autopsych: An R Shiny tool for the reproducible Rasch analysis, differential item functioning, equating, and examination of group effects

• Published in: PloS one Vol. 16; no. 10; p. e0257682
• Main Authors: Courtney, Matthew G. R, Chang, Kevin C. T, Mei, Bing, Meissel, Kane, Rowe, Luke I, Issayeva, Laila B
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 11-10-2021; Public Library of Science (PLoS)
• Subjects: Autopsies; Biology and Life Sciences; Computer and Information Sciences; Constraint modelling; Data analysis; Engineering and Technology; Evaluation; Group effects; Methods; Open source software; People and Places; Personnel selection; Physical Sciences; Public domain; Public software; Quantitative psychology; Rasch model; Reliability analysis; Research and Analysis Methods; Science Policy; Social Sciences; Source code; Spreadsheets; Students; Teacher education; Testing; Variance analysis; Kazakhstan; New Zealand
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0257682

In this paper, we present autopsych, a novel online tool that allows school assessment experts, test developers, and researchers to perform routine psychometric analyses and equating of student test data and to examine the effect of student demographic and group conditions on student test performance. The app extends current open-source software by providing (1) extensive embedded result narration and summaries for written reports, (2) improved handling of partial credit data via customizable item-person Wright maps, (3) customizable item- and person-flagging systems, (4) item-response theory model constraints and controls, (5) many-facets Rasch analysis to examine item bias, (6) Rasch fixed item equating for mapping student ability across test forms, (7) tabbed spreadsheet outputs and immediate options for secondary data analysis, (8) customizable graphical color schemes, (9) extended ANOVA analysis for examining group differences, and (10) inter-rater reliability analyses for the verifying the consistency of rater scoring systems. We present the app's architecture and functionalities and test its performance with simulated and real-world small-, medium-, and large-scale assessment data. Implications and planned future developments are also discussed.