level
Note. N = 150 ( n = 50 for each condition). Participants were on average 39.5 years old ( SD = 10.1), and participant age did not differ by condition.
a Reflects the number and percentage of participants answering “yes” to this question.
Results of Curve-Fitting Analysis Examining the Time Course of Fixations to the Target
Logistic parameter | 9-year-olds | 16-year-olds | (40) |
| Cohen's | ||
Maximum asymptote, proportion | .843 | .135 | .877 | .082 | 0.951 | .347 | 0.302 |
Crossover, in ms | 759 | 87 | 694 | 42 | 2.877 | .006 | 0.840 |
Slope, as change in proportion per ms | .001 | .0002 | .002 | .0002 | 2.635 | .012 | 2.078 |
Note. For each subject, the logistic function was fit to target fixations separately. The maximum asymptote is the asymptotic degree of looking at the end of the time course of fixations. The crossover point is the point in time the function crosses the midway point between peak and baseline. The slope represents the rate of change in the function measured at the crossover. Mean parameter values for each of the analyses are shown for the 9-year-olds ( n = 24) and 16-year-olds ( n = 18), as well as the results of t tests (assuming unequal variance) comparing the parameter estimates between the two ages.
Descriptive Statistics and Correlations for Study Variables
Variable |
|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | |
1. Internal– external status | 3,697 | 0.43 | 0.49 | — | ||||||
2. Manager job performance | 2,134 | 3.14 | 0.62 | −.08 | — | |||||
3. Starting salary | 3,697 | 1.01 | 0.27 | .45 | −.01 | — | ||||
4. Subsequent promotion | 3,697 | 0.33 | 0.47 | .08 | .07 | .04 | — | |||
5. Organizational tenure | 3,697 | 6.45 | 6.62 | −.29 | .09 | .01 | .09 | — | ||
6. Unit service performance | 3,505 | 85.00 | 6.98 | −.25 | −.39 | .24 | .08 | .01 | — | |
7. Unit financial performance | 694 | 42.61 | 5.86 | .00 | −.03 | .12 | −.07 | −.02 | .16 | — |
Means, Standard Deviations, and One-Way Analyses of Variance in Psychological and Social Resources and Cognitive Appraisals
Measure | Urban | Rural | (1, 294) | η | ||
Self-esteem | 2.91 | 0.49 | 3.35 | 0.35 | 68.87 | .19 |
Social support | 4.22 | 1.50 | 5.56 | 1.20 | 62.60 | .17 |
Cognitive appraisals | ||||||
Threat | 2.78 | 0.87 | 1.99 | 0.88 | 56.35 | .20 |
Challenge | 2.48 | 0.88 | 2.83 | 1.20 | 7.87 | .03 |
Self-efficacy | 2.65 | 0.79 | 3.53 | 0.92 | 56.35 | .16 |
*** p < .001.
Results From a Factor Analysis of the Parental Care and Tenderness (PCAT) Questionnaire
PCAT item | Factor loading | ||
1 | 2 | 3 | |
Factor 1: Tenderness—Positive | |||
20. You make a baby laugh over and over again by making silly faces. | .04 | .01 | |
22. A child blows you kisses to say goodbye. | −.02 | −.01 | |
16. A newborn baby curls its hand around your finger. | −.06 | .00 | |
19. You watch as a toddler takes their first step and tumbles gently back down. | .05 | −.07 | |
25. You see a father tossing his giggling baby up into the air as a game. | .10 | −.03 | |
Factor 2: Liking | |||
5. I think that kids are annoying (R) | −.01 | .06 | |
8. I can’t stand how children whine all the time (R) | −.12 | −.03 | |
2. When I hear a child crying, my first thought is “shut up!” (R) | .04 | .01 | |
11. I don’t like to be around babies. (R) | .11 | −.01 | |
14. If I could, I would hire a nanny to take care of my children. (R) | .08 | −.02 | |
Factor 3: Protection | |||
7. I would hurt anyone who was a threat to a child. | −.13 | −.02 | |
12. I would show no mercy to someone who was a danger to a child. | .00 | −.05 | |
15. I would use any means necessary to protect a child, even if I had to hurt others. | .06 | .08 | |
4. I would feel compelled to punish anyone who tried to harm a child. | .07 | .03 | |
9. I would sooner go to bed hungry than let a child go without food. | .46 | −.03 |
Note. N = 307. The extraction method was principal axis factoring with an oblique (Promax with Kaiser Normalization) rotation. Factor loadings above .30 are in bold. Reverse-scored items are denoted with an (R). Adapted from “Individual Differences in Activation of the Parental Care Motivational System: Assessment, Prediction, and Implications,” by E. E. Buckels, A. T. Beall, M. K. Hofer, E. Y. Lin, Z. Zhou, and M. Schaller, 2015, Journal of Personality and Social Psychology , 108 (3), p. 501 ( https://doi.org/10.1037/pspp0000023 ). Copyright 2015 by the American Psychological Association.
Moderator Analysis: Types of Measurement and Study Year
Effect | Estimate |
| 95% CI | ||
Fixed effects | |||||
Intercept | .119 | .040 | .041 | .198 | .003 |
Creativity measurement | .097 | .028 | .042 | .153 | .001 |
Academic achievement measurement | −.039 | .018 | −.074 | −.004 | .03 |
Study year | .0002 | .001 | −.001 | .002 | .76 |
Goal | −.003 | .029 | −.060 | .054 | .91 |
Published | .054 | .030 | −.005 | .114 | .07 |
Random effects | |||||
Within-study variance | .009 | .001 | .008 | .011 | <.001 |
Between-study variance | .018 | .003 | .012 | .023 | <.001 |
Note . Number of studies = 120, number of effects = 782, total N = 52,578. CI = confidence interval; LL = lower limit; UL = upper limit.
Master Narrative Voices: Struggle and Success and Emancipation
Discourse and dimension | Example quote |
Struggle and success | |
Self-actualization as member of a larger gay community is the end goal of healthy sexual identity development, or “coming out” | “My path of gayness ... going from denial to saying, well this is it, and then the process of coming out, and the process of just sort of, looking around and seeing, well where do I stand in the world, and sort of having, uh, political feelings.” (Carl, age 50) |
Maintaining healthy sexual identity entails vigilance against internalization of societal discrimination | “When I'm like thinking of criticisms of more mainstream gay culture, I try to ... make sure it's coming from an appropriate place and not like a place of self-loathing.” (Patrick, age 20) |
Emancipation | |
Open exploration of an individually fluid sexual self is the goal of healthy sexual identity development | “[For heterosexuals] the man penetrates the female, whereas with gay people, I feel like there is this potential for really playing around with that model a lot, you know, and just experimenting and exploring.” (Orion, age 31) |
Questioning discrete, monolithic categories of sexual identity | “LGBTQI, you know, and added on so many letters. Um, and it does start to raise the question about what the terms mean and whether ... any term can adequately be descriptive.” (Bill, age 50) |
Integrated Results Matrix for the Effect of Topic Familiarity on Reliance on Author Expertise
Quantitative results | Qualitative results | Example quote |
When the topic was more familiar (climate change) and cards were more relevant, participants placed less value on author expertise. | When an assertion was considered to be more familiar and considered to be general knowledge, participants perceived less need to rely on author expertise. | Participant 144: “I feel that I know more about climate and there are several things on the climate cards that are obvious, and that if I sort of know it already, then the source is not so critical ... whereas with nuclear energy, I don't know so much so then I'm maybe more interested in who says what.” |
When the topic was less familiar (nuclear power) and cards were more relevant, participants placed more value on authors with higher expertise. | When an assertion was considered to be less familiar and not general knowledge, participants perceived more need to rely on author expertise. | Participant 3: “[Nuclear power], which I know much, much less about, I would back up my arguments more with what I trust from the professors.” |
Note . We integrated quantitative data (whether students selected a card about nuclear power or about climate change) and qualitative data (interviews with students) to provide a more comprehensive description of students’ card selections between the two topics.
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Heterogeneous Graph (HG) is a data structure composed of various types of nodes and rich relational information, which can accurately show complex application scenarios in the real world. Although heterogeneous graph neural networks (HGNNs) have been widely applied to model HGs, there are still some issues that need to be addressed. On the one hand, most of HGNNs ignore the fine-grained information when modeling HGs, such as attribute and topology overcoupling due to the accumulation of multi-source heterogeneous information in message passing. On the other hand, HGNNs are designed from a single view (based on metapath or relation awareness), which undoubtedly leads to information loss and makes it difficult to fully extract potential interactions in HGs. To tackle the aforementioned limitations, a M ulti-view fusion based H eterogeneous G raph N eural N etwork (MHGNN) is proposed, which is modeled from node view, network schema view, and semantics view to mine the information from different granularity in HGs. MHGNN extracts the fine-gained information of nodes, heterogeneous interaction of neighboring nodes, and mutual influence between different semantics from three views respectively. Then, the model integrates these information as the final node representation. To prove the effectiveness of this work, extensive experiments are conducted on four real-world datasets, and comparisons are made with seven competitive baselines. The results demonstrate that the proposed MHGNN significantly outperforms state-of-the-art methods. Source codes are available at https://github.com/ZZY-GraphMiningLab/MHGNN.
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The datasets used in the experiments are publicly available in the online repository.
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This work is supported by National Key R &D Program of China(Grant No.2022ZD0119501); the National Natural Science Foundation of China (Grant No. 62072288, 52374221, 62302277), the Natural Science Foundation of Shandong Province (Grant No. ZR2022MF268, ZR2022QF136, ZR2021QG038), the Taishan Scholar Program of Shandong Province(Grant No.tsqn202211154, ts20190936), the Natural Science Foundation of Shandong Province (Youth Program, Grant No.ZR2022QF136).
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College of electronic and information Engineering, Shandong University of Science and Technology, Qianwangang Road, Qingdao, 266590, Shandong, China
Chao Li, Xiangkai Zhu & Qingtian Zeng
Institute of Information Science, Beijing Jiaotong University, Haidian District, 100044, Beijing, China
College of Computer Science and Engineering, Shandong University of Science and Technology, Qianwangang Road, Qingdao, 266590, Shandong, China
Zhongying Zhao, Lingtao Su & Qingtian Zeng
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Chao Li, Xiangkai Zhu, Yeyu Yan, Zhongying Zhao, Lingtao Su, Qingtian Zeng wrote the main manuscript text; Xiangkai Zhu and Yeyu Yan prepared the result of our experiments; All authors reviewed the manuscript.
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Basics. In APA style, a figure is any representation of information that does not use rows and columns (e.g., a line graph, map, or photograph). Keep the following in mind when including a figure in your paper: The figure number, in bold text, belongs above the figure. The figure title belongs one double-spaced line below the figure number.
Note: For creating your own tables, figures, and images see the Paper Formatting Section of This Guide. If you include or adapt a table, figure, or image you must include: In bold, left hand justified, label as Table # or Figure #. For Example: Table 2, Figure 4. One double spaced line below table number, in italics with all major words ...
Titles should be brief but explain the main function or purpose. Use title case for table titles, which means to capitalize all nouns, verbs, proper nouns, and major words. Minor words less than four letters should be lowercased. The word "Table" and the number should be bolded. Italicize the table title. Table 1.
Reporting Research Results in APA Style | Tips & Examples. Published on December 21, 2020 by Pritha Bhandari.Revised on January 17, 2024. The results section of a quantitative research paper is where you summarize your data and report the findings of any relevant statistical analyses.. The APA manual provides rigorous guidelines for what to report in quantitative research papers in the fields ...
Example: Figure 1. Schematic drawings of a bird's eye view of the table (a) and the test phase of. the choice task (b). Numbers represent the dimensions in centimeters. Adapted from. "Visual Experience Enhances Infants' Use of Task-Relevant Information in an Action. Task," by S.-h.
Number figures consecutively throughout your paper. Figures should be labeled "Figure (number)" ABOVE the figure. Double-space the caption that appears under a figure. General Format 1 (Figure from a Book): Caption under Figure. Note: Descriptive phrase that serves as title and description. Reprinted [or adapted]
A graph can be a useful addition to any research paper, as it provides a visual reference to the point you are trying to convey. Graphs are generally used to display data in an interesting and easy-to-read manner. As with any other piece of research, cite a graph properly per American Psychological Association rules.
Sample results of several t tests table. Sample correlation table. Sample analysis of variance (ANOVA) table. Sample factor analysis table. Sample regression table. Sample qualitative table with variable descriptions. Sample mixed methods table. These sample tables are also available as a downloadable Word file (DOCX, 37KB).
Media Files: APA Sample Student Paper , APA Sample Professional Paper This resource is enhanced by Acrobat PDF files. Download the free Acrobat Reader. Note: The APA Publication Manual, 7 th Edition specifies different formatting conventions for student and professional papers (i.e., papers written for credit in a course and papers intended for scholarly publication).
Citing Information From an Image, Chart, Table or Graph. If you refer to information from an image, chart, table or graph, but do not reproduce it in your paper, create a citation both in-text and on your Reference list. If the information is part of another format, for example a book, magazine article, encyclopedia, etc., cite the work it came ...
Apr 01, 2022 4749. You can find examples of how to cite a graph, table or chart in APA style from the links below.
Basic guidelines for formatting the reference list at the end of a standard APA research paper Author/Authors Rules for handling works by a single author or multiple authors that apply to all APA-style references in your reference list, regardless of the type of work (book, article, electronic resource, etc.) ...
In this section, some notations used in this paper are first defined, as shown in Table 1, then introduce some basic concepts and formulate the problem to be solved in this paper.. Definition 1 (Heterogeneous Graph (HG) [])Heterogeneous graph can be denoted as a network \(G=\left( V,E,X\right) \), where V represents the node set, E represents the edge set, and X represents the attribute set.