Data documentation

Click on each dataset’s name to see its description, license, citation, and link to its original source. Code used to create these data can be found here.

Data from an interactive version of the scales from the International Personality Item Pool designed to measure Cattell’s 16 personality factors. (CC BY 4.0) [link]

Catell’s 16 Personality Factors Test [dataset and codebook]. http://openpsychometrics.org/_rawdata/16PF.zip

Data from a 4th grade math test of German students. (GPL-3) [link]

Robitzsch A (2022). sirt: Supplementary Item Response Theory Models. R package version 3.12-66, https://CRAN.R-project.org/package=sirt.

Ratings of 5 personality traits on 14 items (CC BY 4.0) [link]

Smits, I. A. M., Dolan, C. V., Vorst, H. C. M., Wicherts, J. M., & Timmerman, M. E. (2011). Cohort differences in Big Five personality factors over a period of 25 years. Journal of Personality and Social Psychology, 100(6), 1124–1138. https://doi.org/10.1037/a0022874

Attitudes on abortion (GPL-3) [link]

McGrath, K. and Waterton, J. (1986) British social attitudes, 1983-86 panel survey. London: SCPR.

Data from the ajective check list (GPL-3) [link]

Gough, H. G., & Heilbrun,A. B. (1980) The Adjective Check List, Manual 1980 Edition. Consulting Psychologists Press.

Participants rate photos of smiling and neutral faces of mix-heritage people based on different measures (CC BY 4.0) [link]

Chen, J.M., Norman, J.B. & Nam, Y. Broadening the stimulus set: Introducing the American Multiracial Faces Database. Behav Res (2020). https://doi.org/10.3758/s13428-020-01447-8

Attitudes about capital punishment (GPL-3) [link]

Andrich, D. (1988). The application of an unfolding model of the PIRT type to the measurement of attitude. Applied psychological measurement, 12(1), 33-51.

Reports for PROMIS anxiety measure (GPL-3) [link]

PROMIS Cooperative Group. Unpublished Manual for the Patient-Reported Outcomes Measurement Information System (PROMIS) Version 1.1. October, 2008: http://www.nihpromis.org

Item responses for study 1 of the Approaches to Text Questionnaire wherein participants completed survey items to the 87-item TReAT-Q, need for cognition, and a subset of questions from the adult reading history questionnaire [link]

Calloway RC, Helder A, Perfetti CA. Study 1 TReATQ, RH, NFC Raw Data [data set] from “A measure of individual differences in readers’ approaches to text and its relation to reading experience and reading comprehension”. Behav Res Methods. 2023 Feb;55(2):899-931. https://osf.io/e7xpy/?view_only=0b3180a18cd94803a7c743096c95dd79

Item responses for study 2 of Approaches to Text wherein participants completed a host of surveys, which collected items on participants’ comprehension, vocabulary, reading history, author recognition, etc [link]

Calloway RC, Helder A, Perfetti CA. Study 2 Data [data set] from “A measure of individual differences in readers’ approaches to text and its relation to reading experience and reading comprehension”. Behav Res Methods. 2023 Feb;55(2):899-931. https://osf.io/e7xpy/?view_only=0b3180a18cd94803a7c743096c95dd79

Data from the author recognition test (CC BY 4.0) [link]

McCarron, S.P., Kuperman, V. Is the author recognition test a useful metric for native and non-native English speakers? An item response theory analysis. Behav Res 53, 2226–2237 (2021). https://doi.org/10.3758/s13428-021-01556-y

Data on individual differences in what people enjoy about visual art (CC-BY-NC-SA 4.0) [link]

Artistic Preferences Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/APS_data.zip

Response data for skill-building and non-skill-building items from ASSISTments. Data from SY 2009-10 [link]

Response data for items from ASSISTments. Data from SY 2012-13 [link]

Response data for items from ASSISTments. Data from SY 2015-16 [link]

Response data for items from ASSISTments. Data from ASSISTments Data Mining Competition 2017 [link]

Autonomy support ratings of teachers by students (GPL-3) [link]

Koopman, L., Zijlstra, B. J., & van der Ark, L. A. (2020). Standard errors of two‐level scalability coefficients. British Journal of Mathematical and Statistical Psychology, 73(2), 213-236.

Toddler’s performance on balancing tasks (GPL-3) [link]

Van Maanen, L., Been, P. H., & Sijtsma, K. (1989). Problem solving strategies and the linear logistic test model. In Mathematical psychology in progress (pp. 267-287). Springer.

Data from a big 5 measure (GPL-3) [link]

Dolan, C. V., Oort, F. J., Stoel, R. D., & Wicherts, J. M. (2009). Testing measurement invariance in the target rotated multigroup exploratory factor model. Structural Equation Modeling: A Multidisciplinary Journal, 16(2), 295-314.

Items from a study testing difference between blocked and interleaved instruction for fraction addition and multiplication [link]

Patel, R., Liu, R., Koedinger, K. (2017) Dissertation data [dataset]. DataShop @ CMU. https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=1190

Behavior problems index from NLSY [link]

Kim, M., Winkler, C., & Talley, S. (2021). Binary item CFA of Behavior Problem Index (BPI) using Mplus: A step-by-step tutorial. The Quantitative Methods for Psychology, 17, 141-153.

Data from an interactive version of the Open Hemispheric Brain Dominance Scale survey, a measure of left and right brain cognitive style (CC BY 4.0) [link]

Jorgenson, E. (2016). Open Hemispheric Brain Dominance Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/OHBDS-data.zip

Data from an interactive personality test that combines the scales from 8 different well regarded tests into one.

(CC BY 4.0) [link]

Yarkoni, T. (2010). The abbreviation of personality, or how to measure 200 personality scales with 200 items. Journal of Research in Personality, 44, 180-198.

Coping with noxious factory odor. (GPL-3) [link]

Cavalini, P. M. (1992). It’s an ill wind that brings no good. Studies on odour annoyance and the dispersion of odorant concentrations from industries. Unpublished doctoral disseratation. University of Groningen, The Netherlands.

Data from an English proficiency exam (ECPE) (GPL-3) [link]

Templin, J., & Hoffman, L. (2013). Obtaining diagnostic classification model estimates using Mplus. Educational Measurement: Issues and Practice, 32, 37-50.

Data from a high-stakes reading comprehension test (GPL-3) [link]

Ravand, H., Barati, H., & Widhiarso, W. (2013). Exploring diagnostic capacity of a high stakes reading comprehension test: A pedagogical demonstration. Iranian Journal of Language Testing, 3(1), 1-27.

Subset of PISA reading test with Q matrix. (GPL-3) [link]

Chen, J., & de la Torre, J. (2014). A procedure for diagnostically modeling extant large-scale assessment data: the case of the programme for international student assessment in reading. Psychology, 5(18), 1967-1978.

Subset of TIMSS math items with Q matrix (GPL-3) [link]

Su, Y.-L., Choi, K. M., Lee, W.-C., Choi, T., & McAninch, M. (2013). Hierarchical cognitive diagnostic analysis for TIMSS 2003 mathematics. CASMA Research Report 35. Center for Advanced Studies in Measurement and Assessment (CASMA), University of Iowa.

Subsample of TIMSS items from Australia with Q matrix (GPL-3) [link]

Lee, Y. S., Park, Y. S., & Taylan, D. (2011). A cognitive diagnostic modeling of attribute mastery in Massachusetts, Minnesota, and the US national sample using the TIMSS 2007. International Journal of Testing, 11, 144-177.

Austrian TIMSS data with Q matrix (GPL-3) [link]

George, A. C., & Robitzsch, A. (2014). Multiple group cognitive diagnosis models, with an emphasis on differential item functioning. Psychological Test and Assessment Modeling, 56(4), 405-432

Over 10-week sequence of problem-solving activities, undergraduate students received either (1) fixed sequence of sense-making support and perceptual-fluency, or (2) adaptively assigned supports based on solving interactions. Findings were used to understand which intervention helped facilitate reduced confusion and mistake-making while problem solving in chemistry [link]

Rau, M. A., Zahn, M., Misback, E., Herder, T., & Burstyn, J. (2021). Adaptive support for representational competencies during technology-based problem solving in chemistry [data set]. Journal of the Learning Sciences. doi:10.1080/10508406.2021.1888733

Data from the Amsterdam Chess test (GPL-3) [link]

Van Der Maas, H. L., & Wagenmakers, E. J. (2005). A psychometric analysis of chess expertise. The American journal of psychology, 118(1), 29-60.

Likert scale items evaluating students’ Chinese language learning strategies [link]

Pavlik, P. (2011). Motivation and Metacognition in Chinese Vocabulary Learning, Experiment 4 [data set]. DataShop @ CMU. https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=459

Data from an interactive version of the Experinces in Close Relationships Scale by Kelly Brennan, Catherine Clark and Phillip Shaver. (CC BY 4.0) [link]

(2018). Experiences in Close Relationships Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/ECR-data-1March2018.zip

Data on student performance on math items from an intelligent tutoring system [link]

Stamper, J., Niculescu-Mizil, A., Ritter, S., Gordon, G.J., & Koedinger, K.R. (2010). Algebra I 2005-2006. Development data set from KDD Cup 2010 Educational Data Mining Challenge. Find it at http://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp

Data on student performance on math items from an intelligent tutoring system [link]

Stamper, J., Niculescu-Mizil, A., Ritter, S., Gordon, G.J., & Koedinger, K.R. (2010). Algebra I 2006-2007. Development data set from KDD Cup 2010 Educational Data Mining Challenge. Find it at http://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp

Data on student performance on math items from an intelligent tutoring system [link]

Stamper, J., Niculescu-Mizil, A., Ritter, S., Gordon, G.J., & Koedinger, K.R. (2010). Algebra I 2008-2009. Challenge data set from KDD Cup 2010 Educational Data Mining Challenge. Find it at http://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp

Data on student performance on math items from an intelligent tutoring system [link]

Stamper, J., Niculescu-Mizil, A., Ritter, S., Gordon, G.J., & Koedinger, K.R. (2010). Bridge to Algebra 2006-2007. Development data set from KDD Cup 2010 Educational Data Mining Challenge. Find it at http://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp

Data on student performance on math items from an intelligent tutoring system [link]

Stamper, J., Niculescu-Mizil, A., Ritter, S., Gordon, G.J., & Koedinger, K.R. (2010). Bridge to Algebra 2008-2009. Challenge data set from KDD Cup 2010 Educational Data Mining Challenge. Find it at http://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp

Likert scale responses statements on how actions may effect future consequences (CC BY 4.0) [link]

Consideration of Future Consequences Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/CFCS.zip

Items from a randomized trial to improve reading comprehension, science domain knowledge, and reading engagement via literacy inteventions in 1st grade (CC BY 4.0) [link]

Kim, James S.; Burkhauser, Mary; Mesite, Laura; Asher, Catherine A.; Relyea, Jackie Eunjung; Fitzgerald, Jill; Elmore, Jeff, 2021, “Replication data for: Improving Reading Comprehension, Science Domain Knowledge, and Reading Engagement through a First-Grade Content Literacy Intervention”, https://doi.org/10.7910/DVN/RVJIMX, Harvard Dataverse, V1, UNF:6:BSUJnLmkvmj0YSTQr3MZAA== [fileUNF]

Items from a randomized trial to assess transfer effects on students’ reading comprehension outcomes (CC BY 4.0) [link]

James S. Kim; Mary A. Burkhauser; Jackie E. Relyea; Joshua B. Gilbert; Ethan Scherer; Jill Fitzgerald; Douglas Mosher; Joseph McIntyre, 2022, “Replication Data for: A Longitudinal Randomized Trial of a Sustained Content Literacy Intervention from First to Second Grade: Transfer Effects on Students’ Reading Comprehension Outcomes”, https://doi.org/10.7910/DVN/LAWFFU, Harvard Dataverse, V2, UNF:6:VATSovKxwaqyJAT6ZzzfBw== [fileUNF]

Arithmetic items from MathGarden [link]

Coomans, F., Hofman, A., Brinkhuis, M., van der Maas, H. L., & Maris, G. (2016). Distinguishing fast and slow processes in accuracy-response time data. PloS one, 11(5), e0155149.

Arithmetic items from MathGarden [link]

Coomans, F., Hofman, A., Brinkhuis, M., van der Maas, H. L., & Maris, G. (2016). Distinguishing fast and slow processes in accuracy-response time data. PloS one, 11(5), e0155149.

Tasks from game “Letter Chaos” [link]

Coomans, F., Hofman, A., Brinkhuis, M., van der Maas, H. L., & Maris, G. (2016). Distinguishing fast and slow processes in accuracy-response time data. PloS one, 11(5), e0155149.

Arithmetic items from MathGarden [link]

Coomans, F., Hofman, A., Brinkhuis, M., van der Maas, H. L., & Maris, G. (2016). Distinguishing fast and slow processes in accuracy-response time data. PloS one, 11(5), e0155149.

Tasks from game “Set” [link]

Coomans, F., Hofman, A., Brinkhuis, M., van der Maas, H. L., & Maris, G. (2016). Distinguishing fast and slow processes in accuracy-response time data. PloS one, 11(5), e0155149.

Arithmetic items from MathGarden [link]

Coomans, F., Hofman, A., Brinkhuis, M., van der Maas, H. L., & Maris, G. (2016). Distinguishing fast and slow processes in accuracy-response time data. PloS one, 11(5), e0155149.

Responses and response times from a credentialing dataset with suspected cheating. (GPL-3) [link]

Cizek, G. J., & Wollack, J. A. (Eds.). (2016). Handbook of quantitative methods for detecting cheating on tests. Abingdon, England: Routledge.

Data on language aqcuisition from children. [link]

Hartshorne, J. K., Tenenbaum, J. B., & Pinker, S. (2018). A critical period for second language acquisition: Evidence from 2/3 million English speakers. Cognition, 177, 263-277.

Additional item-level data from the 2019 CSEDM workshop released after challenge end [link]

Rivers, K., Price, T. (2019). 2nd CSEDM Data Challenge - F19 All Data v1.0 [data set]. DataShop @ CMU. https://pslcdatashop.web.cmu.edu/Files?datasetId=2865

Additional item-level data from the 2019 CSEDM workshop released in Spring 2019 [link]

Rivers, K., Price, T. (2019). 2nd CSEDM Data Challenge - S19 All Data v1.0 [data set]. DataShop @ CMU. https://pslcdatashop.web.cmu.edu/Files?datasetId=2866

Programming IRW and process data from the 2019 Education Data Mining in Computer Science Education workshop [link]

Rivers, K., Price, T. (2019). CSEDM Data Challenge v1.1 (Updated 2019/01/29) [data set]. DataShop @ CMU. https://pslcdatashop.web.cmu.edu/Files?datasetId=2865

Survey of leisure time usage (GPL-3) [link]

http://www.labstat.it/home/wp-content/uploads/2015/09/relgoods.txt

Data on undergraduate students’ attitudes towards science pre- and post-participation in a protein-centric CURE (CC BY 4.0) [link]

Eddy, R., Koletar, C., Galport, N., Bell, J. E., Bell, J. K., & Provost, J. (2022). CURE Survey Pretest and Post-Test Student Data. LDbase. https://doi.org/10.33009/ldbase.1667941609.e3ab

Data on mental rotation tasks (GPL-2) [link]

Borst, G., Kievit, R. A., Thompson, W. L., & Kosslyn, S. M. (2011). Mental rotation is not easily cognitively penetrable. Journal of Cognitive Psychology, 23(1), 60-75.

Data on feelings of deception after playing an online game (CC-By Attribution 4.0 International) [link]

Krasnow, M.M., Howard, R.M. & Eisenbruch, A.B. The importance of being honest? Evidence that deception may not pollute social science subject pools after all. Behav Res 52, 1175–1188 (2020). https://doi.org/10.3758/s13428-019-01309-y

Data on professors’ feelings on deception in academia (CC-By Attribution 4.0 International) [link]

Krasnow, M.M., Howard, R.M. & Eisenbruch, A.B. The importance of being honest? Evidence that deception may not pollute social science subject pools after all. Behav Res 52, 1175–1188 (2020). https://doi.org/10.3758/s13428-019-01309-y

Data from an online survey that measures scales of depression anxiety and stress (CC BY 4.0) [link]

(2021). Depression Anxiety Stress Scales [dataset and codebook]. http://openpsychometrics.org/_rawdata/DASS_data_21.02.19.zip

Participants were asked to look at two similar pictures and identify (1) whether there were any differences between the photos, and (2) where the differences were. Participants were monetarily incentivized to spot the differences. [link]

Liu, J., Shen, Q., Zhang, J. et al. UnsolvableItemsSelection_Raw_CombinedSampleN=131 [data set]. Behav Res 53, 1935–1944 (2021). https://doi.org/10.3758/s13428-020-01526-w

Similar to study 1, participants were asked to identify areas of difference between two photos. Unlike study 1, there was no opportunity to cheat. Participants were monetarily incentivized to spot the differences. [link]

Liu, J., Shen, Q., Zhang, J. et al. SolvableItemsSelection_Summary_CombinedSampleN=135 [data set]. Behav Res 53, 1935–1944 (2021). https://doi.org/10.3758/s13428-020-01526-w

Participants watched a die tossing video and asked to predict in advance which numbers the die would land on. Responses record whether their predictions were correct. Participants were monetarily incentivized to spot the differences. [link]

Liu, J., Shen, Q., Zhang, J. et al. DieGuessingTask_Raw [data set]. Behav Res 53, 1935–1944 (2021). https://doi.org/10.3758/s13428-020-01526-w

Participants were presented with a square bisected by a vertical line. Each side of the line had a certain number of dots, and participants were asked to identify which side had more dots. They were told that it was more difficult to assess the number of dots on the left side, and so the monetary reward for guessing the left panel was higher [link]

Liu, J., Shen, Q., Zhang, J. et al. DotsTask_Raw [data set]. Behav Res 53, 1935–1944 (2021). https://doi.org/10.3758/s13428-020-01526-w

Responses to Czech medical school admissions exam (GPL-3) [link]

Drabinova, A. & Martinkova, P. (2017). Detection of differential item functioning with nonlinear regression: A non-IRT approach accounting for guessing. Journal of Educational Measurement, 54(4), 498–517, doi: 10.1111/jedm.12158.

Items from a digital games study to test the theory that fractions should be introduced to students mixed with whole numbers on the same line to demonstrate that they represent magnitudes of the same type of whole numbers [link]

Lomas, D. (2011). Classroom: Exp1 - UCLA-Kentucky [dataset]. DataShop @ CMU. https://pslcdatashop.web.cmu.edu/Files?datasetId=445

DS14 responses from patients with coronary artery disease (GPL-3) [link]

Denollet, J., Pedersen, S. S., Vrints, C. J., & Conraads, V. M. (2013). Predictive value of social inhibition and negative affectivity for cardiovascular events and mortality in patients with coronary artery disease: the Type D personality construct. Psychosomatic Medicine, 75, 873-981.

Modeling of second language acquisition [link]

Settles, Burr, 2018, “Data for the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM)”, https://doi.org/10.7910/DVN/8SWHNO, Harvard Dataverse, V4, https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8SWHNO

Modeling of second language acquisition [link]

Settles, Burr, 2018, “Data for the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM)”, https://doi.org/10.7910/DVN/8SWHNO, Harvard Dataverse, V4, https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8SWHNO

Modeling of second language acquisition [link]

Settles, Burr, 2018, “Data for the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM)”, https://doi.org/10.7910/DVN/8SWHNO, Harvard Dataverse, V4, https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/8SWHNO

Data from Lexile tasks given to 4th graders [link]

Kyngdon, A. (2011). Plausible measurement analogies to some psychometric models of test performance. British Journal of Mathematical & Statistical Psychology, 64(3), 478–497.

Data from Lexile tasks given to 8th graders [link]

Kyngdon, A. (2011). Plausible measurement analogies to some psychometric models of test performance. British Journal of Mathematical & Statistical Psychology, 64(3), 478–497.

Data on Chilean youths’ feelings and self-evaluations of social belonging, academic performance, ambitions, technology use, etc. (3rd round of longitudinal study) [link]

n/a

Emotional reactivity from the Freiburg Complaint Checklist (GPL-3) [link]

ZPID (2013). PsychData of the Leibniz Institute for Psychology Information ZPID. Trier: Center for Research Data in Psychology.

Data from an interactive online version of a combined set of Simon Baron-Cohen’s Empathizing and Systemizing Quotients, which asks participants how much they agree with statements of empathizing and perceiving behaviors (CC BY 4.0) [link]

Empathizing-Systemizing Test [dataset and codebook. http://openpsychometrics.org/_rawdata/EQSQ.zip

Data from the natural science portion of the 2013 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the social science portion of the 2013 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the language portion of the 2013 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the mathematics portion of the 2013 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the natural science portion of the 2014 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the social science portion of the 2014 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the language portion of the 2014 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the mathematics portion of the 2014 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the natural science portion of the 2015 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the social science portion of the 2015 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the language portion of the 2015 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the mathematics portion of the 2015 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the natural science portion of the 2016 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the social science portion of the 2016 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the language portion of the 2016 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the mathematics portion of the 2016 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the natural science portion of the 2017 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the social science portion of the 2017 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the language portion of the 2017 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the mathematics portion of the 2017 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the natural science portion of the 2018 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the social science portion of the 2018 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the language portion of the 2018 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the mathematics portion of the 2018 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the natural science portion of the 2019 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the social science portion of the 2019 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the language portion of the 2019 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the mathematics portion of the 2019 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the natural science portion of the 2020 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the social science portion of the 2020 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the language portion of the 2020 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the mathematics portion of the 2020 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the natural science portion of the 2021 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the social science portion of the 2021 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the language portion of the 2021 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the mathematics portion of the 2021 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the natural science portion of the 2022 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the social science portion of the 2022 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the language portion of the 2022 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Data from the mathematics portion of the 2022 Brazilian ENEM assessment (one million person subsample) (CC BY 4.0) [link]

Martins, D. M. (2021). Estado da arte das pesquisas sobre ENEM no Brasil: State of the art of research on ENEM in Brazil. Latin American Journal of Development, 3(5), 2898-2907.

Eysenck Personality Inventory Impulsivity Subscale (GPL-3) [link]

Ferrando, P.J.(2002). Theoretical and Empirical Comparison between Two Models for Continuous Item Responses. Multivariate Behavioral Research, 37(4), 521-542.

Teaching self-efficacy scale (GPL-3) [link]

Kan, A.(2009). Effect of scale response format on psychometric properties in teaching self-efficacy. Euroasian Journal of Educational Research, 34, 215-228.

Political party preference data from Europeans (GPL-3) [link]

Schuur, H. V. (1984). Structure in political beliefs: A new model for stochastic unfolding with application to European party activists (Doctoral dissertation, Rijksuniversiteit te Groningen).

Data from an experimental IQ test (CC BY 4.0) [link]

Experimental IQ Test [dataset and codebook]. http://openpsychometrics.org/_rawdata/IQ1.zip

Data from test where participants were shown pictures of peoples’ faces and asked to recall whether they had already seen it (CC BY 4.0) [link]

Exposure Based Face Memory Test [dataset and codebook]. http://openpsychometrics.org/_rawdata/EBFMT.zip

Data on free will and determinism (CC BY 4.0) [link]

Liu, Q. L., Wang, F., Yan, W., Peng, K., Sui, J., & Hu, C. (2020). Questionnaire Data From the Revision of a Chinese Version of Free Will and Determinism Plus Scale. Journal of Open Psychology Data. https://osf.io/t2nsw/

Longitudinal data from a modified CESD in the Health and Retirement Study (HRS) [link]

Steffick, D. E., Wallace, R. B., & Herzog, A. R. (2000). Documentation of affective functioning measures in the Health and Retirement Study. Ann Arbor, MI: University of Michigan, 15.

Longitudinal data on chronic conditions, ADL, and IADLs from the Health and Retirement Study (HRS) [link]

RAND HRS Longitudinal File 2018 (V2). Produced by the RAND Center for the Study of Aging, with funding from the National Institute on Aging and the Social Security Administration. Santa Monica, CA (July 2022).

Data on free will and determinism, dualism/anti-reduction subscale (participants: (1) students in an online Intro to Psych course, (2) students in an in-person, university Intro to Psych course) (CC BY 4.0) [link]

Liu, Q. L., Wang, F., Yan, W., Peng, K., Sui, J., & Hu, C. (2020). Questionnaire Data From the Revision of a Chinese Version of Free Will and Determinism Plus Scale. Journal of Open Psychology Data. https://osf.io/t2nsw/

Normative ratings of popular melodies (id=song) (CC BY 4.0) [link]

Belfi, A.M., Kacirek, K. The famous melodies stimulus set. Behav Res 53, 34–48 (2021). https://doi.org/10.3758/s13428-020-01411-6

Data on peoples’ feminist perspectives, answered on a scale (CC BY 4.0) [link]

Henley, N.; Meng, K.; O’Brien, D.; McCarthy, W.; Sockloskie, R. (1998). “Developing a Scale to Measure the Diversity of Feminist Attitudes”. Psychology of Women Quarterly, 22(2), 317-348. ///Feminist Perspectives Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/FPS.zip

The personality test was constructed with the “Big-Five Factor Markers” from the IPIP. https://ipip.ori.org/newBigFive5broadKey.htm (CC BY 4.0) [link]

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The personality test was constructed with the “Big-Five Factor Markers” from the IPIP. https://ipip.ori.org/newBigFive5broadKey.htm (CC BY 4.0) [link]

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The personality test was constructed with the “Big-Five Factor Markers” from the IPIP. https://ipip.ori.org/newBigFive5broadKey.htm (CC BY 4.0) [link]

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The personality test was constructed with the “Big-Five Factor Markers” from the IPIP. https://ipip.ori.org/newBigFive5broadKey.htm (CC BY 4.0) [link]

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Brennan, K., Clark, C. & Shaver, P. (2018). Experiences in Close Relationships Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/ECR-data-1March2018.zip (CC BY 4.0) [link]

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Judges scores for different aspects of international figure skaters’ performances (2016-17 and 2017-18 seasons) (CC BY 4.0) [link]

Singer-Vine, J. & Templon, J. (2021). ISU Figure Skating Score Sheets as Structured Data [data set]. BuzzFeed News. https://github.com/BuzzFeedNews/figure-skating-scores

Math items from FIMS study (Australian and Japanese students) (GPL-3) [link]

Robitzsch A, Kiefer T, Wu M (2022). TAM: Test Analysis Modules. R package version 4.1-4, https://CRAN.R-project.org/package=TAM.

Data from a survey with personality items that correlate with being a firstborn child (CC-BY-NC-SA 4.0) [link]

(2019). Development of the Firstborn Personality Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/FBPS-ValidationData.zip

Data from an online version of the Fisher Temperment Inventory, which is a measure of personality based on the idea that behavior is influenced by the dominance of neurotransmitters in the brain. (CC BY 4.0) [link]

Brown, LL., Acevedo, B., Fisher, HE. Fisher Temperament Inventory [dataset and codebook]. http://openpsychometrics.org/_rawdata/FTI-data.zip///Brown LL, Acevedo B, Fisher HE (2013). Neural Correlates of Four Broad Temperament Dimensions: Testing Predictions for a Novel Construct of Personality. PLoS ONE 8(11): e78734.

Study from Florida about twins 9 years and older. Items related to twins’ behavior and environment (CC BY 4.0) [link]

Schatschneider, C., Lonigan, C., & Taylor, J. E. (2021). Behavior and Environment Survey Codebook. LDbase. http://ldbase.org/documents/c4669c5b-9853-45be-a33f-9dff7102de20

Study from Florida about twins 9 years and older. Items related to twins’ home and school environment, behavioral tendencies, and attitudes about schooling/home/social aspects (CC BY 4.0) [link]

Hart, S. A., Schatschneider, C., & Taylor, J. E. (2021). Wave 1, Wave 2, Wave 3 Child Survey Measures. LDbase. https://doi.org/10.33009/ldbase.1624481381.d9ec

Data from fraction subtraction problems. (GPL-3) [link]

Tatsuoka, C. (2002). Data analytic methods for latent partially ordered classification models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 51(3), 337-350.

Data from an experiment using an online tutor to teach fractions [link]

Aleven, V., Rau, M., Weitekamp, D. (2013) Cleaned logs with modified step names [data set]. DataShop @ CMU. https://pslcdatashop.web.cmu.edu/Files?datasetId=580

Data from C-tests with a testlet structure (GPL-3) [link]

Schroeders, U., Robitzsch, A., & Schipolowski, S. (2014). A Comparison of Different Psychometric Approaches to Modeling Testlet Structures: An Example with C‐Tests. Journal of Educational Measurement, 51(4), 400-418.

Attitudes towards capital punishment [link]

Roberts, J. S. (1995). Item response theory approaches to attitude measurement. (Doctoral dissertation, University of South Carolina, Columbia, 1995). Dissertation Abstracts International, 56, 7089B.

Attitudes about censorship [link]

Roberts, J. S. (1995). Item response theory approaches to attitude measurement. (Doctoral dissertation, University of South Carolina, Columbia, 1995). Dissertation Abstracts International, 56, 7089B.

Data from mental rotation tasks (GPL-3) [link]

Geiser, C., Lehmann, W., & Eid, M. (2006). Separating” rotators” from” nonrotators” in the mental rotations test: A multigroup latent class analysis. Multivariate Behavioral Research, 41(3), 261-293.

Data from an interactive version of the Open Sex-Role Inventory, which measures masculinity and femininity (or gendered personality traits) (CC-BY-NC-SA 4.0) [link]

(2019). Open Sex Role Inventory [dataset and codebook]. https://openpsychometrics.org/_rawdata/OSRI44_dev_data.zip

Items testing knowledge of geography (ODbL 1.0) [link]

Papoušek, J., Pelánek, R., & Stanislav, V. (2016). Adaptive geography practice data set. Journal of Learning Analytics, 3(2), 317-321.

Data from an online geomtry course, which allows students to get hints [link]

Aleven, V., Bllings, B. (2007). Step tables and test scores [data set]. DataShop @ CMU. https://pslcdatashop.web.cmu.edu/Files?datasetId=122

Study 1 of Graph Mapping Measure (CC BY 4.0) [link]

Jastrzębski, J., Ociepka, M., & Chuderski, A. (2022). Graph Mapping: A novel and simple test to validly assess fluid reasoning. Behavior Research Methods, 1-13.

Answers to grit scale items (CC BY 4.0) [link]

Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: perseverance and passion for long-term goals. Journal of personality and social psychology, 92(6), 1087.

Anxiety scale (HADS) administered to oncology patients (GPL-3) [link]

Zigmond, A. and Snaith, R. (1983), The hospital anxiety and depression scale, Acta Psychiatrika Scandinavica, 67, 361-370.

Items from a study that compares a Cognitive Tutor Algebra tutor (used in a handwriting-based interface) to one enhanced with examples [link]

Anthony, L., & Ritter, S. (2008). Handwriting2/Examples Spring 2007. Dataset 165 in DataShop. Retrieved from https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=165.

Items from a study used to assess various cognitive tutor interventions in a handwriting-based interface [link]

Anthony, L., & Ritter, S. (2008). Handwriting/Examples Dec 2006. Dataset 145 in DataShop. Retrieved from https://pslcdatashop.web.cmu.edu/DatasetInfo?datasetId=145.

Item-level data from online large-scale assessments used to understand the effect of educational interventions (CC BY-NC-SA 4.0) [link]

Gilbert, Josh, 2023, “Replication Data for: Modeling Item-Level Heterogeneous Treatment Effects With the Explanatory Item Response Model: Leveraging Large-Scale Online Assessments to Pinpoint the Impact of Educational Interventions”, https://doi.org/10.7910/DVN/QARRYT, Harvard Dataverse, V2, UNF:6:r+hUHhEPWWtUbcqXyELTJw== [fileUNF]

Data from an online tutoring system used to study human learning rates [link]

Koedinger, K. R., Carvalho, P., Liu, R., & McLaughlin, E. A. (2023). Student-step roll up for ds104 from An Astonishing Regularity in Student Learning Rate [data set]. DataShop @ CMU. https://pslcdatashop.web.cmu.edu/Files?datasetId=4629

Answers to the Humor Styles Questionnaire (CC BY 4.0) [link]

Martin, R. A., Puhlik-Doris, P., Larsen, G., Gray, J., & Weir, K. Humor Styles Questionnaire [dataset and codebook]. http://openpsychometrics.org/_rawdata/HSQ.zip///Martin, R. A., Puhlik-Doris, P., Larsen, G., Gray, J., & Weir, K. (2003). Individual differences in uses of humor and their relation to psychological well-being: Development of the Humor Styles Questionnaire. Journal of Research in Personality, 37, 48-75.

Data on agreeability with certain narcisstic personality characteristics (CC BY 4.0) [link]

Jorgenson, E. (2016). Open Hemispheric Brain Dominance Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/OHBDS-data.zip

Attitudes about difference from IAT database (CC BY 4.0) [link]

Xu, K., Nosek, B., & Greenwald, A. (2014). Psychology data from the race implicit association test on the project implicit demo website. Journal of open psychology data, 2(1).

Attitudes towards poverty from IAT database (CC BY 4.0) [link]

Xu, K., Nosek, B., & Greenwald, A. (2014). Psychology data from the race implicit association test on the project implicit demo website. Journal of open psychology data, 2(1).

Sixty items from 4 cognitive ability tests (CC BY 4.0) [link]

Condon, David; Revelle, William, 2015, “Selected ICAR data from the SAPA-Project: Development and initial validation of a public-domain measure”, https://doi.org/10.7910/DVN/AD9RVY, Harvard Dataverse, V2, UNF:6:73sL85mhjMM9L1xA7R6JTA== [fileUNF]

Reviewer ratings for conference papers. (GPL-3) [link]

Kuhlisch, W., Roos, M., Rothe, J., Rudolph, J., Scheuermann, B., & Stoyan, D. (2016). A statistical approach to calibrating the scores of biased reviewers of scientific papers. Metrika, 79, 37-57.

Standard settings ratings (GPL-3) [link]

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Ratings of 2002 olympics pairs figure skating (GPL-3) [link]

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Survey data on public and private university students’ on internet usage (CC BY 4.0) [link]

Mwakilama, Elias; Jamu, Edister; Senganimalunje, Limbika ; Manda, Tiwonge (2022), “Data on Internet Addiction and Mental Health among university students in Malawi”, Mendeley Data, V3, doi: 10.17632/xbfbcy5bhv.3

Participants respond to statements on extraversion and introversion behaviors on a likert scale (CC BY 4.0) [link]

Development of the Multidimensional Introversion-Extraversion ScalesX [dataset and codebook]. http://openpsychometrics.org/_rawdata/MIES_Dev_Data.zip

Across 10 different trials, 617 healthy (no neurological impairments) participants perform the Iowa gambling task (choose among options, each of which was associated with different risks and rewards) (CC BY-SA 4.0) [link]

Steingroever, H., Fridberg, D. J., Horstmann, A., Kjome, K. L., Kumari, V., Lane, S. D., PhD, … Wagenmakers, E.-J. (2015, March 30). Data from 617 Healthy Participants Performing the Iowa Gambling Task: A “Many Labs” Collaboration. Retrieved from osf.io/8t7rm

Data from mental rotation tasks (GPL-3) [link]

Janssen, A. B., & Geiser, C. (2010). On the relationship between solution strategies in two mental rotation tasks. Learning and Individual Differences, 20(5), 473-478.

IPIP (personality test) item response data from Johnson’s 2005 study [link]

Johnson, J. A. (2015, November 4). Data from Johnson, J. A. (2005). Ascertaining the validity of web-based personality inventories. Journal of Research in Personality, 39, 103-129. doi:10.1016/j.jrp.2004.09.009. Retrieved from osf.io/sxeq5

Common responses (i.e., Table 3) to political action survey (NA) [link]

Jöreskog, K. G., & Moustaki, I. (2001). Factor analysis of ordinal variables: A comparison of three approaches. Multivariate Behavioral Research,, 36(3), 347-387.

Item data from exercsies from Junyi Academy, which is an online learning platform [link]

Chang, H., Hsu, H., & Chen, K. (2015). Problem log, from “Modeling Exercise Relationships in E-Learning: A Unified Approach” [data set]. International Conference on Educational Data Mining (EDM). https://pslcdatashop.web.cmu.edu/Files?datasetId=1198

Affective self-report data (brief scales on depression, self-esteem, loneliness, self-derogation) from the “Monitoring the Future” study [link]

Miech, Richard A., Johnston, Lloyd D., Bachman, Jerald G., O’Malley, Patrick M., Schulenberg, John E., and Patrick, Megan E. Monitoring the Future: A Continuing Study of American Youth (12th-Grade Survey), 2021. Inter-university Consortium for Political and Social Research [distributor], 2022-10-31. https://doi.org/10.3886/ICPSR38503.v1

Responses to Mach-4 scale on Machiavellianism (GPL-3) [link]

Hunter, J. E., Gerbing, D. W., & Boster, F. J. (1982). Machiavellian beliefs and personality: Construct invalidity of the Machiavellianism dimension. Journal of personality and social psychology, 43(6), 1293.

De Jong-Gierveld loneliness scale (GPL-3) [link]

G. J. De Jong and T. van Tilburg (1999). Manual of the loneliness scale. Amsterdam: VU University Amsterdam.

Data from LSAT (GPL-3) [link]

Bock, R. and Lieberman, M. (1970) Fitting a response model for n dichotomously scored items. Psychometrika, 35, 179–197.

Million clinical multiaxial inventory (see https://osf.io/e9jrz) (GPL-3) [link]

Rossi, G., Elklit, A., & Simonsen, E. (2010). Empirical evidence for a four factor framework of personality disorder organization: multigroup confirmatory factor analysis of the millon clinical multiaxial inventory—III personality disorder scales across belgian and danish data samples. Journal of Personality Disorders, 24(1), 128-150.

Spelling test results [link]

Andrews, S., Veldre, A. & Clarke, I.E. Measuring Lexical Quality: The Role of Spelling Ability. Behav Res 52, 2257–2282 (2020). https://doi.org/10.3758/s13428-020-01387-3

Survey data on public and private university students’ mental health symptoms as a consequence of internet usage (CC BY 4.0) [link]

Mwakilama, Elias; Jamu, Edister; Senganimalunje, Limbika ; Manda, Tiwonge (2022), “Data on Internet Addiction and Mental Health among university students in Malawi”, Mendeley Data, V3, doi: 10.17632/xbfbcy5bhv.3

In order to accurately and effectively measure the level of anxiety symptoms in the elderly (above 60 years old), from 2010 to 2011, this project translated, test-tested, and used the English version of the Geriatric Anxiety Inventory (GAI) developed in 2007 by Australian scholar Pachana. This dataset includes the responses from 1318 Chinese elderly people living in urban communities, rural communities, and nursing homes in Beijing. [link]

Wang T, Tang D, Gong X, Wang D. Anxiety and Depression Among Older People Dwelling in Nursing Homes Versus Communities. Chinese Journal of Clinical Psychology,2012,20(6):868-870.

Responses to the Kentucky Inventory of Mindfulness Skills Assessment, which asks participants to rate how much they agree with certain statements about self-care and mindfulness practice (CC BY 4.0) [link]

Baer, R. A., Smith G. T., Allen, K. B. Kentucky Inventory of Mindful Skills [dataset and codebook]. http://openpsychometrics.org/_rawdata/KIMS.zip///Baer, R. A., Smith G. T., Allen, K. B. (2004). Assessment of mindfulness by self-report: The Kentucky Inventory of Mindfulness Skills. Assessment, 11, 191-206.

Social mobility for women in Bangladesh (GPL-3) [link]

Huq, N. and Cleland, J. (1990) Bangladesh Fertility Survey, 1989. Dhaka: National Institute of Population Research and Training (NIPORT).

Data from the Moral Foundations Vignette (MFV) [link]

Crone, D.L., Rhee, J.J. & Laham, S.M. Developing brief versions of the Moral Foundations Vignettes using a genetic algorithm-based approach. Behav Res 53, 1179–1187 (2021). https://doi.org/10.3758/s13428-020-01489-y

Random dot motion discrimination (CC BY 4.0) [link]

O’Brien, G., & Yeatman, J. D. (2021). Bridging sensory and language theories of dyslexia: Toward a multifactorial model. Developmental Science, 24(3), e13039.

Data from a scale assessing wisdom (GPL-2) [link]

Levenson, M. R., Jennings, P. A., Aldwin, C. M., & Shiraishi, R. W. (2005). Self-transcendence: conceptualization and measurement. The International Journal of Aging and Human Development, 60, 127-143.

Data from a scale assessing avalanche risk awareness (GPL-2) [link]

Haegeli, P., Gunn, M., & Haider, W. (2012). Identifying a high-risk cohort in a complex and dynamic risk environment: Out-of-bounds skiing–an example from avalanche safety. Prevention Science, 13, 562-573

Sensation-seeking scale from out-of-bounds skiers (GPL-2) [link]

Hoyle, R. H., Stephenson, M. T., Palmgreen, P., Lorch, E. P., & Donohew, R. L. (2002). Reliability and validity of a brief measure of sensation. Personality and Individual Differences, 32, 401-414.

Children responding to empathy scale (GPL-2) [link]

urce Funk, J. B., Fox, C. M., Chang, M., & Curtiss, K. (2008). The development of the Children’s Empathic Attitudes Questionnaire using classical and Rasch analyses. Journal of Applied Developmental Psychology, 29, 187-196.

Condom usage scale (GPL-2) [link]

de Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. Guilford Press, New York

Child-focused response to challenge scale (GPL-2) [link]

Lakes, K. D., & Hoyt, W. T. (2009). Applications of generalizability theory to clinical child and adolescent psychology research. Journal of Clinical Child & Adolescent Psychology, 38, 144-165.

Learning-related emotions for children learning mathematics (GPL-2) [link]

Grand, A., & Dittrich, R. (2015) Modelling assumed metric paired comparison data - application to learning related emotions. Austrian Journal of Statistics, 44, 3-15.

Motivation for working on open-source software (GPL-2) [link]

Mair, P., Hofmann, E., Gruber, K., Zeileis, A., & Hornik, K. (2015) Motivation, values, and work design as drivers of participation in the R open source Project for Statistical Computing. Proceedings of the National Academy of Sciences of the United States of America, 112(48), 14788-14792.

Depression/OCD collected in a hospital from adolescents (GPL-2) [link]

Jones, P. J., Mair, P., Riemann, B. C., Mugno, B. L., & McNally, R. J. (2018). A network perspective on comorbid depression in adolescents with obsessive-compulsive disorder. Journal of Anxiety Disorders, 53, 1-8. #’

Depression/OCD collected in a hospital (GPL-2) [link]

McNally, R. J., Mair, P., Mugno, B. L., and Riemann, B. C. (2017). Comorbid obsessive-compulsive disorder and depression: A Bayesian network approach. Psychological Medicine, 47(7), 1204- 1214.

Work design questionnaire from open-source developers (GPL-2) [link]

Mair, P., Hofmann, E., Gruber, K., Zeileis, A., & Hornik, K. (2015) Motivation, values, and work design as drivers of participation in the R open source Project for Statistical Computing. Proceedings of the National Academy of Sciences of the United States of America, 112(48), 14788-14792.

PTSD data from disaster survivors (GPL-2) [link]

McNally, R. J., Robinaugh, D. J., Wu, G. W. Y., Wang, L., Deserno, M. K., & Borsboom, D. (2015). Mental disorders as causal systems: A network approach to posttraumatic stress disorder. Clinical Psychological Science, 3(6), 836-849.

PVMT task data (GPL-2) [link]

Wilmer, J. B., Germine, L., Chabris, C. F., Chatterjee, G., Gerbasi, M. & Nakayama, K. (2012): Capturing specific abilities as a window into human individuality: The example of face recognition, Cognitive Neuropsychology, 29, 360-392

Wilson-Patterson conservatism scale (GPL-2) [link]

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Youth depression scale (GPL-2) [link]

Vaughn-Coaxum, R. A., Mair, P., & Weisz, J. R. (2015). Racial/ethnic differences in youth depression indicators: An Item Response Theory analysis of symptoms reported by White, Black, Asian, and Latino youths. Clinical Psychological Science, 4, 239-253.

ZAREKI-R test battery (GPL-2) [link]

Koller, I., & Alexandrowicz, R. W. (2010) Eine psychometrische Analyse der ZAREKI-R mittels Rasch-Modellen [A psychometric analysis of the ZAREKI-R using Rasch-models]. Diagnostica 56, 57-67.

Supreme Court voting record (CC BY-NC 3.0 US DEED) [link]

Martin, A. D., & Quinn, K. M. (2002). Dynamic ideal point estimation via Markov chain Monte Carlo for the US Supreme Court, 1953–1999. Political analysis, 10(2), 134-153.

Numerosity task (BY) administered to Mturkers [link]

Ratcliff, R., Hendrickson, A.T. Do data from mechanical Turk subjects replicate accuracy, response time, and diffusion modeling results?. Behav Res 53, 2302–2325 (2021). https://doi.org/10.3758/s13428-021-01573-x

Lexical decision task administered to Mturkers [link]

Ratcliff, R., Hendrickson, A.T. Do data from mechanical Turk subjects replicate accuracy, response time, and diffusion modeling results?. Behav Res 53, 2302–2325 (2021). https://doi.org/10.3758/s13428-021-01573-x

Word recognition task administered to Mturkers [link]

Ratcliff, R., Hendrickson, A.T. Do data from mechanical Turk subjects replicate accuracy, response time, and diffusion modeling results?. Behav Res 53, 2302–2325 (2021). https://doi.org/10.3758/s13428-021-01573-x

Numerosity task (Y25) administered to Mturkers [link]

Ratcliff, R., Hendrickson, A.T. Do data from mechanical Turk subjects replicate accuracy, response time, and diffusion modeling results?. Behav Res 53, 2302–2325 (2021). https://doi.org/10.3758/s13428-021-01573-x

Extract from 1996 NAEP (GPL-3) [link]

Bartolucci, F., Bacci, S. and Gnaldi, M. (2014), MultiLCIRT: An R package for multidimensional latent class item response models, Computational Statistics and Data Analysis, 71, 971-985.

Data on the strength of connection to nature that individuals feel (CC BY 4.0) [link]

Nisbet, E., Zelenski, J. (2018). Nature Relatedness Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/NR6-data-14Nov2018.zip

Attempt to quantify ‘nerdiness’ (CC BY 4.0) [link]

(2015). Development of the Nerdy Personality Attributes Scale [dataset and codebook]. http://openpsychometrics.org/tests/NPAS/development/

Items from a dataset from the 2020 NeurIPS Education Challenge, which was used to predict student responses, determine question quality, etc [link]

Wang, Z., Lamb, A., Saveliev, E., Cameron, P., Zaykov, Y., Hernández-Lobato, J.M., Turner, R.E., Baraniuk, R.G., Barton, C., Jones, S.P., Woodhead, S., & Zhang, C. (2020). Diagnostic questions: The neurips 2020 education challenge [data set]. arXiv preprint arXiv:2007.12061

Items from a dataset from the 2022 NeurIPS Education Challenge, is based on real AB experiment data to uncover causal links between learning one construct to the success of learning another [link]

Gong, W., Smith, D., Wang, Z., Barton C., Woodhead, S., Pawlowski, N., Jennings, J., Zhang, C. (2022). NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education [data set]. arXiv:2208.12610

Decision-making under time pressure based on indicating which of two displayed circles flashes most frequently (CC BY 4.0) [link]

Archambeau, K., Couto, J. & Van Maanen, L. Non-parametric mixture modeling of cognitive psychological data: A new method to disentangle hidden strategies. Behav Res 55, 2232–2248 (2023). https://doi.org/10.3758/s13428-022-01837-0 Miletić, S., & van Maanen, L. (2019). Caution in decision-making under time pressure is mediated by timing ability. Cognitive Psychology, 110, 16-29.

Data on nonverbal behavioral tendencies (CC BY 4.0) [link]

Richmond, V. P., McCroskey, J. C., & Johnson, A. D. (2003). Development of the Nonverbal Immediacy Scale (NIS): Measures of self- and other-perceived nonverbal immediacy. Communication Quarterly, 51, 502-515.

606 Amazon Mechanical Turk were shown a set of numbers and asked what other numbers are likely to belong to the set (CC BY 4.0) [link]

Bigelow, Eric J.; Piantadosi, Steven T., 2018, “Large Dataset of Generalization Patterns in the Number Game”, https://doi.org/10.7910/DVN/A8ZWLF, Harvard Dataverse, V1, UNF:6:zUgVtjc9CKvWc4pB//Qp6A== [fileUNF]

Big five measure from UK university students (CC BY 4.0) [link]

Satchell, L.P., Fido, D., Harper, C.A. et al. Development of an Offline-Friend Addiction Questionnaire (O-FAQ): Are most people really social addicts?. Behav Res 53, 1097–1106 (2021). https://doi.org/10.3758/s13428-020-01462-9

Domain-specific risk taking measure (DOSPERT) from UK university students (CC BY 4.0) [link]

Satchell, L.P., Fido, D., Harper, C.A. et al. Development of an Offline-Friend Addiction Questionnaire (O-FAQ): Are most people really social addicts?. Behav Res 53, 1097–1106 (2021). https://doi.org/10.3758/s13428-020-01462-9

Offline-Friend Addiction Questionnaire (OFAQ) from UK university students (CC BY 4.0) [link]

Satchell, L.P., Fido, D., Harper, C.A. et al. Development of an Offline-Friend Addiction Questionnaire (O-FAQ): Are most people really social addicts?. Behav Res 53, 1097–1106 (2021). https://doi.org/10.3758/s13428-020-01462-9

Grading data from an OLI psychology course [link]

Cheng, H. (2022). Gradebook for OLI Psych course [dataset]. DataShop @ CMU. https://pslcdatashop.web.cmu.edu/Files?datasetId=5255

Data from the Promoting Adolescents’ Comprehension of Text Project, in which 7 - 12 grade students respond to item to gauge the role of cognitive processes, motivation, engagement, etc to improve reading comprehension (CC BY 4.0) [link]

Vaughn, S., Swanson, E., Roberts, G., Martinez, L., Wanzek, J., Simmons, D., Clemens, N., & Fogarty, M. (2021). PACT8 2011-2012, PACT8 2012-2013, PACT11 2011-2012, PACT11 2012-2013, Vocabulary and Comprehension (VoCO) Project [datasets]. LDbase. http://ldbase.org/datasets/7a353ad7-4859-47a0-8aad-000889fed91a

Survey items from dataset used to assess effectiveness of parental text message intervention (CC BY 4.0) [link]

Scherer, Ethan; Catherine Asher; Kim, James, 2021, “Replication Data for: Using a Factorial Design to Maximize the Effectiveness of a Parental Text Messaging Intervention”, https://doi.org/10.7910/DVN/XUEU90, Harvard Dataverse, V1, UNF:6:yfNiyfKzlJz+tCM15Lo+DQ== [fileUNF]

Data from 2011 PIRLS from one booklet and 4 countries. (GPL-3) [link]

Robitzsch A (2022). sirt: Supplementary Item Response Theory Models. R package version 3.12-66, https://CRAN.R-project.org/package=sirt.

Data on math from a large scale international assessment of 15 year olds [link]

Data on read from a large scale international assessment of 15 year olds [link]

Data on science from a large scale international assessment of 15 year olds [link]

Data on math from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on problem solving from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on reading from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on science from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on math from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on reading from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on science from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on math from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on reading from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on science from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on math, science and reading from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on math, science and reading from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on math, science and reading from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on math, science and reading from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on math, science and reading from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Data on math, science and reading from a large scale international assessment of 15 year olds (CC BY-NC-SA 3.0 IGO) [link]

Items about elementary probability theory. (GPL-3) [link]

Heller, J., & Wickelmaier, F. (2013). Minimum discrepancy estimation in probabilistic knowledge structures. Electronic Notes in Discrete Mathematics, 42, 49-56.

Presence(=1) or absence of carcinoma in uterine cervix (ratings by 7 pathologists) (GPL-3) [link]

Agresti, Alan. 2002. Categorical Data Analysis, second edition. Hoboken: John Wiley & Sons.

Responses from undergrads to questions about cheating behaviors (GPL-3) [link]

Dayton, C. Mitchell. 1998. Latent Class Scaling Analysis. Thousand Oaks, CA: SAGE Publications.

Questions about how certain statements fit candidates for 2000 US presidential election (GPL-3) [link]

The National Election Studies (https://electionstudies.org/). THE 2000 NATIONAL ELECTION STUDY [dataset]. Ann Arbor, MI: University of Michigan, Center for Political Studies [producer and distributor].

Survey responses to questions about universal values (GPL-3) [link]

Stouffer, S.A. and J. Toby. 1951. “Role conflict and personality.” American Journal of Sociology. 56: 395:406.

Data on political ideologies (CC BY 4.0) [link]

Brandt, M. J., Turner-Zwinkels, F. M., & Kubin, E. (2021). Political Psychology Data from a 26-wave Yearlong Longitudinal Study (2019–2020). Journal of Open Psychology Data, 9(1). 10.5334/jopd.54

Data on peoples’ activity preferences, which corresponds with different parts of the brain (CC BY 4.0) [link]

Wagner, Rudolph F., and Kelly A. Wells. “A refined neurobehavioral inventory of hemispheric preference.” Journal of clinical psychology 41.5 (1985): 671-676.///Wagner Preference Inventory [dataset and codebook]. http://openpsychometrics.org/_rawdata/Wagner.zip

Children aged 2 - 5 answer item that assess their social and emotional skills, cognitive skills, early math, early reading, and other indicators of kindergarten readiness (CC BY 4.0) [link]

Bailey, C. S., Korucu, İrem, Eveleigh, A., Schnur, G., Costello, L., Tuttle, M., Knox-Lane, T., Cassidy, C., Ondrusek, A., McNaboe, T., Mazhar, A., & Xie, F. (2023). Preschool Social and Emotional Development Study—Connecticut Dataset. LDbase. https://doi.org/10.33009/ldbase.1680213217.8ed0

Data on behavioral and achievement assessment items from elementary school children (CC BY 4.0) [link]

Hart, S. A., Otaiba, S. A., Connor, C., & Norris, C. U. (2021). Project KIDS Item level Data. LDbase. https://doi.org/10.33009/ldbase.1620837890.bcf8

Data from an online survey that collects participants’ sentiments on various “protestant work ethic” statements (CC BY 4.0) [link]

Protestant Work Ethic Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/PWE_data.zip

Data from online participants on the Woodworth Psychoneurotic Inventory, which is considered by some to be the first personality test (CC BY 4.0) [link]

Woodworth Psychoneurotic Inventory [dataset and codebook]. http://openpsychometrics.org/_rawdata/WPI.zip

Measure of belief in conspiracy theories (GPL-3) [link]

Brotherton R, French CC, Pickering AD (2013). Measuring Belief in Conspiracy Theories: The Generic Conspiracist Beliefs Scale. Frontiers in Psychology, 4, 279.

Response to gratitudes scales from youth and adolescents (GPL-3) [link]

Froh, J. J., Fan, J., Emmons, R. A., Bono, G., Huebner, E. S., & Watkins, P. (2011). Measuring gratitude in youth: Assessing the psychometric properties of adult gratitude scales in children and adolescents. Psychological Assessment, 23(2), 311–324. https://doi.org/10.1037/a0021590

Responses to math exam (GPL-3) [link]

Zeileis A, Umlauf N, Leisch F (2014). Flexible Generation of E-Learning Exams in R: Moodle Quizzes, OLAT Assessments, and Beyond. Journal of Statistical Software, 58(1), 1–36. doi:10.18637/jss.v058.i01

Data from the Synthetic Aperture Personality Assessment personality and ability test (GPL-3) [link]

Revelle, William, Dworak, Elizabeth M. and Condon, David (2020) Cognitive ability in everyday life: the utility of open-source measures. Current Directions in Psychological Science, 29, (4) 358-363. Open access at doi:10.1177/0963721420922178.

Gender role self-concept inventory (GPL-3) [link]

Ursula Athenstaedt (2003) On the Content and Structure of the Gender Role Self-Concept: Including Gender-Stereotypical Behaviors in Addition to Traits. Psychology of Women Quarterly, 27, 309-318. doi: 10.1111/1471-6402.00111.

ipip personality data from the SAPA project (GPL-3) [link]

Revelle, W., Wilt, J., and Rosenthal, A. (2010) Individual Differences in Cognition: New Methods for examining the Personality-Cognition Link In Gruszka, A. and Matthews, G. and Szymura, B. (Eds.) Handbook of Individual Differences in Cognition: Attention, Memory and Executive Control, Springer.

Bond’s Logical Operations Test (GPL-3) [link]

T.G. Bond. BLOT:Bond’s Logical Operations Test. Townsville, Australia: James Cook University. (Original work published 1976), 1995

Eysenck Personality Inventory (GPL-3) [link]

Eysenck, H.J. and Eysenck, S. B.G. (1968). Manual for the Eysenck Personality Inventory.Educational and Industrial Testing Service, San Diego, CA.

Gender-related activities survey (GPL-3) [link]

Gruber, Freya M. and Distlberger, Eva and Scherndl, Thomas and Ortner, Tuulia M. and Pletzer, Belinda (2020) Psychometric properties of the multifaceted Gender-Related Attributes Survey (GERAS) European Journal of Psychological Assessment, 36, (4) 612-623.

Motivational State Questionnaire (GPL-3) [link]

Revelle, W. and Anderson, K.J. (1998) Personality, motivation and cognitive performance: Final report to the Army Research Institute on contract MDA 903-93-K-0008. (https://www.personality-project. org/revelle/publications/ra.ari.98.pdf).

State anxiety data over multiple studies (GPL-3) [link]

Revelle, William and Anderson, Kristen Joan (1997) Personality, motivation and cognitive performance: Final report to the Army Research Institute on contract MDA 903-93-K-0008

Ratings of students (Example 3 of ConQuest manual (GPL-3) [link]

n/a

Item responses from Swedish college entrance exam. (GPL-3) [link]

Ramsay J (2023). TestGardener: Optimal Analysis of Test and Rating Scale Data. R package version 3.1.4, https://CRAN.R-project.org/package=TestGardener.

GSS data on abortion attitudes (GPL-3) [link]

n/a

Data from the Holland Code (RIASEC) Test, wherein participants rated how much they would like to perform certain tasks (CC BY 4.0) [link]

(2018). Holland Code (RIASEC) Test [dataset and codebook]. http://openpsychometrics.org/_rawdata/RIASEC_data12Dec2018.zip

Data from a survey that probes facist ideologies to try to understand those regimes and their followers (CC BY 4.0) [link]

Altemeyer, B (2015). Right-wing Authoritarianism Scale [dataset and codebook]. https://openpsychometrics.org/_rawdata/RWAS.zip

Responses to job satisfaction items (GPL-3) [link]

Bartolucci, F., Bacci, S., & Gnaldi, M. (2015). Statistical analysis of questionnaires: A unified approach based on R and Stata (Vol. 34). CRC press.

Responses from a Lexical Decision Task. (CC BY 4.0) [link]

Yeatman, J. D., Tang, K. A., Donnelly, P. M., Yablonski, M., Ramamurthy, M., Karipidis, I. I., … & Domingue, B. W. (2021). Rapid online assessment of reading ability. Scientific reports, 11(1), 6396.

US House votes [link]

Lewis, Jeffrey B., Keith Poole, Howard Rosenthal, Adam Boche, Aaron Rudkin, and Luke Sonnet (2023). Voteview: Congressional Roll-Call Votes Database. https://voteview.com/

US Senate votes [link]

Lewis, Jeffrey B., Keith Poole, Howard Rosenthal, Adam Boche, Aaron Rudkin, and Luke Sonnet (2023). Voteview: Congressional Roll-Call Votes Database. https://voteview.com/

Data from an interactive version of Experinces in Close Relationships Scale, where participants respond to statements about how they behave with those they have close relationships with (CC BY 4.0) [link]

(2014). Roseberg Self-Esteem Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/RSE.zip///Brennan, K.; Clark, C.; Shaver, P. (1998). Self-report measures of adult romantic attachment. In J. Simpson and W. Rholes, Attachment Theory and Close Relationships. New York: Guilford Press.

Luminosity discrimination tasks (GPL-3) [link]

Ratcliff, R., & Rouder, J. N. (1998). Modeling Response Times for Two-Choice Decisions. Psychological Science, 9(5), 347-356. http://doi.org/10.1111/1467-9280.00067

Personality data from the SAPA project (CC BY 4.0) [link]

Condon, David M.; Revelle, William, 2015, “Selected personality data from the SAPA-Project: 08Dec2013 to 26Jul2014”, https://doi.org/10.7910/DVN/SD7SVE, Harvard Dataverse, V4, UNF:6:9B60JNIFXXKXmyLhABjoUA== [fileUNF]

Item responses from the Symptom Distress Scale (GPL-3) [link]

Ramsay J (2023). TestGardener: Optimal Analysis of Test and Rating Scale Data. R package version 3.1.4, https://CRAN.R-project.org/package=TestGardener.

Responses to 1997 Canadian National Election Study regarding ‘traditional values’ (GPL-3) [link]

Fox, J. (2006) Structural equation modeling with the sem package in R. Structural Equation Modeling 13:465–486.

Answers to the Sexual Compulsivity Scale (CC BY 4.0) [link]

Sexual Compulsivity Scale [dataset and codebook]. http://openpsychometrics.org/_rawdata/SCS.zip

Data on participants’ feelings about and self-concept of sexual activities (CC BY 4.0) [link]

“The Multidimensional Sexual Self-Concept Questionnaire” (MSSCQ) by Dr. William E. Snell, Jr. http://www4.semo.edu/snell/scales/MSSCQ.HTM ///Snell Jr., W. The Multidimensional Sexual Self-Concept Questionnaire” (MSSCQ) [dataset and codebook]. http://openpsychometrics.org/_rawdata/MSSCQ.zip

Quality of life items asked of oncology patients (GPL-3) [link]

Ware, J., Kosinski, M., Turner-Bowker, D. and Gandek, B. (2002), SF-12v2. How to score version 2 of the SF-12 health survey, QualityMetric Incorporated: Lincoln.

Data on the dark triad personality traits (CC BY 4.0) [link]

Paulhus, D. L., & Jones, D. N. (2011, January). Introducing a short measure of the Dark Triad. Poster presented at the meeting of the Society for Personality and Social Psychology, San Antonio.///Short Dark Triad [dataset and codebook]. http://openpsychometrics.org/_rawdata/SD3.zip

Employee-helping behavior across 8 countries. (GPL-3) [link]

Fischer, R., & Karl, J. A. (2019). A primer to (cross-cultural) multi-group invariance testing possibilities in R. Frontiers in psychology, 1507.

Data from two digital games (Slingshot Challenge and Star Mines) for testing prisoner’s dilemma [link]

Ribeiro Eulalio Cabral, G., Rodrigues Sampaio, L., Ribeiro Eulalio Cabral, G. et al. Slingshot Challenge and Star Mines: Two digital games as a prisoner’s dilemma to assess cooperation in children. Behav Res 54, 597–610 (2022). https://doi.org/10.3758/s13428-021-01661-y

Abbreviated version of PVQ40 (GPL-3) [link]

Borg, I., Bardi, A., & Schwartz, S. H. (2017). Does the value circle exist within persons or only across persons? Journal of Personality, 85(2), 151-162

General knowledge items (CC BY 4.0) [link]

Buades-Sitjar, F., Boada, R., Guasch, M., Ferré, P., Hinojosa, J. A., & Duñabeitia, J. A. (2022). The predictors of general knowledge: Data from a Spanish megastudy. Behavior Research Methods, 54(2), 898-909.

Response from a state reading assessment (grade 10) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state reading assessment (grade 3) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state reading assessment (grade 4) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state reading assessment (grade 5) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state reading assessment (grade 6) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state reading assessment (grade 7) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state reading assessment (grade 8) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state reading assessment (grade 9) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state math assessment (grade 5) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state math assessment (grade 6) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state math assessment (grade 7) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state math assessment (grade 8) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Response from a state math assessment (grade 9) [link]

Briggs, D. C., & Domingue, B. (2013). The gains from vertical scaling. Journal of Educational and Behavioral Statistics, 38(6), 551-576.

Well-being scale from students about teachers (GPL-3) [link]

Zijsling, D., Keuning, J., Keizer-Mittelhaeuser, M.-A., Naaijer, H., & Timmermans, A. (2017). Cohortonderzoek COOL5-18: Technisch rapport meting VO-3 in 2014. Onderwijs/Onderzoek

Measure of inductive reasoning (CC BY 4.0) [link]

Golino, H. F., & Gomes, C. M. (2015). Investigando estágios de desenvolvimento do raciocínio indutivo usando a análise fatorial confimatória, o Modelo Logístico Simples de Rasch e o modelo de teste logístico linear (Rasch Estendido). In H. F. Golino, C. M. Gomes, A. Amantes, & G. Coelho, Psicometria Contemporânea: Compreendendo os Modelos Rasch (pp. 283-331). São Paulo: Casa do Psicólogo/Pearson.

Video ratings of emotional experience due to a video (CC BY 4.0) [link]

Küster, D., Baker, M., & Krumhuber, E. G. (2022). PDSTD-The Portsmouth Dynamic Spontaneous Tears Database. Behavior Research Methods, 54(6), 2678-2692.

Tenseness from the Freiburg Complaint Checklist (GPL-3) [link]

ZPID (2013). PsychData of the Leibniz Institute for Psychology Information ZPID. Trier: Center for Research Data in Psychology.

Data collected from Estonian “National Intelligence Tests” at two time points (1933/36, 2006) (CC BY 4.0) [link]

Must, O., & Must, A. (2014). Data from “Changes in test-taking patterns over time” concerning the Flynn Effect in Estonia. Journal of Open Psychology Data, 2(1).

Forced choice responses to big 5 items [link]

Bunji, K., Okada, K. Joint modeling of the two-alternative multidimensional forced-choice personality measurement and its response time by a Thurstonian D-diffusion item response model. Behav Res 52, 1091–1107 (2020). https://doi.org/10.3758/s13428-019-01302-5

TIMSS 2011 data from Australian and Taiwanese students (GPL-3) [link]

Robitzsch A, Kiefer T, Wu M (2022). TAM: Test Analysis Modules. R package version 4.1-4, https://CRAN.R-project.org/package=TAM.

Data form the Taylor manifest anxiety scale. (CC BY 4.0) [link]

Taylor, J. (1953). “A personality scale of manifest anxiety”. The Journal of Abnormal and Social Psychology, 48(2), 285-290.

Transitive reasoning tasks (GPL-3) [link]

Verweij, A. C., Sijtsma, K., & Koops, W. (1996). A Mokken scale for transitive reasoning suited for longitudinal research. International Journal of Behavioral Development, 23, 241-264. doi: 10.1177/016502549601900115

Fifteen forest managers (items) classified 387 trees (ids) for removal. (GPL-3) [link]

Pommerening, A., Pallarés Ramos, C., Kędziora, W., Haufe, J., & Stoyan, D. (2018). Rating experiments in forestry: How much agreement is there in tree marking?. PLoS One, 13(3), e0194747.

Self-Reports of whether one would want or do acts of verbal aggression. (GPL-3) [link]

De Boeck and Wilson (2004), Explanatory Item Response Models, Springer.

Receptive vocabulary task (in German) for children 3-8 (CC BY 4.0) [link]

Bohn, M., Prein, J. C., Delikaya, B., Haun, D. B. M., & Gagarina, N. (2022, August 24). oREV: an Item Response Theory based open receptive vocabulary task for 3 to 8-year-old children. https://doi.org/10.31234/osf.io/4z86w

Data on vocabulary knowledge and personality items (CC BY 4.0) [link]

(2018). Vocabulary IQ Test [dataset and codebook]. http://openpsychometrics.org/_rawdata/VIQT_data.zip

Longitudinal, item-level data from the Western Reserve Reading and Math Project, which concerned child development among twins (CC BY 4.0) [link]

Petrill, S., Thompson, L., Plomin, R., DeThorne, L. S., & Schatschneider, C. (2021). WRRMP Full data (all waves). LDbase. https://doi.org/10.33009/ldbase.1643647104.07c5

Attitudinal items involving workplace negotiations. (GPL-3) [link]

Bartholomew, D. (1998) Scaling unobservable constructs in social science. Applied Statistics, 47, 1–13.

Ten item vocabulary test included in General Social Suvey (https://en.wikipedia.org/wiki/Wordsum) [link]

Malhotra, N., Krosnick, J. A., & Haertel, E. (2007). The psychometric properties of the GSS Wordsum vocabulary test. GSS Methodological Report, 11, 1-63.

Items from online algebra tutoring, assessing whether students get the item correct on the first attempt [link]

Heffernan, N. (2010). 2004-2005 question level data [data set]. DataShop @ CMU. https://pslcdatashop.web.cmu.edu/Files?datasetId=92

Data from the Narcissistic Admiration and Rivalry Questionnaire (NARQ) along with Rosenberg self-esteem (RSE) and Multigroup Ethnic Identity Measure (MEIM). [link]

Chou, E., Westberg, D. W., Nguyen, P. L., & Syed, M. (2023, October 28). The Structure and Correlates of the Narcissistic Admiration and Rivalry Questionnaire among U.S. Racial/Ethnic Groups. https://doi.org/10.31234/osf.io/w3rxe

Data from the Anger scale in the Wave 1 PROMIS study. (CC0 1.0) [link]

Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., DeVellis, R., DeWalt, D., Fries, J., Gershon, R., Hahn, E., Pilkonis, P., Revicki, D., Rose, M., Weinfurt, K., and Hays, R. on behalf of the PROMIS Cooperative Group. (2010) Initial adult health item banks and first wave testing of the patient-reported outcomes measurement information system (PROMIS™) Network: 2005–2008. Journal of Clinical Epidemiology. 63 (11): 1179-1194. pmid: PMC2965562

Data from the CESD Scale in the Wave 1 PROMIS study. (CC0 1.0) [link]

Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., DeVellis, R., DeWalt, D., Fries, J., Gershon, R., Hahn, E., Pilkonis, P., Revicki, D., Rose, M., Weinfurt, K., and Hays, R. on behalf of the PROMIS Cooperative Group. (2010) Initial adult health item banks and first wave testing of the patient-reported outcomes measurement information system (PROMIS™) Network: 2005–2008. Journal of Clinical Epidemiology. 63 (11): 1179-1194. pmid: PMC2965562

Data from the fatigue Scale in the Wave 1 PROMIS study. (CC0 1.0) [link]

Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., DeVellis, R., DeWalt, D., Fries, J., Gershon, R., Hahn, E., Pilkonis, P., Revicki, D., Rose, M., Weinfurt, K., and Hays, R. on behalf of the PROMIS Cooperative Group. (2010) Initial adult health item banks and first wave testing of the patient-reported outcomes measurement information system (PROMIS™) Network: 2005–2008. Journal of Clinical Epidemiology. 63 (11): 1179-1194. pmid: PMC2965562

Data from the pain Scale in the Wave 1 PROMIS study. (CC0 1.0) [link]

Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., DeVellis, R., DeWalt, D., Fries, J., Gershon, R., Hahn, E., Pilkonis, P., Revicki, D., Rose, M., Weinfurt, K., and Hays, R. on behalf of the PROMIS Cooperative Group. (2010) Initial adult health item banks and first wave testing of the patient-reported outcomes measurement information system (PROMIS™) Network: 2005–2008. Journal of Clinical Epidemiology. 63 (11): 1179-1194. pmid: PMC2965562

Data from the social roles & social activities Scales in the Wave 1 PROMIS study. (CC0 1.0) [link]

Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., DeVellis, R., DeWalt, D., Fries, J., Gershon, R., Hahn, E., Pilkonis, P., Revicki, D., Rose, M., Weinfurt, K., and Hays, R. on behalf of the PROMIS Cooperative Group. (2010) Initial adult health item banks and first wave testing of the patient-reported outcomes measurement information system (PROMIS™) Network: 2005–2008. Journal of Clinical Epidemiology. 63 (11): 1179-1194. pmid: PMC2965562

Data from the anxiety Scale in the Wave 1 PROMIS study. (CC0 1.0) [link]

Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., DeVellis, R., DeWalt, D., Fries, J., Gershon, R., Hahn, E., Pilkonis, P., Revicki, D., Rose, M., Weinfurt, K., and Hays, R. on behalf of the PROMIS Cooperative Group. (2010) Initial adult health item banks and first wave testing of the patient-reported outcomes measurement information system (PROMIS™) Network: 2005–2008. Journal of Clinical Epidemiology. 63 (11): 1179-1194. pmid: PMC2965562

Data from the depression Scale in the Wave 1 PROMIS study. (CC0 1.0) [link]

Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., DeVellis, R., DeWalt, D., Fries, J., Gershon, R., Hahn, E., Pilkonis, P., Revicki, D., Rose, M., Weinfurt, K., and Hays, R. on behalf of the PROMIS Cooperative Group. (2010) Initial adult health item banks and first wave testing of the patient-reported outcomes measurement information system (PROMIS™) Network: 2005–2008. Journal of Clinical Epidemiology. 63 (11): 1179-1194. pmid: PMC2965562

Data from the HAQ disability Scale in the Wave 1 PROMIS study. (CC0 1.0) [link]

Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., DeVellis, R., DeWalt, D., Fries, J., Gershon, R., Hahn, E., Pilkonis, P., Revicki, D., Rose, M., Weinfurt, K., and Hays, R. on behalf of the PROMIS Cooperative Group. (2010) Initial adult health item banks and first wave testing of the patient-reported outcomes measurement information system (PROMIS™) Network: 2005–2008. Journal of Clinical Epidemiology. 63 (11): 1179-1194. pmid: PMC2965562

Data from the physical functioning Scale in the Wave 1 PROMIS study. (CC0 1.0) [link]

Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., Amtmann, D., Bode, R., Buysse, D., Choi, S., Cook, K., DeVellis, R., DeWalt, D., Fries, J., Gershon, R., Hahn, E., Pilkonis, P., Revicki, D., Rose, M., Weinfurt, K., and Hays, R. on behalf of the PROMIS Cooperative Group. (2010) Initial adult health item banks and first wave testing of the patient-reported outcomes measurement information system (PROMIS™) Network: 2005–2008. Journal of Clinical Epidemiology. 63 (11): 1179-1194. pmid: PMC2965562

Alcohol use disorder scale (CC-BY) [link]

Rousson, V., Trächsel, B., Iglesias, K., & Baggio, S. (2023). Evaluating the cost of simplicity in score building: An example from alcohol research. Plos one, 18(11), e0294671.

Health literacy quiz in an intervention (Creative Commons Attribution) [link]

Woods-Townsend, K., Hardy-Johnson, P., Bagust, L., Barker, M., Davey, H., Griffiths, J., … & Inskip, H. (2021). A cluster-randomised controlled trial of the LifeLab education intervention to improve health literacy in adolescents. PLoS One, 16(5), e0250545.

Outcomes from an intervention on the effects of CBT. (CC BY 4.0) [link]

Blattman, C., Jamison, J. C., & Sheridan, M. (2017). Reducing crime and violence: Experimental evidence from cognitive behavioral therapy in Liberia. American Economic Review, 107(4), 1165-1206.

Student financial proficiency (CC BY 4.0) [link]

Bruhn, M., de Souza Leão, L., Legovini, A., Marchetti, R., & Zia, B. (2016). The impact of high school financial education: Evidence from a large-scale evaluation in Brazil. American Economic Journal: Applied Economics, 8(4), 256-295.

Scale related to intimate partner violence (CC BY 4.0) [link]

Hidrobo, M., Peterman, A., & Heise, L. (2016). The effect of cash, vouchers, and food transfers on intimate partner violence: evidence from a randomized experiment in Northern Ecuador. American Economic Journal: Applied Economics, 8(3), 284-303.

Object matching tasks (CC BY 4.0) [link]

Growns, B., Towler, A., & Martire, K. (2023). The novel object-matching test (NOM Test): A psychometric measure of visual comparison ability. Behavior research methods, 1-10.

Concreteness (is word’s meaning understood through perception and action) of expressions [link]

Muraki, E. J., Abdalla, S., Brysbaert, M., & Pexman, P. M. (2023). Concreteness ratings for 62,000 English multiword expressions. Behavior Research Methods, 55(5), 2522-2531.

Reading self-concept following a literacy intervention (CC BY-NC-SA 4.0) [link]

Kim, J. S., Relyea, J. E., Burkhauser, M. A., Scherer, E., & Rich, P. (2021). Improving elementary grade students’ science and social studies vocabulary knowledge depth, reading comprehension, and argumentative writing: A conceptual replication. Educational Psychology Review, 1-30.

Vocabulary test following a literacy intervention for grade 1 students (CC BY-NC-SA 4.0) [link]

Kim, J. S., Relyea, J. E., Burkhauser, M. A., Scherer, E., & Rich, P. (2021). Improving elementary grade students’ science and social studies vocabulary knowledge depth, reading comprehension, and argumentative writing: A conceptual replication. Educational Psychology Review, 1-30.

Vocabulary test following a literacy intervention for grade 2 students (CC BY-NC-SA 4.0) [link]

Kim, J. S., Relyea, J. E., Burkhauser, M. A., Scherer, E., & Rich, P. (2021). Improving elementary grade students’ science and social studies vocabulary knowledge depth, reading comprehension, and argumentative writing: A conceptual replication. Educational Psychology Review, 1-30.

Vocabulary test following a reading intervention (CC BY-NC-SA 4.0) [link]

Kim, J. S., Gilbert, J. B., Relyea, J. E., Rich, P., Scherer, E., Burkhauser, M. A., & Tvedt, J. N. (2024). Time to transfer: Long-term effects of a sustained and spiraled content literacy intervention in the elementary grades.Developmental Psychology. Advance online publication. https://doi.org/10.1037/dev0001710

Comprehension test following a reading intervention (CC BY-NC-SA 4.0) [link]

Kim, J. S., Gilbert, J. B., Relyea, J. E., Rich, P., Scherer, E., Burkhauser, M. A., & Tvedt, J. N. (2024). Time to transfer: Long-term effects of a sustained and spiraled content literacy intervention in the elementary grades.Developmental Psychology. Advance online publication. https://doi.org/10.1037/dev0001710

Anxiety regarding gastrointestinal symptoms for those with eating pathology [link]

Forney, K. J., Burton Murray, H., Brown, T. A., Guadagnoli, L., Pucci, G., & Taft, T. (2023). Validation of a measure of hypervigilance and anxiety about gastrointestinal symptoms for individuals with elevated eating pathology. Psychological Assessment.