Assessing the generality and durability of interview‐informed functional analyses and treatment

Due to the limited research demonstrating socially valid outcomes of function‐based treatments in ecologically relevant environments (Santiago, Hanley, Moore, & Jin, 2016), we replicated and extended the effects of the interview‐informed functional analysis and skill‐based treatment procedure described by Hanley, Jin, Vanselow, and Hanratty (2014) with two children diagnosed with autism in a home setting. The assessment and treatment was implemented by a home‐based service provider and treatment was extended to the participants’ parents. Following the interview‐informed functional analyses, we taught the participants functional communication responses and to engage in less‐preferred activities when functional communication outcomes were delayed. We observed large reductions in problem behavior following the introduction of the function‐based treatment. The effects extended to novel settings, stimuli, and caregivers and the results maintained at 6‐week follow‐ups.     

Variables latentes et propriétés mentales : pour une épistémologie affirmée pragmatiste et réaliste

The reliability of models with latent variables is questioned by different authors: what is the ontology of mental properties? How to integrate the complex of mental processes with latent variables? All these debates raise the question of the legitimacy of latent variables as methodological approach comparatively to new approaches such as Network analysis. By clearly posing the ontological nature of mental properties as emergent properties of mental processes, and by clearly posing a theory of pragmatist and realistic knowledge, our work seeks to show that latent variables are efficient approaches for inferring mental properties.     

Out-of-bag Prediction Error: A Cross Validation Index for Generalized Structured Component Analysis

Cross validation is a useful way of comparing predictive generalizability of theoretically plausible a priori models in structural equation modeling (SEM). A number of overall or local cross validation indices have been proposed for existing factor-based and component-based approaches to SEM, including covariance structure analysis and partial least squares path modeling. However, there is no such cross validation index available for generalized structured component analysis (GSCA) which is another component-based approach. We thus propose a cross validation index for GSCA, called Out-of-bag Prediction Error (OPE), which estimates the expected prediction error of a model over replications of so-called in-bag and out-of-bag samples constructed through the implementation of the bootstrap method. The calculation of this index is well-suited to the estimation procedure of GSCA, which uses the bootstrap method to obtain the standard errors or confidence intervals of parameter estimates. We empirically evaluate the performance of the proposed index through the analyses of both simulated and real data.     

A General Approach for Fitting Pure Exploratory Bifactor Models

This article proposes a procedure for fitting a pure exploratory bifactor solution in which the general factor is orthogonal to the group factors, but the loadings on the group factors can satisfy any orthogonal or oblique rotation criterion. The proposal combines orthogonal Procrustes rotations with analytical rotations and consists of a sequence of four steps. The basic input is a semispecified target matrix that can be (a) defined by the user, (b) obtained by using Schmid-Leiman orthogonalization, or (c) automatically built from a conventional unrestricted solution based on a prescribed number of factors. The relevance of the proposal and its advantages over existing procedures is discussed and assessed via simulation. Its feasibility in practice is illustrated with two empirical examples in the personality domain.     

The Impact of Item Misspecification and Dichotomization on Class and Parameter Recovery in LCA of Count Data

Mixture analysis of count data has become increasingly popular among researchers of substance use, behavioral analysis, and program evaluation. However, this increase in popularity seems to have occurred along with adoption of some conventions in model specification based on arbitrary heuristics that may impact the validity of results. Findings from a systematic review of recent drug and alcohol publications suggested count variables are often dichotomized or misspecified as continuous normal indicators in mixture analysis. Prior research suggests that misspecifying skewed distributions of continuous indicators in mixture analysis introduces bias, though the consequences of this practice when applied to count indicators has not been studied. The present work describes results from a simulation study examining bias in mixture recovery when count indicators are dichotomized (median split; presence vs. absence), ordinalized, or the distribution is misspecified (continuous normal; incorrect count distribution). All distributional misspecifications and methods of categorizing resulted in greater bias in parameter estimates and recovery of class membership relative to specifying the true distribution, though dichotomization appeared to improve class enumeration accuracy relative to all other specifications. Overall, results demonstrate the importance of accurately modeling count indicators in mixture analysis, as misspecification and categorizing data can distort study outcomes.     

Four Covariance Structure Models for Canonical Correlation Analysis: A COSAN Modeling Approach

The mathematical connection between canonical correlation analysis (CCA) and covariance structure analysis was first discussed through the Multiple Indicators and Multiple Causes (MIMIC) approach. However, the MIMIC approach has several technical and practical challenges. To address these challenges, a comprehensive COSAN modeling approach is proposed. Specifically, we define four COSAN-CCA models to correspond with four possible combinations of the data to be analyzed and the unique parameters to be estimated. In terms of the data, one can analyze either the unstandardized or standardized variables. In terms of the unique parameters, one can estimate either the weights or loadings. Besides the unique parameters of each COSAN-CCA model, all four COSAN-CCA models also estimate the canonical correlations as their common parameters. Taken together, the four COSAN-CCA models provide the correct point estimates and standard error estimates for all commonly used CCA parameters. Two numeric examples are used to compare the standard error estimates obtained from the MIMIC approach and the COSAN modeling approach. Moreover, the standard error estimates from the COSAN modeling approach are validated by a simulation study and the asymptotic theory. Finally, software implementation and future extensions are discussed.     

A Limited Information Estimator for Dynamic Factor Models

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework. We demonstrate the implementation of this estimator and examine its performance under both correct and incorrect model specifications via a small simulation study. The estimates from this estimator are compared to those from the most common system-wide estimators and are found to be more robust to the structural misspecifications considered.     

The joint structure of maladaptive personality traits and psychopathology

We examined the joint structure of symptoms/syndromes of psychopathology (MMPI-2 Restructured-Clinical Scales; MMPI-2-RC; Tellegen et al., 2003) and maladaptive personality traits (Schedule for Nonadaptive and Adaptive Personality; SNAP-2; Clark, Simms, Wu, & Casillas, 2014) via a series of top-down factor analyses—Goldberg (2006) “bass-ackwards approach”—in a sample of 419 mental-health outpatients and 567 military veterans. A three-factor solution consisting of Negative Affectivity (including Oddity), Low Positive Affectivity, and Disinhibition-versus-Constraint best represented the joint symptom-trait structure, consistent with the third level of Markon et al. (2005) joint hierarchical structure of normal and maladaptive personality traits. Our results point to robustness of the structure of adaptive and maladaptive personality traits and symptoms of psychopathology at the three-factor level.     

Can imagined intergroup contact change (internal) dialogues on differences between ingroup and outgroup?

Several positive functions have been ascribed to integrative internal dialogues (IDs), which are based on mutual openness to a partner's viewpoint and a readiness to consider his/her arguments in order to potentially modify one's own stance. As the technique of imagined intergroup contact (IIC) favorably influences attitudes towards outgroup members, it was hypothesized that IIC would have a beneficial impact on IDs with an outgroup member when the dialogue is focused on differences between ingroup and outgroup. In the experiment, 151 people (80 women) participated. It revealed that after IIC, both the dialogue author's confrontational attitude and the interlocutor's integrative attitude decreased. Thus, IIC made participants less inclined to gain an advantage over their imagined outgroup interlocutors and more inclined to give them freedom in IDs. However, the effect was significant only when the author's involvement in ID was high or medium.     

Stereotype threat may not impact women's inhibitory control or mathematical performance: Providing support for the null hypothesis

Underpinned by the findings of Jamieson and Harkins (2007; Experiment 3), the current study pits the mere effort motivational account of stereotype threat against a working memory interference account. In Experiment 1, females were primed with a negative self- or group stereotype pertaining to their visuospatial ability and completed an anti-saccade eye-tracking task. In Experiment 2 they were primed with a negative or positive group stereotype and completed an anti-saccade and mental arithmetic task. Findings indicate that stereotype threat did not significantly impair women's inhibitory control (Experiments 1 and 2) or mathematical performance (Experiment 2), with Bayesian analyses providing support for the null hypothesis. These findings are discussed in relation to potential moderating factors of stereotype threat, such as task difficulty and stereotype endorsement, as well as the possibility that effect sizes reported in the stereotype threat literature are inflated due to publication bias.