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Previous meta-analyses highlight the role of executive functions (EF), encompassing working memory updating, task-switching, and inhibitory control, in reading comprehension, but have not established their role in decoding. Decoding is defined as the use of orthographic patterns to access oral pronunciations. According to the dual route model, decoding involves parallel activation of lexical and phonological routes, which places cognitive demands on EF. We used multivariate meta-analyses to examine associations between decoding, assessed via nonword and word reading tasks, and EF across 65 studies involving children and adolescents (162 effect size estimates, 22,806 observations from 10,173 participants, weighted mean age = 8.8 years). Meta-regression analyses examined a broad set of potential moderators of correlational effect size estimates, including variables related to sample characteristics, task features, and study design. Results indicated significant small-to-moderate correlations between EF constructs and decoding tasks (r[subscript z] ranged from 0.28 to 0.34), with little evidence of moderation. The observed associations between EF and decoding skills in children and adolescents appear to be relatively consistent, even when accounting for moderators related to the sample, task, and study design.

Researchers in the single-case design tradition have debated the size and importance of the observed autocorrelations in those designs. All of the past estimates of the autocorrelation in that literature have taken the observed autocorrelation estimates as the data to be used in the debate. However, estimates of the autocorrelation are subject to great sampling error when the design has a small number of time points, as is typically the situation in single-case designs. Thus, a given observed autocorrelation may greatly over- or underestimate the corresponding population parameter. This article presents Bayesian estimates of the autocorrelation that greatly reduce the role of sampling error, as compared to past estimators. Simpler empirical Bayes estimates are presented first, in order to illustrate the fundamental notions of autocorrelation sampling error and shrinkage, followed by fully Bayesian estimates, and the difference between the two is explained. Scripts to do the analyses are available as supplemental materials. The analyses are illustrated using two examples from the single-case design literature. Bayesian estimation warrants wider use, not only in debates about the size of autocorrelations, but also in statistical methods that require an independent estimate of the autocorrelation to analyze the data. (Contains 2 figures and 1 table.)

The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article, we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of τ, the between-study standard deviation, and the shrunken estimates of the study effects as a function of τ. With a small or moderate number of studies, τ is not estimated with much precision, and parameter estimates and shrunken study effect estimates can vary widely depending on the correct value of τ. The trace plot allows visualization of the sensitivity to τ along with a plot that shows which values of τ are plausible and which are implausible. A comparable frequentist or empirical Bayes version provides similar results. The concepts are illustrated using examples in meta-analysis and meta-regression; implementation in R is facilitated in a Bayesian or frequentist framework using the bayesmeta and metafor packages, respectively. [ABSTRACT FROM AUTHOR]

Muthen and Asparouhov (2012) made a strong case for the advantages of Bayesian methodology in factor analysis and structural equation models. I show additional extensions and adaptations of their methods and show how non-Bayesians can take advantage of many (though not all) of these advantages by using interval restrictions on parameters. By keeping parameters restricted to intervals (such as loadings between -0.3 and 0.3 to produce small loadings), frequentists using standard structural equation modeling software can do something similar to what a Bayesian does by putting prior distributions on these parameters.

Intensive longitudinal data (ILD) have become increasingly common in the social and behavioral sciences; count variables, such as the number of daily smoked cigarettes, are frequently used outcomes in many ILD studies. We demonstrate a generalized extension of growth mixture modeling (GMM) to Poisson-distributed ILD for identifying qualitatively distinct trajectories in the context of developmental heterogeneity in count data. Accounting for the Poisson outcome distribution is essential for correct model identification and estimation. In addition, setting up the model in a way that is conducive to ILD measures helps with data complexities--large data volume, missing observations, and differences in sampling frequency across individuals. We present technical details of model fitting, summarize an empirical example of patterns of smoking behavior change, and describe research questions the generalized GMM helps to address. (Contains 4 tables, 1 footnote and 5 figures.)

Good quantitative evidence does not require large, aggregate group designs. The authors describe ground-breaking work in managing the conceptual and practical demands in developing meta-analytic strategies for single subject designs in an effort to add to evidence-based practice. (Contains 2 figures.)

We demonstrate a model for categorical data that parallels the MIMIC model for continuous data. The model is equivalent to a latent class model with observed covariates; further, it includes simple handling of missing data. The model is used on data from a large-scale study of HIV that had both biological measures of infection and self-report (missing on some cases). The model allows the determination of sensitivity and specificity of each measure, and an assessment of how well true HIV status can be predicted from characteristics of the individuals in the study.

This article examines whether relationships between individual characteristics and HIV status can be identified when self-report data are used as a proxy for HIV serotest results. The analyses use data obtained from HIV serotests and face-to-face interviews with 7,256 out-of-treatment drug users in ten sites from 1992 to 1998. Relationships between 17 individual characteristics and HIV status (measured both by self-report and serotest results) were determined by (a) comparing and evaluating the fit of both standard and "nonstandard" loglinear models, and (b) evaluating the fit of marginal homogeneity models. The loglinear analyses showed that HIV serostatus was related to individual characteristics in 38 percent of the relationships. In most cases, the strength of the relationships between HIV status and individual characteristics did not differ when HIV status was measured with self-report data.

Examined differences between the visibility of drugs and drug use in over 2000 neighborhoods, surveying residents regarding drug- and alcohol-related behaviors and attitudes and comparing the responses of poor, urban, African Americans versus people from comparison neighborhoods. The most disadvantaged neighborhoods had the most visible drug problems, but drug use was nearly equally distributed across all communities. (Contains references.) (SM)

Describes the application of multilevel statistical models to the evaluation of a multi-site community-based intervention and explicates key issues in the design,analysis, and presentation of results. The initiative described, "Fighting Back, is designed to reduce the demand for illegal drugs and alcohol in a community. (SLD)

Used empirical data about HIV risk behaviors from 330 female participants in a drug treatment program to explore the implications and consequences of using various statistical models to describe the association of one ordinal and one dichotomous variable in which data are incomplete for the dichotomous variable. Examined the statistical fit and substantive consequences of these models. Discusses the application of this new methodological approach. (SLD)

A general approach is described for the analysis of categorical data when there are missing values on one or more observed variables. The method is based on generalized linear models with composite links. Situations in which the model can be used are described. (SLD)