Measurement Error and Methodologic Issues in Analyses of the Proportion of Variance Explained in Cognition.

Existing studies examining the predictive ability of biomarkers for cognitive outcomes do not account for variance due to measurement error, which could lead to under-estimates of the proportion of variance explained. We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 1084) to estimate the proportion of variance explained by Alzheimer's disease (AD) imaging biomarkers in four cognitive outcomes: memory, executive functioning, language, and visuospatial functioning. We compared estimates from standard models that do not account for measurement error, and multilevel models that do account for measurement error.

Modeling Misspecification as a Parameter in Bayesian Structural Equation Models.

Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter-a parameter akin to the correlation root mean squared residual. The misspecification parameter can be interpreted on its own terms as a measure of absolute model fit and allows for comparing different models fit to the same data.

Handling dependent samples in meta-analytic structural equation models: A Wishart-based approach.

We present an approach to meta-analytic structural equation models that relies on hierarchical modeling of sample covariance matrices under the assumption that the matrices are Wishart. The approach handles the commonplace fixed- and random-effects meta-analytic SEMs, and solves the problem of dependent covariance matrices where more than one covariance matrix is obtained from a single study or study author. The ability of the approach to adequately recover parameters is examined via a simulation study.

Disentangling the discrimination and mental health relationship among diverse college students: Moderation analyses of shame-proneness across race, gender, and race-by-gender interactions.

Objective: This study investigated shame-proneness as a moderating risk factor within the relationship between perceived discrimination and mental health outcomes. Moderation across race, gender, and race-by-gender intersections was also examined.

Method: Bayesian analysis was employed to examine moderation among African, Latinx, and Asian descent college students ( = 295).

Probability of superiority for comparing two groups of clusters.

The probability of superiority (PS) has been recommended as a simple-to-interpret effect size for comparing two independent samples-there are several methods for computing the PS for this particular study design. However, educational and psychological interventions increasingly occur in clustered data contexts; and a review of the literature returned only one method for computing the PS in such contexts. In this paper, we propose a method for estimating the PS in clustered data contexts.