Simplifying the implementation of modern scale scoring methods with an automated R package: Automated moderated nonlinear factor analysis (aMNLFA)

Abstract

When generating scores to represent latent constructs, analysts have a choice between applying psychometric approaches that are principled but that can be complicated and time-intensive versus applying simple and fast, but less precise approaches, such as sum or mean scoring. We explain the reasons for preferring modern psychometric approaches: namely, use of unequal item weights and severity parameters, the ability to account for local dependence and differential item functioning, and the use of covariate information to more efficiently estimate factor scores. We describe moderated nonlinear factor analysis (MNLFA), a relatively new, highly flexible approach that allows analysts to develop precise factor score estimates that address limitations of sum score, mean score, and traditional factor analytic approaches to scoring. We then outline the steps involved in using the MNLFA scoring approach and discuss the circumstances in which this approach is preferred. To overcome the difficulty of implementing MNLFA models in practice, we developed an R package, aMNLFA, that automates much of the rule-based scoring process. We illustrate the use of aMNLFA with an empirical example of scoring alcohol involvement in a longitudinal study of 6,998 adolescents and compare performance of MNLFA scores with traditional factor analysis and sum scores based on the same set of 12 items. MNLFA scores retain

Longitudinal change in restricted and repetitive behaviors from 8-36 months

“…The implications of such assumptions are broad and potentially costly. Thankfully, with recent simplifications in the implementation of MNLFA [ 21 ], researchers can adjust for non-invariance more easily than ever.…”

Recovering Predictor–Criterion Relations Using Covariate-Informed Factor Score Estimates

“…We have previously offered recommendations that describe a sequential process using likelihood ratio tests (Curran et al, 2014), although this is tedious and can be influenced by the idiosyncratic characteristics of the given sample. Exciting ongoing work is moving toward an automation of this model building process (e.g., Gottfredson et al, 2018). Third, all covariates included in the scoring model should also be included in the second-stage regression model to obtain accurate estimates of both factor score and covariate effects on the outcome measure.…”

A Latent Variable Approach to Measuring Social Dynamics in Adolescence

“…(, ) for fitting an MNLFA and generating scores. Though a detailed description and rationale for these steps is provided elsewhere (Curran et al., , ; Gottfredson et al., in press), we give a general account of the procedure here…”

“… This procedure is automated in a new R package, aMNLFA, which interfaces with Mplus to conduct all of these steps (Gottfredson, et al., in press). However, this package was not yet available at the time of the current analyses, and thus all analyses were conducted in SAS Version 9.3 (SAS Institute, Inc., Cary, NC). …”