The mediating effects of job satisfaction and propensity to leave on role stress-job performance relationships: Combining meta-analysis and structural equation modeling.

This article combines meta-analysis with structural equation modeling to compare alternative models of the relationships among work stress, psychological mediators, and job performance. Specifically, the authors examined the mediating effects of job satisfaction and propensity to leave and their effect on the relationships between role ambiguity, role conflict, and job performance. The meta-analysis included both published and unpublished studies conducted over a period of 25 years, resulting in 113 independent samples with more than 22,000 individuals. As hypothesized, the structural model that best fit the meta-analytic estimates was the partial mediation model, in which stress is related to job performance both directly and indirectly through job satisfaction and propensity to leave and in which all path coefficients were reliably different from zero. The results are discussed in terms of theoretical contributions and implications for future stress-performance research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Masking misfit in confirmatory factor analysis by increasing unique variances: A cautionary note on the usefulness of cutoff values of fit indices.

Fit indices are widely used in order to test the model fit for structural equation models. In a highly influential study, Hu and Bentler (1999) showed that certain cutoff values for these indices could be derived, which, over time, has led to the reification of these suggested thresholds as “golden rules” for establishing the fit or other aspects of structural equation models. The current study shows how differences in unique variances influence the value of the global chi-square model test and the most commonly used fit indices: Root-mean-square error of approximation, standardized root-mean-square residual, and the comparative fit index. Using data simulation, the authors illustrate how the value of the chi-square test, the root-mean-square error of approximation, and the standardized root-mean-square residual are decreased when unique variances are increased although model misspecification is present. For a broader understanding of the phenomenon, the authors used different sample sizes, number of observed variables per factor, and types of misspecification. A theoretical explanation is provided, and implications for the application of structural equation modeling are discussed. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Confidence intervals for the overall effect size in random-effects meta-analysis.

One of the main objectives in meta-analysis is to estimate the overall effect size by calculating a confidence interval (CI). The usual procedure consists of assuming a standard normal distribution and a sampling variance defined as the inverse of the sum of the estimated weights of the effect sizes. But this procedure does not take into account the uncertainty due to the fact that the heterogeneity variance (τ2) and the within-study variances have to be estimated, leading to CIs that are too narrow with the consequence that the actual coverage probability is smaller than the nominal confidence level. In this article, the performances of 3 alternatives to the standard CI procedure are examined under a random-effects model and 8 different τ2 estimators to estimate the weights: the t distribution CI, the weighted variance CI (with an improved variance), and the quantile approximation method (recently proposed). The results of a Monte Carlo simulation showed that the weighted variance CI outperformed the other methods regardless of the τ2 estimator, the value of τ2, the number of studies, and the sample size. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Combining studies using effect sizes and quality scores: application to bone loss in postmenopausal women

This article presents a random effects model that uses effect sizes (ES) and quality scores to integrate results from investigations. An empirical example is given with data obtained from a meta-analysis on the effectiveness of physical activity in the prevention of bone loss in healthy postmenopausal women. A Medline search was performed to locate relevant studies published in French or English between January 1966 and May 1996. Three independent reviewers extracted data from studies. Effect sizes were calculated according to the method of Hedges and Olkin. A modified version of Chalmers' scale was utilized to calculate quality scores. DerSimonian and Laird's method with incorporation of the quality scores was used to estimate the overall effect size. Quality scores and the inverse of the variances were included as weights when combining studies. The overall estimate and standard error (SE) of the effect of physical activity on spinal bone mineral density loss in healthy postmenopausal women was ESoverall = 0.4263 (1.1361). When compared to other meta-analysis methods such as the fixed effects model and the model of DerSimonian and Laird without the quality score (DL), the new model generated comparable estimators (fixed effects model's ESoverall (SE) = 1.2724 (0.0139), DLs ESoverall (SE) = 0.3958 (1.2370)). Due to the heterogeneity that existed between studies, a random effects model was more appropriate then a fixed effects model. However, it resulted in wider confidence intervals, as expected. It was shown empirically that the model using quality scores generated narrower confidence intervals than the model of DL alone. The inclusion of covariates such as quality scores in meta-analyses permits the quantification of the variation between studies.     

Excessive reassurance seeking, depression, and interpersonal rejection: A meta-analytic review.

Coyne's (1976a, 1976b) interactional theory of depression predicts positive associations between excessive reassurance seeking (ERS) and both depression and interpersonal rejection. A growing body of research has supported the ERS model, but this work has yet to be systematically reviewed. A meta-analysis of 38 studies (N = 6,973) revealed an aggregate effect size (r) of .32 between ERS and concurrent depression. Moderator analyses showed effect sizes were significantly stronger for studies with self-report measures, compared with interviews, and for samples with higher percentages of women and were marginally stronger for studies with community samples, compared with clinical samples. A second meta-analysis of 16 studies yielded a weighted mean effect size of .14 between ERS and concurrent rejection, with studies assessing target-reported rejection showing stronger effect sizes than studies assessing informant-reported rejection and studies examining romantic relationships yielding marginally stronger effect sizes than studies examining nonromantic relationships. Prospective studies are qualitatively reviewed. Results support the ERS model (with several important caveats) but underscore the need for methodological diversity in future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Dependent Effect Sizes in Meta-Analysis: Incorporating the Degree of Interdependence.

In the present article, a commonly used meta-analytic procedure for handling dependent effect sizes from a single sample was examined, and 2 revised procedures that estimate and incorporate the degree of interdependence were proposed. The authors' simulation results reveal that the commonly used procedure that averages the effect sizes from a single sample (denoted as the samplewise procedure) underestimates the degree of heterogeneity. The proposed variations are less biased than the samplewise procedure in estimating the degree of heterogeneity in most of the situations that we examined. Future directions to further improve the procedures for handling dependent effect sizes from a single sample are discussed. (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)     

"The influence of sample size, estimation methods, and model specification on goodness-of-fit assessments in structural equation models": Correction to la Du and Tanaka (1989).

Reports an error in "Influence of sample size, estimation method, and model specification on goodness-of-fit assessments in structural equation models" by Terence J. la Du and J. S. Tanaka (Journal of Applied Psychology, 1989[Aug], Vol 74[4], 625-635). Figure 2 (p. 631) summarizes Katzell's work motivation model and indicates where the trivial misspecification (dashed line) and nontrivial misspecification (starred line) occurred in our model specification condition. The error is in the latter. The starred line should be from Operations and Resources to Extrinsic Rewards and not from Rewards for Performance to Fruity. Our findings are not changed by this error, because we were using Katzell's model and accompanying data base to conduct a sampling study on goodness-of-fit indices and not testing his model. Hence, any of the paths were candidates for the nontrivial misspecification condition. (The following abstract of the original article appeared in record 1989-38703-001.) The problem of assessing fit of structural equation models is reviewed, and two sampling studies are reported that examine the effects of sample size, estimation method, and model misspecification on fit indices. In the first study, the behavior of indices in a known-population confirmatory factor analysis model is considered. In the second study, the same problem in an empirical data set is examined by looking at antecedents and consequences of work motivation. The findings across the two studies suggest that (a) as might be expected, sample size is an important determinant in assessing model fit; (b) estimator-specific, as opposed to estimator-general, fit indices provide more accurate indications of model fit; and (c) the studied fit indices are differentially sensitive to model misspecification. Some recommendations for the use of structural equation model fit indices are given. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The power of statistical tests in meta-analysis.

Calculations of the power of statistical tests are important in planning research studies (including meta-analyses) and in interpreting situations in which a result has not proven to be statistically significant. The authors describe procedures to compute statistical power of fixed- and random-effects tests of the mean effect size, tests for heterogeneity (or variation) of effect size parameters across studies, and tests for contrasts among effect sizes of different studies. Examples are given using 2 published meta-analyses. The examples illustrate that statistical power is not always high in meta-analysis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Structural equation analyses of clinical subpopulation differences and comparative treatment outcomes: Characterizing the daily lives of drug addicts.

The use of structural equation modeling (SEM) is illustrated for comparative treatment outcome research conducted with heterogeneous clinical subpopulations within large multimodality treatment settings. All analyses are accomplished with SEM analogs of more familiar classical multivariate techniques. The effect of the early period of treatment on the daily lives of 486 clients in 2 drug abuse treatment modalities (methadone maintenance and outpatient counseling) is evaluated. Structured means analysis is used to assess initial differences between modalities on the latent means of 6 latent constructs reflecting daily life. The effect of treatment modality and attrition from the program on daily life latent constructs is evaluated while initial selection differences are statistically controlled. Effect sizes are computed on the basis of SEM parameter estimates. The advantage of SEM over classic multivariate approaches for correcting for selection bias when assessing comparative outcomes is explained. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causal inference in randomized experiments with mediational processes.

This article links the structural equation modeling (SEM) approach with the principal stratification (PS) approach, both of which have been widely used to study the role of intermediate posttreatment outcomes in randomized experiments. Despite the potential benefit of such integration, the 2 approaches have been developed in parallel with little interaction. This article proposes the cross-model translation (CMT) approach, in which parameter estimates are translated back and forth between the PS and SEM models. First, without involving any particular identifying assumptions, translation between PS and SEM parameters is carried out on the basis of their close conceptual connection. Monte Carlo simulations are used to further clarify the relation between the 2 approaches under particular identifying assumptions. The study concludes that, under the common goal of causal inference, what makes a practical difference is the choice of identifying assumptions, not the modeling framework itself. The CMT approach provides a common ground in which the PS and SEM approaches can be jointly considered, focusing on their common inferential problems. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Influence of sample size, estimation method, and model specification on goodness-of-fit assessments in structural equation models.

[Correction Notice: An erratum for this article was reported in Vol 75(1) of Journal of Applied Psychology (see record 2008-10492-001). An error exists in Figure 2 and the accompanying text of the article. The corrected information is included in the erratum.] The problem of assessing fit of structural equation models is reviewed, and two sampling studies are reported that examine the effects of sample size, estimation method, and model misspecification on fit indices. In the first study, the behavior of indices in a known-population confirmatory factor analysis model is considered. In the second study, the same problem in an empirical data set is examined by looking at antecedents and consequences of work motivation. The findings across the two studies suggest that (a) as might be expected, sample size is an important determinant in assessing model fit; (b) estimator-specific, as opposed to estimator-general, fit indices provide more accurate indications of model fit; and (c) the studied fit indices are differentially sensitive to model misspecification. Some recommendations for the use of structural equation model fit indices are given. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

(Mis-) Direction of effect sizes.

Comments on an article by Dixon et al. (see record 2007-06671-001) regarding the effect sizes they presented in their meta-analysis of psychological interventions for arthritis pain management. The author of this comment claims that some of the individual effect sizes that they presented are erroneous and have therefore undermined their cumulative effect size estimates. After examining findings from other studies, he concludes that the Dixon et al. meta-analysis reports cumulative effect sizes (Hedge’s g) that overestimate the effects of psychological treatments upon arthritis pain. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Racial/ethnic matching of clients and therapists in mental health services: A meta-analytic review of preferences, perceptions, and outcomes.

Across several decades the effects of matching clients with therapists of the same race/ethnicity have been explored using a variety of approaches. We conducted a meta-analysis of 3 variables frequently used in research on racial/ethnic matching: individuals' preferences for a therapist of their own race/ethnicity, clients' perceptions of therapists across racial/ethnic match, and therapeutic outcomes across racial/ethnic match. Across 52 studies of preferences, the average effect size (Cohen's d) was 0.63, indicating a moderately strong preference for a therapist of one's own race/ethnicity. Across 81 studies of individuals' perceptions of therapists, the average effect size was 0.32, indicating a tendency to perceive therapists of one's own race/ethnicity somewhat more positively than other therapists. Across 53 studies of client outcomes in mental health treatment, the average effect size was 0.09, indicating almost no benefit to treatment outcomes from racial/ethnic matching of clients with therapists. These 3 averaged effect sizes were characterized by substantial heterogeneity: The effects of racial/ethnic matching are highly variable. Studies involving African American participants demonstrated the highest effect sizes across all 3 types of evaluations: preferences, perceptions, and outcomes. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Estimating, testing, and comparing specific effects in structural equation models: The phantom model approach.

The phantom model approach for estimating, testing, and comparing specific effects within structural equation models (SEMs) is presented. The rationale underlying this novel method consists in representing the specific effect to be assessed as a total effect within a separate latent variable model, the phantom model that is added to the main model. The following favorable features characterize the method: (a) It enables the estimation, testing, and comparison of arbitrary specific effects for recursive and nonrecursive models with latent and manifest variables; (b) it enables the bootstrapping of confidence intervals; and (c) it can be applied with all standard SEM programs permitting latent variables, the specification of equality constraints, and the bootstrapping of total effects. These features along with the fact that no manipulation of matrices and formulas is required make the approach particularly suitable for applied researchers. The method is illustrated by means of 3 examples with real data sets. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Links between physical fitness and cardiovascular reactivity and recovery to psychological stressors: A meta-analysis.

A meta-analysis of published studies with adult human participants was conducted to evaluate whether physical fitness attenuates cardiovascular reactivity and improves recovery from acute psychological stressors. Thirty-three studies met selection criteria; 18 were included in recovery analyses. Effect sizes and moderator influences were calculated by using meta-analysis software. A fixed effects model was fit initially; however, between-studies heterogeneity could not be explained even after inclusion of moderators. Therefore, to account for residual heterogeneity, a random effects model was estimated. Under this model, fit individuals showed significantly attenuated heart rate and systolic blood pressure reactivity and a trend toward attenuated diastolic blood pressure reactivity. Fit individuals also showed faster heart rate recovery, but there were no significant differences in systolic blood pressure or diastolic blood pressure recovery. No significant moderators emerged. Results have important implications for elucidating mechanisms underlying effects of fitness on cardiovascular disease and suggest that fitness may be an important confound in studies of stress reactivity. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Neurocognition in first-episode schizophrenia: A meta-analytic review.

Compromised neurocognition is a core feature of schizophrenia. Following Heinrichs and Zakzanis’s (1998) seminal meta-analysis of middle-aged and predominantly chronic schizophrenia samples, the aim of this study is to provide a meta-analysis of neurocognitive findings from 47 studies of first-episode (FE) schizophrenia published through October 2007. The meta-analysis uses 43 separate samples of 2,204 FE patients with a mean age of 25.5 and 2,775 largely age- and gender-matched control participants. FE samples demonstrated medium-to-large impairments across 10 neurocognitive domains (mean effect sizes from ?0.64 to ?1.20). Findings indicate that impairments are reliably and broadly present by the FE, approach or match the degree of deficit shown in well-established illness, and are maximal in immediate verbal memory and processing speed. Larger IQ impairments in the FE compared to the premorbid period, but comparable to later phases of illness suggests deterioration between premorbid and FE phases followed by deficit stability at the group level. Considerable heterogeneity of effect sizes across studies, however, underscores variability in manifestations of the illness and a need for improved reporting of sample characteristics to support moderator variable analyses. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Further meta-analytic procedures for assessing cognitive gender differences.

Outlined procedures for assessing the heterogeneity of a set of effect sizes derived from a meta-analysis, testing for trends with contrasts among the effect sizes obtained, and evaluating the practical importance of the average effect size obtained. These procedures were applied to data presented by J. S. Hyde (1981) regarding cognitive gender differences. The authors conclude that (a) for all 4 areas of cognitive skill investigated, effect sizes for gender differences differed significantly across studies; (b) recent studies of gender differences show a substantial gain in cognitive performance by females relative to males; and (c) studies of gender differences show male vs female effect sizes of practical importance equivalent to outcome rates of 60 vs 40%. (6 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Structural equation modeling with interchangeable dyads.

Structural equation modeling (SEM) can be adapted in a relatively straightforward fashion to analyze data from interchangeable dyads (i.e., dyads in which the 2 members cannot be differentiated). The authors describe a general strategy for SEM model estimation, comparison, and fit assessment that can be used with either dyad-level or pairwise (double-entered) dyadic data. They present applications illustrating this approach with the actor-partner interdependence model, confirmatory factor analysis, and latent growth curve analysis. (PsycINFO Database Record (c) 2010 APA, all rights reserved)