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Principles and practice in reporting structural equation analyses
 PSYCHOLOGICAL METHODS
, 2002
"... Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of ident ..."
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Cited by 224 (1 self)
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Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodnessoffit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here.
Mplus: Statistical Analysis with Latent Variables (Version 4.21) [Computer software
, 2007
"... Chapter 3: Regression and path analysis 19 Chapter 4: Exploratory factor analysis 43 ..."
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Cited by 162 (0 self)
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Chapter 3: Regression and path analysis 19 Chapter 4: Exploratory factor analysis 43
An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data
 Psychological Methods
, 2004
"... Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likerttype items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approa ..."
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Cited by 140 (4 self)
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Confirmatory factor analysis (CFA) is widely used for examining hypothesized relations among ordinal variables (e.g., Likerttype items). A theoretically appropriate method fits the CFA model to polychoric correlations using either weighted least squares (WLS) or robust WLS. Importantly, this approach assumes that a continuous, normal latent process determines each observed variable. The extent to which violations of this assumption undermine CFA estimation is not wellknown. In this article, the authors empirically study this issue using a computer simulation study. The results suggest that estimation of polychoric correlations is robust to modest violations of underlying normality. Further, WLS performed adequately only at the largest sample size but led to substantial estimation difficulties with smaller samples. Finally, robust WLS performed well across all conditions. Variables characterized by an ordinal level of measurement are common in many empirical investigations within the social and behavioral sciences. A typical situation involves the development or refinement of a psychometric test or survey in which a set of ordinally scaled items (e.g., 0
Racial and economic factors in attitudes to immigration", CEPR working paper n° 2542 Espenshade T. and Hempstead K.(1996), "Contemporary American attitudes toward U.S
, 2000
"... Hostilitytowards minorities may sometimes haveeconomicrather thanracial motives. Labour market fears, or concerns about the welfare system, may manifest themselves in hostile remarks and actions against population groups that are considered to be competitors for these resources, as well as political ..."
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Cited by 97 (7 self)
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Hostilitytowards minorities may sometimes haveeconomicrather thanracial motives. Labour market fears, or concerns about the welfare system, may manifest themselves in hostile remarks and actions against population groups that are considered to be competitors for these resources, as well as political radicalisation. The question of what are the components of (often hostile) attitudes of majority populations towards minority related questions, like attitudes towards further immigration, are of great importance for implementing appropriate policies, and to identify the sources of hostility seems crucial for understanding the e±cacy of political actions. We try to isolate the components of such attitudes. Our analysis is based on the British Social Attitudes Survey, which includes questions on attitudes towards immigration from di®erent minority groups, as well as attitudes towards related concerns, like job security and bene¯t expenditures. This information allows us to explore the components of attitudes towards immigration. We specify and estimate a multifactor model. The correlation between answers to questions on immigration and on related issues help us separate di®erent aspects to attitudes.
in press). Generalized multilevel structural equation modeling
, 2002
"... A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a respon ..."
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Cited by 63 (12 self)
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A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent variables. The response model generalizes GLMMs to incorporate factor structures in addition to random intercepts and coefficients. As in GLMMs, the data can have an arbitrary number of levels and can be highly unbalanced with different numbers of lowerlevel units in the higherlevel units and missing data. A wide range of response processes can be modeled including ordered and unordered categorical responses, counts, and responses of mixed types. The structural model is similar to the structural part of a SEM except that it may include latent and observed variables varying at different levels. For example, unitlevel latent variables (factors or random coefficients) can be regressed on clusterlevel latent variables. Special cases of this framework are explored and data from the British Social Attitudes Survey are used for illustration. Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata. Key words: multilevel structural equation models, generalized linear mixed models, latent variables, random
Recent developments in the factor analysis of categorical variables
 Journal of Educational Statistics
, 1986
"... ABSTRACT. Despite known shortcomings of the procedure, exploratory factor analysis of dichotomous test items has been limited, until recently, to unweighted analyses of matrices of tetrachoric correlations. Superior methods have begun to appear in the literature, in professional symposia, and in com ..."
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Cited by 55 (0 self)
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ABSTRACT. Despite known shortcomings of the procedure, exploratory factor analysis of dichotomous test items has been limited, until recently, to unweighted analyses of matrices of tetrachoric correlations. Superior methods have begun to appear in the literature, in professional symposia, and in computer programs. This paper places these developments in a unified framework, from a review of the classical common factor model for measured variables through generalized least squares and marginal maximum likelihood solutions for dichotomous data. Further extensions of the model are also reported as work in progress. Under classical Thurstonian factor analysis (Thurstone, 1947), values of p measured variables are modeled as linear functions of some smaller number of m continuous latent variables, the "factors " that account for the correlations among the observed variables. The usual objectives in factor analysis are (a) to determine the number of factors that provide a satisfactory fit to the observed correlation matrix and (b) to estimate the regression coefficients of the observed variables on the factors—all this, it is hoped, leading to a more
Generalized latent trait models
 Psychometrika
, 2000
"... In this paper we discuss a general model framework within which manifest variables with different distributions in the exponential family can be analyzed with a latent trait model. A unified maximum likelihood method for estimating the parameters of the generalized latent trait model will be present ..."
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Cited by 47 (3 self)
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In this paper we discuss a general model framework within which manifest variables with different distributions in the exponential family can be analyzed with a latent trait model. A unified maximum likelihood method for estimating the parameters of the generalized latent trait model will be presented. We discuss in addition the scoring of individuals on the latent dimensions. The general framework presented allows, not only the analysis of manifest variables all of one type but also the simultaneous analysis of a collection of variables with different distributions. The approach used analyzes the data as they are by making assumptions about the distribution of the manifest variables directly. Key words: generalized linear models, latent trait model, EM algorithm, scoring methods. 1.
Assessing factorial invariance in orderedcategorical measures
 Multivariate Behavioral Research
, 2004
"... The factor analysis of orderedcategorical measures has been described in the literature on factor analysis, but the extension of the analysis to the multiplepopulation case is less wellknown. For example, a comprehensive statement of identification conditions for the multiplepopulation case seem ..."
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Cited by 45 (1 self)
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The factor analysis of orderedcategorical measures has been described in the literature on factor analysis, but the extension of the analysis to the multiplepopulation case is less wellknown. For example, a comprehensive statement of identification conditions for the multiplepopulation case seems absent in the literature. We review this multiplepopulation extension here, with an emphasis on model specification and identification. The use of the method in the study of factorial invariance is described. New results on identification are given for a variety of factor structures and types of measures. Two widelyavailable software packages, LISREL 8.52 (Jöreskog & Sörbom, 1996) and Mplus 2.12 (Muthén & Muthén, 1998), are applied in simulated data to illustrate the method. The two programs are shown to have different model specifications for this method, leading to different fit results in some cases. The final section discusses some remaining problems facing researchers who wish to study factorial invariance in orderedcategorical data.
Bayesian Estimation and Testing of Structural Equation Models
 Psychometrika
, 1999
"... The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameter ..."
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Cited by 43 (10 self)
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The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. If the prior distribution over the parameters is uninformative, the posterior is proportional to the likelihood, and asymptotically the inferences based on the Gibbs sample are the same as those based on the maximum likelihood solution, e.g., output from LISREL or EQS. In small samples, however, the likelihood surface is not Gaussian and in some cases contains local maxima. Nevertheless, the Gibbs sample comes from the correct posterior distribution over the parameters regardless of the sample size and the shape of the likelihood surface. With an informative prior distribution over the parameters, the posterior can be used to make inferences about the parameters of underidentified models, as we illustrate on a simple errorsinvariables model.
The Importance of Teacher Quality as a Key Determinant of Students’ Experiences and Outcomes of Schooling
, 2003
"... Much of the traditional and prevailing dogmas surrounding ‘factors’ affecting students ’ experiences and outcomes of schooling throughout their primary and secondary years – especially sociocultural and socioeconomic factors – are now understood to be products of methodological and statistical art ..."
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Cited by 41 (6 self)
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Much of the traditional and prevailing dogmas surrounding ‘factors’ affecting students ’ experiences and outcomes of schooling throughout their primary and secondary years – especially sociocultural and socioeconomic factors – are now understood to be products of methodological and statistical artefact, and amount to little more than ‘religious’ adherence to the moribund ideologies of biological and social determinism. Moreover, postmodernist perspectives espoused by academics promoting the deconstruction of genderspecific pedagogy and ‘middleclass’ curricula, are equally unhelpful. Above all, a good deal of this ‘discourse ’ is not supported by findings from evidencebased research. In this paper, key findings are presented highlighting ‘real’ effects from recent and emerging local and international research on educational effectiveness. For example, whereas students ’ literacy skills, general academic achievements, attitudes, behaviors and experiences of schooling are influenced by their background and intake characteristics – the magnitude of these effects pale into insignificance compared with class/teacher effects. That is, the quality of teaching and learning provision are by far the most salient influences on students’ cognitive, affective, and behavioral outcomes of schooling –