2.1 Why Is Research Important?
Learning objectives.
By the end of this section, you will be able to:
- Explain how scientific research addresses questions about behavior
- Discuss how scientific research guides public policy
- Appreciate how scientific research can be important in making personal decisions
Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people’s authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth’s continents did not move, and that mental illness was caused by possession ( Figure 2.2 ). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.
The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.
While behavior is observable, the mind is not. If someone is crying, we can see behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This chapter explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.
Use of Research Information
Trying to determine which theories are and are not accepted by the scientific community can be difficult, especially in an area of research as broad as psychology. More than ever before, we have an incredible amount of information at our fingertips, and a simple internet search on any given research topic might result in a number of contradictory studies. In these cases, we are witnessing the scientific community going through the process of reaching a consensus, and it could be quite some time before a consensus emerges. For example, the explosion in our use of technology has led researchers to question whether this ultimately helps or hinders us. The use and implementation of technology in educational settings has become widespread over the last few decades. Researchers are coming to different conclusions regarding the use of technology. To illustrate this point, a study investigating a smartphone app targeting surgery residents (graduate students in surgery training) found that the use of this app can increase student engagement and raise test scores (Shaw & Tan, 2015). Conversely, another study found that the use of technology in undergraduate student populations had negative impacts on sleep, communication, and time management skills (Massimini & Peterson, 2009). Until sufficient amounts of research have been conducted, there will be no clear consensus on the effects that technology has on a student's acquisition of knowledge, study skills, and mental health.
In the meantime, we should strive to think critically about the information we encounter by exercising a degree of healthy skepticism. When someone makes a claim, we should examine the claim from a number of different perspectives: what is the expertise of the person making the claim, what might they gain if the claim is valid, does the claim seem justified given the evidence, and what do other researchers think of the claim? This is especially important when we consider how much information in advertising campaigns and on the internet claims to be based on “scientific evidence” when in actuality it is a belief or perspective of just a few individuals trying to sell a product or draw attention to their perspectives.
We should be informed consumers of the information made available to us because decisions based on this information have significant consequences. One such consequence can be seen in politics and public policy. Imagine that you have been elected as the governor of your state. One of your responsibilities is to manage the state budget and determine how to best spend your constituents’ tax dollars. As the new governor, you need to decide whether to continue funding early intervention programs. These programs are designed to help children who come from low-income backgrounds, have special needs, or face other disadvantages. These programs may involve providing a wide variety of services to maximize the children's development and position them for optimal levels of success in school and later in life (Blann, 2005). While such programs sound appealing, you would want to be sure that they also proved effective before investing additional money in these programs. Fortunately, psychologists and other scientists have conducted vast amounts of research on such programs and, in general, the programs are found to be effective (Neil & Christensen, 2009; Peters-Scheffer, Didden, Korzilius, & Sturmey, 2011). While not all programs are equally effective, and the short-term effects of many such programs are more pronounced, there is reason to believe that many of these programs produce long-term benefits for participants (Barnett, 2011). If you are committed to being a good steward of taxpayer money, you would want to look at research. Which programs are most effective? What characteristics of these programs make them effective? Which programs promote the best outcomes? After examining the research, you would be best equipped to make decisions about which programs to fund.
Link to Learning
Watch this video about early childhood program effectiveness to learn how scientists evaluate effectiveness and how best to invest money into programs that are most effective.
Ultimately, it is not just politicians who can benefit from using research in guiding their decisions. We all might look to research from time to time when making decisions in our lives. Imagine that your sister, Maria, expresses concern about her two-year-old child, Umberto. Umberto does not speak as much or as clearly as the other children in his daycare or others in the family. Umberto's pediatrician undertakes some screening and recommends an evaluation by a speech pathologist, but does not refer Maria to any other specialists. Maria is concerned that Umberto's speech delays are signs of a developmental disorder, but Umberto's pediatrician does not; she sees indications of differences in Umberto's jaw and facial muscles. Hearing this, you do some internet searches, but you are overwhelmed by the breadth of information and the wide array of sources. You see blog posts, top-ten lists, advertisements from healthcare providers, and recommendations from several advocacy organizations. Why are there so many sites? Which are based in research, and which are not?
In the end, research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.
NOTABLE RESEARCHERS
Psychological research has a long history involving important figures from diverse backgrounds. While the introductory chapter discussed several researchers who made significant contributions to the discipline, there are many more individuals who deserve attention in considering how psychology has advanced as a science through their work ( Figure 2.3 ). For instance, Margaret Floy Washburn (1871–1939) was the first woman to earn a PhD in psychology. Her research focused on animal behavior and cognition (Margaret Floy Washburn, PhD, n.d.). Mary Whiton Calkins (1863–1930) was a preeminent first-generation American psychologist who opposed the behaviorist movement, conducted significant research into memory, and established one of the earliest experimental psychology labs in the United States (Mary Whiton Calkins, n.d.).
Francis Sumner (1895–1954) was the first African American to receive a PhD in psychology in 1920. His dissertation focused on issues related to psychoanalysis. Sumner also had research interests in racial bias and educational justice. Sumner was one of the founders of Howard University’s department of psychology, and because of his accomplishments, he is sometimes referred to as the “Father of Black Psychology.” Thirteen years later, Inez Beverly Prosser (1895–1934) became the first African American woman to receive a PhD in psychology. Prosser’s research highlighted issues related to education in segregated versus integrated schools, and ultimately, her work was very influential in the hallmark Brown v. Board of Education Supreme Court ruling that segregation of public schools was unconstitutional (Ethnicity and Health in America Series: Featured Psychologists, n.d.).
Although the establishment of psychology’s scientific roots occurred first in Europe and the United States, it did not take much time until researchers from around the world began to establish their own laboratories and research programs. For example, some of the first experimental psychology laboratories in South America were founded by Horatio Piñero (1869–1919) at two institutions in Buenos Aires, Argentina (Godoy & Brussino, 2010). In India, Gunamudian David Boaz (1908–1965) and Narendra Nath Sen Gupta (1889–1944) established the first independent departments of psychology at the University of Madras and the University of Calcutta, respectively. These developments provided an opportunity for Indian researchers to make important contributions to the field (Gunamudian David Boaz, n.d.; Narendra Nath Sen Gupta, n.d.).
When the American Psychological Association (APA) was first founded in 1892, all of the members were White males (Women and Minorities in Psychology, n.d.). However, by 1905, Mary Whiton Calkins was elected as the first female president of the APA, and by 1946, nearly one-quarter of American psychologists were female. Psychology became a popular degree option for students enrolled in the nation’s historically Black higher education institutions, increasing the number of Black Americans who went on to become psychologists. Given demographic shifts occurring in the United States and increased access to higher educational opportunities among historically underrepresented populations, there is reason to hope that the diversity of the field will increasingly match the larger population, and that the research contributions made by the psychologists of the future will better serve people of all backgrounds (Women and Minorities in Psychology, n.d.).
The Process of Scientific Research
Scientific knowledge is advanced through a process known as the scientific method . Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. The types of reasoning within the circle are called deductive and inductive. In deductive reasoning , ideas are tested in the real world; in inductive reasoning , real-world observations lead to new ideas ( Figure 2.4 ). These processes are inseparable, like inhaling and exhaling, but different research approaches place different emphasis on the deductive and inductive aspects.
In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world. If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion). In this example, the hypothesis is correct; therefore, the conclusion is correct as well. Sometimes, however, an incorrect hypothesis may lead to a logical but incorrect conclusion. Consider this argument: all ducks are born with the ability to see. Quackers is a duck. Therefore, Quackers was born with the ability to see. Scientists use deductive reasoning to empirically test their hypotheses. Returning to the example of the ducks, researchers might design a study to test the hypothesis that if all living things require energy to survive, then ducks will be found to require energy to survive.
Deductive reasoning starts with a generalization that is tested against real-world observations; however, inductive reasoning moves in the opposite direction. Inductive reasoning uses empirical observations to construct broad generalizations. Unlike deductive reasoning, conclusions drawn from inductive reasoning may or may not be correct, regardless of the observations on which they are based. For instance, you may notice that your favorite fruits—apples, bananas, and oranges—all grow on trees; therefore, you assume that all fruit must grow on trees. This would be an example of inductive reasoning, and, clearly, the existence of strawberries, blueberries, and kiwi demonstrate that this generalization is not correct despite it being based on a number of direct observations. Scientists use inductive reasoning to formulate theories, which in turn generate hypotheses that are tested with deductive reasoning. In the end, science involves both deductive and inductive processes.
For example, case studies, which you will read about in the next section, are heavily weighted on the side of empirical observations. Thus, case studies are closely associated with inductive processes as researchers gather massive amounts of observations and seek interesting patterns (new ideas) in the data. Experimental research, on the other hand, puts great emphasis on deductive reasoning.
We’ve stated that theories and hypotheses are ideas, but what sort of ideas are they, exactly? A theory is a well-developed set of ideas that propose an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.
A hypothesis is a testable prediction about how the world will behave if our idea is correct, and it is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests Figure 2.5 .
To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later chapter, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.
A scientific hypothesis is also falsifiable , or capable of being shown to be incorrect. Recall from the introductory chapter that Sigmund Freud had lots of interesting ideas to explain various human behaviors ( Figure 2.6 ). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.
In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).
Scientific research’s dependence on falsifiability allows for great confidence in the information that it produces. Typically, by the time information is accepted by the scientific community, it has been tested repeatedly.
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Exploring the Nature and Importance of Psychological Research
Table of Contents
Have you ever wondered why we behave the way we do, or how our minds work? The quest for these answers lies at the heart of psychological research , a field as fascinating as it is fundamental to our understanding of the human and animal psyche. This blog post will take you on a journey through the intricate nature of psychological research, illuminating its methods, applications, and profound impact on various aspects of society.
What is psychological research?
At its core, psychological research is an intricate tapestry of empirical studies and theoretical constructs that seek to decode the vast complexities of behavior and mental processes. Whether it’s the way we learn new information, what motivates us to act, or how we remember past events, psychological research aims to uncover the how and why of these phenomena, setting the stage for a deeper understanding of ourselves and the world around us.
The empirical nature of psychological studies
Psychological research is grounded in empirical evidence , meaning that it relies on observable and measurable data collected through scientific methods. This approach ensures that findings are not just theoretical concepts but are backed by rigorous testing and analysis, lending credibility and reliability to the conclusions drawn.
Theoretical frameworks in psychology
Theories in psychology are not merely abstract ideas but are carefully constructed frameworks that explain and predict behaviors and mental processes. These theories are constantly tested and refined through research, forming the bedrock of our psychological knowledge.
Intersecting psychological research with diverse fields
Psychological research doesn’t exist in a vacuum. Its tentacles reach far into various domains, influencing and being influenced by different fields of study and practice.
Organizational behavior and psychology
In the realm of business and organizational behavior , psychological research helps us understand how to create better work environments, enhance employee motivation, and improve leadership styles. These insights lead to more productive and harmonious workplaces.
Psychology’s role in medical sciences
Medical sciences benefit from psychological research as it offers crucial insights into patient behavior, mental health, and the interplay between psychological well-being and physical health. This multidisciplinary approach enables more holistic healthcare.
Education shaped by psychological findings
Educators and policymakers turn to psychological research to craft curricula that align with how we learn best. From the effectiveness of different teaching methods to the psychological impacts of standardized testing, research informs educational strategies and policies.
The practical applications of psychological research
Understanding the theory is one thing, but seeing it in action is where the true power of psychological research shines. Let’s explore some of the practical ways in which this research shapes our everyday lives and solves real-world problems.
Solving real-world problems
Psychological research has real-world applications that affect every aspect of society. For instance, it can help address social issues such as prejudice and discrimination , improve mental health treatment, and even aid in disaster response strategies by understanding human behavior in crisis situations.
Improving mental health services
The insights gained from psychological studies are crucial in developing effective therapies and interventions for mental health disorders . By understanding the underlying mechanisms of these conditions, psychologists can tailor treatments to better serve those in need.
Enhancing learning and memory
Research into learning and memory has revolutionized educational practices, making them more inclusive and effective. By applying psychological principles, teachers are able to foster environments where students of all backgrounds and abilities can thrive.
Uncovering psychological facts , laws, and theories
Psychological research is a relentless pursuit of knowledge, aiming to establish facts, laws, and theories that explain the inner workings of our minds and behaviors. These foundational elements are critical in building a structured and reliable understanding of psychology.
The quest for psychological facts
A psychological fact is a scientifically verified piece of information about behaviors or mental processes. Through meticulous research, psychologists can determine these facts, which then serve as building blocks for broader laws and theories.
Establishing psychological laws
Laws in psychology are generalizations about behaviors that are consistent and predictable. For example, the law of effect, which states that behaviors followed by positive outcomes are likely to be repeated, is a principle that has stood the test of time and research.
Formulating psychological theories
Theories are comprehensive explanations that connect and make sense of various psychological facts and laws. They are the culmination of extensive research and provide a framework for understanding complex behaviors and mental processes.
This exploration of psychological research underscores its significance in not only advancing our comprehension of human and animal behavior but also in applying this understanding to better our lives and society. It’s an ever-evolving discipline that continues to challenge our perceptions and drive innovation across countless domains.
How do you see psychological research impacting your daily life? And can you think of a problem in your community where psychological insights might provide a solution?
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Research Methods in Psychology
1 Introduction to Psychological Research – Objectives and Goals, Problems, Hypothesis and Variables
- Nature of Psychological Research
- The Context of Discovery
- Context of Justification
- Characteristics of Psychological Research
- Goals and Objectives of Psychological Research
2 Introduction to Psychological Experiments and Tests
- Independent and Dependent Variables
- Extraneous Variables
- Experimental and Control Groups
- Introduction of Test
- Types of Psychological Test
- Uses of Psychological Tests
3 Steps in Research
- Research Process
- Identification of the Problem
- Review of Literature
- Formulating a Hypothesis
- Identifying Manipulating and Controlling Variables
- Formulating a Research Design
- Constructing Devices for Observation and Measurement
- Sample Selection and Data Collection
- Data Analysis and Interpretation
- Hypothesis Testing
- Drawing Conclusion
4 Types of Research and Methods of Research
- Historical Research
- Descriptive Research
- Correlational Research
- Qualitative Research
- Ex-Post Facto Research
- True Experimental Research
- Quasi-Experimental Research
5 Definition and Description Research Design, Quality of Research Design
- Research Design
- Purpose of Research Design
- Design Selection
- Criteria of Research Design
- Qualities of Research Design
6 Experimental Design (Control Group Design and Two Factor Design)
- Experimental Design
- Control Group Design
- Two Factor Design
7 Survey Design
- Survey Research Designs
- Steps in Survey Design
- Structuring and Designing the Questionnaire
- Interviewing Methodology
- Data Analysis
- Final Report
8 Single Subject Design
- Single Subject Design: Definition and Meaning
- Phases Within Single Subject Design
- Requirements of Single Subject Design
- Characteristics of Single Subject Design
- Types of Single Subject Design
- Advantages of Single Subject Design
- Disadvantages of Single Subject Design
9 Observation Method
- Definition and Meaning of Observation
- Characteristics of Observation
- Types of Observation
- Advantages and Disadvantages of Observation
- Guides for Observation Method
10 Interview and Interviewing
- Definition of Interview
- Types of Interview
- Aspects of Qualitative Research Interviews
- Interview Questions
- Convergent Interviewing as Action Research
- Research Team
11 Questionnaire Method
- Definition and Description of Questionnaires
- Types of Questionnaires
- Purpose of Questionnaire Studies
- Designing Research Questionnaires
- The Methods to Make a Questionnaire Efficient
- The Types of Questionnaire to be Included in the Questionnaire
- Advantages and Disadvantages of Questionnaire
- When to Use a Questionnaire?
12 Case Study
- Definition and Description of Case Study Method
- Historical Account of Case Study Method
- Designing Case Study
- Requirements for Case Studies
- Guideline to Follow in Case Study Method
- Other Important Measures in Case Study Method
- Case Reports
13 Report Writing
- Purpose of a Report
- Writing Style of the Report
- Report Writing – the Do’s and the Don’ts
- Format for Report in Psychology Area
- Major Sections in a Report
14 Review of Literature
- Purposes of Review of Literature
- Sources of Review of Literature
- Types of Literature
- Writing Process of the Review of Literature
- Preparation of Index Card for Reviewing and Abstracting
15 Methodology
- Definition and Purpose of Methodology
- Participants (Sample)
- Apparatus and Materials
16 Result, Analysis and Discussion of the Data
- Definition and Description of Results
- Statistical Presentation
- Tables and Figures
17 Summary and Conclusion
- Summary Definition and Description
- Guidelines for Writing a Summary
- Writing the Summary and Choosing Words
- A Process for Paraphrasing and Summarising
- Summary of a Report
- Writing Conclusions
18 References in Research Report
- Reference List (the Format)
- References (Process of Writing)
- Reference List and Print Sources
- Electronic Sources
- Book on CD Tape and Movie
- Reference Specifications
- General Guidelines to Write References
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PSYCH101: Introduction to Psychology
Why research is important.
Read this text, which introduces the scientific method, which involves making a hypothesis or general premise, deductive reasoning, making empirical observations, and inductive reasoning,
Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people's authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth's continents did not move, and that mental illness was caused by possession (Figure 2.2). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.
Figure 2.2 Some of our ancestors, across the world and over the centuries, believed that trephination - the practice of making a hole in the skull, as shown here - allowed evil spirits to leave the body, thus curing mental illness and other disorders.
Use of Research Information
Trying to determine which theories are and are not accepted by the scientific community can be difficult, especially in an area of research as broad as psychology. More than ever before, we have an incredible amount of information at our fingertips, and a simple internet search on any given research topic might result in a number of contradictory studies. In these cases, we are witnessing the scientific community going through the process of reaching a consensus, and it could be quite some time before a consensus emerges. For example, the explosion in our use of technology has led researchers to question whether this ultimately helps or hinders us. The use and implementation of technology in educational settings has become widespread over the last few decades.
Researchers are coming to different conclusions regarding the use of technology. To illustrate this point, a study investigating a smartphone app targeting surgery residents (graduate students in surgery training) found that the use of this app can increase student engagement and raise test scores. Conversely, another study found that the use of technology in undergraduate student populations had negative impacts on sleep, communication, and time management skills. Until sufficient amounts of research have been conducted, there will be no clear consensus on the effects that technology has on a student's acquisition of knowledge, study skills, and mental health. In the meantime, we should strive to think critically about the information we encounter by exercising a degree of healthy skepticism. When someone makes a claim, we should examine the claim from a number of different perspectives: what is the expertise of the person making the claim, what might they gain if the claim is valid, does the claim seem justified given the evidence, and what do other researchers think of the claim? This is especially important when we consider how much information in advertising campaigns and on the internet claims to be based on "scientific evidence" when in actuality it is a belief or perspective of just a few individuals trying to sell a product or draw attention to their perspectives. We should be informed consumers of the information made available to us because decisions based on this information have significant consequences. One such consequence can be seen in politics and public policy. Imagine that you have been elected as the governor of your state. One of your responsibilities is to manage the state budget and determine how to best spend your constituents' tax dollars. As the new governor, you need to decide whether to continue funding early intervention programs. These programs are designed to help children who come from low-income backgrounds, have special needs, or face other disadvantages. These programs may involve providing a wide variety of services to maximize the children's development and position them for optimal levels of success in school and later in life.
While such programs sound appealing, you would want to be sure that they also proved effective before investing additional money in these programs. Fortunately, psychologists and other scientists have conducted vast amounts of research on such programs and, in general, the programs are found to be effective. While not all programs are equally effective, and the short-term effects of many such programs are more pronounced, there is reason to believe that many of these programs produce long-term benefits for participants. If you are committed to being a good steward of taxpayer money, you would want to look at research. Which programs are most effective? What characteristics of these programs make them effective? Which programs promote the best outcomes? After examining the research, you would be best equipped to make decisions about which programs to fund. Ultimately, it is not just politicians who can benefit from using research in guiding their decisions. We all might look to research from time to time when making decisions in our lives. Imagine you just found out that your sister Maria's child, Umberto, was recently diagnosed with autism. There are many treatments for autism that help decrease the negative impact of autism on the individual. Some examples of treatments for autism are applied behavior analysis (ABA), social communication groups, social skills groups, occupational therapy, and even medication options. If Maria asked you for advice or guidance, what would you do? You would likely want to review the research and learn about the efficacy of each treatment so you could best advise your sister. In the end, research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.
Notable Researchers
Psychological research has a long history involving important figures from diverse backgrounds. While the introductory chapter discussed several researchers who made significant contributions to the discipline, there are many more individuals who deserve attention in considering how psychology has advanced as a science through their work (Figure 2.3). For instance, Margaret Floy Washburn (1871–1939) was the first woman to earn a PhD in psychology. Her research focused on animal behavior and cognition. Mary Whiton Calkins (1863–1930) was a preeminent first-generation American psychologist who opposed the behaviorist movement, conducted significant research into memory, and established one of the earliest experimental psychology labs in the United States. Francis Sumner (1895–1954) was the first African American to receive a PhD in psychology in 1920. His dissertation focused on issues related to psychoanalysis. Sumner also had research interests in racial bias and educational justice. Sumner was one of the founders of Howard University's department of psychology, and because of his accomplishments, he is sometimes referred to as the "Father of Black Psychology". Thirteen years later, Inez Beverly Prosser (1895–1934) became the first African American woman to receive a PhD in psychology. Prosser's research highlighted issues related to education in segregated versus integrated schools, and ultimately, her work was very influential in the hallmark Brown v. Board of Education Supreme Court ruling that segregation of public schools was unconstitutional.
Figure 2.3 (a) Margaret Floy Washburn was the first woman to earn a doctorate degree in psychology. (b) The outcome of Brown v. Board of Education was influenced by the research of psychologist Inez Beverly Prosser, who was the first African American woman to earn a PhD in psychology.
The Process of Scientific Research
Scientific knowledge is advanced through a process known as the scientific method. Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. The types of reasoning within the circle are called deductive and inductive. In deductive reasoning , ideas are tested in the real world; in inductive reasoning , real-world observations lead to new ideas (Figure 2.4). These processes are inseparable, like inhaling and exhaling, but different research approaches place different emphasis on the deductive and inductive aspects.
In the scientific context, deductive reasoning begins with a generalization - one hypothesis - that is then used to reach logical conclusions about the real world. If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion). In this example, the hypothesis is correct; therefore, the conclusion is correct as well. Sometimes, however, an incorrect hypothesis may lead to a logical but incorrect conclusion. Consider this argument: all ducks are born with the ability to see. Quackers is a duck. Therefore, Quackers was born with the ability to see. Scientists use deductive reasoning to empirically test their hypotheses. Returning to the example of the ducks, researchers might design a study to test the hypothesis that if all living things require energy to survive, then ducks will be found to require energy to survive. Deductive reasoning starts with a generalization that is tested against real-world observations; however, inductive reasoning moves in the opposite direction. Inductive reasoning uses empirical observations to construct broad generalizations. Unlike deductive reasoning, conclusions drawn from inductive reasoning may or may not be correct, regardless of the observations on which they are based. For instance, you may notice that your favorite fruits - apples, bananas, and oranges - all grow on trees; therefore, you assume that all fruit must grow on trees. This would be an example of inductive reasoning, and, clearly, the existence of strawberries, blueberries, and kiwi demonstrate that this generalization is not correct despite it being based on a number of direct observations. Scientists use inductive reasoning to formulate theories, which in turn generate hypotheses that are tested with deductive reasoning. In the end, science involves both deductive and inductive processes. For example, case studies, which you will read about in the next section, are heavily weighted on the side of empirical observations. Thus, case studies are closely associated with inductive processes as researchers gather massive amounts of observations and seek interesting patterns (new ideas) in the data. Experimental research, on the other hand, puts great emphasis on deductive reasoning. We've stated that theories and hypotheses are ideas, but what sort of ideas are they, exactly? A theory is a well-developed set of ideas that propose an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory. A hypothesis is a testable prediction about how the world will behave if our idea is correct, and it is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests Figure 2.5.
Figure 2.5 The scientific method involves deriving hypotheses from theories and then testing those hypotheses. If the results are consistent with the theory, then the theory is supported. If the results are not consistent, then the theory should be modified and new hypotheses will be generated.
Figure 2.6 Many of the specifics of (a) Freud's theories, such as (b) his division of the mind into id, ego, and superego, have fallen out of favor in recent decades because they are not falsifiable. In broader strokes, his views set the stage for much of psychological thinking today, such as the unconscious nature of the majority of psychological processes.
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The Use of Research Methods in Psychological Research: A Systematised Review
Salomé elizabeth scholtz.
1 Community Psychosocial Research (COMPRES), School of Psychosocial Health, North-West University, Potchefstroom, South Africa
Werner de Klerk
Leon t. de beer.
2 WorkWell Research Institute, North-West University, Potchefstroom, South Africa
Research methods play an imperative role in research quality as well as educating young researchers, however, the application thereof is unclear which can be detrimental to the field of psychology. Therefore, this systematised review aimed to determine what research methods are being used, how these methods are being used and for what topics in the field. Our review of 999 articles from five journals over a period of 5 years indicated that psychology research is conducted in 10 topics via predominantly quantitative research methods. Of these 10 topics, social psychology was the most popular. The remainder of the conducted methodology is described. It was also found that articles lacked rigour and transparency in the used methodology which has implications for replicability. In conclusion this article, provides an overview of all reported methodologies used in a sample of psychology journals. It highlights the popularity and application of methods and designs throughout the article sample as well as an unexpected lack of rigour with regard to most aspects of methodology. Possible sample bias should be considered when interpreting the results of this study. It is recommended that future research should utilise the results of this study to determine the possible impact on the field of psychology as a science and to further investigation into the use of research methods. Results should prompt the following future research into: a lack or rigour and its implication on replication, the use of certain methods above others, publication bias and choice of sampling method.
Introduction
Psychology is an ever-growing and popular field (Gough and Lyons, 2016 ; Clay, 2017 ). Due to this growth and the need for science-based research to base health decisions on (Perestelo-Pérez, 2013 ), the use of research methods in the broad field of psychology is an essential point of investigation (Stangor, 2011 ; Aanstoos, 2014 ). Research methods are therefore viewed as important tools used by researchers to collect data (Nieuwenhuis, 2016 ) and include the following: quantitative, qualitative, mixed method and multi method (Maree, 2016 ). Additionally, researchers also employ various types of literature reviews to address research questions (Grant and Booth, 2009 ). According to literature, what research method is used and why a certain research method is used is complex as it depends on various factors that may include paradigm (O'Neil and Koekemoer, 2016 ), research question (Grix, 2002 ), or the skill and exposure of the researcher (Nind et al., 2015 ). How these research methods are employed is also difficult to discern as research methods are often depicted as having fixed boundaries that are continuously crossed in research (Johnson et al., 2001 ; Sandelowski, 2011 ). Examples of this crossing include adding quantitative aspects to qualitative studies (Sandelowski et al., 2009 ), or stating that a study used a mixed-method design without the study having any characteristics of this design (Truscott et al., 2010 ).
The inappropriate use of research methods affects how students and researchers improve and utilise their research skills (Scott Jones and Goldring, 2015 ), how theories are developed (Ngulube, 2013 ), and the credibility of research results (Levitt et al., 2017 ). This, in turn, can be detrimental to the field (Nind et al., 2015 ), journal publication (Ketchen et al., 2008 ; Ezeh et al., 2010 ), and attempts to address public social issues through psychological research (Dweck, 2017 ). This is especially important given the now well-known replication crisis the field is facing (Earp and Trafimow, 2015 ; Hengartner, 2018 ).
Due to this lack of clarity on method use and the potential impact of inept use of research methods, the aim of this study was to explore the use of research methods in the field of psychology through a review of journal publications. Chaichanasakul et al. ( 2011 ) identify reviewing articles as the opportunity to examine the development, growth and progress of a research area and overall quality of a journal. Studies such as Lee et al. ( 1999 ) as well as Bluhm et al. ( 2011 ) review of qualitative methods has attempted to synthesis the use of research methods and indicated the growth of qualitative research in American and European journals. Research has also focused on the use of research methods in specific sub-disciplines of psychology, for example, in the field of Industrial and Organisational psychology Coetzee and Van Zyl ( 2014 ) found that South African publications tend to consist of cross-sectional quantitative research methods with underrepresented longitudinal studies. Qualitative studies were found to make up 21% of the articles published from 1995 to 2015 in a similar study by O'Neil and Koekemoer ( 2016 ). Other methods in health psychology, such as Mixed methods research have also been reportedly growing in popularity (O'Cathain, 2009 ).
A broad overview of the use of research methods in the field of psychology as a whole is however, not available in the literature. Therefore, our research focused on answering what research methods are being used, how these methods are being used and for what topics in practice (i.e., journal publications) in order to provide a general perspective of method used in psychology publication. We synthesised the collected data into the following format: research topic [areas of scientific discourse in a field or the current needs of a population (Bittermann and Fischer, 2018 )], method [data-gathering tools (Nieuwenhuis, 2016 )], sampling [elements chosen from a population to partake in research (Ritchie et al., 2009 )], data collection [techniques and research strategy (Maree, 2016 )], and data analysis [discovering information by examining bodies of data (Ktepi, 2016 )]. A systematised review of recent articles (2013 to 2017) collected from five different journals in the field of psychological research was conducted.
Grant and Booth ( 2009 ) describe systematised reviews as the review of choice for post-graduate studies, which is employed using some elements of a systematic review and seldom more than one or two databases to catalogue studies after a comprehensive literature search. The aspects used in this systematised review that are similar to that of a systematic review were a full search within the chosen database and data produced in tabular form (Grant and Booth, 2009 ).
Sample sizes and timelines vary in systematised reviews (see Lowe and Moore, 2014 ; Pericall and Taylor, 2014 ; Barr-Walker, 2017 ). With no clear parameters identified in the literature (see Grant and Booth, 2009 ), the sample size of this study was determined by the purpose of the sample (Strydom, 2011 ), and time and cost constraints (Maree and Pietersen, 2016 ). Thus, a non-probability purposive sample (Ritchie et al., 2009 ) of the top five psychology journals from 2013 to 2017 was included in this research study. Per Lee ( 2015 ) American Psychological Association (APA) recommends the use of the most up-to-date sources for data collection with consideration of the context of the research study. As this research study focused on the most recent trends in research methods used in the broad field of psychology, the identified time frame was deemed appropriate.
Psychology journals were only included if they formed part of the top five English journals in the miscellaneous psychology domain of the Scimago Journal and Country Rank (Scimago Journal & Country Rank, 2017 ). The Scimago Journal and Country Rank provides a yearly updated list of publicly accessible journal and country-specific indicators derived from the Scopus® database (Scopus, 2017b ) by means of the Scimago Journal Rank (SJR) indicator developed by Scimago from the algorithm Google PageRank™ (Scimago Journal & Country Rank, 2017 ). Scopus is the largest global database of abstracts and citations from peer-reviewed journals (Scopus, 2017a ). Reasons for the development of the Scimago Journal and Country Rank list was to allow researchers to assess scientific domains, compare country rankings, and compare and analyse journals (Scimago Journal & Country Rank, 2017 ), which supported the aim of this research study. Additionally, the goals of the journals had to focus on topics in psychology in general with no preference to specific research methods and have full-text access to articles.
The following list of top five journals in 2018 fell within the abovementioned inclusion criteria (1) Australian Journal of Psychology, (2) British Journal of Psychology, (3) Europe's Journal of Psychology, (4) International Journal of Psychology and lastly the (5) Journal of Psychology Applied and Interdisciplinary.
Journals were excluded from this systematised review if no full-text versions of their articles were available, if journals explicitly stated a publication preference for certain research methods, or if the journal only published articles in a specific discipline of psychological research (for example, industrial psychology, clinical psychology etc.).
The researchers followed a procedure (see Figure 1 ) adapted from that of Ferreira et al. ( 2016 ) for systematised reviews. Data collection and categorisation commenced on 4 December 2017 and continued until 30 June 2019. All the data was systematically collected and coded manually (Grant and Booth, 2009 ) with an independent person acting as co-coder. Codes of interest included the research topic, method used, the design used, sampling method, and methodology (the method used for data collection and data analysis). These codes were derived from the wording in each article. Themes were created based on the derived codes and checked by the co-coder. Lastly, these themes were catalogued into a table as per the systematised review design.
Systematised review procedure.
According to Johnston et al. ( 2019 ), “literature screening, selection, and data extraction/analyses” (p. 7) are specifically tailored to the aim of a review. Therefore, the steps followed in a systematic review must be reported in a comprehensive and transparent manner. The chosen systematised design adhered to the rigour expected from systematic reviews with regard to full search and data produced in tabular form (Grant and Booth, 2009 ). The rigorous application of the systematic review is, therefore discussed in relation to these two elements.
Firstly, to ensure a comprehensive search, this research study promoted review transparency by following a clear protocol outlined according to each review stage before collecting data (Johnston et al., 2019 ). This protocol was similar to that of Ferreira et al. ( 2016 ) and approved by three research committees/stakeholders and the researchers (Johnston et al., 2019 ). The eligibility criteria for article inclusion was based on the research question and clearly stated, and the process of inclusion was recorded on an electronic spreadsheet to create an evidence trail (Bandara et al., 2015 ; Johnston et al., 2019 ). Microsoft Excel spreadsheets are a popular tool for review studies and can increase the rigour of the review process (Bandara et al., 2015 ). Screening for appropriate articles for inclusion forms an integral part of a systematic review process (Johnston et al., 2019 ). This step was applied to two aspects of this research study: the choice of eligible journals and articles to be included. Suitable journals were selected by the first author and reviewed by the second and third authors. Initially, all articles from the chosen journals were included. Then, by process of elimination, those irrelevant to the research aim, i.e., interview articles or discussions etc., were excluded.
To ensure rigourous data extraction, data was first extracted by one reviewer, and an independent person verified the results for completeness and accuracy (Johnston et al., 2019 ). The research question served as a guide for efficient, organised data extraction (Johnston et al., 2019 ). Data was categorised according to the codes of interest, along with article identifiers for audit trails such as authors, title and aims of articles. The categorised data was based on the aim of the review (Johnston et al., 2019 ) and synthesised in tabular form under methods used, how these methods were used, and for what topics in the field of psychology.
The initial search produced a total of 1,145 articles from the 5 journals identified. Inclusion and exclusion criteria resulted in a final sample of 999 articles ( Figure 2 ). Articles were co-coded into 84 codes, from which 10 themes were derived ( Table 1 ).
Journal article frequency.
Codes used to form themes (research topics).
Social Psychology | 31 | Aggression SP, Attitude SP, Belief SP, Child abuse SP, Conflict SP, Culture SP, Discrimination SP, Economic, Family illness, Family, Group, Help, Immigration, Intergeneration, Judgement, Law, Leadership, Marriage SP, Media, Optimism, Organisational and Social justice, Parenting SP, Politics, Prejudice, Relationships, Religion, Romantic Relationships SP, Sex and attraction, Stereotype, Violence, Work |
Experimental Psychology | 17 | Anxiety, stress and PTSD, Coping, Depression, Emotion, Empathy, Facial research, Fear and threat, Happiness, Humor, Mindfulness, Mortality, Motivation and Achievement, Perception, Rumination, Self, Self-efficacy |
Cognitive Psychology | 12 | Attention, Cognition, Decision making, Impulse, Intelligence, Language, Math, Memory, Mental, Number, Problem solving, Reading |
Health Psychology | 7 | Addiction, Body, Burnout, Health, Illness (Health Psychology), Sleep (Health Psychology), Suicide and Self-harm |
Physiological Psychology | 6 | Gender, Health (Physiological psychology), Illness (Physiological psychology), Mood disorders, Sleep (Physiological psychology), Visual research |
Developmental Psychology | 3 | Attachment, Development, Old age |
Personality | 3 | Machiavellian, Narcissism, Personality |
Psychological Psychology | 3 | Programme, Psychology practice, Theory |
Education and Learning | 1 | Education and Learning |
Psychometrics | 1 | Measure |
Code Total | 84 |
These 10 themes represent the topic section of our research question ( Figure 3 ). All these topics except, for the final one, psychological practice , were found to concur with the research areas in psychology as identified by Weiten ( 2010 ). These research areas were chosen to represent the derived codes as they provided broad definitions that allowed for clear, concise categorisation of the vast amount of data. Article codes were categorised under particular themes/topics if they adhered to the research area definitions created by Weiten ( 2010 ). It is important to note that these areas of research do not refer to specific disciplines in psychology, such as industrial psychology; but to broader fields that may encompass sub-interests of these disciplines.
Topic frequency (international sample).
In the case of developmental psychology , researchers conduct research into human development from childhood to old age. Social psychology includes research on behaviour governed by social drivers. Researchers in the field of educational psychology study how people learn and the best way to teach them. Health psychology aims to determine the effect of psychological factors on physiological health. Physiological psychology , on the other hand, looks at the influence of physiological aspects on behaviour. Experimental psychology is not the only theme that uses experimental research and focuses on the traditional core topics of psychology (for example, sensation). Cognitive psychology studies the higher mental processes. Psychometrics is concerned with measuring capacity or behaviour. Personality research aims to assess and describe consistency in human behaviour (Weiten, 2010 ). The final theme of psychological practice refers to the experiences, techniques, and interventions employed by practitioners, researchers, and academia in the field of psychology.
Articles under these themes were further subdivided into methodologies: method, sampling, design, data collection, and data analysis. The categorisation was based on information stated in the articles and not inferred by the researchers. Data were compiled into two sets of results presented in this article. The first set addresses the aim of this study from the perspective of the topics identified. The second set of results represents a broad overview of the results from the perspective of the methodology employed. The second set of results are discussed in this article, while the first set is presented in table format. The discussion thus provides a broad overview of methods use in psychology (across all themes), while the table format provides readers with in-depth insight into methods used in the individual themes identified. We believe that presenting the data from both perspectives allow readers a broad understanding of the results. Due a large amount of information that made up our results, we followed Cichocka and Jost ( 2014 ) in simplifying our results. Please note that the numbers indicated in the table in terms of methodology differ from the total number of articles. Some articles employed more than one method/sampling technique/design/data collection method/data analysis in their studies.
What follows is the results for what methods are used, how these methods are used, and which topics in psychology they are applied to . Percentages are reported to the second decimal in order to highlight small differences in the occurrence of methodology.
Firstly, with regard to the research methods used, our results show that researchers are more likely to use quantitative research methods (90.22%) compared to all other research methods. Qualitative research was the second most common research method but only made up about 4.79% of the general method usage. Reviews occurred almost as much as qualitative studies (3.91%), as the third most popular method. Mixed-methods research studies (0.98%) occurred across most themes, whereas multi-method research was indicated in only one study and amounted to 0.10% of the methods identified. The specific use of each method in the topics identified is shown in Table 2 and Figure 4 .
Research methods in psychology.
Quantitative | 401 | 162 | 69 | 60 | 52 | 52 | 48 | 28 | 38 | 13 |
Qualitative | 28 | 4 | 1 | 0 | 5 | 2 | 3 | 5 | 0 | 1 |
Review | 11 | 5 | 2 | 0 | 3 | 4 | 1 | 13 | 0 | 1 |
Mixed Methods | 7 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
Multi-method | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Total | 447 | 171 | 72 | 60 | 61 | 58 | 53 | 47 | 39 | 15 |
Research method frequency in topics.
Secondly, in the case of how these research methods are employed , our study indicated the following.
Sampling −78.34% of the studies in the collected articles did not specify a sampling method. From the remainder of the studies, 13 types of sampling methods were identified. These sampling methods included broad categorisation of a sample as, for example, a probability or non-probability sample. General samples of convenience were the methods most likely to be applied (10.34%), followed by random sampling (3.51%), snowball sampling (2.73%), and purposive (1.37%) and cluster sampling (1.27%). The remainder of the sampling methods occurred to a more limited extent (0–1.0%). See Table 3 and Figure 5 for sampling methods employed in each topic.
Sampling use in the field of psychology.
Not stated | 331 | 153 | 45 | 57 | 49 | 43 | 43 | 38 | 31 | 14 |
Convenience sampling | 55 | 8 | 10 | 1 | 6 | 8 | 9 | 2 | 6 | 1 |
Random sampling | 15 | 3 | 9 | 1 | 2 | 2 | 0 | 2 | 1 | 1 |
Snowball sampling | 14 | 4 | 4 | 1 | 2 | 0 | 0 | 3 | 0 | 0 |
Purposive sampling | 6 | 0 | 2 | 0 | 0 | 2 | 0 | 3 | 1 | 0 |
Cluster sampling | 8 | 1 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
Stratified sampling | 4 | 1 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
Non-probability sampling | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Probability sampling | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Quota sampling | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Criterion sampling | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Self-selection sampling | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Unsystematic sampling | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Total | 443 | 172 | 76 | 60 | 60 | 58 | 52 | 48 | 40 | 16 |
Sampling method frequency in topics.
Designs were categorised based on the articles' statement thereof. Therefore, it is important to note that, in the case of quantitative studies, non-experimental designs (25.55%) were often indicated due to a lack of experiments and any other indication of design, which, according to Laher ( 2016 ), is a reasonable categorisation. Non-experimental designs should thus be compared with experimental designs only in the description of data, as it could include the use of correlational/cross-sectional designs, which were not overtly stated by the authors. For the remainder of the research methods, “not stated” (7.12%) was assigned to articles without design types indicated.
From the 36 identified designs the most popular designs were cross-sectional (23.17%) and experimental (25.64%), which concurred with the high number of quantitative studies. Longitudinal studies (3.80%), the third most popular design, was used in both quantitative and qualitative studies. Qualitative designs consisted of ethnography (0.38%), interpretative phenomenological designs/phenomenology (0.28%), as well as narrative designs (0.28%). Studies that employed the review method were mostly categorised as “not stated,” with the most often stated review designs being systematic reviews (0.57%). The few mixed method studies employed exploratory, explanatory (0.09%), and concurrent designs (0.19%), with some studies referring to separate designs for the qualitative and quantitative methods. The one study that identified itself as a multi-method study used a longitudinal design. Please see how these designs were employed in each specific topic in Table 4 , Figure 6 .
Design use in the field of psychology.
Experimental design | 82 | 82 | 3 | 60 | 10 | 12 | 8 | 6 | 4 | 3 |
Non-experimental design | 115 | 30 | 51 | 0 | 13 | 17 | 13 | 13 | 14 | 3 |
Cross-sectional design | 123 | 31 | 12 | 1 | 19 | 17 | 21 | 5 | 13 | 2 |
Correlational design | 56 | 12 | 3 | 0 | 10 | 2 | 2 | 0 | 4 | 2 |
Not stated | 37 | 7 | 3 | 0 | 4 | 2 | 4 | 14 | 1 | 3 |
Longitudinal design | 21 | 6 | 2 | 1 | 1 | 2 | 2 | 0 | 2 | 3 |
Quasi-experimental design | 4 | 1 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 |
Systematic review | 3 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
Cross-cultural design | 3 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
Descriptive design | 2 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
Ethnography | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Literature review | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
Interpretative Phenomenological Analysis (IPA) | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Narrative design | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
Case-control research design | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
Concurrent data collection design | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Grounded Theory | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Narrative review | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Auto-ethnography | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Case series evaluation | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Case study | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Comprehensive review | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Descriptive-inferential | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Explanatory sequential design | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Exploratory mixed-method | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
Grounded ethnographic design | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Historical cohort design | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Historical research | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
interpretivist approach | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Meta-review | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Prospective design | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Qualitative review | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Qualitative systematic review | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Short-term prospective design | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Total | 461 | 175 | 74 | 63 | 63 | 58 | 56 | 48 | 39 | 16 |
Design frequency in topics.
Data collection and analysis —data collection included 30 methods, with the data collection method most often employed being questionnaires (57.84%). The experimental task (16.56%) was the second most preferred collection method, which included established or unique tasks designed by the researchers. Cognitive ability tests (6.84%) were also regularly used along with various forms of interviewing (7.66%). Table 5 and Figure 7 represent data collection use in the various topics. Data analysis consisted of 3,857 occurrences of data analysis categorised into ±188 various data analysis techniques shown in Table 6 and Figures 1 – 7 . Descriptive statistics were the most commonly used (23.49%) along with correlational analysis (17.19%). When using a qualitative method, researchers generally employed thematic analysis (0.52%) or different forms of analysis that led to coding and the creation of themes. Review studies presented few data analysis methods, with most studies categorising their results. Mixed method and multi-method studies followed the analysis methods identified for the qualitative and quantitative studies included.
Data collection in the field of psychology.
Questionnaire | 364 | 113 | 65 | 42 | 40 | 51 | 39 | 24 | 37 | 11 |
Experimental task | 68 | 66 | 3 | 52 | 9 | 5 | 11 | 5 | 5 | 1 |
Cognitive ability test | 9 | 57 | 1 | 12 | 6 | 1 | 5 | 1 | 1 | 0 |
Physiological measure | 3 | 12 | 1 | 6 | 2 | 5 | 3 | 0 | 1 | 0 |
Interview | 19 | 3 | 0 | 1 | 3 | 0 | 2 | 2 | 0 | 1 |
Online scholarly literature | 10 | 4 | 0 | 0 | 3 | 4 | 0 | 10 | 0 | 0 |
Open-ended questions | 15 | 3 | 0 | 1 | 3 | 1 | 2 | 3 | 0 | 0 |
Semi-structured interviews | 10 | 3 | 0 | 0 | 3 | 2 | 1 | 2 | 0 | 1 |
Observation | 10 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
Documents | 5 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 |
Focus group | 6 | 1 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Not stated | 2 | 1 | 1 | 0 | 0 | 0 | 1 | 4 | 0 | 1 |
Public data | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 |
Drawing task | 0 | 2 | 0 | 1 | 1 | 1 | 0 | 2 | 0 | 0 |
In-depth interview | 6 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Structured interview | 0 | 2 | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 0 |
Writing task | 1 | 0 | 0 | 0 | 4 | 0 | 0 | 1 | 0 | 0 |
Questionnaire interviews | 1 | 0 | 1 | 0 | 2 | 0 | 1 | 0 | 0 | 0 |
Non-experimental task | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Tests | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Group accounts | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Open-ended prompts | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Field notes | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Open-ended interview | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Qualitative questions | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
Social media | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Assessment procedure | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Closed-ended questions | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Open discussions | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Qualitative descriptions | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Total | 551 | 273 | 75 | 116 | 79 | 73 | 65 | 60 | 50 | 17 |
Data collection frequency in topics.
Data analysis in the field of psychology.
Not stated | 5 | 1 | 2 | 0 | 0 | 1 | 1 | 5 | 0 | 1 |
Actor-Partner Interdependence Model (APIM) | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Analysis of Covariance (ANCOVA) | 17 | 8 | 1 | 3 | 4 | 2 | 1 | 0 | 0 | 1 |
Analysis of Variance (ANOVA) | 112 | 60 | 16 | 29 | 15 | 17 | 15 | 6 | 5 | 3 |
Auto-regressive path coefficients | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Average variance extracted (AVE) | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Bartholomew's classification system | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Bayesian analysis | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Bibliometric analysis | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Binary logistic regression | 1 | 1 | 0 | 0 | 1 | 4 | 1 | 0 | 0 | 0 |
Binary multilevel regression | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Binomial and Bernoulli regression models | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Binomial mixed effects model | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Bivariate Correlations | 32 | 10 | 3 | 0 | 4 | 3 | 5 | 1 | 1 | 1 |
Bivariate logistic correlations | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Bootstrapping | 39 | 16 | 2 | 3 | 5 | 1 | 6 | 1 | 2 | 1 |
Canonical correlations | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
Cartesian diagram | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Case-wise diagnostics | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Casual network analysis | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Categorisation | 5 | 2 | 0 | 0 | 1 | 1 | 0 | 4 | 0 | 0 |
Categorisation of responses | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Category codes | 3 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Cattell's scree-test | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Chi-square tests | 52 | 20 | 17 | 5 | 6 | 11 | 8 | 7 | 4 | 3 |
Classic Parallel Analysis (PA) | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 |
Cluster analysis | 7 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 |
Coded | 15 | 3 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 0 |
Cohen d effect size | 14 | 5 | 2 | 1 | 3 | 2 | 3 | 1 | 0 | 1 |
Common method variance (CMV) | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Comprehensive Meta-Analysis (CMA) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Confidence Interval (CI) | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Confirmatory Factor Analysis (CFA) | 57 | 13 | 40 | 0 | 2 | 4 | 7 | 1 | 3 | 1 |
Content analysis | 9 | 1 | 0 | 0 | 2 | 1 | 0 | 1 | 0 | 0 |
Convergent validity | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Cook's distance | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Correlated-trait-correlated-method minus one model | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Correlational analysis | 259 | 85 | 44 | 18 | 27 | 31 | 34 | 8 | 33 | 8 |
Covariance matrix | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Covariance modelling | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Covariance structure analyses | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Cronbach's alpha | 61 | 14 | 18 | 6 | 5 | 10 | 8 | 3 | 7 | 5 |
Cross-validation | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Cross-lagged analyses | 1 | 2 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Dependent t-test | 1 | 2 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
Descriptive statistics | 324 | 132 | 43 | 49 | 41 | 43 | 36 | 28 | 29 | 10 |
Differentiated analysis | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Discriminate analysis | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Discursive psychology | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Dominance analysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Expectation maximisation | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Exploratory data Analysis | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
Exploratory Factor Analysis (EFA) | 14 | 5 | 24 | 0 | 1 | 1 | 4 | 0 | 4 | 0 |
Exploratory structural equation modelling (ESEM) | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Factor analysis | 12 | 4 | 16 | 0 | 2 | 1 | 5 | 0 | 2 | 0 |
Measurement invariance testing | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Four-way mixed ANOVA | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Frequency rate | 20 | 1 | 4 | 2 | 1 | 2 | 2 | 2 | 0 | 0 |
Friedman test | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Games-Howell | 2 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
General linear model analysis | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 |
Greenhouse-Geisser correction | 2 | 5 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |
Grounded theory method | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Grounded theory methodology using open and axial coding | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Guttman split-half | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Harman's one-factor test | 13 | 2 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 |
Herman's criteria of experience categorisation | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Hierarchical CFA (HCFA) | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Hierarchical cluster analysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Hierarchical Linear Modelling (HLM) | 76 | 22 | 2 | 3 | 7 | 6 | 7 | 4 | 4 | 1 |
Huynh-Felt correction | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Identified themes | 3 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Independent samples t-test | 38 | 9 | 4 | 4 | 4 | 8 | 3 | 3 | 1 | 1 |
Inductive open coding | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Inferential statistics | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Interclass correlation | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Internal consistency | 3 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Interpreted and defined | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Interpretive Phenomenological Analysis (IPA) | 2 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Item fit analysis | 1 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
K-means clustering | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Kaiser-meyer-Olkin measure of sampling adequacy | 2 | 0 | 8 | 0 | 0 | 0 | 2 | 0 | 2 | 0 |
Kendall's coefficients | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Kolmogorov-Smirnov test | 1 | 2 | 1 | 1 | 2 | 2 | 0 | 0 | 1 | 0 |
Lagged-effects multilevel modelling | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Latent class differentiation (LCD) | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Latent cluster analysis | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Latent growth curve modelling (LGCM) | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
Latent means | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Latent Profile Analysis (LPA) | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Linear regressions | 69 | 19 | 4 | 10 | 3 | 12 | 5 | 3 | 13 | 0 |
Linguistic Inquiry and Word Count | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Listwise deletion method | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Log-likelihood ratios | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Logistic mixed-effects model | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Logistic regression analyses | 17 | 0 | 1 | 0 | 4 | 2 | 1 | 0 | 0 | 1 |
Loglinear Model | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Mahalanobis distances | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Mann-Whitney U tests | 6 | 4 | 2 | 1 | 2 | 0 | 2 | 4 | 0 | 0 |
Mauchly's test | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 1 |
Maximum likelihood method | 11 | 3 | 9 | 0 | 1 | 3 | 2 | 3 | 1 | 0 |
Maximum-likelihood factor analysis with promax rotation | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Measurement invariance testing | 4 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Mediation analysis | 29 | 7 | 1 | 2 | 4 | 3 | 5 | 0 | 3 | 0 |
Meta-analysis | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Microanalysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Minimum significant difference (MSD) comparison | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Mixed ANOVAs | 19 | 6 | 0 | 10 | 1 | 2 | 1 | 4 | 1 | 0 |
Mixed linear model | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
Mixed-design ANCOVA | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Mixed-effects multiple regression models | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Moderated hierarchical regression model | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Moderated regression analysis | 8 | 4 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 |
Monte Carlo Markov Chains | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multi-group analysis | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multidimensional Random Coefficient Multinomial Logit (MRCML) | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multidimensional Scaling | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multiple-Group Confirmatory Factor Analysis (MGCFA) | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
Multilevel latent class analysis | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Multilevel modelling | 7 | 2 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 0 |
Multilevel Structural Equation Modelling (MSEM) | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multinominal logistic regression (MLR) | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multinominal regression analysis | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
Multiple Indicators Multiple Causes (MIMIC) | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 |
Multiple mediation analysis | 2 | 6 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 |
Multiple regression | 34 | 15 | 3 | 0 | 3 | 4 | 5 | 0 | 7 | 2 |
Multivariate analysis of co-variance (MANCOVA) | 12 | 2 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 0 |
Multivariate Analysis of Variance (MANOVA) | 38 | 8 | 4 | 5 | 5 | 6 | 9 | 1 | 1 | 2 |
Multivariate hierarchical linear regression | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multivariate linear regression | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Multivariate logistic regression analyses | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Multivariate regressions | 2 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Nagelkerke's R square | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Narrative analysis | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Negative binominal regression with log link | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Newman-Keuls | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Nomological Validity Analysis | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
One sample t-test | 8 | 10 | 1 | 7 | 4 | 6 | 4 | 0 | 1 | 0 |
Ordinary Least-Square regression (OLS) | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Pairwise deletion method | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Pairwise parameter comparison | 4 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 |
Parametric Analysis | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Partial Least Squares regression method (PLS) | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Path analysis | 21 | 9 | 0 | 1 | 2 | 4 | 5 | 1 | 2 | 0 |
Path-analytic model test | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Phenomenological analysis | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Polynomial regression analyses | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Fisher LSD | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Principal axis factoring | 2 | 1 | 4 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Principal component analysis (PCA) | 8 | 1 | 12 | 1 | 1 | 0 | 3 | 2 | 5 | 1 |
Pseudo-panel regression | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Quantitative content analysis | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Receiver operating characteristic (ROC) curve analysis | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
Relative weight analysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Repeated measures analyses of variances (rANOVA) | 18 | 22 | 1 | 7 | 5 | 2 | 1 | 1 | 1 | 1 |
Ryan-Einot-Gabriel-Welsch multiple F test | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Satorra-Bentler scaled chi-square statistic | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Scheffe's test | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Sequential multiple mediation analysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Shapiro-Wilk test | 2 | 3 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 |
Sobel Test | 13 | 5 | 0 | 1 | 0 | 2 | 4 | 0 | 0 | 0 |
Squared multiple correlations | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Squared semi-partial correlations (sr2) | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Stepwise regression analysis | 3 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 0 |
Structural Equation Modelling (SEM) | 56 | 22 | 3 | 3 | 3 | 5 | 5 | 0 | 5 | 3 |
Structure analysis | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Subsequent t-test | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Systematic coding- Gemeinschaft-oriented | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Task analysis | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Thematic analysis | 11 | 2 | 0 | 0 | 3 | 0 | 2 | 2 | 0 | 0 |
Three (condition)-way ANOVA | 0 | 4 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
Three-way hierarchical loglinear analysis | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Tukey-Kramer corrections | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 |
Two-paired sample t-test | 7 | 6 | 1 | 1 | 0 | 3 | 1 | 1 | 0 | 1 |
Two-tailed related t-test | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
Unadjusted Logistic regression analysis | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Univariate generalized linear models (GLM) | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Variance inflation factor (VIF) | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Variance-covariance matrix | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Wald test | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Ward's hierarchical cluster method | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Weighted least squares with corrections to means and variances (WLSMV) | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Welch and Brown-Forsythe F-ratios | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
Wilcoxon signed-rank test | 3 | 3 | 0 | 2 | 0 | 0 | 0 | 2 | 0 | 1 |
Wilks' Lamba | 6 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
Word analysis | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
Word Association Analysis | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
scores | 5 | 6 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 |
Total | 1738 | 635 | 329 | 192 | 198 | 237 | 225 | 117 | 152 | 55 |
Results of the topics researched in psychology can be seen in the tables, as previously stated in this article. It is noteworthy that, of the 10 topics, social psychology accounted for 43.54% of the studies, with cognitive psychology the second most popular research topic at 16.92%. The remainder of the topics only occurred in 4.0–7.0% of the articles considered. A list of the included 999 articles is available under the section “View Articles” on the following website: https://methodgarden.xtrapolate.io/ . This website was created by Scholtz et al. ( 2019 ) to visually present a research framework based on this Article's results.
This systematised review categorised full-length articles from five international journals across the span of 5 years to provide insight into the use of research methods in the field of psychology. Results indicated what methods are used how these methods are being used and for what topics (why) in the included sample of articles. The results should be seen as providing insight into method use and by no means a comprehensive representation of the aforementioned aim due to the limited sample. To our knowledge, this is the first research study to address this topic in this manner. Our discussion attempts to promote a productive way forward in terms of the key results for method use in psychology, especially in the field of academia (Holloway, 2008 ).
With regard to the methods used, our data stayed true to literature, finding only common research methods (Grant and Booth, 2009 ; Maree, 2016 ) that varied in the degree to which they were employed. Quantitative research was found to be the most popular method, as indicated by literature (Breen and Darlaston-Jones, 2010 ; Counsell and Harlow, 2017 ) and previous studies in specific areas of psychology (see Coetzee and Van Zyl, 2014 ). Its long history as the first research method (Leech et al., 2007 ) in the field of psychology as well as researchers' current application of mathematical approaches in their studies (Toomela, 2010 ) might contribute to its popularity today. Whatever the case may be, our results show that, despite the growth in qualitative research (Demuth, 2015 ; Smith and McGannon, 2018 ), quantitative research remains the first choice for article publication in these journals. Despite the included journals indicating openness to articles that apply any research methods. This finding may be due to qualitative research still being seen as a new method (Burman and Whelan, 2011 ) or reviewers' standards being higher for qualitative studies (Bluhm et al., 2011 ). Future research is encouraged into the possible biasness in publication of research methods, additionally further investigation with a different sample into the proclaimed growth of qualitative research may also provide different results.
Review studies were found to surpass that of multi-method and mixed method studies. To this effect Grant and Booth ( 2009 ), state that the increased awareness, journal contribution calls as well as its efficiency in procuring research funds all promote the popularity of reviews. The low frequency of mixed method studies contradicts the view in literature that it's the third most utilised research method (Tashakkori and Teddlie's, 2003 ). Its' low occurrence in this sample could be due to opposing views on mixing methods (Gunasekare, 2015 ) or that authors prefer publishing in mixed method journals, when using this method, or its relative novelty (Ivankova et al., 2016 ). Despite its low occurrence, the application of the mixed methods design in articles was methodologically clear in all cases which were not the case for the remainder of research methods.
Additionally, a substantial number of studies used a combination of methodologies that are not mixed or multi-method studies. Perceived fixed boundaries are according to literature often set aside, as confirmed by this result, in order to investigate the aim of a study, which could create a new and helpful way of understanding the world (Gunasekare, 2015 ). According to Toomela ( 2010 ), this is not unheard of and could be considered a form of “structural systemic science,” as in the case of qualitative methodology (observation) applied in quantitative studies (experimental design) for example. Based on this result, further research into this phenomenon as well as its implications for research methods such as multi and mixed methods is recommended.
Discerning how these research methods were applied, presented some difficulty. In the case of sampling, most studies—regardless of method—did mention some form of inclusion and exclusion criteria, but no definite sampling method. This result, along with the fact that samples often consisted of students from the researchers' own academic institutions, can contribute to literature and debates among academics (Peterson and Merunka, 2014 ; Laher, 2016 ). Samples of convenience and students as participants especially raise questions about the generalisability and applicability of results (Peterson and Merunka, 2014 ). This is because attention to sampling is important as inappropriate sampling can debilitate the legitimacy of interpretations (Onwuegbuzie and Collins, 2017 ). Future investigation into the possible implications of this reported popular use of convenience samples for the field of psychology as well as the reason for this use could provide interesting insight, and is encouraged by this study.
Additionally, and this is indicated in Table 6 , articles seldom report the research designs used, which highlights the pressing aspect of the lack of rigour in the included sample. Rigour with regards to the applied empirical method is imperative in promoting psychology as a science (American Psychological Association, 2020 ). Omitting parts of the research process in publication when it could have been used to inform others' research skills should be questioned, and the influence on the process of replicating results should be considered. Publications are often rejected due to a lack of rigour in the applied method and designs (Fonseca, 2013 ; Laher, 2016 ), calling for increased clarity and knowledge of method application. Replication is a critical part of any field of scientific research and requires the “complete articulation” of the study methods used (Drotar, 2010 , p. 804). The lack of thorough description could be explained by the requirements of certain journals to only report on certain aspects of a research process, especially with regard to the applied design (Laher, 20). However, naming aspects such as sampling and designs, is a requirement according to the APA's Journal Article Reporting Standards (JARS-Quant) (Appelbaum et al., 2018 ). With very little information on how a study was conducted, authors lose a valuable opportunity to enhance research validity, enrich the knowledge of others, and contribute to the growth of psychology and methodology as a whole. In the case of this research study, it also restricted our results to only reported samples and designs, which indicated a preference for certain designs, such as cross-sectional designs for quantitative studies.
Data collection and analysis were for the most part clearly stated. A key result was the versatile use of questionnaires. Researchers would apply a questionnaire in various ways, for example in questionnaire interviews, online surveys, and written questionnaires across most research methods. This may highlight a trend for future research.
With regard to the topics these methods were employed for, our research study found a new field named “psychological practice.” This result may show the growing consciousness of researchers as part of the research process (Denzin and Lincoln, 2003 ), psychological practice, and knowledge generation. The most popular of these topics was social psychology, which is generously covered in journals and by learning societies, as testaments of the institutional support and richness social psychology has in the field of psychology (Chryssochoou, 2015 ). The APA's perspective on 2018 trends in psychology also identifies an increased amount of psychology focus on how social determinants are influencing people's health (Deangelis, 2017 ).
This study was not without limitations and the following should be taken into account. Firstly, this study used a sample of five specific journals to address the aim of the research study, despite general journal aims (as stated on journal websites), this inclusion signified a bias towards the research methods published in these specific journals only and limited generalisability. A broader sample of journals over a different period of time, or a single journal over a longer period of time might provide different results. A second limitation is the use of Excel spreadsheets and an electronic system to log articles, which was a manual process and therefore left room for error (Bandara et al., 2015 ). To address this potential issue, co-coding was performed to reduce error. Lastly, this article categorised data based on the information presented in the article sample; there was no interpretation of what methodology could have been applied or whether the methods stated adhered to the criteria for the methods used. Thus, a large number of articles that did not clearly indicate a research method or design could influence the results of this review. However, this in itself was also a noteworthy result. Future research could review research methods of a broader sample of journals with an interpretive review tool that increases rigour. Additionally, the authors also encourage the future use of systematised review designs as a way to promote a concise procedure in applying this design.
Our research study presented the use of research methods for published articles in the field of psychology as well as recommendations for future research based on these results. Insight into the complex questions identified in literature, regarding what methods are used how these methods are being used and for what topics (why) was gained. This sample preferred quantitative methods, used convenience sampling and presented a lack of rigorous accounts for the remaining methodologies. All methodologies that were clearly indicated in the sample were tabulated to allow researchers insight into the general use of methods and not only the most frequently used methods. The lack of rigorous account of research methods in articles was represented in-depth for each step in the research process and can be of vital importance to address the current replication crisis within the field of psychology. Recommendations for future research aimed to motivate research into the practical implications of the results for psychology, for example, publication bias and the use of convenience samples.
Ethics Statement
This study was cleared by the North-West University Health Research Ethics Committee: NWU-00115-17-S1.
Author Contributions
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Understanding Methods for Research in Psychology
A Psychology Research Methods Study Guide
Types of Research in Psychology
- Cross-Sectional vs. Longitudinal Research
- Reliability and Validity
Glossary of Terms
Research in psychology focuses on a variety of topics , ranging from the development of infants to the behavior of social groups. Psychologists use the scientific method to investigate questions both systematically and empirically.
Research in psychology is important because it provides us with valuable information that helps to improve human lives. By learning more about the brain, cognition, behavior, and mental health conditions, researchers are able to solve real-world problems that affect our day-to-day lives.
At a Glance
Knowing more about how research in psychology is conducted can give you a better understanding of what those findings might mean to you. Psychology experiments can range from simple to complex, but there are some basic terms and concepts that all psychology students should understand.
Start your studies by learning more about the different types of research, the basics of experimental design, and the relationships between variables.
Research in Psychology: The Basics
The first step in your review should include a basic introduction to psychology research methods . Psychology research can have a variety of goals. What researchers learn can be used to describe, explain, predict, or change human behavior.
Psychologists use the scientific method to conduct studies and research in psychology. The basic process of conducting psychology research involves asking a question, designing a study, collecting data, analyzing results, reaching conclusions, and sharing the findings.
The Scientific Method in Psychology Research
The steps of the scientific method in psychology research are:
- Make an observation
- Ask a research question and make predictions about what you expect to find
- Test your hypothesis and gather data
- Examine the results and form conclusions
- Report your findings
Research in psychology can take several different forms. It can describe a phenomenon, explore the causes of a phenomenon, or look at relationships between one or more variables. Three of the main types of psychological research focus on:
Descriptive Studies
This type of research can tell us more about what is happening in a specific population. It relies on techniques such as observation, surveys, and case studies.
Correlational Studies
Correlational research is frequently used in psychology to look for relationships between variables. While research look at how variables are related, they do not manipulate any of the variables.
While correlational studies can suggest a relationship between two variables, finding a correlation does not prove that one variable causes a change in another. In other words, correlation does not equal causation.
Experimental Research Methods
Experiments are a research method that can look at whether changes in one variable cause changes in another. The simple experiment is one of the most basic methods of determining if there is a cause-and-effect relationship between two variables.
A simple experiment utilizes a control group of participants who receive no treatment and an experimental group of participants who receive the treatment.
Experimenters then compare the results of the two groups to determine if the treatment had an effect.
Cross-Sectional vs. Longitudinal Research in Psychology
Research in psychology can also involve collecting data at a single point in time, or gathering information at several points over a period of time.
Cross-Sectional Research
In a cross-sectional study , researchers collect data from participants at a single point in time. These are descriptive type of research and cannot be used to determine cause and effect because researchers do not manipulate the independent variables.
However, cross-sectional research does allow researchers to look at the characteristics of the population and explore relationships between different variables at a single point in time.
Longitudinal Research
A longitudinal study is a type of research in psychology that involves looking at the same group of participants over a period of time. Researchers start by collecting initial data that serves as a baseline, and then collect follow-up data at certain intervals. These studies can last days, months, or years.
The longest longitudinal study in psychology was started in 1921 and the study is planned to continue until the last participant dies or withdraws. As of 2003, more than 200 of the partipants were still alive.
The Reliability and Validity of Research in Psychology
Reliability and validity are two concepts that are also critical in psychology research. In order to trust the results, we need to know if the findings are consistent (reliability) and that we are actually measuring what we think we are measuring (validity).
Reliability
Reliability is a vital component of a valid psychological test. What is reliability? How do we measure it? Simply put, reliability refers to the consistency of a measure. A test is considered reliable if we get the same result repeatedly.
When determining the merits of a psychological test, validity is one of the most important factors to consider. What exactly is validity? One of the greatest concerns when creating a psychological test is whether or not it actually measures what we think it is measuring.
For example, a test might be designed to measure a stable personality trait but instead measures transitory emotions generated by situational or environmental conditions. A valid test ensures that the results accurately reflect the dimension undergoing assessment.
Review some of the key terms that you should know and understand about psychology research methods. Spend some time studying these terms and definitions before your exam. Some key terms that you should know include:
- Correlation
- Demand characteristic
- Dependent variable
- Hawthorne effect
- Independent variable
- Naturalistic observation
- Placebo effect
- Random assignment
- Replication
- Selective attrition
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Stanford Magazine. The vexing legacy of Lewis Terman .
By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
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8 Why Is Research Important?
Learning outcomes.
By the end of this section, you will be able to:
- Explain how scientific research addresses questions about behavior
- Discuss how scientific research guides public policy
- Appreciate how scientific research can be important in making personal decisions
Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people’s authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth’s continents did not move, and that mental illness was caused by possession ( Figure ). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.
The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.
While behavior is observable, the mind is not. If someone is crying, we can see behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This chapter explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.
USE OF RESEARCH INFORMATION
Trying to determine which theories are and are not accepted by the scientific community can be difficult, especially in an area of research as broad as psychology. More than ever before, we have an incredible amount of information at our fingertips, and a simple internet search on any given research topic might result in a number of contradictory studies. In these cases, we are witnessing the scientific community going through the process of reaching a consensus, and it could be quite some time before a consensus emerges. For example, the hypothesized link between exposure to media violence and subsequent aggression has been debated in the scientific community for roughly 60 years. Even today, we will find detractors, but a consensus is building. Several professional organizations view media violence exposure as a risk factor for actual violence, including the American Medical Association, the American Psychiatric Association, and the American Psychological Association (American Academy of Pediatrics, American Academy of Child & Adolescent Psychiatry, American Psychological Association, American Medical Association, American Academy of Family Physicians, American Psychiatric Association, 2000).
In the meantime, we should strive to think critically about the information we encounter by exercising a degree of healthy skepticism. When someone makes a claim, we should examine the claim from a number of different perspectives: what is the expertise of the person making the claim, what might they gain if the claim is valid, does the claim seem justified given the evidence, and what do other researchers think of the claim? This is especially important when we consider how much information in advertising campaigns and on the internet claims to be based on “scientific evidence” when in actuality it is a belief or perspective of just a few individuals trying to sell a product or draw attention to their perspectives.
We should be informed consumers of the information made available to us because decisions based on this information have significant consequences. One such consequence can be seen in politics and public policy. Imagine that you have been elected as the governor of your state. One of your responsibilities is to manage the state budget and determine how to best spend your constituents’ tax dollars. As the new governor, you need to decide whether to continue funding the D.A.R.E. (Drug Abuse Resistance Education) program in public schools ( Figure ). This program typically involves police officers coming into the classroom to educate students about the dangers of becoming involved with alcohol and other drugs. According to the D.A.R.E. website (www.dare.org), this program has been very popular since its inception in 1983, and it is currently operating in 75% of school districts in the United States and in more than 40 countries worldwide. Sounds like an easy decision, right? However, on closer review, you discover that the vast majority of research into this program consistently suggests that participation has little, if any, effect on whether or not someone uses alcohol or other drugs (Clayton, Cattarello, & Johnstone, 1996; Ennett, Tobler, Ringwalt, & Flewelling, 1994; Lynam et al., 1999; Ringwalt, Ennett, & Holt, 1991). If you are committed to being a good steward of taxpayer money, will you fund this particular program, or will you try to find other programs that research has consistently demonstrated to be effective?
Watch this news report to learn more about some of the controversial issues surrounding the D.A.R.E. program.
Ultimately, it is not just politicians who can benefit from using research in guiding their decisions. We all might look to research from time to time when making decisions in our lives. Imagine you just found out that a close friend has breast cancer or that one of your young relatives has recently been diagnosed with autism. In either case, you want to know which treatment options are most successful with the fewest side effects. How would you find that out? You would probably talk with your doctor and personally review the research that has been done on various treatment options—always with a critical eye to ensure that you are as informed as possible.
In the end, research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.
THE PROCESS OF SCIENTIFIC RESEARCH
Scientific knowledge is advanced through a process known as the scientific method . Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. The types of reasoning within the circle are called deductive and inductive. In deductive reasoning , ideas are tested against the empirical world; in inductive reasoning , empirical observations lead to new ideas ( Figure ). These processes are inseparable, like inhaling and exhaling, but different research approaches place different emphasis on the deductive and inductive aspects.
In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world. If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion). In this example, the hypothesis is correct; therefore, the conclusion is correct as well. Sometimes, however, an incorrect hypothesis may lead to a logical but incorrect conclusion. Consider this argument: all ducks are born with the ability to see. Quackers is a duck. Therefore, Quackers was born with the ability to see. Scientists use deductive reasoning to empirically test their hypotheses. Returning to the example of the ducks, researchers might design a study to test the hypothesis that if all living things require energy to survive, then ducks will be found to require energy to survive.
Deductive reasoning starts with a generalization that is tested against real-world observations; however, inductive reasoning moves in the opposite direction. Inductive reasoning uses empirical observations to construct broad generalizations. Unlike deductive reasoning, conclusions drawn from inductive reasoning may or may not be correct, regardless of the observations on which they are based. For instance, you may notice that your favorite fruits—apples, bananas, and oranges—all grow on trees; therefore, you assume that all fruit must grow on trees. This would be an example of inductive reasoning, and, clearly, the existence of strawberries, blueberries, and kiwi demonstrate that this generalization is not correct despite it being based on a number of direct observations. Scientists use inductive reasoning to formulate theories, which in turn generate hypotheses that are tested with deductive reasoning. In the end, science involves both deductive and inductive processes.
For example, case studies, which you will read about in the next section, are heavily weighted on the side of empirical observations. Thus, case studies are closely associated with inductive processes as researchers gather massive amounts of observations and seek interesting patterns (new ideas) in the data. Experimental research, on the other hand, puts great emphasis on deductive reasoning.
Play this “Deal Me In” interactive card game to practice using inductive reasoning.
We’ve stated that theories and hypotheses are ideas, but what sort of ideas are they, exactly? A theory is a well-developed set of ideas that propose an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.
A hypothesis is a testable prediction about how the world will behave if our idea is correct, and it is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests Figure .
To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later chapter, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.
A scientific hypothesis is also falsifiable , or capable of being shown to be incorrect. Recall from the introductory chapter that Sigmund Freud had lots of interesting ideas to explain various human behaviors ( Figure ). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.
In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).
Scientific research’s dependence on falsifiability allows for great confidence in the information that it produces. Typically, by the time information is accepted by the scientific community, it has been tested repeatedly.
Visit this website to apply the scientific method and practice its steps by using them to solve a murder mystery, determine why a student is in trouble, and design an experiment to test house paint.
Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. In this way, research enables scientists to separate fact from simple opinion. Having good information generated from research aids in making wise decisions both in public policy and in our personal lives.
Review Questions
Scientific hypotheses are ________ and falsifiable.
________ are defined as observable realities.
Scientific knowledge is ________.
A major criticism of Freud’s early theories involves the fact that his theories ________.
- were too limited in scope
- were too outrageous
- were too broad
- were not testable
Critical Thinking Questions
In this section, the D.A.R.E. program was described as an incredibly popular program in schools across the United States despite the fact that research consistently suggests that this program is largely ineffective. How might one explain this discrepancy?
The scientific method is often described as self-correcting and cyclical. Briefly describe your understanding of the scientific method with regard to these concepts.
Personal Application Questions
Healthcare professionals cite an enormous number of health problems related to obesity, and many people have an understandable desire to attain a healthy weight. There are many diet programs, services, and products on the market to aid those who wish to lose weight. If a close friend was considering purchasing or participating in one of these products, programs, or services, how would you make sure your friend was fully aware of the potential consequences of this decision? What sort of information would you want to review before making such an investment or lifestyle change yourself?
[glossary-page] [glossary-term]deductive reasoning:[/glossary-term] [glossary-definition]results are predicted based on a general premise[/glossary-definition]
[glossary-term]empirical:[/glossary-term] [glossary-definition]grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing[/glossary-definition]
[glossary-term]fact:[/glossary-term] [glossary-definition]objective and verifiable observation, established using evidence collected through empirical research[/glossary-definition]
[glossary-term]falsifiable:[/glossary-term] [glossary-definition]able to be disproven by experimental results[/glossary-definition]
[glossary-term]hypothesis:[/glossary-term] [glossary-definition](plural: hypotheses) tentative and testable statement about the relationship between two or more variables[/glossary-definition]
[glossary-term]inductive reasoning:[/glossary-term] [glossary-definition]conclusions are drawn from observations[/glossary-definition]
[glossary-term]opinion:[/glossary-term] [glossary-definition]personal judgements, conclusions, or attitudes that may or may not be accurate[/glossary-definition]
[glossary-term]theory:[/glossary-term] [glossary-definition]well-developed set of ideas that propose an explanation for observed phenomena[/glossary-definition] [/glossary-page]
General Psychology Copyright © by Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.
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10 Why is Research Important?
Learning Objectives
- Explain how scientific research addresses questions about behaviour
- Discuss how scientific research guides public policy
- Appreciate how scientific research can be important in making personal decisions
Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people’s authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth’s continents did not move, and that mental illness was caused by possession ( Figure PR.2 ). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.
The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behaviour, as well as the cognitive (mental) and physiological (body) processes that underlie behaviour. In contrast to other methods that people use to understand the behaviour of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : it is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.
While behaviour is observable, the mind is not. If someone is crying, we can see behaviour. However, the reason for the behaviour is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behaviour by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behaviour. This chapter explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.
Use of Research Information
Trying to determine which theories are and are not accepted by the scientific community can be difficult, especially in an area of research as broad as psychology. More than ever before, we have an incredible amount of information at our fingertips, and a simple internet search on any given research topic might result in a number of contradictory studies. In these cases, we are witnessing the scientific community going through the process of reaching a consensus, and it could be quite some time before a consensus emerges. For example, the explosion in our use of technology has led researchers to question whether this ultimately helps or hinders us. The use and implementation of technology in educational settings has become widespread over the last few decades. Researchers are coming to different conclusions regarding the use of technology. To illustrate this point, a study investigating a smartphone app targeting surgery residents (graduate students in surgery training) found that the use of this app can increase student engagement and raise test scores (Shaw & Tan, 2015). Conversely, another study found that the use of technology in undergraduate student populations had negative impacts on sleep, communication, and time management skills (Massimini & Peterson, 2009). Until sufficient amounts of research have been conducted, there will be no clear consensus on the effects that technology has on a student’s acquisition of knowledge, study skills, and mental health.
In the meantime, we should strive to think critically about the information we encounter by exercising a degree of healthy skepticism. When someone makes a claim, we should examine the claim from a number of different perspectives: what is the expertise of the person making the claim, what might they gain if the claim is valid, does the claim seem justified given the evidence, and what do other researchers think of the claim? This is especially important when we consider how much information in advertising campaigns and on the internet claims to be based on “scientific evidence” when in actuality it is a belief or perspective of just a few individuals trying to sell a product or draw attention to their perspectives.
We should be informed consumers of the information made available to us because decisions based on this information have significant consequences. One such consequence can be seen in politics and public policy. Imagine that you have been elected as the Premier of your province. One of your responsibilities is to manage the provincial budget and determine how to best spend your constituents’ tax dollars. As the new Premier, you need to decide whether to continue funding early intervention programs. These programs are designed to help children who come from low-income backgrounds, have special needs, or face other disadvantages. These programs may involve providing a wide variety of services to maximize the children’s development and position them for optimal levels of success in school and later in life (Blann, 2005). While such programs sound appealing, you would want to be sure that they also proved effective before investing additional money in these programs. Fortunately, psychologists and other scientists have conducted vast amounts of research on such programs and, in general, the programs are found to be effective (Neil & Christensen, 2009; Peters-Scheffer, Didden, Korzilius, & Sturmey, 2011). While not all programs are equally effective, and the short-term effects of many such programs are more pronounced, there is reason to believe that many of these programs produce long-term benefits for participants (Barnett, 2011). If you are committed to being a good steward of taxpayer money, you would want to look at research. Which programs are most effective? What characteristics of these programs make them effective? Which programs promote the best outcomes? After examining the research, you would be best equipped to make decisions about which programs to fund.
LINK TO LEARNING
Ultimately, it is not just politicians who can benefit from using research in guiding their decisions. We all might look to research from time to time when making decisions in our lives. Imagine you just found out that a close friend has breast cancer or that one of your young relatives has recently been diagnosed with autism. In either case, you want to know which treatment options are most successful with the fewest side effects. How would you find that out? You would probably talk with your doctor and personally review the research that has been done on various treatment options—always with a critical eye to ensure that you are as informed as possible.
In the end, research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.
The Process of Scientific Research
Scientific knowledge is advanced through a process known as the scientific method . Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. The types of reasoning within the circle are called deductive and inductive. In deductive reasoning , ideas are tested in the real world; in inductive reasoning , real-world observations lead to new ideas ( Figure PR.3 ). These processes are inseparable, like inhaling and exhaling, but different research approaches place different emphasis on the deductive and inductive aspects.
In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world. If the hypothesis is supported, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion). In this example, the hypothesis is correct; therefore, the conclusion is correct as well. Sometimes, however, an incorrect hypothesis may lead to a logical but incorrect conclusion. Consider this argument: all ducks are born with the ability to see. Quackers is a duck. Therefore, Quackers was born with the ability to see. Scientists use deductive reasoning to empirically test their hypotheses. Returning to the example of the ducks, researchers might design a study to test the hypothesis that if all living things require energy to survive, then ducks will be found to require energy to survive.
Deductive reasoning starts with a generalization that is tested against real-world observations; however, inductive reasoning moves in the opposite direction. Inductive reasoning uses empirical observations to construct broad generalizations. Unlike deductive reasoning, conclusions drawn from inductive reasoning may or may not be correct, regardless of the observations on which they are based. For instance, you may notice that your favourite fruits—apples, bananas, and oranges—all grow on trees; therefore, you assume that all fruit must grow on trees. This would be an example of inductive reasoning, and, clearly, the existence of strawberries, blueberries, and kiwi demonstrate that this generalization is not correct despite it being based on a number of direct observations. Scientists use inductive reasoning to formulate theories, which in turn generate hypotheses that are tested with deductive reasoning. In the end, science involves both deductive and inductive processes.
For example, case studies, which you will read about in the next section, are heavily weighted on the side of empirical observations. Thus, case studies are closely associated with inductive processes as researchers gather massive amounts of observations and seek interesting patterns (new ideas) in the data. Experimental research, on the other hand, puts great emphasis on deductive reasoning.
We’ve stated that theories and hypotheses are ideas, but what sort of ideas are they, exactly? A theory is a well-developed set of ideas that propose an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.
A hypothesis is a testable prediction about how the world will behave if our idea is correct, and it is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests Figure PR.4 .
Introduction to Psychology & Neuroscience Copyright © 2020 by Edited by Leanne Stevens is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.
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7 Why Is Research Important?
[latexpage]
Learning Objectives
By the end of this section, you will be able to:
- Explain how scientific research addresses questions about behavior
- Discuss how scientific research guides public policy
- Appreciate how scientific research can be important in making personal decisions
Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people’s authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth’s continents did not move, and that mental illness was caused by possession ( [link] ). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.
The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.
While behavior is observable, the mind is not. If someone is crying, we can see behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes we can learn the reason for someone’s behavior by simply asking a question, like “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This chapter explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.
USE OF RESEARCH INFORMATION
Trying to determine which theories are and are not accepted by the scientific community can be difficult, especially in an area of research as broad as psychology. More than ever before, we have an incredible amount of information at our fingertips, and a simple internet search on any given research topic might result in a number of contradictory studies. In these cases, we are witnessing the scientific community going through the process of reaching a consensus, and it could be quite some time before a consensus emerges. For example, the hypothesized link between exposure to media violence and subsequent aggression has been debated in the scientific community for roughly 60 years. Even today, we will find detractors, but a consensus is building. Several professional organizations view media violence exposure as a risk factor for actual violence, including the American Medical Association, the American Psychiatric Association, and the American Psychological Association (American Academy of Pediatrics, American Academy of Child & Adolescent Psychiatry, American Psychological Association, American Medical Association, American Academy of Family Physicians, American Psychiatric Association, 2000).
In the meantime, we should strive to think critically about the information we encounter by exercising a degree of healthy skepticism. When someone makes a claim, we should examine the claim from a number of different perspectives: what is the expertise of the person making the claim, what might they gain if the claim is valid, does the claim seem justified given the evidence, and what do other researchers think of the claim? This is especially important when we consider how much information in advertising campaigns and on the internet claims to be based on “scientific evidence” when in actuality it is a belief or perspective of just a few individuals trying to sell a product or draw attention to their perspectives.
We should be informed consumers of the information made available to us because decisions based on this information have significant consequences. One such consequence can be seen in politics and public policy. Imagine that you have been elected as the governor of your state. One of your responsibilities is to manage the state budget and determine how to best spend your constituents’ tax dollars. As the new governor, you need to decide whether to continue funding the D.A.R.E. (Drug Abuse Resistance Education) program in public schools ( [link] ). This program typically involves police officers coming into the classroom to educate students about the dangers of becoming involved with alcohol and other drugs. According to the D.A.R.E. website (www.dare.org), this program has been very popular since its inception in 1983, and it is currently operating in 75% of school districts in the United States and in more than 40 countries worldwide. Sounds like an easy decision, right? However, on closer review, you discover that the vast majority of research into this program consistently suggests that participation has little, if any, effect on whether or not someone uses alcohol or other drugs (Clayton, Cattarello, & Johnstone, 1996; Ennett, Tobler, Ringwalt, & Flewelling, 1994; Lynam et al., 1999; Ringwalt, Ennett, & Holt, 1991). If you are committed to being a good steward of taxpayer money, will you fund this particular program, or will you try to find other programs that research has consistently demonstrated to be effective?
Watch this news report to learn more about some of the controversial issues surrounding the D.A.R.E. program.
Ultimately, it is not just politicians who can benefit from using research in guiding their decisions. We all might look to research from time to time when making decisions in our lives. Imagine you just found out that a close friend has breast cancer or that one of your young relatives has recently been diagnosed with autism. In either case, you want to know which treatment options are most successful with the fewest side effects. How would you find that out? You would probably talk with your doctor and personally review the research that has been done on various treatment options—always with a critical eye to ensure that you are as informed as possible.
In the end, research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.
THE PROCESS OF SCIENTIFIC RESEARCH
Scientific knowledge is advanced through a process known as the scientific method . Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those empirical observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. The types of reasoning within the circle are called deductive and inductive. In deductive reasoning , ideas are tested against the empirical world; in inductive reasoning , empirical observations lead to new ideas ( [link] ). These processes are inseparable, like inhaling and exhaling, but different research approaches place different emphasis on the deductive and inductive aspects.
In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world. If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion). In this example, the hypothesis is correct; therefore, the conclusion is correct as well. Sometimes, however, an incorrect hypothesis may lead to a logical but incorrect conclusion. Consider this argument: all ducks are born with the ability to see. Quackers is a duck. Therefore, Quackers was born with the ability to see. Scientists use deductive reasoning to empirically test their hypotheses. Returning to the example of the ducks, researchers might design a study to test the hypothesis that if all living things require energy to survive, then ducks will be found to require energy to survive.
Deductive reasoning starts with a generalization that is tested against real-world observations; however, inductive reasoning moves in the opposite direction. Inductive reasoning uses empirical observations to construct broad generalizations. Unlike deductive reasoning, conclusions drawn from inductive reasoning may or may not be correct, regardless of the observations on which they are based. For instance, you may notice that your favorite fruits—apples, bananas, and oranges—all grow on trees; therefore, you assume that all fruit must grow on trees. This would be an example of inductive reasoning, and, clearly, the existence of strawberries, blueberries, and kiwi demonstrate that this generalization is not correct despite it being based on a number of direct observations. Scientists use inductive reasoning to formulate theories, which in turn generate hypotheses that are tested with deductive reasoning. In the end, science involves both deductive and inductive processes.
For example, case studies, which you will read about in the next section, are heavily weighted on the side of empirical observations. Thus, case studies are closely associated with inductive processes as researchers gather massive amounts of observations and seek interesting patterns (new ideas) in the data. Experimental research, on the other hand, puts great emphasis on deductive reasoning.
Play this “Deal Me In” interactive card game to practice using inductive reasoning.
We’ve stated that theories and hypotheses are ideas, but what sort of ideas are they, exactly? A theory is a well-developed set of ideas that propose an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once; instead, researchers create hypotheses to test specific aspects of a theory.
A hypothesis is a testable prediction about how the world will behave if our idea is correct, and it is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests [link] .
To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later chapter, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.
A scientific hypothesis is also falsifiable , or capable of being shown to be incorrect. Recall from the introductory chapter that Sigmund Freud had lots of interesting ideas to explain various human behaviors ( [link] ). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable; for example, it is impossible to imagine empirical observations that would disprove the existence of the id, the ego, and the superego—the three elements of personality described in Freud’s theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.
In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).
Scientific research’s dependence on falsifiability allows for great confidence in the information that it produces. Typically, by the time information is accepted by the scientific community, it has been tested repeatedly.
Visit this website to apply the scientific method and practice its steps by using them to solve a murder mystery, determine why a student is in trouble, and design an experiment to test house paint.
Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. In this way, research enables scientists to separate fact from simple opinion. Having good information generated from research aids in making wise decisions both in public policy and in our personal lives.
Review Questions
Scientific hypotheses are ________ and falsifiable.
________ are defined as observable realities.
Scientific knowledge is ________.
A major criticism of Freud’s early theories involves the fact that his theories ________.
- were too limited in scope
- were too outrageous
- were too broad
- were not testable
Critical Thinking Questions
In this section, the D.A.R.E. program was described as an incredibly popular program in schools across the United States despite the fact that research consistently suggests that this program is largely ineffective. How might one explain this discrepancy?
There is probably tremendous political pressure to appear to be hard on drugs. Therefore, even though D.A.R.E. might be ineffective, it is a well-known program with which voters are familiar.
The scientific method is often described as self-correcting and cyclical. Briefly describe your understanding of the scientific method with regard to these concepts.
This cyclical, self-correcting process is primarily a function of the empirical nature of science. Theories are generated as explanations of real-world phenomena. From theories, specific hypotheses are developed and tested. As a function of this testing, theories will be revisited and modified or refined to generate new hypotheses that are again tested. This cyclical process ultimately allows for more and more precise (and presumably accurate) information to be collected.
Personal Application Questions
Healthcare professionals cite an enormous number of health problems related to obesity, and many people have an understandable desire to attain a healthy weight. There are many diet programs, services, and products on the market to aid those who wish to lose weight. If a close friend was considering purchasing or participating in one of these products, programs, or services, how would you make sure your friend was fully aware of the potential consequences of this decision? What sort of information would you want to review before making such an investment or lifestyle change yourself?
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IMAGES
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Learning Objectives. By the end of this section, you will be able to: Explain how scientific research addresses questions about behavior. Discuss how scientific research guides public policy. Appreciate how scientific research can be important in making personal decisions.
Table of Contents. Have you ever wondered why we behave the way we do, or how our minds work? The quest for these answers lies at the heart of psychological research, a field as fascinating as it is fundamental to our understanding of the human and animal psyche.
Psychological research relies on both inductive and deductive reasoning. In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world.
Back to '1.3: The Importance of Psychological Research and Its Approaches\' Why Research Is Important. Read this text, which introduces the scientific method, which involves making a hypothesis or general premise, deductive reasoning, making empirical observations, and inductive reasoning,
Learning Objectives. Explain how scientific research addresses questions about behavior. Discuss how scientific research guides public policy. Appreciate how scientific research can be important in making personal decisions. Scientific research is a critical tool for successfully navigating our complex world.
According to literature, what research method is used and why a certain research method is used is complex as it depends on various factors that may include paradigm (O'Neil and Koekemoer, 2016), research question (Grix, 2002), or the skill and exposure of the researcher (Nind et al., 2015).
Research in psychology is important because it provides us with valuable information that helps to improve human lives. By learning more about the brain, cognition, behavior, and mental health conditions, researchers are able to solve real-world problems that affect our day-to-day lives.
Psychological research relies on both inductive and deductive reasoning. In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world.
Explain how scientific research addresses questions about behaviour; Discuss how scientific research guides public policy; Appreciate how scientific research can be important in making personal decisions
Learning Objectives. By the end of this section, you will be able to: Explain how scientific research addresses questions about behavior. Discuss how scientific research guides public policy. Appreciate how scientific research can be important in making personal decisions.