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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 98 (1 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 63 (2 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 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 55 (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.
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 53 (0 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...
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 27 (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.
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 24 (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
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 16 (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
On the Use, Usefulness, and Ease of Use of Structural Equation Modeling
 in MIS Research: A Note of Caution.” MIS Quarterly
, 1995
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
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Cited by 14 (0 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
Mean and Covariance Structure Analysis: Theoretical and Practical Improvements
, 1995
"... The most widely used multivariate statistical models in the social and behavioral sciences involve linear structural relations among observed and latent variables. In practice, these variables are generally nonnormally distributed, and hence classical multivariate analysis, based on multinormal erro ..."
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Cited by 12 (4 self)
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The most widely used multivariate statistical models in the social and behavioral sciences involve linear structural relations among observed and latent variables. In practice, these variables are generally nonnormally distributed, and hence classical multivariate analysis, based on multinormal errorfree variables having no simultaneous interrelations, is not adequate to deal with such data. Since structural relations among variables imply a structure for the multivariate product moments of the variables, general methods for the analysis of mean and covariance structures have been proposed to estimate and test particular model structures. Unfortunately, extant statistical tests, such as the likelihood ratio test (LRT) and a test based on asymptotically distribution free (ADF) covariance structure analysis, have been found to be virtually useless in practical model evaluation at finite sample sizes with nonnormal data. For example, in one condition of a simulation on confirmatory facto...
On the meaning and use of kurtosis
 Psychological Methods
, 1997
"... For symmetric unimodal distributions, positive kurtosis indicates heavy tails and peakedness relative to the normal distribution, whereas negative kurtosis indicates light tails and flatness. Many textbooks, however, describe or illustrate kurtosis incompletely or incorrectly. In this article, kurto ..."
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Cited by 11 (0 self)
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For symmetric unimodal distributions, positive kurtosis indicates heavy tails and peakedness relative to the normal distribution, whereas negative kurtosis indicates light tails and flatness. Many textbooks, however, describe or illustrate kurtosis incompletely or incorrectly. In this article, kurtosis is illustrated with wellknown distributions, and aspects of its interpretation a d misinterpretation are discussed. The role of kurtosis in testing univariate and multivariate normality; as a measure of departures from normality; in issues of robustness, outliers, and bimodality; in generalized tests and estimators, as well as limitations of and alternatives to the kurtosis measure [32, are discussed. It is typical ly noted in introductory statistics courses that distributions can be characterized in terms of central tendency, variability, and shape. With respect o shape, virtually every textbook defines and illustrates kewness. On the other hand, another aspect of shape, which is kurtosis, is either not discussed or, worse yet, is often described or illustrated incorrectly. Kurtosis is also frequently not reported in research articles, in spite of the fact that virtually every statistical package provides a measure of kurtosis. This occurs most likely because kurtosis is not well understood and because the role of kurtosis in various aspects of statistical analysis is not widely recognized. The purpose of this article is to clarify the meaning of kurtosis and to show why and how it is useful. On the Mean ing o f Kurtosis Kurtosis can be formally defined as the standardized fourth population moment about the mean, E (X IX)4 IX4