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Latent variable model

About: Latent variable model is a research topic. Over the lifetime, 3589 publications have been published within this topic receiving 235061 citations.


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Journal ArticleDOI
TL;DR: This paper used a three-level hierarchical linear model to estimate latent true score measures of students' perceptions of goal structures, appropriately adjusted for their nested structure, and examined the inter-correlations among the student and classroom level variables, and predictors of each.

108 citations

Journal ArticleDOI
TL;DR: In this paper, Latent variable interaction modeling with continuous observed variables is presented using two different approaches: LISREL 8.30 and PRELIS2 and SIMPLIS programs.
Abstract: Latent variable interaction modeling with continuous observed variables is presented using 2 different approaches. The 1st approach analyzes data using a LISREL 8.30 program where the latent interaction variable is defined by multiplying pairs of observed variables. The 2nd approach analyzes data using PRELIS2 and SIMPLIS programs where the latent interaction variable is defined by multiplying the latent variable scores of the exogeneous latent independent variables. The programs used to create the multivariate normal observed variables and conduct the analyses for the 2 different approaches are given in the appendixes. The product indicant and latent variable score approach produced similar gamma coefficients in their hypothesized models but differed in their standard errors for the gamma coefficients. The latent variable score approach holds the promise of being easier to implement and can be applied to more complex latent variable interaction models.

107 citations

Journal ArticleDOI
TL;DR: The RMSEA-P and its confidence interval are incorporated into James, Mulaik, and Brett's (1982) framework for model testing, providing evidence that many of the conclusions based upon the goodness of fit of the overall model may be inaccurate.
Abstract: Goodness-of-fit indices have an important role in structural equation model evaluation. However, some studies (e.g., McDonald & Ho, 2002; Mulaik et al., 1989) have raised concerns that overall fit values primarily reflect the fit of the measurement model, and this allows significant misspecification among the latent variables to be masked. Using an approach analogous to Anderson and Gerbing's (1988) 2-step approach that isolates the measurement component of a composite model, we present the rationale and evidence for the root mean square error of approximation of the path component (RMSEA-P), a relatively new fit index that isolates the path component. We reviewed 5 of the top organizational behavior/human resources journals from 2001 to 2008 and identified 43 studies using structural equation modeling in which the overall composite model could be decomposed into its measurement and path components. The RMSEA-P for these studies generally showed unfavorable results, with many values failing to meet commonly accepted standards. Incorporating the RMSEA-P and its confidence interval into James, Mulaik, and Brett's (1982) framework for model testing, we provide evidence that many of the conclusions based upon the goodness of fit of the overall model may be inaccurate. We conclude with recommendations for how researchers can focus more attention on path models and latent variable relations and improve their model evaluation process.

107 citations

Journal ArticleDOI
TL;DR: One particular form of data imputation based on latent variable modeling, which is called Multivariate Imputation, is highlighted as holding great promise for dealing with missing data in the context of multivariate analysis.
Abstract: Missing data constitute a common but widely underappreciated problem in both cross-sectional and longitudinal research. Furthermore, both the gravity of the problems associated with missing data and the availability of the applicable solutions are greatly increased by the use of multivariate analysis. The most common approaches to dealing with missing data are reviewed, such as data deletion and data imputation, and their relative merits and limitations are discussed. One particular form of data imputation based on latent variable modeling, which we call Multivariate Imputation, is highlighted as holding great promise for dealing with missing data in the context of multivariate analysis. The recent theoretical extension of latent variable modeling to growth curve analysis also permitted us to extend the same kind of solution to the problem of missing data in longitudinal studies. Data simulations are used to compare the results of multivariate imputation to other common approaches to missing data.

107 citations

Journal ArticleDOI
TL;DR: In this paper, the relationship between government spending on health care and education and selected social indicators was estimated using a latent variable model, and it was shown that increases in public spending do have a positive impact on social outcomes.
Abstract: Using data for a sample of developing countries and transition economies, this paper estimates the relationship between government spending on health care and education and selected social indicators. Unlike previous studies, where social indicators are used as proxies for the unobservable health and education status of the population, this paper estimates a latent variable model. The findings suggest that public spending is an important determinant of social outcomes, particularly in the education sector. Overall, the latent variable approach yields better estimates of a social production function than the traditional approach, with higher elasticities of social indicators with respect to income and spending, therefore providing stronger evidence that increases in public spending do have a positive impact on social outcomes. Copyright # 2003 John Wiley & Sons, Ltd.

106 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202375
2022143
2021137
2020185
2019142
2018159