Misspecification in Latent Change Score Models: Consequences for Parameter Estimation,Model Evaluation,and Predicting Change

Latent change score models (LCS) are conceptually powerful tools for analyzing longitudinal data (McArdle & Hamagami, 2001). However, applications of these models typically include constraints on key parameters over time. Although practically useful, strict invariance over time in these parameters is unlikely in real data. This study investigates the robustness of LCS when invariance over time is incorrectly imposed on key change-related parameters. Monte Carlo simulation methods were used to explore the impact of misspecification on parameter estimation, predicted trajectories of change, and model fit in the dual change score model, the foundational LCS. When constraints were incorrectly applied, several parameters, most notably the slope (i.e., constant change) factor mean and autoproportion coefficient, were severely and consistently biased, as were regression paths to the slope factor when external predictors of change were included. Standard fit indices indicated that the misspecified models fit well, partly because mean level trajectories over time were accurately captured. Loosening constraint improved the accuracy of parameter estimates, but estimates were more unstable, and models frequently failed to converge. Results suggest that potentially common sources of misspecification in LCS can produce distorted impressions of developmental processes, and that identifying and rectifying the situation is a challenge.     

Accounting for Missing Correlation Coefficients in Fixed-Effects MASEM

Meta-analytic structural equation modeling (MASEM) is increasingly applied to advance theories by synthesizing existing findings. MASEM essentially consists of two stages. In Stage 1, a pooled correlation matrix is estimated based on the reported correlation coefficients in the individual studies. In Stage 2, a structural model (such as a path model) is fitted to explain the pooled correlations. Frequently, the individual studies do not provide all the correlation coefficients between the research variables. In this study, we modify the currently optimal MASEM-method to deal with missing correlation coefficients, and compare its performance with existing methods. This study is the first to evaluate the performance of fixed-effects MASEM methods under different levels of missing correlation coefficients. We found that the often used univariate methods performed very poorly, while the multivariate methods performed well overall.     

What if they are right? Network approval,expectations, and relationship maintenance behaviors

This study drew on uncertainty reduction and decision‐making theories to investigate how perceptions of approval of romantic relationships from family and friends can influence romantic partners' dynamics. Using a dyadic approach, the authors examined whether expectations of a partner's behavior in the relationship mediated the associations between perceived social network approval and relationship maintenance behaviors in a sample of 137 couples. The actor–partner interdependence mediation model (APIMeM) was applied. Results showed that women's and men's perceptions of approval from their own and their partner's network were associated with their own level of expectations of their partner's behaviors. In turn, women's and men's expectations were associated with their own and their partner's maintenance behaviors.     

Images of the disciplining of psychology, 1890–1940

Over the course of psychology’s first several decades, the language used to convey the subject matter gradually shifted from being free and literary to being strictly constrained and disciplined by increasingly focused theoretical demands. The project described here, “Disciplining Psychology,” aimed to depict this transformation by generating images of the faces of three highly influential psychologists—William James, Sigmund Freud, and B. F. Skinner. Each image is composed of the words used in one of each individual’s most important books. The tightening of the disciplinary vocabulary is revealed in the differences among the three arrays of words themselves, but I have also striven to reflect it in the aesthetic aspects of each image. The method used here could easily be extended to a wider array of authors, texts, and psychological topics.     

Developmental models for children's temperament: alternatives to chronometric polynomial curves

Partridge and Lerner (2007), in a secondary analysis of the New York Longitudinal Study, employed a chronometric polynomial growth curve model to argue that the developmental course of difficult temperament follows a non‐linear trajectory over the first 5 years of life. The free curve slope intercept (FCSI) growth curve model of Meredith and Tisak (1990) is presented as a preferable conceptual alternative because it contains a number of currently popular statistical models, including repeated measures multivariate analysis of variance, factor mean, linear growth, linear factor analysis, and hierarchical linear models as special cases. As such, researchers can compare the fit of each of these models relative to the FCSI model, and, at times, to each other. The present paper conducts a re‐analysis of the data, and establishes that fit of the FCSI model is arguably better than other statistical alternatives. The FCSI model is also used as the basis for identifying subgroups of individuals with their qualitatively distinct growth patterns within a growth mixture modeling framework. Copyright © 2010 John Wiley & Sons, Ltd.     

Contrast effects in typicality judgements: A hierarchical Bayesian approach

We examine the influence of contrast categories on the internal graded membership structure of everyday concepts using computational models proposed in the artificial category learning tradition. In particular, the generalized context model (Nosofsky, 1986), which assumes that only members of a given category contribute to the typicality of a category member, is contrasted to the similarity–dissimilarity generalized context model (SD-GCM; Stewart & Brown, 2005), which assumes that members of other categories are also influential in determining typicality. The models are compared in a hierarchical Bayesian framework in their account of the typicality gradient of five animal categories and six artefact categories. For each target category, we consider all possible relevant contrast categories. Three separate issue are examined: (a) whether contrast effects can be found, (b) which categories are responsible for these effects, and (c) whether more than one category influences the typicality. Results indicate that the internal category structure is codetermined by dissimilarity towards potential contrast categories. In most cases, only a single contrast category contributed to the typicality. The present findings suggest that contrast effects might be more widespread than has previously been assumed. Further, they stress the importance of characteristics particular of everyday concepts, which require careful consideration when applying computational models of representation of the artificial category learning tradition to everyday concepts.     

Functional analysis of aberrant behavior through measurement of separate response topographies

Functional analysis results for multiple topographies of aberrant behavior were graphed in an aggregate fashion and then separately for 48 clients. The results indicated that multiple topographies of behavior may be maintained by different contingencies. These results indicate that graphing functional analysis data in an aggregate fashion and then separately may improve the accuracy of their interpretation.     

The Question of Questions: What is a Gene? Comments on Rolston and Griffths & Stotz

If the question “What is a gene?” proves to be worth asking it must be able to elicit an answer which both recognizes and address the reasons why the concept of the gene ever seemed to be something worth getting excited about in the first place as well analyzing and evaluating the latest develops in the molecular biology of DNA. Each of the preceding papers fails to do one of these and sufferrs the consequences. Where Rolston responds to the apparent failure of molecular biology to make good on the desideratum of the classical gene by veering off into fanciful talk about “cybernetic genes,” Griffiths and Stotz lose themselves in the molecular fine print and forget to ask themselves why “genes” should be of any special interst anyway.     

Asymptotic biases in exploratory factor analysis and structural equation modeling

Formulas for the asymptotic biases of the parameter estimates in structural equation models are provided in the case of the Wishart maximum likelihood estimation for normally and nonnormally distributed variables. When multivariate normality is satisfied, considerable simplification is obtained for the models of unstandardized variables. Formulas for the models of standardized variables are also provided. Numerical examples with Monte Carlo simulations in factor analysis show the accuracy of the formulas and suggest the asymptotic robustness of the asymptotic biases with normality assumption against nonnormal data. Some relationships between the asymptotic biases and other asymptotic values are discussed.The author is indebted to the editor and anonymous reviewers for their comments, corrections, and suggestions on this paper, and to Yutaka Kano for discussion on biases.