Showing papers on "Latent variable model published in 1994"
TL;DR: This paper proposed a framework for representing personality constructs at four levels of abstraction, i.e., partial disaggregation, total aggregation, partial aggregation and total disaggregation models, where each dimension is either freely correlated with the other dimensions or loading on one or more order factors.
Abstract: This article proposes a framework for representing personality constructs at four levels of abstraction. The total aggregation model is the composite formed by the sum of scores on all items in a scale. The partial aggregation model treats separate dimensions of a personality construct as indicators of a single latent variable, with each dimension being an aggregation of items. The partial disaggregation model represents each dimension as a separate latent variable, either freely correlated with the other dimensions or loading on one or more than one higher order factor; the measures of the dimensions are multiple indicators formed as aggregates of subsets of items. The total disaggregation model also represents each dimension as a separate latent variable but, unlike the partial disaggregation model, uses each item in the scale as an indicator of its respective factor. Illustrations of the models are provided on the State Self‐Esteem Scale—including tests of psychometric properties, invariance, and gener...
1,507 citations
TL;DR: In this paper, a variant of the Gibbs sampler is used to draw from the exact posterior of the multinomial probit model with correlated errors, which avoids direct evaluation of the likelihood and thus avoids the problems associated with calculating choice probabilities which affect both the standard likelihood and method of simulated moments approaches.
574 citations
TL;DR: In this article, a stochastic volatility model is used to estimate daily asset price dynamics, and the model is estimated by integrating latent volatility out of the joint density of prices and volatility to obtain the marginal density.
394 citations
TL;DR: In this paper, an example of a latent-variable structural equation approach is presented as a more appropriate strategy for analyzing method effects that circumvents problems associated with prior statistical techniques.
Abstract: Recent research in the area of organizational behavior on social desirability and negative affectivity as potential sources of artifactual covariance is reviewed. Next, an example of a latent-variable structural equation approach is presented as a more appropriate strategy for analyzing method effects that circumvents problems associated with prior statistical techniques. This example illustrates the specification of a structural equation model with and without method effects and describes the sequence of model comparisons that provides direct tests for the presence of method effects and for the impact of these effects on estimates of substantive relationships. Finally, this latent-variable approach is discussed with regard to other potential applications involving method effects. An important stream of research on method variance in organizational behavior studies has attempted to directly measure some of the variables associated with common effects of method
357 citations
TL;DR: In this article, a simulation approach is used to investigate a multiple-indicator regression model in the context of covariance structure analysis, which is a commonly used type of model in applications of structure analysis.
Abstract: A frequently used type of model in applications of covariance structure analysis is one referred to as a multiple‐indicator regression model. This study takes a simulation approach to investigate s...
258 citations
TL;DR: This article describes the use of structural equation modeling with latent variables to examine group differences and test competing models about cause-effect relationships in passive longitudinal designs and compared with several other statistical methods including analysis of cross-lagged panel correlations, regression analysis, and path analysis.
Abstract: This article describes the use of structural equation modeling with latent variables to examine group differences and test competing models about cause-effect relationships in passive longitudinal designs. This approach is compared with several other statistical methods including analysis of cross-lagged panel correlations, regression analysis, and path analysis. The mechanics and advantages of structural equation modeling are illustrated using an example based on a 3-wave longitudinal study of adolescents' alcohol use. Within this example, the generalizability of the measurement model and structural model are assessed across gender and time, and competing models about the causes and consequences of adolescents' alcohol use are tested. The article concludes with a discussion of some of the strengths and limitations of using structural equation modeling with longitudinal data.
208 citations
TL;DR: In this paper, the use of structural equation modeling (SEM) for comparative treatment outcome research conducted with heterogeneous clinical subpopulations within large multimodal treatment settings is illustrated.
Abstract: The use of structural equation modeling (SEM) is illustrated for comparative treatment outcome research conducted with heterogeneous clinical subpopulations within large multimodality treatment settings. All analyses are accomplished with SEM analogs of more familiar classical multivariate techniques. The effect of the early period of treatment on the daily lives of 486 clients in two drug abuse treatment modalities (methadone maintenance and outpatient counseling) is evaluated. Structured means analysis is used to assess initial differences between modalities on the latent means of 6 latent constructs reflecting daily life. The effect of treatment modality and attrition from the program on daily life latent constructs is evaluated while initial selection differences are statistically controlled. Effect sizes are computed on the basis of SEM parameter estimates. The advantage of SEM over classic multivariate approaches for correcting for selection bias when assessing comparative outcomes is explained.
179 citations
TL;DR: This paper proposed a loglinear IRT model that relates polytomously scored item responses to a multidimensional latent space, where the analyst may specify a response function for each response, indicating which latent abilities are necessary to arrive at that response.
Abstract: A loglinear IRT model is proposed that relates polytomously scored item responses to a multidimensional latent space The analyst may specify a response function for each response, indicating which latent abilities are necessary to arrive at that response Each item may have a different number of response categories, so that free response items are more easily analyzed Conditional maximum likelihood estimates are derived and the models may be tested generally or against alternative loglinear IRT models
120 citations
TL;DR: In this article, a set of 10 theoretical structural predictions for the Figural Intersections Test (FIT) were derived, which stipulate relations between mental attentional resources (mental power: Mp) and the systematically varied mental demand of items (mental demand: Md), as they jointly codetermine probable performance.
Abstract: A dialectical constructivist model of mental attention ("effort") and of working memory is briefly presented, and used to explicate subjects' processing in misleading test items. We illustrate with task analyses of the Figural Intersections Test (FIT). We semantically derive a set of 10 Theoretical Structural Predictions (TSP) that stipulate relations between mental attentional resources (mental-power: Mp) and the systematically varied mental demand of items (mental-demand: Md), as they jointly codetermine probable performance (conditional probabilities of passing and failing). These predictions are evaluated on first approximation using a known family of ordered Latent Class models, all probabilistic versions of Guttman's unidimensional scale. Parameters of these models were estimated using the Categorical Data Analysis System of Eliason (1990). Main results are: (1) Data fit Lazarsfeld's latent-distance model, providing initial support for our 10 predictions; (2) The M-power of children (latent Mp-class...
118 citations
TL;DR: Investigation of developmental trends in adolescent alcohol, marijuana, and cigarette use across a 5-year period using multiple-group latent growth modeling revealed that common developmental trends existed for all three substances.
Abstract: Longitudinal data sets typically suffer from attrition and other forms of missing data. When this common problem occurs, several researchers have demonstrated that correct maximum likelihood estimation with missing data can be obtained under mild assumptions concerning the missing data mechanism. With reasonable substantive theory, a mixture of cross-sectional and longitudinal methods developed within multiple-group structural equation modeling can provide a strong basis for inference about developmental change. Using an approach to the analysis of missing data, the present study investigated developmental trends in adolescent (N = 759) alcohol, marijuana, and cigarette use across a 5-year period using multiple-group latent growth modeling. An associative model revealed that common developmental trends existed for all three substances. Age and gender were included in the model as predictors of initial status and developmental change. Findings discuss the utility of latent variable structural equation modeling techniques and missing data approaches in the study of developmental change.
116 citations
TL;DR: In this article, an example application of latent growth methodology analyzing developmental change in adolescent alcohol consumption is presented, with particular reference to the utility of Latent Growth Curve models for assessing de velopmental processes at both the inter- and intra-indi-vidual level across a variety of behavioral domains.
Abstract: Recent advances in latent growth modeling allow for the testing of complex models regarding develop mental trends from both an inter- and intra-individual perspective. The interpretation of model parameters for the latent growth specification is illustrated with a simple two-factor model. An example application of latent growth methodology analyzing developmental change in adolescent alcohol consumption is presented. Findings are discussed with particular reference to the utility of latent growth curve models for assessing de velopmental processes at both the inter- and intra-indi vidual level across a variety of behavioral domains. Index terms: alcohol consumption, change measure ment, developmental models, growth measurement, la tent growth models.
TL;DR: Results indicate that greater autonomy is significantly associated with greater perceived quality of life and that greater quality‐of‐life ratings are associated with more community tenure.
Abstract: Services are intended to maintain patients in the community while improving the quality of their lives. The purpose of this study was to determine the extent to which psychiatric patients' diagnoses, levels of autonomy, objective living conditions and degree of service utilization are associated with their perceived quality of life and, in turn, community tenure. A latent variable causal model was developed and tested using data from 152 schizophrenic and affective psychotic patients. Information was obtained using a semistructured interview, a quality of life scale, ratings on the Global Assessment Scale and patient hospital records. Results indicate that greater autonomy is significantly associated with greater perceived quality of life and that greater quality-of-life ratings are associated with greater community tenure.
TL;DR: In this article, a questionnaire assessing two enduring aspects of mood (mood level and mood reactivity) is reported, and a correlation between the latent trait variable underlying the mood level scale and the expectation of repeatedly measured mood states is estimated.
TL;DR: In this article, a series of rules that can be applied easily to measurement models of complexity one to demonstrate the identifiability of their parameters are presented, where the model is not identified, the rules pinpoint the parameters that are not identified and thus, help researchers formulate a testable model.
Abstract: Although computer programs may estimate values for unidentified parameters, a parameter must be identified in order for there to exist a unique point estimate of its value. We provide a series of rules that can be applied easily to measurement models of complexity one to demonstrate the identifiability of their parameters. These rules can be applied to models that contain one or more latent variables and that contain observed variables with correlated measurement errors. If the model is not identified, the rules pinpoint the parameters that are not identified and, thus, help researchers formulate a testable model.
TL;DR: In this article, a method for computing multidimensional information and providing examples of how different aspects of test information can be displayed graphically to form a profile of a test in a two-dimensional latent space.
Abstract: In some cognitive testing situations it is believed, despite reporting only a single score, that the test items differentiate levels of multiple traits. In such situa tions, the reported score may represent quite disparate composites of these multiple traits. Thus, when attempting to interpret a single score from a set of multidimensional items, several concerns naturally arise. First, it is important to know what composite of traits is being measured at all levels of the reported score scale. Second, it is also necessary to discern that all examinees, no matter where they lie in the latent trait space, are being measured on the same composite of traits. Thus, the role of multidimensionality in the interpretation or meaning given to various score levels must be examined. This paper presents a method for computing multidimensional information and provides examples of how different aspects of test information can be displayed graphically to form a profile of a test in a two-dimensional latent space. Index t...
01 Oct 1994
TL;DR: This article concerns multi-group covariance structure analysis with structured means of latent selection as a special case of phenotypic selection, that is, selection based not on latent variables, but on observed variables.
Abstract: This article concerns multi-group covariance structure analysis with structured means. The traditional latent selection model is formulated as a special case of phenotypic selection, that is, selection based not on latent variables, but on observed variables. This formulation has the advantage that it enables one to test very specific hypotheses concerning selection on latent variables. Illustrations are given using simulated and real data.
TL;DR: Latent trait models were fitted to data for schizophrenic or schizophreniform inpatients and it was found that a reduction in the numbers of rating categories led to consistencies in response while deletion of several items led to consistent scales of symptoms that accorded with an item response characterization.
Abstract: Latent trait models were fitted to data for 149 schizophrenic or schizophreniform inpatients rated on the Scale for the Assessment of Positive Symptoms (SAPS) and the Scale for the Assessment of Negative Symptoms (SANS) using the Rasch Extended Logistic Model. It was found that a reduction in the numbers of rating categories, from six to three or four led to consistencies in response while deletion of several items led to consistent scales of symptoms that accorded with an item response characterization. Behaviours included in the final scales varied in the numbers of categories, and in the range of symptom level covered by a category. Relationships between scores representing symptoms were found to be modelled better by a factor structure that included a third overlapping 'cognitive' factor in addition to the now traditional positive and negative factors, than by the original positive and negative factors alone.
TL;DR: In this paper, a general structural modeling approach is discussed, which allows estimation of theoretically and empirically relevant interrelationship indexes between growth or decline in longitudinally assessed psychological constructs and additional variables.
Abstract: This paper is concerned with the study of correlates and predictors of change in a multiwave design. A general structural modeling approach is discussed, which allows estimation of theoretically and empirically relevant interrelationship indexes between growth or decline in longitudinally assessed psychological constructs and additional variables. Several classical test theory-based structural models are discussed. The models permit consistent and efficient estimation of, and tests about, the degree of covariation between change in one or more repeatedly measured latent dimensions and other variables, such as studied or presumed correlates of growth or decline in the longitudinally observed constructs. These models are useful in developmental studies with multiple assessment points, in which variables that are correlated with, and can be used to predict, change in measured abilities in repeatedly assessed psychological characteristics are to be identified. The approach is illustrated with data from a cogn...
01 Jan 1994
TL;DR: This paper investigated the dimensionality of the 1992 NAEP mathematics test in the context of subgroup differences using a multidimensional model with dimensions corresponding to both content-specific and format-specific factors.
Abstract: This report investigates the dimensionality of the 1992 NAEP mathematics test in the context of subgroup differences. A multidimensional model is supported by these data with dimensions corresponding to both content-specific and format-specific factors. The analysis approach of this paper utilizes key grouping variables of the NAEP reports (e.g., gender, ethnicity) but has the advantage that subgroup comparisons are done not only in a univariate manner, using one grouping variable at a time, but using the set of grouping variables jointly. This is carried out within a structural model with latent variables, which relates the information on the test items to background information via a set of factors. It is found that the different factors relate differently to the background variables. Multidimensional latent variable modeling also suggests a new way of reporting results with respect to math performance in specific content areas. For content-specific performance, the subscores are related to overall performance, considering content-specific scores conditional on overall scores. For a given overall score, a subgroup difference is considered with respect to a certain content area. This conditional approach may be of value for revealing differences in opportunity to learn or differences in curricular emphases. Conditional differences may be viewed as “unrealized potential” for performance in a specific content area.
TL;DR: In this paper, differences between the positive and negative affect dimensions of Tellegen's (1985) model of mood were examined with 713 undergraduates, using findings from latent trait analysis.
01 Jan 1994
TL;DR: In this article, the authors considered the situation in which k tests are given to n subjects and the test scores are Poisson distributed random variables, where the Poisson parameter is assumed to be a product of a test difficulty and a subject ability parameter.
Abstract: This paper concerns the situation in which k tests are given to n subjects and the test scores are Poisson distributed random variables. The Poisson parameter is assumed to be a product of a test difficulty and a subject ability parameter. The test parameters are supposed to be fixed, and the ability parameters random. A number of different latent distributions are discussed, and we show how the parameters of these distributions can be estimated and the fit to the data checked.
TL;DR: In this article, the static approach is extended using multiple-indicator Markov chain models for longitudinal data obtained from repeated measure ments across time, and the applicability of these models is demonstrated using data from a lon gitinal study on solving arithmetic word problems.
Abstract: Macready & Dayton (1980) showed that state mas tery models are handled optimally within the general latent class framework for data from a single time point. An extension of this idea is presented here for longitudinal data obtained from repeated measure ments across time. The static approach is extended using multiple-indicator Markov chain models. The approach presented here emphasizes the dynamic as pects of the process of change, such as growth, decay, and stability. The general approach is presented, and models with purely categorical and ordered categorical states and several extensions of these models are dis cussed. Problems of estimation, identification, assess ment of model fit, and hypothesis testing associated with these models also are discussed. The applicability of these models is demonstrated using data from a lon gitudinal study on solving arithmetic word problems. The advantages and disadvantages of using the ap proach presented here are discussed. Index terms: arithmetic word problems, ...
01 Jan 1994
TL;DR: In this article, search procedures for finding a pure measurement model and using this pure model to determine features of the structural model are discussed and implemented as the Purify and MIMbuild modules of the TETRAD II program.
Abstract: Linear structural equation models with latent variables are common in psychology, econometrics, sociology, and political science. Such models have two parts, a measurement model that specifies how the latent variables are measured, and a structural model which specifies the causal relations among the latent variables. In this paper I discuss search procedures for finding a ‘pure’ measurement model and for using this pure model to determine features of the structural model. The procedures are implemented as the Purify and MIMbuild modules of the TETRAD II program.