Bias, Type I Error Rates, and Statistical Power of a Latent Mediation Model in the Presence of Violations of Invariance

WISC-V Measurement Invariance According to Sex and Age: Advancing the Understanding of Intergroup Differences in Cognitive Performance

This study sought to verify whether the constructs measured on the WISC-V are equivalent according to sex and age group in Chilean students to substantiate intergroup comparisons. For this, the measurement invariance of two variants of the five-factor intelligence model was explored with the ten primary subtests (hierarchical and oblique) using multigroup confirmatory factor analysis. Seven hundred and forty participants between 6 and 16 years of age from the Chilean standardization sample were assessed. The results show complete invariance according to sex, but incomplete according to the age group. The implications of these findings in both the professional area of psychology and future research are discussed.

The Effect of Noninvariance on the Estimation of the Mediated Effect in the Two-Wave Mediation Model

The two-wave mediation model is the most suitable model for examining mediation effects in a randomized intervention and includes measures taken at pretest and posttest. When using self-report measures, the meaning of responses may change for the treatment group over the course of the intervention and result in noninvariance across groups at posttest, a phenomenon referred to as response shift. We investigate how the mediated effect would be impacted by noninvariance when using sum scores (i.e., assuming invariance). In a Monte Carlo simulation study, the magnitude and proportion of items that had noninvariant intercepts, the direction of noninvariance, number of items, effect size of the mediated effect and sample size were varied. Results showed increased Type I and Type II errors due to a biased estimate of the intervention effect on the mediator resulting from noninvariance. Thus, measurement noninvariance could lead to erroneous conclusions about the process underlying the intervention.

Clarifying the Implicit Assumptions of Two-Wave Mediation Models via the Latent Change Score Specification: An Evaluation of Model Fit Indices

Statistical mediation analysis is used to investigate mechanisms through which a randomized intervention causally affects an outcome variable. Mediation analysis is often carried out in a pretest-posttest control group design because it is a common choice for evaluating experimental manipulations in the behavioral and social sciences. There are four different two-wave (i.e., pretest-posttest) mediation models that can be estimated using either linear regression or a Latent Change Score (LCS) specification in Structural Equation Modeling: Analysis of Covariance, difference and residualized change scores, and a cross-sectional model. Linear regression modeling and the LCS specification of the two-wave mediation models provide identical mediated effect estimates but the two modeling approaches differ in their assumptions of model fit. Linear regression modeling assumes each of the four two-wave mediation models fit the data perfectly whereas the LCS specification allows researchers to evaluate the model constraints implied by the difference score, residualized change score, and cross-sectional models via model fit indices. Therefore, the purpose of this paper is to provide a conceptual and statistical comparison of two-wave mediation models. Models were compared on the assumptions they make about time-lags and cross-lagged effects as well as statistically using both standard measures of model fit (χ2, RMSEA, and CFI) and newly proposed T-size measures of model fit for the two-wave mediation models. Overall, the LCS specification makes clear the assumptions that are often implicitly made when fitting two-wave mediation models with regression. In a Monte Carlo simulation, the standard model fit indices and newly proposed T-size measures of model fit generally correctly identified the best fitting two-wave mediation model.

The measurement of the mediator and its influence on statistical mediation conclusions

In psychology, the causal process between 2 variables can be studied with statistical mediation analysis. To make a causal interpretation about the relation between variables, researchers who use the statistical mediation model make many assumptions about the variables in the model, among which are measurement assumptions about the mediator. For example, researchers often assume that the measure of the mediator yields scores that are reliable and that have a valid interpretation. In this article, we address how several measurement challenges affect the conclusions of statistical mediation analysis, and how researchers can use different psychometric models to study theoretically different causal processes. We use simulated data sets to illustrate how 10 well-fitting and theoretically sound statistical mediation models could significantly detect the indirect effect or miss it entirely depending on how the mediator is represented in the model. In the example, power to detect the indirect effect varied by the amount of true mediator variance that the psychometric model of the mediator was able to isolate. Different strategies to incorporate psychometric methods into mediation research are discussed and future directions are considered. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Confounding in statistical mediation analysis: What it is and how to address it

Psychology researchers are often interested in mechanisms underlying how randomized interventions affect outcomes such as substance use and mental health. Mediation analysis is a common statistical method for investigating psychological mechanisms that has benefited from exciting new methodological improvements over the last 2 decades. One of the most important new developments is methodology for estimating causal mediated effects using the potential outcomes framework for causal inference. Potential outcomes-based methods developed in epidemiology and statistics have important implications for understanding psychological mechanisms. We aim to provide a concise introduction to and illustration of these new methods and emphasize the importance of confounder adjustment. First, we review the traditional regression approach for estimating mediated effects. Second, we describe the potential outcomes framework. Third, we define what a confounder is and how the presence of a confounder can provide misleading evidence regarding mechanisms of interventions. Fourth, we describe experimental designs that can help rule out confounder bias. Fifth, we describe new statistical approaches to adjust for measured confounders of the mediator-outcome relation and sensitivity analyses to probe effects of unmeasured confounders on the mediated effect. All approaches are illustrated with application to a real counseling intervention dataset. Counseling psychologists interested in understanding the causal mechanisms of their interventions can benefit from incorporating the most up-to-date techniques into their mediation analyses. (PsycINFO Database Record