Finite mixtures in confirmatory factor-analysis models

In this paper, various types of finite mixtures of confirmatory factor-analysis models are proposed for handling data heterogeneity. Under the proposed mixture approach, observations are assumed to be drawn from mixtures of distinct confirmatory factor-analysis models. But each observation does not need to be identified to a particular model prior to model fitting. Several classes of mixture models are proposed. These models differ by their unique representations of data heterogeneity. Three different sampling schemes for these mixture models are distinguished. A mixed type of the these three sampling schemes is considered throughout this article. The proposed mixture approach reduces to regular multiple-group confirmatory factor-analysis under a restrictive sampling scheme, in which the structural equation model for each observation is assumed to be known. By assuming a mixture of multivariate normals for the data, maximum likelihood estimation using the EM (Expectation-Maximization) algorithm and the AS (Approximate-Scoring) method are developed, respectively. Some mixture models were fitted to a real data set for illustrating the application of the theory. Although the EM algorithm and the AS method gave similar sets of parameter estimates, the AS method was found computationally more efficient than the EM algorithm. Some comments on applying the mixture approach to structural equation modeling are made.Note: This paper is one of the Psychometric Society's 1995 Dissertation Award papers.—EditorThis article is based on the dissertation of the author. The author would like to thank Peter Bentler, who was the dissertation chair, for guidance and encouragement of this work. Eric Holman, Robert Jennrich, Bengt Muthén, and Thomas Wickens, who served as the committee members for the dissertation, had been very supportive and helpful. Michael Browne is appreciated for discussing some important points about the use of the approximate information in the dissertation. Thanks also go to an anonymous associate editor, whose comments were very useful for the revision of an earlier version of this article.     

Between-individual comparisons in performance evaluation: a perspective from prospect theory

This article examines how between-individual comparisons influence performance evaluations in rating tasks. The authors demonstrated a systematic change in the perceived difference across ratees as a result of changing the way performance information is expressed. Study 1 found that perceived performance difference between 2 individuals was greater when their objective performance levels were presented with small numbers (e.g., absence rates of 2% vs. 5%) than when they were presented with large numbers (e.g., attendance rates of 98% vs. 95%). Extending this finding to situations involving trade-offs between multiple performance attributes across ratees, Study 2 showed that the relative preference for 1 ratee over another actually reversed when the presentation format of the performance information changed. The authors draw upon prospect theory to offer a theoretical framework describing the between-individual comparison aspect of performance evaluation.     

On the limitations of fixed‐step‐size adaptive methods with response confidence

The family of (non‐parametric, fixed‐step‐size) adaptive methods, also known as ‘up–down’ or ‘staircase’ methods, has been used extensively in psychophysical studies for threshold estimation. Extensions of adaptive methods to non‐binary responses have also been proposed. An example is the three‐category weighted up–down (WUD) method (Kaernbach, 2001) and its four‐category extension (Klein, 2001). Such an extension, however, is somewhat restricted, and in this paper we discuss its limitations. To facilitate the discussion, we characterize the extension of WUD by an algorithm that incorporates response confidence into a family of adaptive methods. This algorithm can also be applied to two other adaptive methods, namely Derman's up–down method and the biased‐coin design, which are suitable for estimating any threshold quantiles. We then discuss via simulations of the above three methods the limitations of the algorithm. To illustrate, we conduct a small scale of experiment using the extended WUD under different response confidence formats to evaluate the consistency of threshold estimation.     

Does a Tired Mind Help Avoid a Decision Bias? The Effect of Ego Depletion on Escalation of Commitment

In this research, we investigated the effect of ego depletion on escalation of commitment. Specifically, we conducted two laboratory experiments and obtained evidence that ego depletion decreases escalation of commitment. In Study 1, we found that individuals were less susceptible to escalation of commitment after completing an ego depletion task. In Study 2, we confirmed the effect observed in Study 1 using a different manipulation of ego depletion and a different subject pool. Contrary to the fundamental assumption of bounded rationality that people have a tendency to make decision errors when mental resources are scarce, the findings of this research show that a tired mind can help reduce escalation bias.     

Fitting direct covariance structures by the MSTRUCT modeling language of the CALIS procedure

This paper demonstrates the usefulness and flexibility of the general structural equation modelling (SEM) approach to fitting direct covariance patterns or structures (as opposed to fitting implied covariance structures from functional relationships among variables). In particular, the MSTRUCT modelling language (or syntax) of the CALIS procedure (SAS/STAT version 9.22 or later: SAS Institute, 2010) is used to illustrate the SEM approach. The MSTRUCT modelling language supports a direct covariance pattern specification of each covariance element. It also supports the input of additional independent and dependent parameters. Model tests, fit statistics, estimates, and their standard errors are then produced under the general SEM framework. By using numerical and computational examples, the following tests of basic covariance patterns are illustrated: sphericity, compound symmetry, and multiple‐group covariance patterns. Specification and testing of two complex correlation structures, the circumplex pattern and the composite direct product models with or without composite errors and scales, are also illustrated by the MSTRUCT syntax. It is concluded that the SEM approach offers a general and flexible modelling of direct covariance and correlation patterns. In conjunction with the use of SAS macros, the MSTRUCT syntax provides an easy‐to‐use interface for specifying and fitting complex covariance and correlation structures, even when the number of variables or parameters becomes large.     

Understanding the emotional aspects of escalation of commitment: the role of negative affect

Despite the importance of understanding the emotional aspects of organizational decision making, prior research has paid scant attention to the role of emotion in escalation of commitment. This article attempts to fill this gap by examining the relationship between negative affect and escalation of commitment. Results showed that regardless of whether negative affect was measured as a dispositional trait (Neuroticism) in Studies 1 and 2 or as a transient mood state in Study 3, it was negatively correlated with escalation tendency when one was personally responsible for a prior decision. This pattern of results is consistent with the predictions derived from the coping perspective, suggesting that people seek to escape from the unpleasant emotions that are associated with escalation situations.     

A Computationally Efficient State Space Approach to Estimating Multilevel Regression Models and Multilevel Confirmatory Factor Models

Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multilevel factor models. A brief outline of the state space formulation is provided and then state space forms for univariate and multivariate multilevel regression models, and a multilevel confirmatory factor model, are illustrated. The utility of the state space approach is demonstrated with either a simulated or real example for each multilevel model. It is concluded that the results from the state space approach are essentially identical to those from specialized multilevel regression modeling and structural equation modeling software. More importantly, the state space approach offers researchers a computationally more efficient alternative to fit multilevel regression models with a large number of Level 1 units within each Level 2 unit or a large number of observations on each subject in a longitudinal study.     

An examination of premarital sexual behavior among college students in hong kong

Abstract

This study examined the determinants of Hong Kong Chinese college students' intentions to engage in premarital sexual behavior. Fishbein and Ajzen's (1975) theory of reasoned action (TOFA) and Ajzen's (1985) perceived behavioral control were used as the theoretical framework for our investigation. Two hundred and thirty eight students completed a questionnaire designed to measure constructs of the two theories. Results from the regression analysis: (a) support the applicability of TORA in predicting these students' intentions to engage in premarital sex; (b) suggest that while attitudes toward engaging in premarital sex were more important in predicting male students' intentions, subjective norms were more important for female students; (c) reveal that the additional prediction contributed by the behavioral control components was relatively weak. Other findings were basically consistent with premarital sex research conducted in Western societies. Given the fact that promoting abstinence is one way to stop the transmission of the AIDS virus, both the theoretical and applied implications of our results for health intervention are discussed.     

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.