observation is pivotal in understanding complex phenomena by delving into the intricacies of human behavior, social interactions, and cultural norms [ 9 , 11 ] .

Through qualitative observation, researchers can capture the natural setting and the context in which individuals operate, which often leads to more authentic and nuanced findings.

However, it has its limitations.

The very features that make qualitative observation powerful can also be seen as its shortcomings.

The findings from qualitative observation are not generalizable due to the small sample sizes typically employed [ 11 ] .

Additionally, this form of observation is also highly dependent on the skills and biases of the researcher, which can influence the data collection and interpretation process [ 7 ] .

See the table below for a summary of the key pros and cons:

Up Next: An Introduction to Qualitative Research

[1] Seim, J. (2021). Participant observation, observant participation, and hybrid ethnography.  Sociological Methods & Research , 0049124120986209. ( Source )

[2] Handley, M., Bunn, F., Lynch, J., & Goodman, C. (2020). Using non-participant observation to uncover mechanisms: Insights from a realist evaluation.  Evaluation ,  26 (3), 380-393. ( Source )

[3] Carey, A. L., Rentscher, K. E., & Mehl, M. R. (2020). Naturalistic observation of social interactions.  The Wiley encyclopedia of health psychology , 373-383. ( Source )

[4] Cyr, J. (2019). Focus Groups for the Social Science Researcher . Cambridge University Press.

[5] Mishra, L. (2016). Focus group discussion in qualitative research.  TechnoLearn: An International Journal of Educational Technology ,  6 (1), 1-5. ( Source )

[6] Geertz, C. (1973). Thick Description: Towards an Interprative theory of culture.  The Interpretation of Cultures , 3-31.

[7] Damaskinidis, G. (2017). Qualitative research and subjective impressions in educational contexts.  American Journal of Educational Research ,  5 (12), 1228-1233.

[8] Farid, S. (2022). Observation. In  Principles of Social Research Methodology  (pp. 365-375). Singapore: Springer Nature Singapore.

[9] Daniel, B. K., & Harland, T. (2017). Higher Education Research Methodology: A Step-by-Step Guide to the Research Process . Taylor & Francis.

[10] Kirner, K., & Mills, J. (2019). Introduction to Ethnographic Research: A Guide for Anthropology. SAGE Publications.

[11] Bryman, A. (2012). Social Research Methods . OUP Oxford.

[12] Kelly, G. J., & Green, J. L. (Eds.). (2018). Theory and Methods for Sociocultural Research in Science and Engineering Education . Taylor & Francis.

[13] Schneider, N. C., Coates, W. C., & Yarris, L. M. (2017). Taking your qualitative research to the next level: a guide for the medical educator.  AEM education and training ,  1 (4), 368-378. ( Source )

[14] Thomson, R., & McLeod, J. (2015). New frontiers in qualitative longitudinal research: An agenda for research.  International Journal of Social Research Methodology ,  18 (3), 243-250. ( Source )

[15] Islam, M. R., Khan, N. A., & Baikady, R. (Eds.). (2022). Principles of Social Research Methodology . Springer Nature Singapore.

[16] Njie, B., & Asimiran, S. (2014). Case study as a choice in qualitative methodology.  Journal of research & method in Education ,  4 (3), 35-40.

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Behavioral Research Blog

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Two examples of on-site observational studies with older persons

In some cases, it is necessary to bring your research to your participants. For example, you might want to observe people in a natural setting: at home , in a shop, in the classroom, or in the office.

Another case where on-site research would be beneficial  is when your participants are experiencing health issues, preventing them from travelling to your lab. Conducting your research on location enables you to study people that are otherwise difficult to reach.

These factors should be taken into consideration when choosing the location for your research, especially when conducting studies with older age groups.

Practical aspects of on-site observational studies

If you want to conduct your study in another location, there are some practical aspects to think about. For example, you have to set the right lighting conditions and camera position. You also have to make sure that you capture voices and other sounds accurately.

Do you want to learn more how to set up an AV lab? Read on for the perfect tips & tricks!

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Observational research examples

The first study we describe in this blog post explores how familiarity influences the use of electronic devices in different age groups. In the second study, researchers compared different methods of observing pain expressions in dementia patients.

Example 1: Familiarity and intuitive use

Improving products for older age groups.

It is generally assumed that older adults have difficulty using modern electronic devices, such as mobile telephones or computers. Because this age group is growing in most countries, changing products and processes to adapt to their needs is increasingly more important.  

Improving technological experiences in older age groups supports their social inclusion, productivity, and their independence. To gain more insight in the learning processes involved in using electronic devices, Lawry and his colleagues compared levels of familiarity between age groups.

Comparing familiarity between older and younger adults

Familiarity describes the way an action is recognized or understood, based on prior experience and knowledge. It develops from a general level of knowledge, to knowledge that is based on experience, and finally to effortless and unconscious action.

The researchers identified several behaviors that suggest familiarity with an action. These include anticipation and planning, relative speed, verbalization, and task attention. They recorded these behaviors during a verbal report of how participants thought they would perform a task, as well as during the performance of the task itself.

Specifically, participants were asked to use a product they were familiar with and a product that was new to them.  The study included 32 participants with different educational backgrounds, who were divided into different age groups (18-44, 45-59, 60-74, and 75+).

Observing familiarity at home

What better place to study familiarity than in the familiar place of home? By conducting part of their research in the participants’ homes, researcher Lawry and his team created a more realistic learning environment, had easy access to familiar products, and were able to recruit older participants more easily.

They coded verbal and visual data from both studies using The Observer XT , and used this software to calculate inter-rater reliability as well. Results showed significant differences in familiarity between age groups, concerning both known and new products.

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Differences between younger and older adults

When using their own products, the youngest adult group showed more familiar behaviors than the two oldest groups. These younger participants also showed more familiarity when using a new product, compared to all other age groups.

Importantly, these results indicate that intuitive use of new products declines as early as during middle age. Therefore, the researchers advise product designers to incorporate more features that are based on prior knowledge of both middle-aged and older adults.

Example 2: Pain expressions in dementia

Observing pain expressions.

In dementia, severe cognitive impairment can lead to an inability to communicate verbally. When these patients can’t tell their caregivers about the pain they feel, an accurate assessment of pain expressions becomes essential.

Browne and her colleagues examined how the angle of observation influences this assessment, both in trained and untrained observers. It is widely assumed that a front view provides the most information on pain. However, caregivers also often observe patients from the side.

Not only can this information be used to improve human observations, but it can also support the development of computer vision systems, further improving care for people with dementia.

On-site observational study

The researchers included 102 adults over the age of 65 in their study, with and without dementia.

Video recordings were made from both the front and profile of their faces, during a physiotherapy examination and a baseline period. Observers used this video data to assess pain expressions with two different coding systems.

They made their observations in long-term care facilities and an outpatient physiotherapy clinic. This approach provided opportunities to observe participants in their beds, as well as during their treatments.

When analyzing their data, Browne and her colleagues used The Observer XT to code the video data and calculate reliability between raters.

Benefits of a profile view

Both trained and untrained observers were able to discriminate between pain and pain-free situations. When comparing assessments between these observers, results showed that undergraduate students relied less on specific pain cues when making their observations.

During physiotherapy, the students also rated pain intensity higher and more accurately when viewing patients’ profiles. This suggests that the assessments of less experienced observers could improve when including a profile view.

Observer accuracy may also benefit from the use of computerized systems, particularly when viewing patients from the front.

Download the free FaceReader methodology note to learn more about facial expression analysis theory.

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Interested in human behavior research? Read more on 'How to study human behavior' !

• Lawry, S.; Popovic, V.; Blackler, A.; Thompson, H. (2019). Age, familiarity, and intuitive use: an empirical investigation. Applied Ergonomics , 74 , 74-84.
• Browne, E.; Hadjistavropoulos, T.; Prkachin, K.; Ashraf, A.; Taati, B. (2019). Pain expressions in dementia: validity of observers’ pain judgments as a function of angle of observation. Journal of Nonverbal Behavior , https://doi.org/10.1007/s10919-019-00303-4.

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• AI, Your Side Hustle Hero to Make Money from Home

Think AI is just for super-smart scientists? Nope! There are tons of ways you can use AI to make some serious cash from home.

Ready to turn your tech-love into a side hustle? Here are some amazing ideas:

The Content Creation Powerhouse

1. ai, your writing buddy.

Imagine having a writing assistant that never gets tired, bored, or has writer’s block! AI tools like Jasper and Rytr help you crank out blog posts, website copy, product descriptions, and even creative stuff like poems or short stories.

You can write paid articles for websites or companies, or use AI to make your own online business shine with tons of fresh content. AI can even help you find ideas and make sure your writing is on point!

Read : 5 ChatGPT Prompts to Drive Business Growth and Innovation

2. Pics in a Flash

Ever wish you could draw anything you imagine? AI image generators like DALL-E and Midjourney let you do just that!

Just type in what you want to see – like “a cat flying a spaceship” or “a watercolor landscape of a hidden waterfall” – and the AI will create it.

You can sell your images on stock photo sites, use them to make your blog posts stand out, or turn them into cool digital art pieces you can sell online.

3. Movie Magician

AI can help you become the next editing superstar! Services can cut and paste video clips, add background music, and even turn your dialogue into subtitles for different languages.

Imagine helping YouTubers make their videos snappier, or editing short videos for businesses– it’s a skill you could even be paid for!

AI Expert Services

1. the global chat champ.

If you know another language (or more!), AI translation tools become your BFFs. Instead of taking forever on translations, AI does the basic work, and you fine-tune it for accuracy and style.

Get gigs translating websites so they reach worldwide markets, translating important documents, or even adding subtitles so movies and videos can be enjoyed by everyone!

2. Meet Your Robot Assistant

Businesses want to offer help 24/7, but that’s impossible for humans! That’s where chatbots come in.

Platforms like Dialogflow let you “train” little AI assistants to answer common questions, take orders, or gather information from potential customers even when everyone’s asleep.

It’s like coding and customer service rolled into one cool job.

3. Data Detective

AI is amazing at spotting patterns way too huge for humans to see alone. You can offer services by using AI to analyze mountains of social media chatter to see what people really think about products or brands.

Investors might pay you to use AI to spot stock market trends. Businesses might want you to use AI to track their rivals and see what sneaky plans they might be hatching!

Affiliate Marketing with a Techy Twist

1. ai reviewer: the ultimate comparison tool.

Imagine being able to gather tons of information on different products in a flash! That’s what AI lets you do. Let’s say you want to review the best robot vacuums.

AI tools can help you scrape customer reviews, compare features across different brands, and even summarize the pros and cons. You write awesome reviews, include your special affiliate links, and whenever someone clicks your link and buys, you earn cash.

It’s like being a super-helpful shopping guide and getting paid for it!

2. Super-Niche Websites: Your Secret Weapon

Sometimes the biggest money isn’t in the broadest topics, but the super-focused ones. Imagine a website not about pets, but all about the cutest outfits for teacup poodles! AI tools help you find these “micro-niches” – topics huge groups of people are obsessed with but might have fewer websites dedicated to them.

Once you’ve picked your niche, AI can help with everything else. It can find keywords that help people find your site, suggest tons of article ideas, and even help you write some of the content.

AI can also help you find products related to your niche to promote with affiliate links, making your awesome website into a money-making machine.

Build Your AI Empire

1. tool time: coding for cash.

If you have some coding skills, you can build super-useful, bite-sized AI tools that people will happily pay for. Think of common problems people have: resizing a ton of images is a pain, long articles can be a drag to read, and catching every single grammar error is tough.

You could build simple tools that offer AI-powered solutions – a quick image resizer, an article summarizer, or an extra-smart grammar checker. Sell these tools on online marketplaces, and suddenly you’re not just using AI, you’re selling it!

2. Plugin Power: Supercharge Popular Programs

Do you know your way around popular software like Photoshop, Excel, or even game design programs? You can become a plugin superstar! Create little add-ons that use AI to do cool new things.

Maybe your plugin adds AI filters to Photoshop, or teaches Excel to predict future patterns based on the data. Find platforms that allow developers to sell plugins for their software, and your creations could make you money while helping others work smarter.

AI Business Sensei: The Ultimate Consultant

If you get really good with AI, and understand how businesses work, you could make serious money as a consultant. Companies often have no idea how to start using AI to their advantage.

You could be the expert that helps them! Teach them how AI can find them new customers, help them analyze huge amounts of data to make better decisions, or even automate some parts of their business to save them time and money.

As AI gets more important, companies will be desperate for consultants like you!

Things to Remember:

Humans still needed: the ai hype is real, but….

AI is a powerful tool, but it’s still just that – a tool. It can mess up, make stuff that’s just plain weird, or even be used for harmful things if we’re not careful. That’s where you come in!

Your job is to double-check AI’s work, make sure it sounds natural and makes sense, add your own creative spark, and be the one to make sure the AI is doing good, not harm.

Find Your Thing: Be the Specialist

Trying to be an expert in everything AI-related is a recipe for a headache. Instead, become known as THE person for something specific.

Are you the best AI product reviewer for tech gadgets? The go-to person for building customer service chatbots? The genius who finds hidden stock market patterns using AI?

Specializing makes it easier for clients to find you and know exactly what you can do for them.

Never Stop Learning: The AI Train Keeps Rolling

AI technology changes at lightning speed! New tools, techniques, and updates are happening all the time. To stay ahead of the game, you’ve got to be curious and willing to learn.

Subscribe to tech newsletters, mess around with new AI programs as they come out, and take online courses. The more you know about cutting-edge AI, the more valuable your skills become!

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• Modern Ways to Earn Money from Home in 2024

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Writing Lab Report Observations: Your Ultimate Guide

Published 30 Aug 2023

The key to an effective observation is the absence of subjectivity — it ensures all the data is consistent and accurate. That’s when some students start looking for answers on how to write observations in a lab report and make them adhere to all the best standards.  

While the observations section may be the humble beginning of a lab report, it forms the bedrock of scientific investigation . It ensures that your research is built upon a foundation of transparency, trust, and precision, making it an indispensable component of the scientific process.

In this introduction, we'll explore the role the observations section plays, along with practical steps and examples for making an observation.  

Why are lab report observations important for working with data? 

The "Observations" section in a lab report often appears deceptively simple: it’s a collection of notes about what you saw, heard, smelled, or otherwise experienced during an experiment. 

However, beneath its straightforward surface lies a crucial component of scientific inquiry . This section captures the raw data from your observations during the experiment, unfiltered by interpretation or analysis. This can include anything from temperature readings and color changes to the time it took for a reaction to occur or the physical appearance of a specimen.

The purpose of this section is twofold:

• First, it serves as a factual record that allows anyone reading the report to understand precisely what transpired during the experiment. 
• Second, it plays a vital role in research integrity. Providing a transparent account of your observations allows others to verify and replicate your experiment ─ a cornerstone of the scientific method.

The observation section is linked to the scientific method, the systematic process scientists use to investigate natural phenomena. In this context, it fits snugly into the "observation" phase, where researchers gather data through their senses before moving on to analysis and conclusions.

One of the primary principles of the scientific method is transparency. Science thrives on open access to information and reproducibility. The observations section embodies this principle by documenting what was witnessed, heard, or measured without interpretation. When you make an observation , others should be able to replicate your experiment precisely based on your observations, fostering trust and reliability in the scientific community.

Preparing to write observations: the importance of accuracy and objectivity

Before diving into the writing of observations in a lab report, it's essential to understand the role accuracy and objectivity play in the scientific process. Observations serve as the cornerstone of your experiment, and any inaccuracies or biases can compromise the integrity of your research.

Why is accuracy important?

• Accurate observations are the foundation of valid data. They provide a reliable representation of what occurred during the experiment, allowing others to trust and replicate your results.
• Inaccurate observations can undermine the credibility of your entire study. Precision and attention to detail are essential to demonstrate your commitment to rigorous scientific inquiry.

Why is objectivity important? 

• Objectivity in observations helps guard against bias. Researchers should strive to record what they see, hear, or measure without injecting personal opinions, expectations, or interpretations.
• Objectivity enables the reproducibility of experiments. When others can rely on your observations as an impartial account, it becomes easier for them to replicate your study accurately.

Take notes during the experiment like a pro 

Now that we've underscored the importance of accuracy and objectivity in observations, let's explore some practical tips for taking notes during the experiment. 

Be detailed

Record as much relevant information as possible. Include measurements, times, quantities, and any unusual occurrences. The more comprehensive your notes, the better you can reconstruct the experiment later.

Use clear language

Avoid vague terms like "a lot" or "a little" and opt for specific descriptions such as "5 milliliters" or "a 20-degree angle."

Stay unbiased

You should be making a lab report hypothesis , but make sure you stay objective. Describe what you observe without making judgments or interpretations. Stick to the facts, even if they don't meet your expectations.

Document changes

If there are changes or developments during the experiment, note them promptly. Document it in real-time, whether it's a color change, a sudden temperature drop, or a reaction time.

Include timestamps for significant events or changes. This can help establish the timeline of the experiment accurately.

Photographic evidence

If applicable, take photographs or videos of critical stages or results. These visual records can be valuable supplements to your written observations.

Writing observations for a lab report: ultimate guide 

Writing the observations section is a step that requires clarity, precision, and adherence to the scientific method. Here's a step-by-step guide to help you craft this essential component effectively:

1. Begin with a clear heading.

Start your Observations section with a clear and descriptive heading. For example, "Observations of Chemical Reaction Experiment."

2. Provide context.

Explain the experiment or procedure briefly to give readers context for your observations. Additionally, explain what you were investigating and why.

3. Structure your observations.

Organize your observations systematically, using headings or subheadings to separate different aspects of it ─ this step will also help you further with the lab report analysis . This could include time-based observations, physical changes, measurements, and more.

4. Include raw data.

Present your observations in a raw, unprocessed form. Include measurements, quantities, and precise data points. For example, if you measured a temperature, record it as "25°C" rather than "warm."

5. Differentiate between observations and interpretations.

Maintain a clear distinction between what you observed and any interpretations or conclusions. Describe what you saw, heard, or measured without offering explanations or analysis at this stage.

6. Be objective.

Maintain objectivity throughout your Observations section. Stick to the facts and avoid injecting personal opinions, hypotheses, or conclusions. Your goal is to provide neutral and unbiased findings. 

7. Ask for a peer review.

Consider having a colleague or mentor review your observations section for feedback and engage them in discussion to ensure it is clear and follows the scientific method.

Effective VS ineffective observations: examples 

What makes an observation good? In the example of the  lab report for chemistry , you can see that observation is specific and quantitative, using descriptive language that conveys precise details about what was observed. Such reports avoid vague terms and subjective judgments. Ineffective observations. On the other hand, they lack specificity, use imprecise language, and often rely on subjective interpretations without clear, factual descriptions.

Effective observations

Physics: "The pendulum reached its highest point on the third swing and then gradually began to slow down, eventually coming to a complete stop after 12 swings."

• Why it's effective: It provides a specific count of swings and clearly describes what happened during the experiment.

Chemistry: "Upon adding 10 ml of sulfuric acid to the solution, a vigorous effervescence occurred, releasing gas bubbles with a distinct, pungent odor."

• Why it's effective: It quantifies the amount added (10 ml), describes the reaction (effervescence), and even adds sensory details (distinct, pungent odor).

Ineffective observations

Physics: "The pendulum swung for a while, and then it stopped."

• Why it's ineffective: It lacks specificity and a precise count of swings, making it unclear and imprecise.

Chemistry: "The reaction was really cool; it bubbled up and smelled bad."

• Why it's ineffective: It uses vague language ("cool," "bubbled up") and doesn't quantify the reactants or describe the odor accurately.

What leads to good observations in a lab report? 

It’s not uncommon for students and researchers to make certain mistakes in lab report writing that can affect the clarity and quality of their reports. Here are some common mistakes and tips on how to avoid them:

🚫 Using overly complex language or technical jargon when simpler terms suffice can make your report less accessible.

How to solve : Straightforwardly explain complex concepts and define technical terms when first introduced.

🚫 Providing a hasty or incomplete conclusion can leave readers with unanswered questions.

How to solve: Take the time to analyze your data and provide a well-considered conclusion thoroughly. Discuss the implications of your findings and their significance in the broader context of your research.

🚫 Omitting important data or failing to record observations thoroughly can weaken the validity of your conclusions .

How to solve: Carefully record all data during the experiment, and ensure that your observations are comprehensive. Include all relevant information, even if it seems insignificant at the time.

Wrapping up

Observations lay the foundation for the entire scientific process. They provide the raw data upon which hypotheses are tested, experiments are replicated, and breakthroughs are made. Key points to remember include:

• Accuracy and objectivity are paramount in observations. Your goal is to provide precise, unbiased, and uninterpreted data.
• When preparing to write observations, effective note-taking is crucial. Be detailed, use clear language, document changes, and organize your notes.
• Observations should include raw data and measurements presented clearly and concisely.
• Maintain a neutral tone, avoiding subjective language.

Meticulous and unbiased observations let others build upon your work, verify your findings, and collectively advance human knowledge. By prioritizing clarity and precision in your observations, you contribute to science's growth and integrity. If you ask for help, you can search for the services of lab report writers   ─ they will gladly help you with observations!

Additional resources

• Purdue OWL offers comprehensive guidance on writing lab reports, including a sample report and observation tips.
• Science Buddies provides a collection of sample lab reports, including observation sections, for various experiments.
• Online writing tools and software designed for scientific writing that can assist in formatting and structuring your lab reports. Tools like LaTeX, Overleaf, or Scrivener are popular choices among researchers.

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Observational Research Examples (Tips for Students)

June 28, 2023 by Chukwuemeka Gabriel Leave a Comment

What is observational research, what are the examples, and ad why is it important to do observational research?

We will be discussing observational research and a few examples that have been conducted to monitor behaviours.

Observational research involves monitoring the behaviour of people or animals in their natural environment. Famous English primatologist. Dame Jane Morris Goodall spent over 5 decades observing chimpanzees in Africa.

Just like the famous English primatologist, other researchers also observe individuals in their natural environment. For example, an educational researcher will conduct observation in the classroom to better understand how students learn.

What Is Observational Research?

Observational research involves monitoring the action of individuals or animals in their natural environment. It also refers to several types of non-experimental studies where behaviour is observed and recorded as well.

The aim of observational research is to define a variable or sets of variables. Observational research is usually conducted to obtain specific characteristics of an individual or a group.

Researchers can also conduct observational research to observe the behaviours of animals in the wild.

Generally, the data collected in observational research studies are usually quantitative.

Also Read: 10 Free Rider Problem Examples (Tips for Students)

Why Do Observational Research?

Anthropologists and psychologists usually conduct observational research because;

Anthropologists usually study cultures and indigenous people in remote areas. Some of the isolated indigenous people live in the Amazon, Central Africa, and several islands on the Pacific.

Living in isolation means that the only way to observe these indigenous people’s behaviour is to visit their natural environment.

For psychologists, observing a person’s behaviour in a laboratory setting is not natural. It feels more artificial to study an individual’s character in a lab.

However, people will change their behaviour once they know their behaviour will later be supervised and analysed by a psychologist.

While observational research also involves observing animal behaviour, there are several phenomena which cannot be observed in a laboratory. For example, observing an animal’s foraging behaviour as well as mate selection.

The only option to determine animal foraging behaviour or mate selection is observational research in their natural environment.

Observational Research Examples

How do researchers observe certain behaviours and early development in humans? One we will look at is linguistic development in children.

Linguistics Development of Children

Children are born with natural survival abilities but learn how to do other things while growing up. For example, an infant only needs his or her mother’s direction to the food source. With their innate abilities, they can suck from their mother’s breast.

However, they need to learn how to speak as they grow up. How they learn to speak from a young age can only be observed while growing up at home.

Before a child gets to first grade, his language skills are already well developed and can communicate just fine with an adult.

For researchers to conduct their study, they need to observe a child at home with the parents. A data collector needs to be around the child to take detailed notes on how parents speak to the child.

Data collected from such observation may be codded to describe the number of words the parents spoke. After analysing the collected data, it might reveal how patterns of parental comments help a child’s linguistic development.

Consumer Product Design

Consumer product design is among the examples of observational research.

This involves a company conducting extensive analyses of how new products will be used by consumers before releasing the product to the market.

The aim is to know and understand consumers’ experiments while using the new product. Certain questions the company need to ask themselves are, does the new product fit perfectly to serve its suppose, and will more consumers buy the new product?

Additionally, the company will invite a small group of prospective customers to the lab. In the lab is a two-way mirror with a qualified team behind it, observing and taking detailed notes of what the small invited group is doing.

Also Read : Institutional Raci sm Examples (Tips for Students)

Jane Goodall’s Research

Famous English primatologist and anthropologist, Dame Jane Morris Goodall observed chimpanzees in the wild for decades.

Goodall carried out her observation by entering the natural habitat of the animals. She took detailed notes of every behaviour she observed in the wild.

Her observations were later transformed into research papers that help the world understand more about animal behaviour.

Goodall’s amazing discovery of chimpanzees using twigs to hunt termites was an outstanding discovery.

Spying on Farms

With the help of infrared imaging software from satellites, companies are able to observe crops anywhere on Earth.

Images from the satellite provide measures of chlorophyll absorption which can help predict yields.

The images also help analysts to know the number of acres and crops planted across the world. In commodities like corn and wheat, the prediction can yield huge profits.

Decision-Making Group Dynamics

When big corporations make huge decisions, there are usually some consequences that are detrimental to the company’s survival.

It’s important to understand the decision-making process and how it works.

Psychological research conducted over the decades has primarily focused on statements that people usually make to each other during important meetings.

To closely observe the interactions of people in meetings, professional observers need to watch meetings from a two-way mirror or observe through CCTV.

This can help identify who said what to whom ad the type of statements made.

Also Read: Checks and Balances Examples (Tips for Students)

Satellite Image of Walmart

Observational research can lead people to spend millions of dollars observing businesses.

According to a report by NPR , stock market analysts observed Walmart parking lots to predict its earnings. Analyst purchase will go on to purchase satellites of selected car parks in a region, a country, or even the entire world.

With the collected data and their market experience, it will be easier to understand customer purchasing patterns.

Ethnographic Research

Ethnography is a kind of observational research which allows the researchers to automatically become part of a specific group or society.

The researcher operates undercover, collects data, and interacts with the group as a member. The researcher keeps his primary duty hidden from the community and secretly observes the natural behaviour of members of the community.

This type of research can help us understand how small groups will accept a stranger in their midst.

Time and Motion Studies

This type of observational research involves observing the work process in the office. The aim is to make procedures to be more efficient. Achieving this may involve reducing the number of movements required to complete a task.

By reducing the movement needed to complete a task, efficiency increases and productivity improves. A time and motion study can point out safety issues that might harm employees.

Frank Bunker Gilbreth and his wife Lilian Gilbreth were the early pioneers of the time and motion study.

Also Read: 11 Natural Monopoly Examples (Tips for Students)

A case is a form of observational research which involves the researcher spending more time with a single individual to acquire a detailed understanding of their character.

The researcher may decide to conduct interviews with the individual, take detailed notes, or take video records of the individual’s behaviour.

Generally, case studies provide detailed information which cannot be acquired by studying a large group of people.

Infant Attachment

This type of observational research involves observing mother/infant bonding, one of the examples that involve close research in a natural environment.

Just like Goodall, Mary Ainsworth conducted her research to better understand mother/infant bonding.

In 1954, Mary Ainsworth travelled to Uganda to study mother/infant bonding. Her research took her to several family homes for two years. She interviewed mothers to find out about their parenting practices and took detailed notes as well.

Mary Ainsworth’s research papers were transformed into academic papers and formed the basis for the Strange Situation test.

Observational research involves monitoring the action of individuals closely in their natural environment. Several researchers have conducted observational research to understand certain behaviours.

Observational research tends to provide more insight than observing a subject in a laboratory.

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Gabriel Chukwuemeka is a graduate of Physics; he loves Geography and has in-depth knowledge of Astrophysics. Gabriel is an ardent writer who writes for Stay Informed Group and enjoys looking at the world map when he is not writing.

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• What Is Qualitative Observation? | Definition & Examples

What Is Qualitative Observation? | Definition & Examples

Published on March 18, 2023 by Tegan George . Revised on June 22, 2023.

Qualitative observation is a research method where the characteristics or qualities of a phenomenon are described without using any quantitative measurements or data. Rather, the observation is based on the observer’s subjective interpretation of what they see, hear, smell, taste, or feel.

Qualitative observations can be done using various methods, including direct observation, interviews , focus groups , or case studies . They can provide rich and detailed information about the behavior, attitudes, perceptions, and experiences of individuals or groups.

Table of contents

When to use qualitative observation, examples of qualitative observation, types of qualitative observations, advantages and disadvantages of qualitative observations, other interesting articles, frequently asked questions.

Qualitative observation is a type of observational study , often used in conjunction with other types of research through triangulation . It is often used in fields like social sciences, education, healthcare, marketing, and design. This type of study is especially well suited for gaining rich and detailed insights into complex and/or subjective phenomena.

A qualitative observation could be a good fit for your research if:

• You are conducting exploratory research . If the goal of your research is to gain a better understanding of a phenomenon, object, or situation, qualitative observation is a good place to start.
• When your research topic is complex, subjective, or cannot be examined numerically. Qualitative observation is often able to capture the complexity and subjectivity of human behavior, particularly for topics like emotions, attitudes, perceptions, or cultural practices. These may not be quantifiable or measurable through other methods.
• You are relying on triangulation within your research approach. Qualitative observation is a solid addition to triangulation approaches, where multiple sources of data are used to validate and verify research findings.

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Qualitative observation is commonly used in marketing to study consumer behavior, preferences, and attitudes towards products or services.

During the focus group, you focus particularly on qualitative observations, taking note of the participants’ facial expressions, body language, word choice, and tone of voice.

Qualitative observation is often also used in design fields, to better understand user needs, preferences, and behaviors. This can aid in the development of products and services that better meet user needs.

You are particularly focused on any usability issues that could impact customer satisfaction. You run a series of testing sessions, focusing on reactions like facial expressions, body language, and verbal feedback.

There are several types of qualitative observation. Here are some of the most common types to help you choose the best one for your work.

Qualitative observations are a great choice of research method for some projects, but they definitely have their share of disadvantages to consider.

Advantages of qualitative observations

• Qualitative observations allow you to generate rich and nuanced qualitative data —aiding you in understanding a phenomenon or object and providing insights into the more complex and subjective aspects of human experience.
• Qualitative observation is a flexible research method that can be adjusted based on research goals and timeline. It also has the potential to be quite non-intrusive, allowing observation of participants in their natural settings without disrupting or influencing their behavior.
• Qualitative observation is often used in combination with other research methods, such as interviews or surveys , to provide a more complete picture of the phenomenon being studied. This triangulation can help improve the reliability and validity of the research findings.

Disadvantages of qualitative observations

• Like many observational studies, qualitative observations are at high risk for many research biases , particularly on the side of the researcher in the case of observer bias . These biases can also bleed over to the participant size, in the case of the Hawthorne effect or social desirability bias .
• Qualitative observations are typically based on a small sample size , which makes them very unlikely to be representative of the larger population. This greatly limits the generalizability of the findings if used as a standalone method, and the data collection process can be long and onerous.
• Like other human subject research, qualitative observation has its share of ethical considerations to keep in mind and protect, particularly informed consent, privacy, and confidentiality.

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If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

Data analysis in qualitative observation often involves searching for any recurring patterns, themes, and categories in your data. This process may involve coding the data, developing conceptual frameworks or models, and conducting thematic analysis . This can help you generate strong hypotheses or theories based on your data.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

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Associations between deep venous thrombosis and thyroid diseases: a two-sample bidirectional Mendelian randomization study

• Lifeng Zhang 1   na1 ,
• Kaibei Li 2   na1 ,
• Qifan Yang 1 ,
• Yao Lin 1 ,
• Caijuan Geng 1 ,
• Wei Huang 1 &
• Wei Zeng 1  

European Journal of Medical Research volume  29 , Article number:  327 ( 2024 ) Cite this article

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Some previous observational studies have linked deep venous thrombosis (DVT) to thyroid diseases; however, the findings were contradictory. This study aimed to investigate whether some common thyroid diseases can cause DVT using a two-sample Mendelian randomization (MR) approach.

This two-sample MR study used single nucleotide polymorphisms (SNPs) identified by the FinnGen genome-wide association studies (GWAS) to be highly associated with some common thyroid diseases, including autoimmune hyperthyroidism (962 cases and 172,976 controls), subacute thyroiditis (418 cases and 187,684 controls), hypothyroidism (26,342 cases and 59,827 controls), and malignant neoplasm of the thyroid gland (989 cases and 217,803 controls. These SNPs were used as instruments. Outcome datasets for the GWAS on DVT (6,767 cases and 330,392 controls) were selected from the UK Biobank data, which was obtained from the Integrative Epidemiology Unit (IEU) open GWAS project. The inverse variance weighted (IVW), MR-Egger and weighted median methods were used to estimate the causal association between DVT and thyroid diseases. The Cochran’s Q test was used to quantify the heterogeneity of the instrumental variables (IVs). MR Pleiotropy RESidual Sum and Outlier test (MR-PRESSO) was used to detect horizontal pleiotropy. When the causal relationship was significant, bidirectional MR analysis was performed to determine any reverse causal relationships between exposures and outcomes.

This MR study illustrated that autoimmune hyperthyroidism slightly increased the risk of DVT according to the IVW [odds ratio (OR) = 1.0009; p  = 0.024] and weighted median methods [OR = 1.001; p  = 0.028]. According to Cochran’s Q test, there was no evidence of heterogeneity in IVs. Additionally, MR-PRESSO did not detect horizontal pleiotropy ( p  = 0.972). However, no association was observed between other thyroid diseases and DVT using the IVW, weighted median, and MR-Egger regression methods.

Conclusions

This study revealed that autoimmune hyperthyroidism may cause DVT; however, more evidence and larger sample sizes are required to draw more precise conclusions.

Introduction

Deep venous thrombosis (DVT) is a common type of disease that occurs in 1–2 individuals per 1000 each year [ 1 ]. In the post-COVID-19 era, DVT showed a higher incidence rate [ 2 ]. Among hospitalized patients, the incidence rate of this disease was as high as 2.7% [ 3 ], increasing the risk of adverse events during hospitalization. According to the Registro Informatizado Enfermedad Tromboembolica (RIETE) registry, which included data from ~ 100,000 patients from 26 countries, the 30-day mortality rate was 2.6% for distal DVT and 3.3% for proximal DVT [ 4 ]. Other studies have shown that the one-year mortality rate of DVT is 19.6% [ 5 ]. DVT and pulmonary embolism (PE), collectively referred to as venous thromboembolism (VTE), constitute a major global burden of disease [ 6 ].

Thyroid diseases are common in the real world. Previous studies have focused on the relationship between DVT and thyroid diseases, including thyroid dysfunction and thyroid cancer. Some case reports [ 7 , 8 , 9 ] have demonstrated that hyperthyroidism is often associated with DVT and indicates a worse prognosis [ 10 ]. The relationship between thyroid tumors and venous thrombosis has troubled researchers for many years. In 1989, the first case of papillary thyroid carcinoma presenting with axillary vein thrombosis as the initial symptom was reported [ 11 ]. In 1995, researchers began to notice the relationship between thyroid tumors and hypercoagulability [ 12 ], laying the foundation for subsequent extensive research. However, the aforementioned observational studies had limitations, such as small sample sizes, selection bias, reverse causality, and confounding factors, which may have led to unreliable conclusions [ 13 ].

Previous studies have explored the relationship of thyroid disease and DVT and revealed that high levels of thyroid hormones may increase the risk of DVT. Hyperthyroidism promotes a procoagulant and hypofibrinolytic state by affecting the von Willebrand factor, factors VIII, IV, and X, fibrinogen, and plasminogen activator inhibitor-1 [ 14 , 15 ]. At the molecular level, researchers believe that thyroid hormones affect coagulation levels through an important nuclear thyroid hormone receptor (TR), TRβ [ 16 ], and participate in pathological coagulation through endothelial dysfunction. Thyroid hormones may have non-genetic effects on the behavior of endothelial cells [ 17 , 18 ]. In a study regarding tumor thrombosis, Lou [ 19 ] found that 303 circular RNAs were differentially expressed in DVT using microarray. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the most significantly enriched pathways included thyroid hormone-signaling pathway and endocytosis, and also increased level of proteoglycans in cancer. This indicated that tumor cells and thyroid hormones might interact to promote thrombosis. Based on these studies, we speculated that thyroid diseases, including thyroid dysfunction and thyroid tumors, may cause DVT.

Mendelian randomization (MR) research is a causal inference technique that can be used to assess the causal relationship and reverse causation between specific exposure and outcome factors. If certain assumptions [ 20 ] are fulfilled, genetic variants can be employed as instrumental variables (IVs) to establish causal relationships. Bidirectional MR analysis can clarify the presence of reverse causal relationships [ 21 ], making the conclusions more comprehensive. Accordingly, we aimed to apply a two-sample MR strategy to investigate whether DVT is related to four thyroid diseases, including autoimmune hyperthyroidism, subacute thyroiditis, hypothyroidism, and thyroid cancer.

Study design

MR relies on single nucleotide polymorphisms (SNPs) as IVs. The IVs should fulfill the following three criteria [ 22 ]: (1) IVs should be strongly associated with exposure. (2) Genetic variants must be independent of unmeasured confounding factors that may affect the exposure–outcome association. (3) IVs are presumed to affect the outcome only through their associations with exposure (Fig.  1 ). IVs that met the above requirements were used to estimate the relationship between exposure and outcome. Our study protocol conformed to the STROBE-MR Statement [ 23 ], and all methods were performed in accordance with the relevant guidelines and regulations.

The relationship between instrumental variables, exposure, outcome, and confounding factors

Data sources and instruments

Datasets (Table  1 ) in this study were obtained from a publicly available database (the IEU open genome-wide association studies (GWAS) project [ 24 ] ( https://gwas.mrcieu.ac.uk )). There was no overlap in samples between the data sources of outcome and exposures. Using de-identified summary-level data, privacy information such as overall age and gender were hidden. Ethical approval was obtained for all original work. This study complied with the terms of use of the database.

MR analysis was performed using the R package “TwoSampleMR”. SNPs associated with each thyroid disease at the genome-wide significance threshold of p  < 5.0 × 10 –8 were selected as potential IVs. To ensure independence between the genetic variants used as IVs, the linkage disequilibrium (LD) threshold for grouping was set to r 2  < 0.001 with a window size of 10,000 kb. The SNP with the lowest p -value at each locus was retained for analyses.

Statistical analysis

Multiple MR methods were used to infer causal relationships between thyroid diseases and DVT, including the inverse variance weighted (IVW), weighted median, and MR-Egger tests, after harmonizing the SNPs across the GWASs of exposures and outcomes. The main analysis was conducted using the IVW method. Heterogeneity and pleiotropy were also performed in each MR analysis. Meanwhile, the MR-PRESSO Global test [ 25 ] was utilized to detect horizontal pleiotropy. The effect trend of SNP was observed through a scatter plot, and the forest plot was used to observe the overall effects. When a significant causal relationship was confirmed by two-sample MR analysis, bidirectional MR analysis was performed to assess reverse causal relationships by swapping exposure and outcome factors. Parameters were set the same as before. All abovementioned statistical analyses were performed using the package TwoSampleMR (version 0.5.7) in the R program (version 4.2.1).

After harmonizing the SNPs across the GWASs for exposures and outcomes, the IVW (OR = 1.0009, p  = 0.024, Table  2 ) and weighted median analyses (OR = 1.001, p  = 0.028) revealed significant causal effects between autoimmune hyperthyroidism and DVT risk. Similar results were observed using the weighted median approach Cochran’s Q test, MR-Egger intercept, and MR-PRESSO tests suggested that the results were not influenced by pleiotropy and heterogeneity (Table  2 ). However, the leave-one-out analysis revealed a significant difference after removing some SNPs (rs179247, rs6679677, rs72891915, and rs942495, p  < 0.05, Figure S2a), indicating that MR results were dependent on these SNPs (Figure S2, Table S1). No significant effects were observed in other thyroid diseases (Table  2 ). The estimated scatter plot of the association between thyroid diseases and DVT is presented in Fig.  2 , indicating a positive causal relationship between autoimmune hyperthyroidism and DVT (Fig.  2 a). The forest plots of single SNPs affecting the risk of DVT are displayed in Figure S1.

The estimated scatter plot of the association between thyroid diseases and DVT. MR-analyses are derived using IVW, MR-Egger, weighted median and mode. By fitting different models, the scatter plot showed the relationship between SNP and exposure factors, predicting the association between SNP and outcomes

Bidirectional MR analysis was performed to further determine the relationship between autoimmune hyperthyroidism and DVT. The reverse causal relationship was not observed (Table S2), which indicated that autoimmune hyperthyroidism can cause DVT from a mechanism perspective.

This study used MR to assess whether thyroid diseases affect the incidence of DVT. The results showed that autoimmune hyperthyroidism can increase the risk of DVT occurrence, but a reverse causal relationship was not observed between them using bidirectional MR analysis. However, other thyroid diseases, such as subacute thyroiditis, hypothyroidism, and thyroid cancer, did not show a similar effect.

Recently, several studies have suggested that thyroid-related diseases may be associated with the occurrence of DVT in the lower extremities, which provided etiological clues leading to the occurrence of DVT in our subsequent research. In 2006, a review mentioned the association between thyroid dysfunction and coagulation disorders [ 26 ], indicating a hypercoagulable state in patients with hyperthyroidism. In 2011, a review further suggested a clear association between hypothyroidism and bleeding tendency, while hyperthyroidism appeared to increase the risk of thrombotic events, particularly cerebral venous thrombosis [ 27 ]. A retrospective cohort study [ 28 ] supported this conclusion, but this study only observed a higher proportion of concurrent thyroid dysfunction in patients with cerebral venous thrombosis. The relationship between thyroid function and venous thromboembolism remains controversial. Krieg VJ et al. [ 29 ] found that hypothyroidism has a higher incidence rate in patients with chronic thromboembolic pulmonary hypertension and may be associated with more severe disease, which seemed to be different from previous views that hyperthyroidism may be associated with venous thrombosis. Alsaidan [ 30 ] also revealed that the risk of developing venous thrombosis was almost increased onefold for cases with a mild-to-moderate elevation of thyroid stimulating hormone and Free thyroxine 4(FT4). In contrast, it increased twofold for cases with a severe elevation of thyroid stimulating hormone and FT4. Raised thyroid hormones may increase the synthesis or secretion of coagulation factors or may decrease fibrinolysis, which may lead to the occurrence of coagulation abnormality.

Other thyroid diseases are also reported to be associated with DVT. In a large prospective cohort study [ 31 ], the incidence of venous thromboembolism was observed to increase in patients with thyroid cancer over the age of 60. However, other retrospective studies did not find any difference compared with the general population [ 32 ]. In the post-COVID-19 era, subacute thyroiditis has received considerable attention from researchers. New evidence suggests that COVID-19 may be associated with subacute thyroiditis [ 33 , 34 ]. Mondal et al. [ 35 ] found that out of 670 COVID-19 patients, 11 presented with post-COVID-19 subacute thyroiditis. Among them, painless subacute thyroiditis appeared earlier and exhibited symptoms of hyperthyroidism. Another case report also indicated the same result, that is, subacute thyroiditis occurred after COVID-19 infection, accompanied by thyroid function changes [ 36 ]. This led us to hypothesize that subacute thyroiditis may cause DVT through alterations in thyroid function.

This study confirmed a significant causal relationship between autoimmune hyperthyroidism and DVT ( p  = 0.02). The data were tested for heterogeneity and gene pleiotropy using MR-Egger, Cochran’s Q, and MR-PRESSO tests. There was no evidence that the results were influenced by pleiotropy or heterogeneity. In the leave-one-out analysis, four of the five selected SNPs showed significant effects of autoimmune hyperthyroidism on DVT, suggesting an impact of these SNPs on DVT outcome. Previous studies have focused on the relationship between hyperthyroidism and its secondary arrhythmias and arterial thromboembolism [ 37 , 38 ]. This study emphasized the risk of DVT in patients with hyperthyroidism, which has certain clinical implications. Prophylactic anticoagulant therapy was observed to help prevent DVT in patients with hyperthyroidism. Unfortunately, the results of this study did not reveal any evidence that suggests a relationship between other thyroid diseases and DVT occurrence. This may be due to the limited database, as this study only included the GWAS data from a subset of European populations. Large-scale multiracial studies are needed in the future.

There are some limitations to this study. First, it was limited to participants of European descent. Consequently, further investigation is required to confirm these findings in other ethnicities. Second, this study did not reveal the relationship between complications of hyperthyroidism and DVT. Additionally, this study selected IVs from the database using statistical methods rather than selecting them from the real population. This may result in weaker effects of the screened IVs and reduce the clinical significance of MR analysis. Moreover, the definitions of some diseases in this study were not clear in the original database, and some of the diseases were self-reported, which may reduce the accuracy of diagnosis. Further research is still needed to clarify the causal relationship between DVT and thyroid diseases based on prospective cohort and randomized controlled trials (RCTs).

This study analyzed large-scale genetic data and provided evidence of a causal relationship between autoimmune hyperthyroidism and the risk of DVT, Compared with the other thyroid diseases investigated. Prospective RCTs or MR studies with larger sample sizes are still needed to draw more precise conclusions.

Availability of data and materials

The IEU open gwas project, https://gwas.mrcieu.ac.uk/

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Lifeng Zhang and Kaibei Li have contributed equally to this work and share the first authorship.

Authors and Affiliations

Department of Vascular Surgery, Hospital of Chengdu University of Traditional Chinese Medicine, No. 39, Shierqiao Road, Jinniu District, Chengdu, 610072, Sichuan, People’s Republic of China

Lifeng Zhang, Qifan Yang, Yao Lin, Caijuan Geng, Wei Huang & Wei Zeng

Disinfection Supply Center, Hospital of Chengdu University of Traditional Chinese Medicine, No. 39, Shierqiao Road, Jin Niu District, Chengdu, 610072, Sichuan, People’s Republic of China

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Conception and design: LFZ and WZ. Analysis and interpretation: LFZ, KBL and WZ. Data collection: LFZ, QFY, YL, CJG and WH. Writing the article: LFZ, KBL. Critical revision of the article: LFZ, GFY and WZ. Final approval of the article: LFZ, KBL, YL, CJG, WH, QFY and WZ. Statistical analysis: YL, QFY.

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Correspondence to Wei Zeng .

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Zhang, L., Li, K., Yang, Q. et al. Associations between deep venous thrombosis and thyroid diseases: a two-sample bidirectional Mendelian randomization study. Eur J Med Res 29 , 327 (2024). https://doi.org/10.1186/s40001-024-01933-1

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Received : 12 September 2023

Accepted : 09 June 2024

Published : 14 June 2024

DOI : https://doi.org/10.1186/s40001-024-01933-1

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New study bolsters evidence rare genetic mutation can delay early Alzheimer’s

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F or members of a large extended Colombian family, an early Alzheimer’s diagnosis is practically a grim guarantee. But new research further supports the idea that a rare genetic mutation can delay the devastating disease’s onset.

An international team of researchers identified 27 individuals within this extended family who carried both a genetic variant that guaranteed they’d develop Alzheimer’s and a single copy of the so-called Christchurch mutation. They found that people with a single copy of this rare mutation, or heterozygotes, developed mild cognitive impairment at median ages of 52 and dementia at 54, while members of this family who were destined to develop Alzheimer’s and lacked the Christchurch mutation showed signs of cognitive impairment and dementia at 47 and 50, respectively.

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Home  >  Blog  >  Update: CVE-2024-4577 quickly weaponized to distribute “TellYouThePass” Ransomware  

Update: CVE-2024-4577 quickly weaponized to distribute “TellYouThePass” Ransomware

Gabi Sharadin

, Daniel Johnston

Jun 10, 2024 2 min read

Introduction

Recently, Imperva Threat Research reported on attacker activity leveraging the new PHP vulnerability, CVE-2024-4577 . From as early as June 8th, we have detected attacker activity leveraging this vulnerability to deliver malware, which we have now identified to be a part of the “TellYouThePass” ransomware campaign.

TellYouThePass is a ransomware that has been seen since 2019, targeting businesses and individuals, in a campaign for both Windows and Linux systems. It commonly leverages CVE-2021-44228 (Apache Log4j), and has also been seen using CVE-2023-46604, among others.

Attack Vector

As we analyzed attacks exploiting this vulnerability, we noticed a few campaigns, including WebShell upload attempts and several attempts to place ransomware on a target system.

The attackers used the known exploit for CVE-2024-3577 to execute arbitrary PHP code on the target system, leveraging the code to use the “ system ” function to run an HTML application file hosted on an attacker-controlled web server via the mshta.exe binary. mshta.exe is a native Windows binary that can execute remote payloads, pointing to the attackers operating in a “living off the land” style.

Sample Analysis

The “TellYouThePass” ransomware campaign has been in operation since 2019 and has taken various forms over the years . Recently observed variants have taken the form of .NET samples delivered using HTML applications [1] [2] .

The initial infection is performed with the use of an HTA file (dd3.hta), which contains a malicious VBScript. The VBScript contains a long base64 encoded string, which when decoded reveals bytes of a binary, which are loaded into memory during runtime. 

When the bytes are extracted and inspected, we can deduce the intent behind the infection. The extracted bytes reveal a serialized method, which loads a Portable Executable (PE) into memory during runtime. 

Analysis of this executable reveals a .NET variant of the “TellYouThePass” ransomware, which upon further analysis, reveals the core functionality.

Upon initial execution, the sample sends an HTTP request to the command-and-control (C2) server containing details about the infected machine as a notification of infection. The callback masquerades as a request to retrieve CSS resources likely designed to evade detection.

The C2 IP is hardcoded in the sample, and as of the time of writing is relatively unknown:

The binary then enumerates directories, kills processes, generates encryption keys and encrypts files within each enumerated directory that has a defined file extension. Finally the malware then publishes a ReadMe message in the web root directory as “READ_ME10.html”, containing the details required to “TellYouThePass”.

Community Response

Within hours of the initial detection, Imperva Threat Research was able to track discussions about this ransomware on several different online communities, including threads on Bleeping Computer Forums and X .

Imperva Threat Research is tracking this threat, and will update if there are any new developments. 

Recommendations

With new vulnerabilities and ransomware campaigns discovered every day, it’s crucial to defend your systems against threats. 

• Be aware of your applications, and patch rising vulnerabilities as soon as possible.
• Leverage products like Imperva’s Web Application Firewall to stop attacks minutes after they’re discovered in the wild.
• Use Anti-Virus programs to provide a first line of defense against malware campaigns like TellYouThePass. 

URL: hxxp:/88.218.76[.]13/dd3.hta

C2 IP: 88.218.76[.]13

Hash (HTA sample): 95279881525d4ed4ce25777bb967ab87659e7f72235b76f9530456b48a00bac3

Hash (HTA sample): 5a2b9ddddea96f21d905036761ab27627bd6db4f5973b006f1e39d4acb04a618

Hash Extracted .NET binary: 9562AD2C173B107A2BAA7A4986825B52E881A935DEB4356BF8B80B1EC6D41C53

Bitcoin Wallet address: bc1qnuxx83nd4keeegrumtnu8kup8g02yzgff6z53l

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