Structural Invariance in Multigroup Latent Class Analysis: Perception of Disability Status and Academic Expectations

• In Event: Issues of Measurement Invariance in Mixture Models for Educational Data

Abstract

Measurement invariance (MI) in the latent class (LCA) context has a number of unique challenges both in application and conceptualization (Kankaras, Moors, & Vermut, 2011). This is in contrast to the factor analytic context which is well established and applied (Vandenberg & Lance, 2000). For example, LCA models typically have a large number of parameters to be tested for invariance, making ‘full measurement invariance’ less likely to be supported or practically meaningful. Further, it is unclear how violations of full measurement invariance (e.g., partial MI) would translate to bias when making substantive group comparisons.

The current paper illustrates an application of measurement invariance in the multigroup context with analyses conducted using Mplus 8.3 (Muthén & Muthén, 1998-2017). The sample consists of two groups of 10th grade students: 1) students who are perceived to have disabilities (POD) and 2) those who are not (non-POD). Here the research focus is on understanding how academic expectations of these students vary based on their perceived disability status.

Authors

• Adam Garber, University of California - Santa Barbara

• Karen L. Nylund-Gibson, University of California - Santa Barbara