TY - JOUR
T1 - tidyLPA: An R Package to Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software
AU - Rosenberg, Joshua
AU - Beymer, Patrick
AU - Anderson, Daniel
AU - Van Lissa, Caspar J.
AU - Schmidt, Jennifer
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
U2 - 10.21105/joss.00978
DO - 10.21105/joss.00978
M3 - Article
SN - 2475-9066
VL - 3
SP - 978
JO - Journal of Open Source Software
JF - Journal of Open Source Software
IS - 30
ER -