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Evaluating the use of exploratory factor analysis in psychological research
 Psychological Methods
, 1999
"... Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of ..."
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Cited by 162 (3 self)
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Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of these decisions has important consequences for the obtained results. Recommendations that have been made in the methodological literature are discussed. Analyses of 3 existing empirical data sets are used to illustrate how questionable decisions in conducting factor analyses can yield problematic results. The article presents a survey of 2 prominent journals that suggests that researchers routinely conduct analyses using such questionable methods. The implications of these practices for psychological research are discussed, and the reasons for current practices are reviewed. Since its initial development nearly a century ago (Spearman, 1904, 1927), exploratory factor analysis (EFA) has been one of the most widely used statistical procedures in psychological research. Despite this
The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis
 Psychological Methods
, 1996
"... Monte Carlo computer simulations were used to investigate the performance of three X 2 test statistics in confirmatory factor analysis (CFA). Normal theory maximum likelihood)~2 (ML), Browne's asymptotic distribution free X 2 (ADF), and the SatorraBentler rescaled X 2 (SB) were examined under ..."
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Cited by 97 (5 self)
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Monte Carlo computer simulations were used to investigate the performance of three X 2 test statistics in confirmatory factor analysis (CFA). Normal theory maximum likelihood)~2 (ML), Browne's asymptotic distribution free X 2 (ADF), and the SatorraBentler rescaled X 2 (SB) were examined under varying conditions of sample size, model specification, and multivariate distribution. For properly specified models, ML and SB showed no evidence of bias under normal distributions across all sample sizes, whereas ADF was biased at all but the largest sample sizes. ML was increasingly overestimated with increasing nonnormality, but both SB (at all sample sizes) and ADF (only at large sample sizes) showed no evidence of bias. For misspecified models, ML was again inflated with increasing nonnormality, but both SB and ADF were underestimated with increasing nonnormality. It appears that the power of the SB and ADF test statistics to detect a model misspecification is attenuated given nonnormally distributed data. Confirmatory factor analysis (CFA) has become an increasingly popular method of investigating the structure of data sets in psychology. In contrast to traditional exploratory factor analysis that does not place strong a priori restrictions on the structure of the model being tested, CFA requires the investigator to specify both the number of factors
A Scaled Difference Chisquare Test Statistic for Moment Structure Analysis
"... A family of scaling corrections aimed to improve the chisquare approximation of goodnessoffit test statistics in small samples, large models, and nonnormal data was proposed in Satorra and Bentler (1994). For structural equations models, SatorraBentler's (SB) scaling corrections are availab ..."
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Cited by 86 (1 self)
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A family of scaling corrections aimed to improve the chisquare approximation of goodnessoffit test statistics in small samples, large models, and nonnormal data was proposed in Satorra and Bentler (1994). For structural equations models, SatorraBentler's (SB) scaling corrections are available in standard computer software. Often, however, the interest is not on the overall fit of a model, but on a test of the restrictions that a null model say M 0 implies on a less restricted one M 1 .IfT 0 and T 1 denote the goodnessoffit test statistics associated to M 0 and M 1 , respectively, then typically the difference T d = T 0 ; T 1 is used as a chisquare test statistic with degrees of freedom equal to the difference on the number of independent parameters estimated under the models M 0 and M 1 . As in the case of the goodnessoffit test, it is of interest to scale the statistic T d in order to improveitschisquare approximation in realistic, i.e., nonasymptotic and nonn...
Evaluating the fit of structural equation models: Tests of significance and descriptive goodnessoffit measures
 Methods of Psychological Research
, 2003
"... For structural equation models, a huge variety of fit indices has been developed. These indices, however, can point to conflicting conclusions about the extent to which a model actually matches the observed data. The present article provides some guidelines that should help applied researchers to e ..."
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Cited by 59 (0 self)
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For structural equation models, a huge variety of fit indices has been developed. These indices, however, can point to conflicting conclusions about the extent to which a model actually matches the observed data. The present article provides some guidelines that should help applied researchers to evaluate the adequacy of a given structural equation model. First, as goodnessoffit measures depend on the method used for parameter estimation, maximum likelihood (ML) and weighted least squares (WLS) methods are introduced in the context of structural equation modeling. Then, the most common goodnessoffit indices are discussed and some recommendations for practitioners given. Finally, we generated an artificial data set according to a "true" model and analyzed two misspecified and two correctly specified models as examples of poor model fit, adequate fit, and good fit.
A linear nongaussian acyclic model for causal discovery
 J. Machine Learning Research
, 2006
"... In recent years, several methods have been proposed for the discovery of causal structure from nonexperimental data. Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, we show how to ..."
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Cited by 56 (24 self)
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In recent years, several methods have been proposed for the discovery of causal structure from nonexperimental data. Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, we show how to discover the complete causal structure of continuousvalued data, under the assumptions that (a) the data generating process is linear, (b) there are no unobserved confounders, and (c) disturbance variables have nonGaussian distributions of nonzero variances. The solution relies on the use of the statistical method known as independent component analysis, and does not require any prespecified timeordering of the variables. We provide a complete Matlab package for performing this LiNGAM analysis (short for Linear NonGaussian Acyclic Model), and demonstrate the effectiveness of the method using artificially generated data and realworld data.
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 40 (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
Relationships of job and family involvement, family social support, and work–family conflict with job and life satisfaction
 Journal of Applied Psychology
, 1996
"... A model of the relationship between work and family that incorporates variables from both the workfamily conflict and social support literatures was developed and empirically tested. This model related bidirectional workfamily conflict, family instrumental and emotional social support, and job an ..."
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Cited by 35 (0 self)
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A model of the relationship between work and family that incorporates variables from both the workfamily conflict and social support literatures was developed and empirically tested. This model related bidirectional workfamily conflict, family instrumental and emotional social support, and job and family involvement to job and life satisfaction. Data came from 163 workers who were living with at least 1 family member. Results suggested that relationships between work and family can have an important effect on job and life satisfaction and that the level of involvement the worker assigns to work and family roles is associated with this relationship. The results also suggested that the relationship between work and family can be simultaneously characterized by conflict and support. Higher levels of work interfering with family predicted lower levels of family emotional and instrumental support. Higher levels of family emotional and instrumental support were associated with lower levels of family interfering with work. The growing body of occupational stress research regarding the relationship between work and family has suggested that there are interconnecting and possibly reciprocal influences between these two domains
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 30 (8 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 Robustness of LISREL Modeling Revisited
 Structural equation modeling: Present and future: A Festschrift in honor of Karl Jöreskog (pp. 139–168). Chicago: Scientific Software International
, 2001
"... Somer obustness questions in str uctur al equation modeling (SEM) ar intr duced. Factor that a#ect the occuruv ce of nonconver gence and impr: er solutions arr/7 ewed in detail. ..."
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Cited by 27 (2 self)
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Somer obustness questions in str uctur al equation modeling (SEM) ar intr duced. Factor that a#ect the occuruv ce of nonconver gence and impr: er solutions arr/7 ewed in detail.
Latent variable interaction and quadratic effect estimation: a twostep technique using structural equation.” (http://www.wright.edu/robert.ping/) Updated from Ping R
 Psychological Bulletin
, 1996
"... The author proposes an alternative estimation technique for latent variable interactions and quadraties. Available techniques for specifying these variables in structural equation models require adding variables or constraint equations that can produce specification tedium and errors or estimation d ..."
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Cited by 21 (0 self)
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The author proposes an alternative estimation technique for latent variable interactions and quadraties. Available techniques for specifying these variables in structural equation models require adding variables or constraint equations that can produce specification tedium and errors or estimation difficulties. The proposed technique avoids these difficulties and may be useful for EQS, LISREL 7, and LISREL 8 users. First, measurement parameters for indicator Ioadings and errors of linear latent variables are estimated in a measurement model that excludes the interaction and quadratic variables. Next, these estimates are used to calculate values for the indicator loadings and error variances ofthe interaction and quadratic latent variables. Then, these calculated values are specified as constants in the structural model containing the interaction and quadratic variables. Interaction and quadratic effects are routinely reported for categorical independent variables (i.e., in analysis of variance) frequently to aid in the interpretation of significant main effects. However, interaction and quadratic effects are less frequently reported for continuous independent variables. Researchers have called for the inclusion of interaction and quadratic variables in models with continuous independent