tidyLPA: An R Package to Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Joshua M. Rosenberg, Patrick N. Beymer, Daniel J. Anderson, Jennifer A. Schmidt
Journal/Conference Name J. Open Source Software
Paper Category
Paper Abstract Researchers are often interested in identifying homogeneous subgroups within heterogeneous samples on the basis of a set of measures, such as profiles of individuals’ motivation (i.e., their values, competence beliefs, and achievement goals). Latent Profile Analysis (LPA) is a statistical method for identifying such groups, or latent profiles, and is a special case of the general mixture model where all measured variables are continuous (Harring & Hodis, 2016; Pastor, Barron, Miller, & Davis, 2007). The tidyLPA package allows users to specify different models that determine whether and how different parameters (i.e., means, variances, and covariances) are estimated, and to specify and compare different solutions based on the number of profiles extracted.
Date of publication 2018
Code Programming Language R
Comment

Copyright Researcher 2021