Code/Syntax: Functions IPU & CI.IPU - Index of Proximity to Unidimensionality (lavaan)

DOI

This R-code complements - and allows to reproduce and flexibly implement - Raykov & Bluemke's (2021, DOI: 10.1177/0013164420940764 ) procedure for examining proximity to unidimensionality for multicomponent measuring instruments with multidimensional structure. The seminal publication provides Mplus code, whereas here we show how the method can be implemented in an open-science framework. The R-code uses latent variable modeling with the help of the lavaan-package and allows one to point and interval estimate an explained variance proportion-based index that may be considered a measure of proximity to unidimensional structure. The approach is readily utilized in educational, behavioral, and social research when it is of interest to evaluate whether a more general structure scale, test, or measuring instrument could be treated as being associated with an approximately unidimensional latent structure for some empirical purposes. Raykov, T., & Bluemke, M. (2021). Examining Multidimensional Measuring Instruments for Proximity to Unidimensional Structure Using Latent Variable Modeling. Educational and Psychological Measurement, 81(2), 319-339. https://doi.org/10.1177/0013164420940764

Identifier
DOI https://doi.org/10.7802/2597
Source https://search.gesis.org/research_data/SDN-10.7802-2597?lang=de
Metadata Access https://datacatalogue.cessda.eu/oai-pmh/v0/oai?verb=GetRecord&metadataPrefix=oai_ddi25&identifier=6b4fef150dfd5cc944310a9b9a0ecb7fd8463d6c117a6be326a2be9162ef84c9
Provenance
Creator Bluemke, Matthias; Urban, Julian
Publisher GESIS Data Archive for the Social Sciences; GESIS Datenarchiv für Sozialwissenschaften
Publication Year 2023
Rights Free access (without registration) - The research data can be downloaded directly by anyone without further limitations. CC BY-NC 4.0: Attribution – NonCommercial (https://creativecommons.org/licenses/by-nc/4.0/deed.de); Freier Zugang (ohne Registrierung) - Die Forschungsdaten können von jedem direkt heruntergeladen werden. CC BY-NC 4.0: Attribution – NonCommercial (https://creativecommons.org/licenses/by-nc/4.0/deed.de)
OpenAccess true
Contact http://www.gesis.org/
Representation
Discipline Social Sciences