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Factor score regression (FSR) is widely used as a convenient alternative to traditional structural equation modeling (SEM) for assessing structural relations between latent variables. But when latent variables are simply replaced by factor scores, biases in the structural parameter estimates often have to be corrected, due to the measurement error in the factor scores. The method of Croon (MOC) is a well-known bias correction technique. However, its standard implementation can render poor quality estimates in small samples (e.g. less than 100). This article aims to develop a small sample correction (SSC) that integrates two different modifications to the standard MOC. We conducted a simulation study to compare the empirical performance of (a) standard SEM, (b) the standard MOC, (c) naive FSR, and (d) the MOC with the proposed SSC. In addition, we assessed the robustness of the performance of the SSC in various models with a different number of predictors and indicators. The results showed that the MOC with the proposed SSC yielded smaller mean squared errors than SEM and the standard MOC in small samples and performed similarly to naive FSR. However, naive FSR yielded more biased estimates than the proposed MOC with SSC, by failing to account for measurement error in the factor scores. [ABSTRACT FROM AUTHOR]

Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the recently developed EGA with various traditional factor retention criteria. We use both continuous and binary data, as evidence regarding the accuracy of such criteria in the latter case is scarce. Simulation results, based on scenarios resulting from varying sample size, communalities from major factors, interfactor correlations, skewness, and correlation measure, show that EGA outperforms the traditional factor retention criteria considered in most cases in terms of bias and accuracy. In addition, we show that factor retention decisions for binary data are preferably made using Pearson, instead of tetrachoric, correlations, which is contradictory to popular belief. [ABSTRACT FROM AUTHOR]

In the presence of omitted variables or similar validity threats, regression estimates are biased. Unbiased estimates (the causal effects) can be obtained in large samples by fitting instead the Instrumental Variables Regression (IVR) model. The IVR model can be estimated using structural equation modeling (SEM) software or using Econometric estimators such as two-stage least squares (2SLS). We describe 2SLS using SEM terminology, and report a simulation study in which we generated data according to a regression model in the presence of omitted variables and fitted (a) a regression model using ordinary least squares, (b) an IVR model using maximum likelihood (ML) as implemented in SEM software, and (c) an IVR model using 2SLS. Coverage rates of the causal effect using regression methods are always unacceptably low (often 0). When using the IVR model, accurate coverage is obtained across all conditions when N = 500. Even when the IVR model is misspecified, better coverage than regression is generally obtained. Differences between 2SLS and ML are small and favor 2SLS in small samples (N ≤ 100). [ABSTRACT FROM AUTHOR]

Estimation of the direct effect of an exposure on an outcome requires adjustment for confounders of the exposure–outcome and mediator–outcome relationships. When some of the latter confounders have been affected by the exposure, then standard regression adjustment is prone to possibly severe bias. The use of inverse probability weighting under so-called marginal structural models has recently been suggested as a solution in the psychological literature. In this article, we show how progress can alternatively be made via G-estimation. We show that this estimation method can be easily embedded within the structural equation modeling framework and could in particular be used for estimating direct effects in the presence of latent variables. Moreover, by avoiding inverse probability weighting, it accommodates the typical problem of unstable weights in the alternative estimation approaches based on marginal structural models. We illustrate the approach both by simulations and by the analysis of a longitudinal study in individiduals who ended a romantic relationship. In this example we explore whether the effect of attachment anxiety during the relationship on mental distress 2 years after the breakup is mediated by rumination or not. [ABSTRACT FROM PUBLISHER]

The present study examined the influence of features differing across problems (problem size and operation) and across individuals (gender, amount of daily arithmetic practice, calculator use, and arithmetic skill) on simple arithmetic performance. Regression analyses were used to investigate the role of these variables in both strategy selection and strategy efficiency. Results show that more skilled and highly practiced students used memory retrieval more often and executed their strategies more efficiently than did less skilled and practiced students. Furthermore, calculator use correlated with both retrieval and procedural strategy efficiency but not with strategy selection. Only very small associations with gender were observed, with boys retrieving slightly faster than girls. Implications of the present findings for models of mental arithmetic are discussed. [ABSTRACT FROM AUTHOR]

Background Although numerous studies recognize the importance of social network support in engaging substance abusers into treatment, there is only limited knowledge of the impact of network involvement and support during treatment. The primary objective of this research was to enhance retention in Therapeutic Community treatment utilizing a social network intervention. Aims The specific goals of this study were (1) to determine whether different pre-treatment factors predicted treatment retention in a Therapeutic Community; and (2) to determine whether participation of significant others in a social network intervention predicted treatment retention. Design, setting and participants Consecutive admissions to four long-term residential Therapeutic Communities were assessed at intake ( n = 207); the study comprised a mainly male (84.9%) sample of polydrug (41.1%) and opiate (20.8%) abusers, of whom 64.4% had ever injected drugs. Assessment involved the European version of the Addiction Severity Index (EuropASI), the Circumstances, Motivation, Readiness scales (CMR), the Dutch version of the family environment scale (GKS/FES) and an in-depth interview on social network structure and perceived social support. Network members of different cohorts were assigned to a social network intervention, which consisted of three elements (a video, participation at an induction day and participation in a discussion session). Findings Hierarchical regression analyses showed that client-perceived social support ( F1,198 = 10.9, P = 0.001) and treatment motivation and readiness ( F1,198 = 8.8; P = 0.003) explained a significant proportion of the variance in treatment retention (model fit: F7,197 = 4.4; P = 0.000). By including the variable ‘significant others’ participation in network intervention’ (network involvement) in the model, the fit clearly improved ( F1,197 = 6.2; P = 0.013). At the same time, the impact of perceived social support decreased ( F1,197 = 2.9; P = 0.091). Conclusions Participation in the social network intervention was associated with improved treatment retention controlling for other client characteristics. This suggests that the intervention may be of benefit in the treatment of addicted individuals. [ABSTRACT FROM AUTHOR]