RaviSheel04/Psychology-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Summary

Psych-101 is a data set of natural language transcripts from human psychological experiments. It comprises trial-by-trial data from 160 psychological experiments and 60,092 participants, making 10,681,650 choices. Human choices are encapsuled in "<<" and ">>" tokens.

Paper: Centaur: a foundation model of human cognition Point of Contact: Marcel Binz

  Example Prompt

You will be presented with triplets of objects, which will be assigned to the keys D… See the full description on the dataset page: https://huggingface.co/datasets/marcelbinz/Psych-101.

The vast majority of scientific articles published to-date have not been accompanied by concomitant publication of the underlying research data upon which they are based. This state of affairs precludes the routine re-use and re-analysis of research data, undermining the efficiency of the scientific enterprise, and compromising the credibility of claims that cannot be independently verified. It may be especially important to make data available for the most influential studies that have provided a foundation for subsequent research and theory development. Therefore, we launched an initiative—the Data Ark—to examine whether we could retrospectively enhance the preservation and accessibility of important scientific data. Here we report the outcome of our efforts to retrieve, preserve, and liberate data from 111 of the most highly-cited articles published in psychology and psychiatry between 2006–2011 (n = 48) and 2014–2016 (n = 63). Most data sets were not made available (76/111, 68%, 95% CI [60, 77]), some were only made available with restrictions (20/111, 18%, 95% CI [10, 27]), and few were made available in a completely unrestricted form (15/111, 14%, 95% CI [5, 22]). Where extant data sharing systems were in place, they usually (17/22, 77%, 95% CI [54, 91]) did not allow unrestricted access. Authors reported several barriers to data sharing, including issues related to data ownership and ethical concerns. The Data Ark initiative could help preserve and liberate important scientific data, surface barriers to data sharing, and advance community discussions on data stewardship.

Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.

Methods To investigate the data-related practices of psychology researchers, we adapted a survey developed as part of our previous study of neuroimaging researchers. The survey was distributed via Qualtrics (http://www.qualtrics.com) from January 25 to March 25, 2019. Before beginning the survey, participants were required to verify that they were at least 18 years old and gave their informed consent to participate. Participants who did not meet these inclusion criteria or who did not complete at least the first section of the survey were not included in the final data analysis. After filtering, 274 psychology researchers from 31 countries participated in our survey.

All code for data collection and visualization is included in the Jupyter notebooks included here.

SPSS Data sets for study 1 to 3

This dataset is about books. It has 1 row and is filtered where the book is Data analysis for psychology. It features 7 columns including author, publication date, language, and book publisher.

This dataset appears to contain a variety of features related to text analysis, sentiment analysis, and psychological indicators, likely derived from posts or text data. Some features include readability indices such as Automated Readability Index (ARI), Coleman Liau Index, and Flesch-Kincaid Grade Level, as well as sentiment analysis scores like sentiment compound, negative, neutral, and positive scores. Additionally, there are features related to psychological aspects such as economic stress, isolation, substance use, and domestic stress. The dataset seems to cover a wide range of linguistic, psychological, and behavioural attributes, potentially suitable for analyzing mental health-related topics in online communities or text data.

Benefits of using this dataset:

• Insight into Mental Health: The dataset provides valuable insights into mental health by analyzing linguistic patterns, sentiment, and psychological indicators in text data. Researchers and data scientists can gain a better understanding of how mental health issues manifest in online communication.
• Predictive Modeling: With a wide range of features, including sentiment analysis scores and psychological indicators, the dataset offers opportunities for developing predictive models to identify or predict mental health outcomes based on textual data. This can be useful for early intervention and support.
• Community Engagement: Mental health is a topic of increasing importance, and this dataset can foster community engagement on platforms like Kaggle. Data enthusiasts, researchers, and mental health professionals can collaborate to analyze the data and develop solutions to address mental health challenges.
• Data-driven Insights: By analyzing the dataset, users can uncover correlations and patterns between linguistic features, sentiment, and mental health indicators. These insights can inform interventions, policies, and support systems aimed at promoting mental well-being.
• Educational Resource: The dataset can serve as a valuable educational resource for teaching and learning about mental health analytics, sentiment analysis, and text mining techniques. It provides a real-world dataset for students and practitioners to apply data science skills in a meaningful context.

Project abstract: Many situations involve processing social and non-social information simultaneously. However, is not known how performance is affected in such situations. Here, we examined how our ability to process social information is affected by the need to keep track of non-social information. Participants were instructed to carry out two tasks within each trial. The social task involved referential communication – requiring participants to use social cues to guide their decisions. At the same time, cognitive load was manipulated by requiring participants to remember non-social information in the form of either one or three two-digit numbers visually presented before each social task stimulus. Results indicate that the cognitive demands of simultaneously processing social and non-social information impair social information processing. Specifically, keeping in mind three numbers slowed participants' ability to use another person's perspective to guide decisions. These results suggest that social information processing requires domain-general resources that are depleted under cognitive load. Data: These files include our dataset, as well as the scripts used to analyze the data and create graphs of the results. You will need to download R (http://www.r-project.org/) to use these files. Data are from 29 adult participants. Participants completed an adapted version of the “Director Task” (Dumontheil, Hillebrandt, Apperly, & Blakemore, 2012) with an embedded working memory (WM) Task component. Afterwards, participants completed a verbal reverse digit-span task as a measure of WM capacity and the Interpersonal Reactivity Index questionnaire to assess individual differences in trait perspective taking (Davis, 1980). Data Analysis: We used the lme4 package in R (Bates, Maechler, & Bolker, 2013) to perform a linear mixed effects analysis on the relationship between our factors of interest and accuracy and RT for both tasks. RT data from correct trials only were analyzed. To create approximately normally distributed residuals, we used a log or reciprocal function to transform RT data. We performed a two-step procedure: first, we created a global model including main and interactive effects of cognitive load (low vs. high), condition (Director Present vs. Director Absent), trial type (1-object vs. 3-object), and perspective (same vs. different) as fixed effects, and each model included a random intercept for each participant. We then compared all possible combinations[1] of the variables within our global model using an automated model selection procedure (MuMIn1.9.0; Barton, 2013). Models were ranked using Second-order Akaike Information Criterion (AICc; Burnham & Anderson, 2002). Second, after determining the best fitting model for each outcome of interest, we tested whether WM capacity or trait perspective taking explained any additional variance through likelihood ratio tests. All p-values were obtained by likelihood ratio tests comparing the best fitting model against a baseline model.[1] Interactions were always accompanied by their respective main effects and all lower order terms

Update (August 8, 2013): There was a minor error in the original SocialDualTaskData.R file, which has now been corrected.

SPSS Data Sets Study 1 & 2(Glück, J. & Scherpf, A. (2022). Intelligence and wisdom: Age-Related Differences and Nonlinear Relationships. Manuscript submitted for publication (copy on file with author).

The COVID-19 Psychological Research Consortium (C19PRC) Study aims to monitor and assess the long-term psychological, social, political and economic impact of the COVID-19 pandemic on the UK general population. A longitudinal, internet panel survey was designed to assess: (1) COVID-19 related knowledge, attitudes and behaviours, (2) the occurrence of common mental health disorders, as well as the role of (3) psychological factors, and (4) social and political attitudes in influencing the public’s response to the pandemic. Quota sampling was used to recruit a nationally representative sample of adults in terms of age, sex and household income. The first C19PRC survey was launched on 23 March 2020 (Wave 1), the day that a strict lockdown was enforced across the UK, and recruited 2025 UK adults. As of February 2022, six follow-up surveys have been conducted: Wave 2, April/May 2020; Wave 3, July/August 2020; Wave 4, Nov/Dec 2020; Wave 5, March/April 2021; Wave 6, Aug/Sept 2021; and Wave 7, Nov/Dec 2021. The baseline sample was representative of the UK population in relation to economic activity, ethnicity, and household composition. Data collection for the C19PRC Study is ongoing, with subsequent follow-up surveys being conducted during 2022 (Waves 8 and 9). C19PRC Study data has strong generalisability to facilitate and stimulate interdisciplinary research on important pandemic-related public health questions. It will allow changes in mental health and psychosocial functioning to be investigated from the beginning of the pandemic, identifying vulnerable groups in need of support. Find out more about the study at https://www.sheffield.ac.uk/psychology-consortium-covid19

knkrn5/wealthpsychology-tokenized-data dataset hosted on Hugging Face and contributed by the HF Datasets community

This unpublished dataset is for peer review only. Every other use is currently prohibited. Upon publication, it will be made publicly available for reproducing the results.

Macks Psychology Group is a comprehensive mental health organization providing diagnostic testing, therapeutic services, and educational support to individuals of all ages. Led by a team of experienced providers, the group offers a range of services including psychological testing, individual and family therapy, speech and language therapy, and social skills groups. Their team of experts specializes in various areas, including autism spectrum disorder, attention deficit hyperactivity disorder (ADHD), anxiety, depression, and traumatic brain injury.

With a focus on providing personalized care, Macks Psychology Group utilizes evidence-based practices to address the unique needs of each individual. Their services are designed to promote emotional, social, and cognitive development, as well as academic and professional success. With locations in West Chester, Ohio, and Cincinnati, Ohio, Macks Psychology Group is dedicated to empowering individuals and families to reach their full potential.

This record contains the underlying research data for the publication "Estimating the reproducibility of psychological science" and the full-text is available from: https://ink.library.smu.edu.sg/lkcsb_research/5257Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.

This SPSS dataset is from a 2019 survey conducted via Prolific. There are 323 participants in the file, 306 with complete data for the key measures. Measures include the Big Five Inventory, the Interest/Deprivation Curiosity Scale, the Need for Cognition Scale, the Need to Belong Scale, the Basic Psychological Need Satisfaction Scale, the General Belongingness Scale, the Meaning in Life Questionnaire, the Mindful Attention Awareness Scale, the Smartphone Addiction Scale, and some questions about listening to podcasts.

In relation to podcasts, participants were first asked if they had ever listened to a podcast. Those who said yes (N = 240) were asked questions related to amount of listening, categories and format of podcasts, setting of listening, device used, social engagement around podcasts, and parasocial relationships with their favourite podcast host. Participants also indicated their age, gender, and country of residence.

The datafile contains item ratings and scale scores for all measures. Item wording and response labels are provided in the variable view tab of the downloaded file. Other files available on the OSF site include a syntax file related to the analyses reported in a published paper and a copy of the survey.

This observational study sought to document the statistical content taught to undergraduate psychology students in the UK. We searched for module syllabi from psychology undergraduate programmes in the UK and assessed whether the module syllabi mentioned each of 32 quantitative and statistical topics.

This dataset was collected from K-12 teachers via online surveys (Qualtrics). The statistical analyses were conducted in R-programing.

In the present research, we tested whether a combination of getting perspective and exposure to relevant incremental theories can mitigate the consequences of bias on discipline decisions. We call this combination of approaches a “Bias-Consequence Alleviation” (BCA) intervention. The present research sought to determine how the following components can be integrated to reduce the process by which bias contributes to racial inequality in discipline decisions: (1) getting a misbehaving student’s perspective, “student-perspective”; (2) belief that others’ personalities can change, “student-growth”; and (3) belief that one’s own ability to sustain positive relationships can change, “relationship-growth.” Can a combination of these three components curb troublemaker-labeling and pattern-prediction responses to a Black student’s misbehavior (Exp...

This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

In 2023, around 31.6 percent of women and 20 percent of men in Germany were especially interested in information about social interactions and psychology. This data is based on a survey conducted in Germany that year. The Allensbach Market and Advertising Media Analysis (Allensbacher Markt- und Werbeträgeranalyse or AWA in German) determines attitudes, consumer habits and media usage of the population in Germany on a broad statistical basis.

Questions, answers, and metadata collected from 49,159 16 Factor Personality Tests. The data was hosted on OpenPsychometrics.org a nonprofit effort to educate the public about psychology and to collect data for psychological research. Their notes on the data collected in the codebook.html.

From Wikipedia:

The Sixteen Personality Factor Questionnaire (16PF) is a self-report personality test developed over several decades of empirical research by Raymond B. Cattell, Maurice Tatsuoka and Herbert Eber. The 16PF provides a measure of normal personality and can also be used by psychologists, and other mental health professionals, as a clinical instrument to help diagnose psychiatric disorders, and help with prognosis and therapy planning. The 16PF can also provide information relevant to the clinical and counseling process, such as an individual’s capacity for insight, self-esteem, cognitive style, internalization of standards, openness to change, capacity for empathy, level of interpersonal trust, quality of attachments, interpersonal needs, attitude toward authority, reaction toward dynamics of power, frustration tolerance, and coping style. Thus, the 16PF instrument provides clinicians with a normal-range measurement of anxiety, adjustment, emotional stability and behavioral problems. Clinicians can use 16PF results to identify effective strategies for establishing a working alliance, to develop a therapeutic plan, and to select effective therapeutic interventions or modes of treatment. It can also be used within other areas of psychology, such as career and occupational selection.