Testing Mediational Models With Longitudinal Data: Questions and Tips in the Use of Structural Equation Modeling
David A. Cole
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.
The present analysis, using the NLSY97, attempts to model the structural relationship between the latent second-order g factor extracted from the 12 ASVAB subtests, the parental SES latent factor from 3 indicators of parental SES, and the GPA latent factor from 5 domains of grade point averages. A structural equation modeling (SEM) bootstrapping approach combined with a Predictive Mean Matching (PMM) multiple imputation has been employed. The structural path from parental SES to GPA, independently of g, appears to be trivial in the black, hispanic, and white population. The analysis is repeated for the 3 ACT subtests, yielding an ACT-g latent factor. The same conclusion is observed. Most of the effect of SES on GPA appears to be mediated by g. Adding grade variable substantially increases the contribution of parental SES on the achievement factor, which was partially mediated by g. Missing data is handled with PMM multiple imputation. Univariate and multivariate normality tests are carried out in SPSS and AMOS, and through bootstrapping. Full result provided in EXCEL at the end of the article.
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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.
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.
In the present article, I demonstrate that processing speed (using ASVAB speeded subtests) has a modest predictive validity over the g factor extracted from the ASVAB (non-speeded subtests) in predicting overall GPA in the NLSY97, within black, hispanic and the white sample. Next, I investigate the mediation of speed in the black-white difference in IQ (g). For both analyses, processing speed accounts for a modest portion of these associations. Nonetheless, some issues related with such ‘psychometric speed’ measures need to be clarified.
School-level genetic variation predicts school-level verbal IQ scores: Results from a sample of American middle and high schools
Kevin M. Beaver, John Paul Wright (2011)
Research has consistently revealed that average IQ scores vary significantly across macro-level units, such as states and nations. The reason for this variation in IQ, however, has remained at the center of much controversy. One of the more provocative explanations is that IQ across macro-level units is the result of genetic differences, but empirical studies have yet to examine this possibility directly. The current study partially addresses this gap in the literature by examining whether average IQ scores across thirty-six schools are associated with differences in the allelic distributions of dopaminergic polymorphisms across schools. Analysis of data drawn from subjects (ages 12–19 years) participating in the National Longitudinal Study of Adolescent Health provides support in favor of this perspective, where variation in school-level IQ scores was predicted by school-level genetic variation. This association remained statistically significant even after controlling for the effects of race.
This was a recent article published at The Economist. Basically, I am not surprised at all about scientific misconduct and biases. This is why I don’t generally trust scientists. Anyway :