Testing Mediational Models With Longitudinal Data: Questions and Tips in the Use of Structural Equation Modeling

Testing Mediational Models With Longitudinal Data: Questions and Tips in the Use of Structural Equation Modeling

David A. Cole
Vanderbilt University
Scott E. Maxwell
University of Notre Dame

R. M. Baron and D. A. Kenny (1986) provided clarion conceptual and methodological guidelines for testing mediational models with cross-sectional data. Graduating from cross-sectional to longitudinal designs enables researchers to make more rigorous inferences about the causal relations implied by such models. In this transition, misconceptions and erroneous assumptions are the norm. First, we describe some of the questions that arise (and misconceptions that sometimes emerge) in longitudinal tests of mediational models. We also provide a collection of tips for structural equation modeling (SEM) of mediational processes. Finally, we suggest a series of 5 steps when using SEM to test mediational processes in longitudinal designs: testing the measurement model, testing for added components, testing for omitted paths, testing the stationarity assumption, and estimating the mediational effects.

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L’hypothèse toutes choses étant égales, The all other things being equal assumption

(English text in bold.)

Il semblerait que bon nombre de gens, en particulier sur Internet, confondent absolument tout quand il s’agit de parler d’un sujet concernant des études de corrélations dans les sciences sociales. Ils proposent un point de vue tronqué, biaisé, incomplet de la réalité telle qu’elle est actuellement.

I get the impression that a lot of people around the web confound absolutely everything when talking about correlations on the topic of social sciences. They supply a biased, incomplete, or truncated reality of what actually really is.

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Introduction to Structural Equation Modeling: Issues and Practical Considerations

Introduction to Structural Equation Modeling: Issues and Practical Considerations

Pui-Wa Lei and Qiong Wu, The Pennsylvania State University (Fall 2007)

Structural equation modeling (SEM) is a versatile statistical modeling tool. Its estimation techniques, modeling capacities, and breadth of applications are expanding rapidly. This module introduces some common terminologies. General steps of SEM are discussed along with important considerations in each step. Simple examples are provided to illustrate some of the ideas for beginners. In addition, several popular specialized SEM software programs are briefly discussed with regard to their features and availability. The intent of this module is to focus on foundational issues to inform readers of the potentials as well as the limitations of SEM. Interested readers are encouraged to consult additional references for advanced model types and more application examples.

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Des arguments fallacieux dans le débat Race-QI : Some misleading arguments in the Race-IQ debate

The english text is in bold.

Plus souvent que l’inverse, je remarque que les arguments ad hoc viennent des environnementalistes, qui posent l’idée que les différences raciales n’ont pas de composantes génétiques (substantielles). Nous allons voir à quoi ces arguments ressemblent et pourquoi ils ne fonctionnent pas.

More often than not, I notice that ad hoc arguments seem to come from the environmentalists, which posit than group differences have no (substantial) genetic components. We will see what those arguments look like and why they are bogus.

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Comprendre les études sur le QI : un lexique

Dans les débats ayant à voir avec les races, et le QI plus particulièrement, des termes techniques (et abréviations) sont souvent utilisés. Sans ce bagage technique, il peut parfois s’avérer difficile de comprendre les discussions. Voici donc une liste des termes souvent employés, et leur équivalent en anglais, par ordre alphabétique. La liste n’est évidemment pas exhaustive, mais ce sont les termes qui reviennent le plus souvent, me semble-t-il, dans les discussions et les études publiées sur le sujet.

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