The excel file contains means for each participant and condition across Experiments 1-3.

This new data in Excel file were from a study regarding cyberbullying victimization, resilience and depression among university students in Saudi Arabia. The data are valuable as it shows the trend of cyberbullying behaviour among university students and it relates to psychological problems such as depression. However, having resilience can buffer this negative effect. Results showed significant positive correlations between cyberbullying victimization and depression, and a significant negative correlation between resilience and depression. This means that if students have higher resilience, they obtain lower scores in depression.

This repository contains the raw dataset associated to the scientific article "Stable psychological traits predict psychological perceived stress to COVID-19 outbreak”, by L. Flesia, V. Fietta, B. Segatto, M. Monaro. Data are contained in the excel file and organized as follows:

- the entire dataset used by the authors to perform statistical analysis

- the training set used by the authors to train and validate ML models

- the test set used by the authors to test the ML models

The "Legend" file contains the description of each variable in the excel file.

The step by step instructions to replicate the results of ML classification models, which are reported in the paper, including two .arff files containing the training and test set od data that can be directly run in WEKA software 3.9.

The "COVID-19 QUESTIONNAIRE" file contains the English version of the questions administered to participants.

raw data for statistical analysis

Effect size base data in excel

This dataset is derived from a questionnaire survey on the psychological dependence of college students on generative AI software. The data was collected in the form of an online questionnaire from January 24, 2025 to February 6, 2025, covering college students from multiple universities in Yunnan Province. The questionnaire design includes multiple dimensions such as basic information, usage behavior, psychological dependence, negative emotional experience, self-efficacy, etc., with a total of 1110 valid sample records. The data has been anonymized and does not contain any personal identification information. All responses were filled out by the participants themselves. In the data file, each row represents the complete answer of a respondent, and column labels include serial number, gender, grade level, major category, whether generative AI has been used, commonly used software types, frequency of use, start time, motivation for use, impact on learning efficiency, recommendation intention, attitude towards prohibition of use, future use intention, level of trust in AI, dependency behavior, anxiety and emotional reactions, self-efficacy, and other aspects. Some of the questions in the questionnaire were scored with the Likert five point scale, and some were Single choice question or multiple choice questions. Some questions, such as "Have you used Generative AI before?", are automatically skipped if not used, resulting in a missing value of "0" in the corresponding column, which is a reasonable loss in design logic. There may be self-report bias in the data collection process, and some questions involve subjective evaluations of psychological states, resulting in certain subjective errors. In the data processing stage, preliminary cleaning has been carried out for issues such as outliers and duplicate submissions to ensure the validity and consistency of the data. The data file is in Excel format (. xlsx) and can be opened and processed using common spreadsheet software such as Microsoft Excel, WPS spreadsheets, Google Sheets, etc. This dataset is suitable for empirical research in fields such as educational technology, psychology, and information behavior, especially for exploring the psychological and behavioral characteristics of college students during their interaction with generative AI.

This zip file contains data files for 3 activities described in the accompanying PPT slides 1. an excel spreadsheet for analysing gain scores in a 2 group, 2 times data array. this activity requires access to –https://campbellcollaboration.org/research-resources/effect-size-calculator.html to calculate effect size.2. an AMOS path model and SPSS data set for an autoregressive, bivariate path model with cross-lagging. This activity is related to the following article: Brown, G. T. L., & Marshall, J. C. (2012). The impact of training students how to write introductions for academic essays: An exploratory, longitudinal study. Assessment & Evaluation in Higher Education, 37(6), 653-670. doi:10.1080/02602938.2011.5632773. an AMOS latent curve model and SPSS data set for a 3-time latent factor model with an interaction mixed model that uses GPA as a predictor of the LCM start and slope or change factors. This activity makes use of data reported previously and a published data analysis case: Peterson, E. R., Brown, G. T. L., & Jun, M. C. (2015). Achievement emotions in higher education: A diary study exploring emotions across an assessment event. Contemporary Educational Psychology, 42, 82-96. doi:10.1016/j.cedpsych.2015.05.002andBrown, G. T. L., & Peterson, E. R. (2018). Evaluating repeated diary study responses: Latent curve modeling. In SAGE Research Methods Cases Part 2. Retrieved from http://methods.sagepub.com/case/evaluating-repeated-diary-study-responses-latent-curve-modeling doi:10.4135/9781526431592

The Search_2 dataset consists of (1) a set of 44 high-resolution digital color images of different complex natural scenes, (2) the ground truth corresponding to each of these scenes, and (3) the results of psychophysical experiments on each of these images. The images in the Search_2 dataset are a subset of a larger set that was used in a visual search and detection experiment. Each scene (image) contains a single military vehicle that serves as a search target. Areport describes the images in detail, and presents the corresponding ground truth and observer data. The image dataset, an Excel file with the ground truth and observer data, and a copy of this report are included in the dataset. The complete dataset can be used to validate (1) digital metrics that compute the visual distinctness (contrast, conspicuity, or saliency) of targets in complex scenes, and (2) models of human visual search and detection.

Files used for full data (APRILEXCEL1), for full data without outliers (April Excel No Outliers 1), and for subgroup analyses. Includes R script (MetaBH).

Research investigating whether depression is an adaptation or a disorder has been hindered by the lack of an experimental paradigm that can test causal relationships. Moreover, studies attempting to induce the syndrome often fail to capture the suite of feelings, thoughts, and behaviours that characterize depression. An experimental paradigm for triggering depressive symptoms can improve our etiological understanding of the syndrome. The present study attempts to induce core symptoms of depression, particularly those related to rumination, in a healthy, non-clinical sample through a controlled social experiment. These symptoms are sad or depressed mood, anhedonia, feelings of worthlessness or guilt, and difficulty concentrating. 134 undergraduate students were randomly assigned to either an Exclusion (EX) or Inclusion (IN) group. Participants in the Exclusion group were exposed to a modified Cyberball paradigm, designed to make them feel socially excluded, followed by a dual-interferenc..., Our datasets were collected through LimeSurvey, an open source on-line statistical survey web app. We first exported our data as excel files which contain participants' self-reported data. We also added the scores for each participants' writing task provided by our blind-raters. We have presented the data from our primary study and our pilot study in two seperate files., , # Data from: An experimental paradigm for triggering a depressive syndrome

https://doi.org/10.5061/dryad.v6wwpzh2v

Description of the data and file structure

# Title of Dataset: PilotExperiment

This file is our overall dataset for the pilot study detailed in our manuscript and supplementary materials.

## Description of the Data and file structure

The following is a description of each of the variables found in our data set, organized by column:Â

Note: any ‘.’ within the dataset refers to missing data.Â

**Date: **The date the participant completed the study.

**ID: **The randomized ID given to each participant.

**Cond: **The condition that the participant was assigned to (1=Exclusion; 0=Inclusion).

**Mood Variables: **Scores for the following mood variables at time 1 are found in their respective columns. These same mood variables, at time 2, have the same names but have a ‘2’ afterwards. Variables over 7 characters were...

The files are in reference to this paper:

Peira, Nathalie, Armita Golkar, Arne Öhman, Silke Anders, and Stefan Wiens. Emotional Responses in Spider Fear Are Closely Related to Picture Awareness. Cognition & Emotion 26, no. 2 (February 2012): 252–60. https://doi.org/10.1080/02699931.2011.579087.

Note that the actual number of subjects was 37 (20 fearful), not 36 (as stated in the paper).

The "Peira2012Rawdata.xls" contains the raw data (as Excelfile; https://microsoft-excel.en.softonic.com/). The same data are also saved as separate tab-separated textfiles. Thus, the Excelfile is not necessary but provided for convenience.

The main tabs/files are: General: Id FearGr 0=no spider fear, 1=spider fear fear=([1,3,4,7,8,9,10,12,13,20,21,22,24,25,28,30,31,32,34,36]); nofear=([2,5,6,11,14,15,16,17,18,19,23,26,27,29,33,35,37]); spweb modified SAS questionnaire before participation SPQ spider fear questionnaire STAI trait anxiety DS disgust sensitivity

Detect: mean recognition ratings Rate: mean emotion ratings SCLmean: mean skin conductance levels for the three tasks (view, detect, rate) HRmean: mean heart rates for the three tasks (view, detect, rate)

The variable names are: f20 f40 f70 f200 s20 s40 s70 s200 Note that f refers to flower and s to spider. 20,40,70, and 200 refer to different SOAs (stimulus onset asynchronies).

The files in the Peira2012SPSS.zip are not necessary. They just illustrate how we conducted some of the analyses in SPSS (https://www.ibm.com/products/spss-statistics).

This dataset supports the analysis of a vignette experiment investigating teacher expectations of students (currently in review). It includes structured data and analysis scripts used to study how teachers form expectations based on manipulations of student engagement, characteristics of social group membership and instructional context. This repository includes an R script used for the analysis of the experimental data, along with an accompanying Excel codebook file detailing variable definitions and data structure. Both resources are designed to facilitate reproducibility and provide clear documentation of the study's methodology.

Two experiments investigated Estes and Maddox’ theory (2002) that word frequency mirror effect in episodic recognition memory is due to word likeness rather than frequency of experience with a word. In Experiment 1, sixteen first year psychology students at the University of Newcastle studied lists of high and low frequency words crossed with high-neighbourhood-density and low-neighbourhood-density words and were given an episodic recognition test and asked to rate words as new or old and provide ratings of confidence according to a three point scale with six possible responses: sure old, probably old, possibly old, possibly new, probably new and sure new. Experiment 2 included twenty-three first year psychology students at the University of Newcastle who were tested using lexical decision task lists of words and nonwords. Testing was undertaken on a computer that presented the stimuli and recorded the participants’ responses using a program written in Turbo Pascal 6.0 with millisecond accurate timing. The dataset contains one Microsoft Excel file in .xls format containing data for Experiments 1 and 2.

The data supporting the findings of the article is available here to be published in the open psychology journal.

the data set is excel file generated from google form

1. Excel Files “experiment*.xlsx” files contain raw data. In ‘Notes’ an explanation of the relevant variables is offered “Parameters.xlsx” file contains the best-fitting parameters of the selective integration model (the version that omits early noise). Each tab corresponds to a different experiment. 2. Matlab files Matlab files in the ‘code’ file allow one to reproduce simulation results in fig1b, fig3b and fig3d. The ‘selective_sim.m’ file offers a basic simulation of the selective integration model. For questions and further requests please contact Konstantinos Tsetsos: k.tsetsos62@gmail.com

Hipsley, C. A. & Sherratt, E. Literature and survey data for: Psychology, not technology, is our biggest challenge to open digital morphology data. Excel file containing 3 worksheets:1) Literature summary: Data accompanying Figure 1a. Number of publications per year retrieved through Web of Science’s Science Citation Index Expanded using the topic search terms: x-ray comput* tomograph* OR CT; magnetic resonance imag* OR MRI; synchrotron; photogrammetr*. Articles were limited to the Web of Science categories Anatomy Morphology, Evolutionary Biology, Paleontology, and Zoology. Results for 2018 are not shown. Only three articles prior to 1980 were recovered. Search performed on 25 June 2018.2) CT papers: Data accompanying Figure 1b. 50 publications including CT data published in the past 5 years in Anatomical Record, Journal of Anatomy, Journal of Morphology, Journal of Vertebrate Paleontology, and Zoological Journal of the Linnean Society (10 papers each). These journals were among the top 10% of publishers including CT data recovered in our literature search.3) Survey responses: Includes data accompanying Figure 2. Responses to an online survey generated in Google Docs and posted to the MORPHMET and geomorph discussion groups. Results can also be found here:https://docs.google.com/forms/d/1hZr2QyN3a7Sh1BlaXOPumnUlqLm_MN-SkwmyuQLsO3c/edit#responses

The dataset includes one master Excel spreadsheet containing literatures searched, catalogued and summarised in the fields of Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology, which contains 770 selected texts. The aims of the data collected are to produce an integrated theory that builds on the findings of different disciplines (Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology) focused on the understanding of factors and processes (from the macro social level to the social and psychological level), within the different life contexts, that promote or hinder youth active citizenship in EU. It is possible that similar databases of literature around Europe, Young People and Active Citizenship across the fields of Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology exist in other forms, perhaps collected for studies on one or more of the included disciplines, but we do not currently have access to a similar repository. With that said, it is highly unlikely that an exact dataset corresponding to the specifics of this study exist in any form elsewhere, thus justifying the creation of new data for this study in the absence of suitable existing data. Data collected here will bridge the gap between global aggregated literatures on youth and citizenship separated by discipline on the one hand, and a new dataset offering an integrated literature analysis of different fields of study. The data sources are available in bibliographic format and attached via Excel document. The dataset relies on the following information taken from the data sources: specific identifying information about the text itself (title/author/year/publisher); abstract or summarizing information either taken directly from the text or summarized by the researcher; and keywords either taken directly from the text or summarized by the researcher. Finally, the aggregated literature review spreadsheet constitutes raw data which can be reused by researchers who want to compare our data with similar data collected in different countries, or to perform textual analysis (content analysis and/or data mining) on our data.

This database is comprised of 951 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 509 males (54%) and 442 females (46%). Their ages ranged from 12 to 16 years (M = 13.69, SD = 0.72). Seven participants did not report their age. The majority were born in Australia (N = 849, 89%). The next most common countries of birth were China (N = 24, 2.5%), the UK (N = 23, 2.4%), and the USA (N = 9, 0.9%). Data were drawn from students at five Australian independent secondary schools.

The data contains item responses for the Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. The Social media question asked about frequency of use with the question “How often do you use social media?”. The response options ranged from constantly to once a week or less. Items measuring Fear of Missing Out were included and incorporated the following five questions based on the APS Stress and Wellbeing in Australia Survey (APS, 2015). These were “When I have a good time it is important for me to share the details online; I am afraid that I will miss out on something if I don’t stay connected to my online social networks; I feel worried and uncomfortable when I can’t access my social media accounts; I find it difficult to relax or sleep after spending time on social networking sites; I feel my brain burnout with the constant connectivity of social media. Internal consistency for this measure was α = .81. Self compassion was measured using the 12-item short-form of the Self-Compassion Scale (SCS-SF; Raes et al., 2011).

The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

References:

Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. _{https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4}

Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. _{https://doi.org/10.1002/cpp.702}

Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. _{https://doi.org/10.1016/S0005-7967(98)00034-5}

As part of research into longitudinal change in sleep and daytime sleepiness in postpartum women, longitudinal self reporting, consisting of seven consecutive day sleep/wake diaries for postpartem weeks 6,12 and 18 was provided by thirty-three healthy postpartum women. Data from the study is provided as Excel datasets. Dependent variables were analysed using a repeated measures ANOVA in SPSS 20.0 statistical software.

The folder contain data files and R analysis code for three eye movement experiment that investigate the perceptual span of young and older adult Chinese readers. The experiments used the moving window paradigm, in which text is shown normally within a region (window) around each fixation and text outside this window is masked. Window size was word based in the present experiment and manipulation so that text was presented entirely as normal, within only the fixated word visible, the fixated word and the word to its left visible, the fixated word and one word to its right visible, or the fixated word and two words to its right visible. Text outside the window was replaced by a mask in Experiment 1, visually similar characters in Experiment 2, or blurred to be unreadable in Experiment 3. Separate excel spreadsheets of data are included for Experiments 1-3, along with the R analysis script, and a Bayes Factor analysis script for quantifying the evidence for null and positive effects.Reference: Xie, F., McGowan, V.A., Chang, M., Li, L., White, S.J., Paterson, K.B., Wang, J., & Warrington, K.L. (2020). Revealing similarities in the perceptual span of young and older Chinese readers. The Quarterly Journal of Experimental Psychology, in press.