Using multilevel analyses with sibling data to increase analytic power: An illustration and simulation study.

This study investigates the extent to which analytic power can be increased through the inclusion of siblings in a data set and the concomitant use of random coefficient multilevel models. Analyses of real-world data regarding the predictors of young adult alcohol use illustrate how parallel single-level analyses of a 1-child-per-family data set and multilevel analyses of a data set including all siblings in each family would be conducted. A simulation study, closely based on the illustrative analyses, compares the empirical power to detect main, moderation, and mediation effects under three conditions: (a) single-level analyses of 1-child-per-family data, (b) multilevel analyses of all-siblings data, and (c) single-level analyses of independent data with sample size equivalent to the all-siblings condition. Supplementary analyses are conducted to determine the conditions under which greater analytic power could be achieved with the addition of siblings to a data set than with the addition of a lesser number of independent individuals at equivalent cost. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations.

The authors propose new procedures for evaluating direct, indirect, and total effects in multilevel models when all relevant variables are measured at Level 1 and all effects are random. Formulas are provided for the mean and variance of the indirect and total effects and for the sampling variances of the average indirect and total effects. Simulations show that the estimates are unbiased under most conditions. Confidence intervals based on a normal approximation or a simulated sampling distribution perform well when the random effects are normally distributed but less so when they are nonnormally distributed. These methods are further developed to address hypotheses of moderated mediation in the multilevel context. An example demonstrates the feasibility and usefulness of the proposed methods. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Reporting results of latent growth modeling and multilevel modeling analyses: Some recommendations for rehabilitation psychology.

Objective: There has been a general increase in interest and use of modeling techniques that treat data as nested, whether it is people nested within larger units, such as families or treatment centers, or observations nested under people. The popularity can be witnessed by noting the number of new textbooks and articles related to latent growth curve modeling and multilevel modeling. This paper discusses both of these techniques in the context of longitudinal research designs, with the main purposes of highlighting some benefits and issues related to the use of these models and outlining guidelines for reporting results from studies using multilevel modeling or latent growth modeling. Implications: These longitudinal analytic techniques can be greatly beneficial to researchers conducting rehabilitation studies, but there are several issues related to their use and reporting that need to be taken into consideration. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis.

There are a number of significant challenges researchers encounter when studying development over an extended period of time, including subject attrition, the changing of measurement structures across groups and developmental periods, and the need to invest substantial time and money. Integrative data analysis is an emerging set of methodologies that allows researchers to overcome many of the challenges of single-sample designs through the pooling of data drawn from multiple existing developmental studies. This approach is characterized by a host of advantages, but this also introduces several new complexities that must be addressed prior to broad adoption by developmental researchers. In this article, the authors focus on methods for fitting measurement models and creating scale scores using data drawn from multiple longitudinal studies. The authors present findings from the analysis of repeated measures of internalizing symptomatology that were pooled from three existing developmental studies. The authors describe and demonstrate each step in the analysis and conclude with a discussion of potential limitations and directions for future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Evaluating model fit for growth curve models: Integration of fit indices from SEM and MLM frameworks.

Evaluating overall model fit for growth curve models involves 3 challenging issues. (a) Three types of longitudinal data with different implications for model fit may be distinguished: balanced on time with complete data, balanced on time with data missing at random, and unbalanced on time. (b) Traditional work on fit from the structural equation modeling (SEM) perspective has focused only on the covariance structure, but growth curve models have four potential sources of misspecification: within-individual covariance matrix, between-individuals covariance matrix, marginal mean structure, and conditional mean structure. (c) Growth curve models can be estimated in both the SEM and multilevel modeling (MLM) frameworks; these have different emphases for the evaluation of model fit. In this article, the authors discuss the challenges presented by these 3 issues in the calculation and interpretation of SEM- and MLM-based fit indices for growth curve models and conclude by identifying some lines for future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Using latent-variable models to analyze smoking cessation clinical trial data: An example among the methadone maintained.

The relationship between tobacco and illicit drug use was examined among 166 methadone-maintained persons participating in a smoking cessation intervention. Latent-growth latent-variable models showed a significant relationship during treatment between rates of change in heroin and rates of change in tobacco use, with increased heroin use corresponding with increased tobacco use. Although levels of cocaine use were related to levels of tobacco use, there was no significant relationship between the rates of change of the 2 substances. A more traditional longitudinal structural equation model demonstrated a significant relationship between more heroin use during treatment and negative smoking cessation outcomes at 6-month follow-up. Findings demonstrate the utility of latent-growth models for analyzing short-term clinical trial data and strongly suggest that successful smoking cessation in this population requires a concurrent focus on reducing heroin use. (PsycINFO Database Record (c) 2010 APA, all rights reserved)