Planning, motivation, and evaluation in orientation to the future: A latent structure analysis

The paper presents a model of orientation to the future in terms of three processes; motivation, planning, and evaluation. To test this model a total of 154 11-, 15- and 18-year-old adolescents were interviewed about their goals and hopes for the future. Seven observed variables were estimated and a model including three latent constructs was tested using the LISREL VI computer program. The planning construct consisted of the amount of knowledge, the complexity of plans and their level of realization. The evaluation construct included internality, estimation of the likelihood that the goals would be realized and an overall emotional evaluation of the future. The motivation construct consisted of one observed variable, extension. Confirmatory factor analysis showed that the model fitted the data, thus providing support for the theoretical model.     

On measures of explained variance in nonrecursive structural equation models

Whereas measures of explained variance in a regression and an equation of a recursive structural equation model can be simply summarized by a standard R2 measure, this is not possible in nonrecursive models in which there are reciprocal interdependencies among variables. This article provides a general approach to defining variance explained in latent dependent variables of nonrecursive linear structural equation models. A new method of its estimation, easily implemented in EQS or LISREL and available in EQS 6, is described and illustrated.     

A Bayesian analysis of finite mixtures in the LISREL model

In this paper, we propose a Bayesian framework for estimating finite mixtures of the LISREL model. The basic idea in our analysis is to augment the observed data of the manifest variables with the latent variables and the allocation variables. The Gibbs sampler is implemented to obtain the Bayesian solution. Other associated statistical inferences, such as the direct estimation of the latent variables, establishment of a goodness-of-fit assessment for a posited model, Bayesian classification, residual and outlier analyses, are discussed. The methodology is illustrated with a simulation study and a real example.This research was supported by a Hong Kong UGC Earmarked grant CUHK 4026/97H. The authors are indebted to the Editor, the Associate Editor, and three anonymous reviewers for constructive comments in improving the paper, and also to ICPSR and the relevant funding agency for allowing the use of the data. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.     

The Latent Structure of Substance Use Among American Indian Adolescents: An Example Using Categorical Variables

Researchers frequently have only categorical data to analyze and cannot, for theoretical or methodological reasons, assume that the observed variables are discrete representations of an underlying continuous variable. We present latent class analysis as an alternative method of measuring latent variables in these circumstances. Latent class analysis does not require the assumptions of factor analyses about the nature of manifest and latent variables, but does allow the use of more precise model selection than techniques such as cluster analysis. We modeled the lifetime substance use of American Indian youth. The latent class model of American Indian teenagers' substance use had four classes: Abstaining, Predominantly Alcohol, Predominantly Alcohol and Marijuana, and Plural Substance. We then demonstrated the usefulness of this latent variable by using it to differentiate levels of several variables in a manner consistent with Social Cognitive Theory.     

Bohr as a Phenomenological Realist

There is confusion among scholars of Bohr as to whether he should be categorized as an instrumentalist (see Faye 1991) or a realist (see Folse 1985). I argue that Bohr is a realist, and that the confusion is due to the fact that he holds a very special view of realism, which did not coincide with the philosophers’ views. His approach was sometimes labelled instrumentalist and other times realist, because he was an instrumentalist on the theoretical level, but a realist on the level of models. Such a realist position is what I call phenomenological realism. In this paper, and by taking Bohr’s debate with Einstein as a paradigm, I try to prove that Bohr was such a realist.

An alternative two stage least squares (2SLS) estimator for latent variable equations

The Maximum-likelihood estimator dominates the estimation of general structural equation models. Noniterative, equation-by-equation estimators for factor analysis have received some attention, but little has been done on such estimators for latent variable equations. I propose an alternative 2SLS estimator of the parameters in LISREL type models and contrast it with the existing ones. The new 2SLS estimator allows observed and latent variables to originate from nonnormal distributions, is consistent, has a known asymptotic covariance matrix, and is estimable with standard statistical software. Diagnostics for evaluating instrumental variables are described. An empirical example illustrates the estimator. I gratefully acknowledge support for this research from the Sociology Program of the National Science Foundation (SES-9121564) and the Center for Advanced Study in the Behavioral Sciences, Stanford, California. This paper was presented at the Interdisciplinary Consortium for Statistical Applications at Indiana University at Bloomington (March 2, 1994) and at the RMD Conference on Causal Modeling at Purdue University, West Lafayette, Indiana (March 3-5, 1994).     

Understanding the “cognitive revolution” in psychology

In this paper it is argued that the “cognitive revolution” in psychology is not best represented either as a Kuhnian “paradigm shift,” or as a movement from an instrumentalist to a realist conception of psychological theory, or as a continuous evolution out of more “liberalized” forms of behaviorism, or as a return to the form of “structuralist” psychology practiced by Wundt and Titchener. It is suggested that the move from behaviorism to cognitivism is best represented in terms of the replacement of (operationally defined) “intervening variables” by genuine “hypothetical constructs” possessing cognitive “surplus meaning,” and that the “cognitive revolution” of the 1950s continued a cognitive tradition that can be traced back to the 1920s. © 1999 John Wiley & Sons, Inc.     

A latent state-trait anxiety model and its application to determine consistency and specificity coefficients

Abstract

In the present study the consistency model (Steyer, 1987) was applied to data gathered with the German version of the State-Trait Anxiety Inventory (Laux, Glanzmann, Schaffner, and Spielberger, 1981). The questionnaire was presented twice to 64 university students with an interval of two months between first and second testing. The consistency and specificity coefficients, estimated by LISREL (Jöreskog and Sörbom, 1984), support the state-trait distinction. The state variables have high specificity and consistency coefficients; the trait variables, in contrast, have high consistency coefficients but low or even negligible specificity coefficients. The discussion points out the advantages of the consistency model over the stability model; the most important advantage is that the theoretical structure of the consistency model is more appropriate for the type of application considered. It contains a state factor for each occasion of measurement and a trait factor common to all occasions of measurement. In the stability model, only latent states, but no latent traits occur. Consistency, specificity, and reliability can be identified as proportions of variance determined by latent variables specified in the model. Therefore, data analysis provides statistics concerning the practical significance of the trait and of the effects of situations and/or interactions.     

Latent Growth Modeling for Logistic Response Functions

Throughout much of the social and behavioral sciences, latent growth modeling (latent curve analysis) has become an important tool for understanding individuals' longitudinal change. Although nonlinear variations of latent growth models appear in the methodological and applied literature, a notable exclusion is the treatment of growth following logistic (sigmoidal; S-shape) response functions. Such trajectories are assumed in a variety of psychological and educational settings where learning occurs over time, and yet applications using the logistic model in growth modeling methodology have been sparse. The logistic function, in particular, may not be utilized as often because software options remain limited. In this article we show how a specialized version of the logistic function can be modeled using conventional structural equation modeling software. The specialization is a reparameterization of the logistic function whose new parameters correspond to scientifically interesting characteristics of the growth process. In addition to providing an example using simulated data, we show how this nonlinear functional form can be fit using transformed subject-level data obtained through a learning task from an air traffic controller simulation experiment. LISREL syntax for the empirical example is provided.     

Path model analyzed with ordinary least squares multiple regression versus LISREL

The data of a specified path model using the variables of voice, perceived organizational support, being heard, and procedural justice were subjected to the two separate structural equation modeling analytic techniques--that of ordinary least squares regression and LISREL. A comparison of the results and differences between the analyses is discussed, with the LISREL approach being stronger from both theoretical and statistical perspectives.     

Longitudinal Analysis of Select Influences on Adolescents' Occupational Aspirations

The National Education Longitudinal Study of 1988 was used to investigate the longitudinal influence of select demographic and latent variables on the development of adolescents' occupational aspirations at three critical points in the career development process—early, mid-, and late adolescence. Linear structural equation (LISREL) analysis examined the contributions of family status, academic achievement, and social psychological variables. Occupational aspirations of adolescents were relatively stable across the 4-year time period. Further, earlier aspirations offered significant predictive power for subsequent ones. Structural coefficients for social demographic variables indicated that socioeconomic status (SES) had significant effects on adolescents' aspirations. In contrast, two latent variables, academic achievement and self-evaluation, initially represented only modest effects on aspirations which then decreased consistently over time.     

The theoretical status of latent variables

This article examines the theoretical status of latent variables as used in modern test theory models. First, it is argued that a consistent interpretation of such models requires a realist ontology for latent variables. Second, the relation between latent variables and their indicators is discussed. It is maintained that this relation can be interpreted as a causal one but that in measurement models for interindividual differences the relation does not apply to the level of the individual person. To substantiate intraindividual causal conclusions, one must explicitly represent individual level processes in the measurement model. Several research strategies that may be useful in this respect are discussed, and a typology of constructs is proposed on the basis of this analysis. The need to link individual processes to latent variable models for interindividual differences is emphasized.     

Equivalence of a measurement model of cognitive abilities in U.S. standardization and Australian neuroscience samples

A psychological measurement model provides an explicit definition of (a) the theoretical and (b) the numerical relationships between observed scores and the latent variables that underlie the observed scores. Examination of the metric invariance of a measurement model involves testing the hypothesis that all components of the model relating observed scores to latent variables are equal across groups. The assumption of metric invariance is necessary for simple interpretation of scores. Establishing metric invariance also has implications for interpretation of convergent and divergent validity and patterns of deficit or disability. In this study the equivalence of the measurement model derived from the U.S. Wechsler Adult Intelligence Scale-III standardization sample was compared with a heterogeneous neurosciences sample in Australia. A pattern of strict metric invariance was observed across samples. These results provide evidence of the generality of the model underlying measurement of cognitive abilities.     

Causal Models of Physical Exercise Participation: Testing the Predictive Power of the Construct "Pressure to Change"

This study examines 2 different causal models to predict physical exercise motivation and behavior under a longitudinal perspective. The first model includes 5 latent variables that were hypothesized to have an impact on exercise intention and behavior: behavior-specific social support, exercise self-efficacy, perceived health benefits, perceived barriers, and subjective vulnerability to cardiovascular disease. The second model was based on all variables of the first model, but additionally included the new variable "pressure to change." Pressure to change was defined as the extent to which a person feels the necessity that specific personal life circumstances (e.g., health status, social relations) may not remain as they are and ought to be changed. It was hypothesized that the inclusion of health-related pressure to change would result in a better prediction of exercise intention. The proposed causal models were tested separately at the stages of exercise adoption and maintenance. Covariance structure analyses (LISREL) confirmed that pressure to change may be an important factor in the motivational process that leads to the adoption of regular physical exercise. Adding this latent variable to the basic model improved the amount of explained variance in exercise intention by 6%. Furthermore, the results did not support the assumption that cognitive control is critical especially during the acquisition of exercise behaviors, but may be less influential once the behavioral routines have been established. Our data rather indicate that regular physical exercise, even if performed on a regular basis for years, always remains a behavior that requires a high level of cognitive guidance.     

On the explaining‐away phenomenon in multivariate latent variable models

Many probabilistic models for psychological and educational measurements contain latent variables. Well‐known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the ‘explaining‐away’ phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well‐known latent variable models by using both theoretical and real data examples.     

The trade-off between accuracy and precision in latent variable models of mediation processes

Social psychologists place high importance on understanding mechanisms and frequently employ mediation analyses to shed light on the process underlying an effect. Such analyses can be conducted with observed variables (e.g., a typical regression approach) or latent variables (e.g., a structural equation modeling approach), and choosing between these methods can be a more complex and consequential decision than researchers often realize. The present article adds to the literature on mediation by examining the relative trade-off between accuracy and precision in latent versus observed variable modeling. Whereas past work has shown that latent variable models tend to produce more accurate estimates, we demonstrate that this increase in accuracy comes at the cost of increased standard errors and reduced power, and examine this relative trade-off both theoretically and empirically in a typical 3-variable mediation model across varying levels of effect size and reliability. We discuss implications for social psychologists seeking to uncover mediating variables and provide 3 practical recommendations for maximizing both accuracy and precision in mediation analyses.     

Growth modeling with nonignorable dropout: alternative analyses of the STAR*D antidepressant trial

This article uses a general latent variable framework to study a series of models for nonignorable missingness due to dropout. Nonignorable missing data modeling acknowledges that missingness may depend not only on covariates and observed outcomes at previous time points as with the standard missing at random assumption, but also on latent variables such as values that would have been observed (missing outcomes), developmental trends (growth factors), and qualitatively different types of development (latent trajectory classes). These alternative predictors of missing data can be explored in a general latent variable framework with the Mplus program. A flexible new model uses an extended pattern-mixture approach where missingness is a function of latent dropout classes in combination with growth mixture modeling. A new selection model not only allows an influence of the outcomes on missingness but allows this influence to vary across classes. Model selection is discussed. The missing data models are applied to longitudinal data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, the largest antidepressant clinical trial in the United States to date. Despite the importance of this trial, STAR*D growth model analyses using nonignorable missing data techniques have not been explored until now. The STAR*D data are shown to feature distinct trajectory classes, including a low class corresponding to substantial improvement in depression, a minority class with a U-shaped curve corresponding to transient improvement, and a high class corresponding to no improvement. The analyses provide a new way to assess drug efficiency in the presence of dropout.     

Developing and testing a model of loneliness

This article presents a model of loneliness that incorporates characteristics of the social network, background variables, personality characteristics, and evaluative aspects. The most salient aspect of this approach is its emphasis on cognitive processes that mediate between characteristics of the social network and the experience of loneliness. A total of 554 adult men and women served as respondents. The program LISREL, a causal modelling approach, was used to analyze the data. The LISREL program includes a goodness-of-fit test that indicates the degree of fit between a particular model and the data. The hypothesized model made a valuable contribution to the understanding of loneliness: It accounted for 52.3% of the variance in the data set. One of the model's major advantages is its ability to disentangle both the direct and the indirect causal influences of the various factors on loneliness.     

A latent variable approach to executive control in healthy ageing

It is a well-established finding that the central executive is fractionated in at least three separable component processes: Updating, Shifting, and Inhibition of information (Miyake et al., 2000). However, the fractionation of the central executive among the elderly has been less well explored, and Miyake's et al. latent structure has not yet been integrated with other models that propose additional components, such as access to long-term information. Here we administered a battery of classic and newer neuropsychological tests of executive functions to 122 healthy individuals aged between 48 and 91 years. The test scores were subjected to a latent variable analysis (LISREL), and yielded four factors. The factor structure obtained was broadly consistent with Miyake et al.'s three-factor model. However, an additional factor, which was labeled 'efficiency of access to long-term memory', and a mediator factor ('speed of processing') were apparent in our structural equation analysis. Furthermore, the best model that described executive functioning in our sample of healthy elderly adults included a two-factor solution, thus indicating a possible mechanism of dedifferentiation, which involves larger correlations and interdependence of latent variables as a consequence of cognitive ageing. These results are discussed in the light of current models of prefrontal cortex functioning.