Evaluating Response Shift in Statistical Mediation Analysis

Researchers and prevention scientists often develop interventions to target intermediate variables (known as mediators) that are thought to be related to an outcome. When researchers target a mediating construct measured by self-report, the meaning of the self-report measure could change from pretest to posttest for the individuals who received the intervention—which is a phenomenon referred to as response shift. As a result, any observed changes on the mediator measure across groups or across time might reflect a combination of true change on the construct and response shift. Although previous studies have focused on identifying the source and type of response shift in measures after an intervention, there has been limited research on how using sum scores in the presence of response shift affects the estimation of mediated effects via statistical mediation analysis, which is critical for explaining how the intervention worked. In this article, we focus on recalibration response shift, which is a change in internal standards of measurement and affects how respondents interpret the response scale. We provide background on the theory of response shift and the methodology used to detect response shift (i.e., tests of measurement invariance). In addition, we used simulated data sets to provide an illustration of how recalibration in the mediator can bias estimates of the mediated effect and affect Type I error and power.

The Indirect Effect is Omitted Variable Bias: A Cautionary Note on the Interpretability of Significant Results in Non-Experimental Mediation Analyses

This paper intends to remind communication scientists that the indirect effect as estimated in mediation analyses is a statistical synonym for omitted variable bias (i.e., confounding or suppression). This simple fact questions the interpretability of statistically significant ‘indirect effects’ in observational designs: in social reality all variables correlate with each other to some extent - the so-called ‘crud factor’ - which means that omitted variable bias and ‘indirect effects’ at the population level are virtually guaranteed regardless of the actual variables involved in the statistical mediation model. As a result, there can be no inferential link between the observation of a significant indirect effect and a theoretical claim of mediation. Through this argument the paper hopes to cultivate a more critical attitude toward the interpretation of ‘indirect effects’ in observational communication science.

Statistical Mediation in Lifespan Developmental Analyses

Lifespan developmental research studies how individuals change throughout their lifetime and how intraindividual or interindividual change leads to future outcomes. Lifespan researchers are interested in how developmental processes unfold and how specific developmental pathways lead to an outcome. Developmental processes have been previously studied using developmental cascade models, concepts of equifinality and multifinality, and developmental interventions. Statistical mediation analysis also provides a framework for studying developmental processes and developmental pathways by identifying intermediate variables, known as mediators, that transmit the effect between early exposures and future outcomes. The role of statistical mediation in lifespan developmental research is either to explain how the developmental process unfolds, or to identify mediators that researchers can target in interventions so that individuals change developmental pathways. The statistical mediation model is inherently causal, so the relations between the exposures, mediators, and outcomes have to be correctly specified, and ruling out alternative explanations for the relations is of upmost importance. The statistical mediation model can be extended to deal with longitudinal data. For example, the autoregressive mediation model can represent change through time by examining lagged relations in multiwave datasets. On the other hand, the multilevel mediation model can deal with the clustering of repeated measures within individuals to study intraindividual and interindividual change. Finally, the latent growth curve mediation model can represent the variability of linear and nonlinear trajectories for individuals in the variables in the mediation model through time. As a result, developmental researchers have access to a range of models that could describe the theory of change they want to study. Researchers are encouraged to consider mechanisms of change and to formulate mediation hypotheses about lifespan development.