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.

Easy Statistical Mediation Analysis With Distinguishable Dyadic Data

Research in communication and other social science disciplines that relies on measuring each member of a dyad on putative causes and effects can require complex analyses to illuminate how members of the dyad influence one another. Dyadic mediation analysis is a branch of mediation analysis that focuses on establishing the mechanism(s) by which mutual influence operates. Relying on the similarity between dyadic mediation analysis using structural equation modeling and mediation analysis with ordinary least squares regression, we developed MEDYAD, an easy-to-use computational tool for SPSS, SAS, and R that conducts dyadic mediation analysis with distinguishable dyadic data. MEDYAD implements the Actor-Partner Interdependence Model Extended to Mediation (APIMeM), as well as simpler and more complex dyadic mediation models. Bootstrapping methods are implemented for inferences about indirect effects. Additional features include methods for conducting all possible pairwise comparisons between indirect effects, heteroskedasticity-robust inference, and saving bootstrap estimates of parameters for further analysis.

Two-Condition Within-Participant Statistical Mediation Analysis: A Path-Analytic Framework

Researchers interested in testing mediation often use designs where participants are measured on a dependent variable Y and a mediator M in both of two different circumstances. The dominant approach to assessing mediation in such a design, proposed by Judd, Kenny, and McClelland (2001), relies on a series of hypothesis tests about components of the mediation model and is not based on an estimate of or formal inference about the indirect effect. In this paper we recast Judd et al.’s approach in the path-analytic framework that is now commonly used in between-participant mediation analysis. By so doing, it is apparent how to estimate the indirect effect of a within-participant manipulation on some outcome through a mediator as the product of paths of influence. This path analytic approach eliminates the need for discrete hypothesis tests about components of the model to support a claim of mediation, as Judd et al’s method requires, because it relies only on an inference about the product of paths— the indirect effect. We generalize methods of inference for the indirect effect widely used in between-participant designs to this within-participant version of mediation analysis, including bootstrap confidence intervals and Monte Carlo confidence intervals. Using this path analytic approach, we extend the method to models with multiple mediators operating in parallel and serially and discuss the comparison of indirect effects in these more complex models. We offer macros and code for SPSS, SAS, and Mplus that conduct these analyses.

Obamacares: Candidate Traits, Descriptive Representation, and Black Political Participation

While a number of studies demonstrate that black candidates have the ability to increase black political participation, a growing literature is investigatingwhydescriptive representation matters. This paper contributes to this discussion by exploring whether perceptions of candidate traits play a mediating role between the presence of an African American candidate on the ballot and increases in black political activity. I test this trait hypothesis using data from the 1992–2012 American National Election Study, a survey experiment, and statistical mediation analysis. The results indicate that perceptions of black candidates as being better leaders, more empathetic, knowledgeable, intelligent, honest, and moral explain a substantial amount of why descriptive representation increases black political participation across a range of different political activities. In the conclusion, I discuss the importance of the psychological link between blacks and their co-racial representatives in inspiring higher levels of political participation.

The cross-frequency mediation mechanism of intracortical information transactions

1. AbstractIn a seminal paper by von Stein and Sarnthein (2000), it was hypothesized that “bottom-up” information processing of “content” elicits local, high frequency (beta-gamma) oscillations, whereas “top-down” processing is “contextual”, characterized by large scale integration spanning distant cortical regions, and implemented by slower frequency (theta-alpha) oscillations. This corresponds to a mechanism of cortical information transactions, where synchronization of beta-gamma oscillations between distant cortical regions is mediated by widespread theta-alpha oscillations. It is the aim of this paper to express this hypothesis quantitatively, in terms of a model that will allow testing this type of information transaction mechanism. The basic methodology used here corresponds to statistical mediation analysis, originally developed by (Baron and Kenny 1986). We generalize the classical mediator model to the case of multivariate complex-valued data, consisting of the discrete Fourier transform coefficients of signals of electric neuronal activity, at different frequencies, and at different cortical locations. The “mediation effect” is quantified here in a novel way, as the product of “dual frequency RV-coupling coefficients”, that were introduced in (Pascual-Marqui et al 2016, http://arxiv.org/abs/1603.05343). Relevant statistical procedures are presented for testing the cross-frequency mediation mechanism in general, and in particular for testing the von Stein & Sarnthein hypothesis.