Understanding communication processes is the goal of most communication researchers. Rarely are we satisfied merely ascertaining whether messages have an effect on some outcome of focus in a specif...

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Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium

Several methods for testing mediation hypotheses with 2-level nested data have been proposed by researchers using a multilevel modeling (MLM) paradigm. However, these MLM approaches do not accommodate mediation pathways with Level-2 outcomes and may produce conflated estimates of between- and within-level components of indirect effects. Moreover, these methods have each appeared in isolation, so a unified framework that integrates the existing methods, as well as new multilevel mediation models, is lacking. Here we show that a multilevel structural equation modeling (MSEM) paradigm can overcome these 2 limitations of mediation analysis with MLM. We present an integrative 2-level MSEM mathematical framework that subsumes new and existing multilevel mediation approaches as special cases. We use several applied examples and accompanying software code to illustrate the flexibility of this framework and to show that different substantive conclusions can be drawn using MSEM versus MLM.

/pdf/a-general-multilevel-sem-framework-for-assessing-multilevel-4kljtbetjd.pdf

A General Multilevel SEM Framework for Assessing Multilevel Mediation

I describe a test of linear moderated mediation in path analysis based on an interval estimate of the parameter of a function linking the indirect effect to values of a moderator-a parameter that I call the index of moderated mediation. This test can be used for models that integrate moderation and mediation in which the relationship between the indirect effect and the moderator is estimated as linear, including many of the models described by Edwards and Lambert ( 2007 ) and Preacher, Rucker, and Hayes ( 2007 ) as well as extensions of these models to processes involving multiple mediators operating in parallel or in serial. Generalization of the method to latent variable models is straightforward. Three empirical examples describe the computation of the index and the test, and its implementation is illustrated using Mplus and the PROCESS macro for SPSS and SAS.

An Index and Test of Linear Moderated Mediation.

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Applied Longitudinal Data Analysis Modeling Change And Event Occurrence

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Applied Missing Data Analysis

Monte Carlo simulation is a useful but underutilized method of constructing confidence intervals for indirect effects in mediation analysis. The Monte Carlo confidence interval method has several distinct advantages over rival methods. Its performance is comparable to other widely accepted methods of interval construction, it can be used when only summary data are available, it can be used in situations where rival methods (e.g., bootstrapping and distribution of the product methods) are difficult or impossible, and it is not as computer-intensive as some other methods. In this study we discuss Monte Carlo confidence intervals for indirect effects, report the results of a simulation study comparing their performance to that of competing methods, demonstrate the method in applied examples, and discuss several software options for implementation in applied settings.

/pdf/advantages-of-monte-carlo-confidence-intervals-for-indirect-3uz1laawh3.pdf

Advantages of Monte Carlo Confidence Intervals for Indirect Effects

Mediation models are used to describe the mechanism(s) by which one variable influences another. These models can be useful in developmental research to explicate the relationship between variables, developmental processes, or combinations of variables and processes. In this article we describe aspects of mediation effects specific to developmental research. We focus on three central issues in longitudinal mediation models: the theory of change for variables in the model, the role of time in the model, and the types of indirect effects in the model. We use these themes as we describe three different models for examining mediation in longitudinal data.

/pdf/mediation-models-for-longitudinal-data-in-developmental-1kvlyub4qn.pdf

Mediation models for longitudinal data in developmental research.

We review fundamental issues in one traditional structural equation modeling (SEM) approach to analyzing longitudinal data — cross-lagged panel designs. We then discuss a number of new developments in SEM that are applicable to analyzing panel designs. These issues include setting appropriate scales for latent variables, specifying an appropriate null model, evaluating factorial invariance in an appropriate manner, and examining both direct and indirect (mediated), effects in ways better suited for panel designs. We supplement each topic with discussion intended to enhance conceptual and statistical understanding.

/pdf/new-developments-in-latent-variable-panel-analyses-of-3e9shhq77q.pdf

New developments in latent variable panel analyses of longitudinal data.

Autoregressive and cross-lagged panel analysis for longitudinal data

The use of manipulatives to teach mathematics is often prescribed as an efficacious teaching strategy. To examine the empirical evidence regarding the use of manipulatives during mathematics instruction, we conducted a systematic search of the literature. This search identified 55 studies that compared instruction with manipulatives to a control condition where math instruction was provided with only abstract math symbols. The sample of studies included students from kindergarten to college level (N = 7,237). Statistically significant results were identified with small to moderate effect sizes, as measured by Cohen's d, in favor of the use of manipulatives when compared with instruction that only used abstract math symbols. However, the relationship between teaching mathematics with concrete manipulatives and student learning was moderated by both instructional and methodological characteristics of the studies. Additionally, separate analyses conducted for specific learning outcomes of retention (k = 53, N = 7,140), problem solving (k = 9, N = 477), transfer (k = 13, N = 3,453), and justification (k = 2, N = 109) revealed moderate to large effects on retention and small effects on problem solving, transfer, and justification in favor of using manipulatives over abstract math symbols.

James P. Selig

Papers

Advantages of Monte Carlo Confidence Intervals for Indirect Effects

Mediation models for longitudinal data in developmental research.

New developments in latent variable panel analyses of longitudinal data.

Autoregressive and cross-lagged panel analysis for longitudinal data

A meta-analysis of the efficacy of teaching mathematics with concrete manipulatives