Tag: SEM

The GRoLTS-Checklist: Guidelines for Reporting on Latent Trajectory Studies

Estimating models within the mixture model framework, like latent growth mixture modeling (LGMM) or latent class growth analysis (LCGA), involves making various decisions throughout the estimation process. This has led to a wide variety in how results of latent trajectory analysis are reported.

Testing Small Variance Priors Using Prior-Posterior Predictive P-values

Muthen and Asparouhov (2012) propose to evaluate model fit in structural equation models based on approximate (using small variance priors) instead of exact equality of (combinations of) parameters to zero. This is an important development that adequately addresses Cohen’s (1994) “The earth is round (p < .05)”, which stresses that point null-hypotheses are so precise that small and irrelevant differences from the null-hypothesis may lead to their rejection.

Analyzing indirect effects in cluster randomized trials. The effect of estimation method, number of groups and group sizes on accuracy and power

Cluster randomized trials assess the effect of an intervention that is carried out at the group or cluster level. Ajzen’s theory of planned behavior is often used to model the effect of the intervention as an indirect effect mediated in turn by attitude, norms and behavioral intention.

Facing off with Scylla and Charybdis: a comparison of scalar, partial, and the novel possibility of approximate measurement invariance

Measurement invariance (MI) is a pre-requisite for comparing latent variable scores across groups. The current paper introduces the concept of approximate MI building on the work of Muthén and Asparouhov and their application of Bayesian Structural Equation Modeling (BSEM) in the software Mplus.

Bayesian evaluation of informative hypotheses in SEM using Mplus: A black bear story

Half in jest we use a story about a black bear to illustrate that there are some discrepancies between the formal use of the p-value and the way it is often used in practice. We argue that more can be learned from data by evaluating informative hypotheses, than by testing the traditional null hypothesis.

Bayesian Evaluation of Inequality-Constrained Hypotheses in SEM Models using Mplus

Researchers in the behavioral and social sciences often have expectations that can be expressed in the form of inequality constraints among the parameters of a structural equation model resulting in an informative hypothesis. The questions they would like an answer to are “Is the hypothesis Correct” or “Is the hypothesis incorrect”?