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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 235 (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.
Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data
, 2004
"... This chapter gives an overview of recent advances in latent variable analysis. Emphasis is placed on the strength of modeling obtained by using a flexible combination of continuous and categorical latent variables. ..."
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Cited by 160 (16 self)
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This chapter gives an overview of recent advances in latent variable analysis. Emphasis is placed on the strength of modeling obtained by using a flexible combination of continuous and categorical latent variables.
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 147 (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
Beyond SEM: General latent variable modeling
 Behaviormetrika
, 2002
"... This article gives an overview of statistical analysis with latent variables. Using traditional structural equation modeling as a starting point, it shows how the idea of latent variables captures a wide variety of statistical concepts, including random e&ects, missing data, sources of variatio ..."
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Cited by 116 (9 self)
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This article gives an overview of statistical analysis with latent variables. Using traditional structural equation modeling as a starting point, it shows how the idea of latent variables captures a wide variety of statistical concepts, including random e&ects, missing data, sources of variation in hierarchical data, hnite mixtures, latent classes, and clusters. These latent variable applications go beyond the traditional latent variable useage in psychometrics with its focus on measurement error and hypothetical constructs measured by multiple indicators. The article argues for the value of integrating statistical and psychometric modeling ideas. Di&erent applications are discussed in a unifying framework that brings together in one general model such di&erent analysis types as factor models, growth curve models, multilevel models, latent class models and discretetime survival models. Several possible combinations and extensions of these models are made clear due to the unifying framework. 1.
General growth mixture modeling for randomized preventive interventions
, 2002
"... This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials. Growth mixture modeling represents unobserved heterogeneity among the subjects using a finitemixture random effects model. The methodology allows one to examine the impact of an interventio ..."
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Cited by 96 (21 self)
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This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials. Growth mixture modeling represents unobserved heterogeneity among the subjects using a finitemixture random effects model. The methodology allows one to examine the impact of an intervention on subgroups characterized by different types of growth trajectories. Such modeling is informative when examining effects on populations that contain individuals who have normative growth as well as nonnormative growth. The analysis identifies subgroup membership and allows theorybased modeling of intervention effects in the different subgroups. An example is presented concerning a randomized
Mental health provider attitudes toward adoption of evidencebased practice: The EvidenceBased Practice Attitude
 Scale (EBPAS). Mental Health Services Research
, 2004
"... Mental health provider attitudes toward organizational change have not been well studied. Dissemination and implementation of evidencebased practices (EBPs) into realworld settings represent organizational change that may be limited or facilitated by provider attitudes toward adoption of new trea ..."
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Cited by 93 (6 self)
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Mental health provider attitudes toward organizational change have not been well studied. Dissemination and implementation of evidencebased practices (EBPs) into realworld settings represent organizational change that may be limited or facilitated by provider attitudes toward adoption of new treatments, interventions, and practices. A brief measure of mental health provider attitudes toward adoption of EBPs was developed and attitudes were examined in relation to a set of provider individual difference and organizational characteristics. Methods: Participants were 322 public sector clinical service workers from 51 programs providing mental health services to children and adolescents and their families. Results: Four dimensions of attitudes toward adoption of EBPs were identified: (1) intuitive Appeal of EBP, (2) likelihood of adopting EBP given Requirements to do so, (3) Openness to new practices, and (4) perceived Divergence of usual practice with researchbased/academically developed interventions. Provider attitudes varied by education level, level of experience, and organizational context. Conclusions: Attitudes toward adoption of EBPs can be reliably measured and vary in relation to individual differences and service context. EBP implementation plans should include consideration of mental health service provider attitudes as a potential aid to improve the process and effectiveness of dissemination efforts. KEY WORDS: evidencebased practice; attitudes; dissemination; mental health; child; adolescent; organization; services.
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 73 (13 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
Investigating population heterogeneity with factor mixture models
 Psychological Methods
, 2005
"... Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed ..."
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Cited by 73 (4 self)
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Sources of population heterogeneity may or may not be observed. If the sources of heterogeneity are observed (e.g., gender), the sample can be split into groups and the data analyzed with methods for multiple groups. If the sources of population heterogeneity are unobserved, the data can be analyzed with latent class models. Factor mixture models are a combination of latent class and common factor models and can be used to explore unobserved population heterogeneity. Observed sources of heterogeneity can be included as covariates. The different ways to incorporate covariates correspond to different conceptual interpretations. These are discussed in detail. Characteristics of factor mixture modeling are described in comparison to other methods designed for data stemming from heterogeneous populations. A stepbystep analysis of a subset of data from the Longitudinal Survey of American Youth illustrates how factor mixture models can be applied in an exploratory fashion to data collected at a single time point. The populations investigated in the behavioral sciences and related fields of research are often heterogeneous. A sample may consist of explicitly defined groups such as experimental and control groups, and the aim is to compare these groups. On the other hand, the sources of population heterogeneity may not be known beforehand. Test scores on a cognitive test may reflect two types of children in the sample: those who master the knowledge required to solve the items (masters) and those who lack this critical knowledge (nonmasters). The interest may be to decide to which of the subpopulations a given child most likely belongs. In addition, it may be of interest to characterize masters and nonmasters using background variables to develop specific
Sensitivity of goodness of fit indexes to lack of measurement invariance
 Structural Equation Modeling
, 2007
"... Two Monte Carlo studies were conducted to examine the sensitivity of goodness of fit indexes to lack of measurement invariance at 3 commonly tested levels: factor loadings, intercepts, and residual variances. Standardized root mean square residual (SRMR) appears to be more sensitive to lack of invar ..."
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Cited by 66 (0 self)
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Two Monte Carlo studies were conducted to examine the sensitivity of goodness of fit indexes to lack of measurement invariance at 3 commonly tested levels: factor loadings, intercepts, and residual variances. Standardized root mean square residual (SRMR) appears to be more sensitive to lack of invariance in factor loadings than in intercepts or residual variances. Comparative fit index (CFI) and root mean square error of approximation (RMSEA) appear to be equally sensitive to all 3 types of lack of invariance. The most intriguing finding is that changes in fit statistics are affected by the interaction between the pattern of invariance and the proportion of invariant items: when the pattern of lack of invariance is uniform, the relation is nonmonotonic, whereas when the pattern of lack of invariance is mixed, the relation is monotonic. Unequal sample sizes affect changes across all 3 levels of invariance: Changes are bigger when sample sizes are equal rather than when they are unequal. Cutoff points for testing invariance at different levels are recommended. Measurement invariance is a prerequisite for comparing different cultural, ethnic, gender, age, or experimental versus control groups. When groups are compared based on instruments that do not measure the same constructs, inference problems occur. In other words, the conclusions drawn from a study may be biased or invalid if the measures that we rely on do not have the same meanings across different groups. Consequently, an effective health prevention program might be deemed harmful whereas a detrimental early education program might be considered beneficial. For example, in a pioneering study (Millsap & Kwok, 2004) on the impact of lack of measurement invariance on group comparisons, selection bias under varying conditions of lack of invariance was examined. The
Are There Better Indices for Evaluation Purposes than the hindex? A Comparison of Nine Different Variants of the h Index Using Data from Biomedicine
, 2008
"... In this study, we examined empirical results on the h index and its most important variants in order to determine whether the variants developed are associated with an incremental contribution for evaluation purposes. The results of a factor analysis using bibliographic data on postdoctoral resear ..."
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Cited by 64 (2 self)
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In this study, we examined empirical results on the h index and its most important variants in order to determine whether the variants developed are associated with an incremental contribution for evaluation purposes. The results of a factor analysis using bibliographic data on postdoctoral researchers in biomedicine indicate that regarding the h index and its variants, we are dealing with two types of indices that load on one factor each. One type describes the most productive core of a scientist’s output and gives the number of papers in that core. The other type of indices describes the impact of the papers in the core. Because an index for evaluative purposes is a useful yardstick for comparison among scientists if the index corresponds strongly with peer assessments, we calculated a logistic regression analysis with the two factors resulting from the factor analysis as independent variables and peer assessment of the postdoctoral researchers as the dependent variable. The results of the regression analysis show that peer assessments can be predicted better using the factor ‘impact of the productive core ’ than using the factor ‘quantity of the productive core.’