The Effectiveness of Psychosocial Interventions Delivered by Teachers in Schools: A Systematic Review and Meta-Analysis

The growing mental health needs of students within schools have resulted in teachers increasing their involvement in the delivery of school-based, psychosocial interventions. Current research reports mixed findings concerning the effectiveness of psychosocial interventions delivered by teachers for mental health outcomes. This article presents a systematic review and meta-analysis that examined the effectiveness of school-based psychosocial interventions delivered by teachers on internalizing and externalizing outcomes and the moderating factors that influence treatment effects on these outcomes. Nine electronic databases, major journals, and gray literature (e.g., websites, conference abstract) were searched and field experts were contacted to locate additional studies. Twenty-four studies that met the study inclusion criteria were coded into internalizing or externalizing outcomes and further analyzed using robust variance estimation in meta-regression. Both publication and risk of bias of studies were further assessed. The results showed statistically significant reductions in students’ internalizing outcomes (d = .133, 95% CI [.002, .263]) and no statistical significant effect for externalizing outcomes (d = .15, 95% CI [?.037, .066]). Moderator analysis with meta-regression revealed that gender (%male, b = ?.017, p < .05), race (% Caucasian, b = .002, p < .05), and the tier of intervention (b = .299, p = .06) affected intervention effectiveness. This study builds on existing literature that shows that teacher-delivered Tier 1 interventions are effective interventions but also adds to this literature by showing that interventions are more effective with internalizing outcomes than on the externalizing outcomes. Moderator analysis also revealed treatments were more effective with female students for internalizing outcomes and more effective with Caucasian students for externalizing outcomes.     

Use of factor mixture modeling to capture Spearman's law of diminishing returns

Spearman's law of diminishing returns (SLODR) posits that at higher levels of general cognitive ability the general factor (g) performs less well in explaining individual differences in cognitive test performance. Research has generally supported SLODR, but previous research has required the a priori division of respondents into separate ability or IQ groups. The present study sought to obviate this limitation through the use of factor mixture modeling to investigate SLODR in the Kaufman Assessment Battery for Children-Second Edition (KABC-II). A second-order confirmatory factor model was modeled as a within-class factor structure. The fit and parameter estimates of several models with varying number of classes and factorial invariance restrictions were compared. Given SLODR, a predictable pattern of findings should emerge when factor mixture modeling is applied. Our results were consistent with these SLODR-based predictions, most notably the g factor variance was less in higher g mean classes. Use of factor mixture modeling was found to provide support for SLODR while improving the model used to investigate SLODR.     

From a single-level analysis to a multilevel analysis of single-case experimental designs

Multilevel modeling provides one approach to synthesizing single-case experimental design data. In this study, we present the multilevel model (the two-level and the three-level models) for summarizing single-case results over cases, over studies, or both. In addition to the basic multilevel models, we elaborate on several plausible alternative models. We apply the proposed models to real datasets and investigate to what extent the estimated treatment effect is dependent on the modeling specifications and the underlying assumptions. By considering a range of plausible models and assumptions, researchers can determine the degree to which the effect estimates and conclusions are sensitive to the specific assumptions made. If the same conclusions are reached across a range of plausible assumptions, confidence in the conclusions can be enhanced. We advise researchers not to focus on one model but conduct multiple plausible multilevel analyses and investigate whether the results depend on the modeling options.     

Modeling external events in the three-level analysis of multiple-baseline across-participants designs: A simulation study

In this study, we focus on a three-level meta-analysis for combining data from studies using multiple-baseline across-participants designs. A complicating factor in such designs is that results might be biased if the dependent variable is affected by not explicitly modeled external events, such as the illness of a teacher, an exciting class activity, or the presence of a foreign observer. In multiple-baseline designs, external effects can become apparent if they simultaneously have an effect on the outcome score(s) of the participants within a study. This study presents a method for adjusting the three-level model to external events and evaluates the appropriateness of the modified model. Therefore, we use a simulation study, and we illustrate the new approach with real data sets. The results indicate that ignoring an external event effect results in biased estimates of the treatment effects, especially when there is only a small number of studies and measurement occasions involved. The mean squared error, as well as the standard error and coverage proportion of the effect estimates, is improved with the modified model. Moreover, the adjusted model results in less biased variance estimates. If there is no external event effect, we find no differences in results between the modified and unmodified models.     

Estimation of a Latent Variable Regression Growth Curve Model for Individuals Cross-Classified by Clusters

The cross-classified multiple membership latent variable regression (CCMM-LVR) model is a recent extension to the three-level latent variable regression (HM3-LVR) model which can be utilized for longitudinal data that contains individuals who changed clusters over time (for instance, student mobility across schools). The HM3-LVR model can include the initial status on growth effect as varying across those clusters and allows testing of more flexible hypotheses about the influence of initial status on growth and of factors that might impact that relationship, but only in the presence of pure clustering of participants within higher-level units. This Monte Carlo study was conducted to evaluate model estimation under a variety of conditions and to measure the impact of ignoring cross-classified data when estimating the incorrectly specified HM3-LVR model in a scenario in which true values for parameters are known. Furthermore, results from a real-data analysis were used to inform the design of the simulation. Overall, it would be recommended for researchers to utilize the CCMM-LVR model over the HM3-LVR model when individuals are cross-classified, and to use a bare minimum of more than 100 clustering units in order to avoid overestimation of the level-3 variance component estimates.     

Multilevel meta-analysis of single-subject experimental designs: A simulation study

One way to combine data from single-subject experimental design studies is by performing a multilevel meta-analysis, with unstandardized or standardized regression coefficients as the effect size metrics. This study evaluates the performance of this approach. The results indicate that a multilevel meta-analysis of unstandardized effect sizes results in good estimates of the effect. The multilevel meta-analysis of standardized effect sizes, on the other hand, is suitable only when the number of measurement occasions for each subject is 20 or more. The effect of the treatment on the intercept is estimated with enough power when the studies are homogeneous or when the number of studies is large; the power of the effect on the slope is estimated with enough power only when the number of studies and the number of measurement occasions are large.     

Incorporating Student Mobility in Achievement Growth Modeling: A Cross-Classified Multiple Membership Growth Curve Model

Multiple membership random effects models (MMREMs) have been developed for use in situations where individuals are members of multiple higher level organizational units. Despite their availability and the frequency with which multiple membership structures are encountered, no studies have extended the MMREM approach to hierarchical growth curve modeling (GCM). This study introduces a cross-classified multiple membership growth curve model (CCMM-GCM) for modeling, for example, academic achievement trajectories in the presence of student mobility. Real data are used to demonstrate and compare growth curve model estimates using the CCMM-GCM and a conventional GCM that ignores student mobility. Results indicate that the CCMM-GCM represents a promising option for modeling growth for multiple membership data structures.     

Meta-analytic methods of pooling correlation matrices for structural equation modeling under different patterns of missing data

Three methods of synthesizing correlations for meta-analytic structural equation modeling (SEM) under different degrees and mechanisms of missingness were compared for the estimation of correlation and SEM parameters and goodness-of-fit indices by using Monte Carlo simulation techniques. A revised generalized least squares (GLS) method for synthesizing correlations, weighted-covariance GLS (W-COV GLS), was compared with univariate weighting with untransformed correlations (univariate r) and univariate weighting with Fisher's z-transformed correlations (univariate z). These 3 methods were crossed with listwise and pairwise deletion. Univariate z and W-COV GLS performed similarly, with W-COV GLS providing slightly better estimation of parameters and more correct model rejection rates. Missing not at random data produced high levels of relative bias in correlation and model parameter estimates and higher incorrect SEM model rejection rates. Pairwise deletion resulted in inflated standard errors for all synthesis methods and higher incorrect rejection rates for the SEM model with univariate weighting procedures.