"Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models": Clarification to Bauer and Hussong (2009).

Reports a clarification to "Psychometric approaches for developing commensurate measures across independent studies: Traditional and new models" by Daniel J. Bauer and Andrea M. Hussong (Psychological Methods, 2009[Jun], Vol 14[2], 101-125). In this article, the authors wrote, "To our knowledge, the multisample framework is the only available option within these [latent variable] programs that allows for the moderation of all types of parameters, and this approach requires a single categorical moderator variable to define the samples.” Bengt Muthén has clarified for the authors that some programs, including Mplus and Mx, can allow for continuous moderation through the implementation of nonlinear constraints involving observed variables, further enlarging the class of MNLFA models that can be fit with these programs. (The following abstract of the original article appeared in record 2009-08072-001.) When conducting an integrative analysis of data obtained from multiple independent studies, a fundamental problem is to establish commensurate measures for the constructs of interest. Fortunately, procedures for evaluating and establishing measurement equivalence across samples are well developed for the linear factor model and commonly used item response theory models. A newly proposed moderated nonlinear factor analysis model generalizes these models and procedures, allowing for items of different scale types (continuous or discrete) and differential item functioning across levels of categorical and/or continuous variables. The potential of this new model to resolve the problem of measurement in integrative data analysis is shown via an empirical example examining changes in alcohol involvement from ages 10 to 22 years across 2 longitudinal studies. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Short and long views of integrative data analysis: Comments on contributions to the special issue.

The author comments on the potential of integrative data analysis (IDA) as a new methodological activity and on some of the topics that were discussed in the 5 articles in this special issue. One topic is the extent to which IDA will be used to provide conclusive summaries regarding the strength of evidence for well-specified questions versus to provide new information that goes beyond the simple sum of individual studies. Another is the meaning of variances of effects that are observed over studies and sample strata. A 3rd is the potential to enhance understanding of construct validity by fitting measurement models described in the special issue. The author concludes by recommending critical examination of model-based inferences from IDA through sensitivity analyses and by noting that IDA can promote collaboration and networks that yield data that are more amenable to integrated analyses in the future. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Item factor analysis: Current approaches and future directions.

The rationale underlying factor analysis applies to continuous and categorical variables alike; however, the models and estimation methods for continuous (i.e., interval or ratio scale) data are not appropriate for item-level data that are categorical in nature. The authors provide a targeted review and synthesis of the item factor analysis (IFA) estimation literature for ordered-categorical data (e.g., Likert-type response scales) with specific attention paid to the problems of estimating models with many items and many factors. Popular IFA models and estimation methods found in the structural equation modeling and item response theory literatures are presented. Following this presentation, recent developments in the estimation of IFA parameters (e.g., Markov chain Monte Carlo) are discussed. The authors conclude with considerations for future research on IFA, simulated examples, and advice for applied researchers. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Taxometric and factor analytic models of anxiety sensitivity: Integrating approaches to latent structural research.

This study represents an effort to better understand the latent structure of anxiety sensitivity (AS), as indexed by the 16-item Anxiety Sensitivity Index (ASI; S. Reiss, R. A. Peterson, M. Gursky, & R. J. McNally, 1986), by using taxometric and factor-analytic approaches in an integrative manner. Taxometric analyses indicated that AS has a taxonic latent class structure (i.e., a dichotomous latent class structure) in a large sample of North American adults (N = 2,515). As predicted, confirmatory factor analyses indicated that a multidimensional 3-factor model of AS provided a good fit for the AS complement class (normative or low-risk form) but not the AS taxon class (high-risk form). Exploratory factor analytic results suggested that the AS taxon may demonstrate a unique, unidimensional factor solution, though there are alternative indications that it may be characterized by a 2-factor solution. Findings suggest that the latent structural nature of AS can be conceptualized as a taxonic latent class structure composed of 2 types or forms of AS, each of these forms characterized by its own unique latent continuity and dimensional structure. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Multiphase mixed-effects models for repeated measures data.

Behavior that develops in phases may exhibit distinctively different rates of change in one time period than in others. In this article, a mixed-effects model for a response that displays identifiable regimes is reviewed. An interesting component of the model is the change point. In substantive terms, the change point is the time when development switches from one phase to another. In a mixed-effects model, the change point can be a random coefficient. This possibility allows individuals to make the transition from one phase to another at different ages or after different lengths of time in treatment. Two examples are reviewed in detail, both of which can be estimated with software that is widely available. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Pooling data from multiple longitudinal studies: The role of item response theory in integrative data analysis.

There are a number of significant challenges researchers encounter when studying development over an extended period of time, including subject attrition, the changing of measurement structures across groups and developmental periods, and the need to invest substantial time and money. Integrative data analysis is an emerging set of methodologies that allows researchers to overcome many of the challenges of single-sample designs through the pooling of data drawn from multiple existing developmental studies. This approach is characterized by a host of advantages, but this also introduces several new complexities that must be addressed prior to broad adoption by developmental researchers. In this article, the authors focus on methods for fitting measurement models and creating scale scores using data drawn from multiple longitudinal studies. The authors present findings from the analysis of repeated measures of internalizing symptomatology that were pooled from three existing developmental studies. The authors describe and demonstrate each step in the analysis and conclude with a discussion of potential limitations and directions for future research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Nonparametric alternatives for testing main effects hypotheses: A model for combining data across independent studies.

Describes G. A. Mack and J. H. Skillings's (1980) and A. Benard and P. Van Elteren's (1953) forms of the combined Kruskal-Wallis test, 2 nonparametric tests that can be used to make comparisons across K groups in a design with B blocks or in B independent studies. It is suggested that the dependent variables across studies may be alternative measures of the same underlying construct, and an application of the models to B. G. Davis's (1972, 1974) data on the clinical skills of 3 groups of nurses is described. (12 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A critique of the correlated trait-correlated method and correlated uniqueness models for multitrait-multimethod data.

The correlated trait-correlated method (CT-CM) and correlated uniqueness (CU) confirmatory factor analysis models for multitrait-multimethod data are critiqued. Although the CU model often returns convergent and admissible factor solutions when the CT-CM model does not, the CU model is shown to have theoretical and substantive shortcomings. On the basis of this critique, the authors recommend that the CT-CM model be regarded as the generally preferred model and that the CU model be invoked only when the CT-CM model fails. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Integrative data analysis: The simultaneous analysis of multiple data sets.

There are both quantitative and methodological techniques that foster the development and maintenance of a cumulative knowledge base within the psychological sciences. Most noteworthy of these techniques is meta-analysis, which allows for the synthesis of summary statistics drawn from multiple studies when the original data are not available. However, when the original data can be obtained from multiple studies, many advantages stem from the statistical analysis of the pooled data. The authors define integrative data analysis (IDA) as the analysis of multiple data sets that have been pooled into one. Although variants of IDA have been incorporated into other scientific disciplines, the use of these techniques is much less evident in psychology. In this article the authors present an overview of IDA as it may be applied within the psychological sciences, discuss the relative advantages and disadvantages of IDA, describe analytic strategies for analyzing pooled individual data, and offer recommendations for the use of IDA in practice. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Measurement equivalence: A comparison of methods based on confirmatory factor analysis and item response theory.

Current interest in the assessment of measurement equivalence emphasizes 2 major methods of analysis. The authors offer a comparison of a linear method (confirmatory factor analysis) and a nonlinear method (differential item and test functioning using item response theory) with an emphasis on their methodological similarities and differences. The 2 approaches test for the equality of true scores (or expected raw scores) across 2 populations when the latent (or factor) score is held constant. Both approaches can provide information about when measurrment nonequivalence exists and the extent to which it is a problem. An empirical example is used to illustrate the 2 approaches. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Effect size measures for mediation models: Quantitative strategies for communicating indirect effects.

The statistical analysis of mediation effects has become an indispensable tool for helping scientists investigate processes thought to be causal. Yet, in spite of many recent advances in the estimation and testing of mediation effects, little attention has been given to methods for communicating effect size and the practical importance of those effect sizes. Our goals in this article are to (a) outline some general desiderata for effect size measures, (b) describe current methods of expressing effect size and practical importance for mediation, (c) use the desiderata to evaluate these methods, and (d) develop new methods to communicate effect size in the context of mediation analysis. The first new effect size index we describe is a residual-based index that quantifies the amount of variance explained in both the mediator and the outcome. The second new effect size index quantifies the indirect effect as the proportion of the maximum possible indirect effect that could have been obtained, given the scales of the variables involved. We supplement our discussion by offering easy-to-use R tools for the numerical and visual communication of effect size for mediation effects. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Three Cs in measurement models: Causal indicators, composite indicators, and covariates.

In the last 2 decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that one can classify indicators into 2 categories: effect (reflective) indicators and causal (formative) indicators. We argue that the dichotomous view is too simple. Instead, there are effect indicators and 3 types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the “Three Cs”). Causal indicators have conceptual unity, and their effects on latent variables are structural. Covariates are not concept measures, but are variables to control to avoid bias in estimating the relations between measures and latent variables. Composite (formative) indicators form exact linear combinations of variables that need not share a concept. Their coefficients are weights rather than structural effects, and composites are a matter of convenience. The failure to distinguish the Three Cs has led to confusion and questions, such as, Are causal and formative indicators different names for the same indicator type? Should an equation with causal or formative indicators have an error term? Are the coefficients of causal indicators less stable than effect indicators? Distinguishing between causal and composite indicators and covariates goes a long way toward eliminating this confusion. We emphasize the key role that subject matter expertise plays in making these distinctions. We provide new guidelines for working with these variable types, including identification of models, scaling latent variables, parameter estimation, and validity assessment. A running empirical example on self-perceived health illustrates our major points. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Differentiation of cognitive abilities across the life span.

Existing representations of cognitive ability structure are exclusively based on linear patterns of interrelations. However, a number of developmental and cognitive theories predict that abilities are differentially related across ages (age differentiation–dedifferentiation) and across levels of functioning (ability differentiation). Nonlinear factor analytic models were applied to multivariate cognitive ability data from 6,273 individuals, ages 4 to 101 years, who were selected to be nationally representative of the U.S. population. Results consistently supported ability differentiation but were less clear with respect to age differentiation–dedifferentiation. Little evidence for age modification of ability differentiation was found. These findings are particularly informative about the nature of individual differences in cognition and about the developmental course of cognitive ability level and structure. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Quantifying construct validity: Two simple measures.

Construct validity is one of the most central concepts in psychology. Researchers generally establish the construct validity of a measure by correlating it with a number of other measures and arguing from the pattern of correlations that the measure is associated with these variables in theoretically predictable ways. This article presents 2 simple metrics for quantifying construct validity that provide effect size estimates indicating the extent to which the observed pattern of correlations in a convergent-discriminant validity matrix matches the theoretically predicted pattern of correlations. Both measures, based on contrast analysis, provide simple estimates of validity that can be compared across studies, constructs, and measures meta-analytically, and can be implemented without the use of complex statistical procedures that may limit their accessibility. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The Gambling Urge Scale: Development, Confirmatory Factor Validation, and Psychometric Properties.

The urge to gamble is a physiological, psychological, or emotional motivational state, often associated with continued gambling. The authors developed and validated the 6-item Gambling Urge Questionnaire (GUS), which was based on the 8-item Alcohol Urge Questionnaire (M. J. Bohn, D. D. Krahn, & B. A. Staehler, 1995), using 968 community-based participants. Exploratory factor analysis using half of the sample indicated a 1-factor solution that accounted for 55.18% of the total variance. This was confirmed using confirmatory factor analysis with the other half of the sample. The GUS had a Cronbach's alpha coefficient of .81. Concurrent, predictive, and criterion-related validity of the GUS were good, suggesting that the GUS is a valid and reliable instrument for assessing gambling urges among nonclinical gamblers. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The process of Retirement Planning Scale (PRePS): Development and validation.

Although a substantial proportion of the western population is approaching retirement age, little is known about how they are preparing for the future. Much attention has been paid to the consumption of educational material and retirement wealth in the present literature, but the process of retirement planning has been ignored. S. L. Friedman and E. K. Scholnick's (1997) theoretical model provided the basis for a comprehensive measure of retirement planning. According to their process theory, individuals develop an understanding of the problem, set goals, make a decision to start preparing, and finally undertake the behaviors needed to fulfill their goals. Fifty-two items were developed to assess each stage of the planning process for financial, health, lifestyle, and psychosocial retirement planning. These were tested on a population sample of 1,449 New Zealanders aged 49–60. Confirmatory factor analysis, bivariate correlations, and hierarchical regression provided support for the valid use of the measure. Necessary antecedents, such as the tendency to look to the future, and locus of control were significantly related to the Process of Retirement Planning Scale (PRePS). The PRePS also outperformed retirement planning measures used in the Health and Retirement Study (F. T. Juster & R. Suzman, 1995) after controlling for socioeconomic and psychological variables. This measure will enable social policy makers to determine which stages of retirement planning require support and intervention. The PRePS will also help to determine which domains of retirement planning predict well-being in later life and the factors which differentiate those who are planning from those who are not. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The criterion problem and creativity in film: Psychometric characteristics of various measures.

Creativity in movies is a topic of growing interest in both the psychological and the marketing literature. Much research has been invested into determining the impact of cinematic quality on film success and finding successful predictors of cinematic creativity. For these reasons, research into the way creativity is measured in film is of considerable importance. This study examines a variety of measures of cinematic quality (movie ratings from a variety of sources) and determines the degree of agreement among different types of measures, the predictive value of these measures, and the effect of the timing of these measures on their predictive value. Results indicate that there is a high degree of agreement among types of movie ratings, that reviews through release day tend to be marginally higher than those that appear later, and that reviews are more highly correlated with later box office success (gross) than with early box office success. A surprising result of this study was that the number of ratings a movie received was a slightly better predictor of box office success than the actual movie ratings. Possible explanations for and implications of these results are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Factorial invariance of a campus climate measure across race, gender, and student classification.

Invariance analyses using multigroup confirmatory factor analysis were conducted to test a model of campus climate perceptions for its equivalence in a combined sample of 2,634 undergraduate and graduate university students across race, gender, and student status. Results suggested that a multidimensional model of campus climate comprised of psychological and behavioral climate dimensions appears to be supported for both undergraduate and graduate students across race/ethnicity and gender. Nonequivalence of factor loadings seen in all three invariance comparisons indicated that relationships between items and the underlying factors differed in magnitude on some climate dimensions between males and females, White and ethnic minority students, and graduate versus undergraduate students. Implications for future climate measurement and higher education policy and practice are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Comparison of factor structure models for the Beck Depression Inventory--II.

Factor analyses of the Beck Depression Inventory--II (A. T. Beck, R. A. Steer, & G. K. Brown, 1996) have frequently produced 2 different 2-factor oblique structures. The author used confirmatory factor analyses to compare these structures with a general-factor model with 2 orthogonal group factors. The general-factor model fit as well as or better than the 2-factor models when applied to item data from previous studies (3 clinical and 2 college samples). Communalities associated with the General Depression factor ranged from 71% to 82%. Cognitive and Somatic group factors were indicative of intropunitiveness and fatigue. It was concluded that the general-factor model gives an acceptable empirical explanation of item covariance structure and offers a conceptual interpretation that is well suited to clinical practice and research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)