Results 1  10
of
50
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 ..."
Abstract

Cited by 97 (5 self)
 Add to MetaCart
(Show Context)
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
Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement invariance
 Psychological Bulletin
, 1989
"... Addresses issues related to partial measurement in variance using a tutorial approach based on the LISREL confirmatory factor analytic model. Specifically, we demonstrate procedures for (a) using "sensitivity analyses " to establish stable and substantively wellfitting baseline models, (b ..."
Abstract

Cited by 84 (4 self)
 Add to MetaCart
(Show Context)
Addresses issues related to partial measurement in variance using a tutorial approach based on the LISREL confirmatory factor analytic model. Specifically, we demonstrate procedures for (a) using "sensitivity analyses " to establish stable and substantively wellfitting baseline models, (b) determining partially invariant measurement parameters, and (c) testing for the invariance of factor covariance and mean structures, given partial measurement invariance. We also show, explicitly, the transformation of parameters from an all^fto an ally model specification, for purposes of testing mean structures. These procedures are illustrated with multidimensional selfconcept data from low ( « = 248) and high (n = 582) academically tracked high school adolescents. An important assumption in testing for mean differences is that the measurement (Drasgow & Kanfer, 1985; Labouvie,
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 ..."
Abstract

Cited by 59 (0 self)
 Add to MetaCart
(Show Context)
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.
Multilevel modeling of individual and group level mediated effects
 Multivariate Behavioral Research
, 2001
"... This article combines procedures for singlelevel mediational analysis with multilevel modeling techniques in order to appropriately test mediational effects in clustered data. A simulation study compared the performance of these multilevel mediational models with that of singlelevel mediational mo ..."
Abstract

Cited by 42 (2 self)
 Add to MetaCart
(Show Context)
This article combines procedures for singlelevel mediational analysis with multilevel modeling techniques in order to appropriately test mediational effects in clustered data. A simulation study compared the performance of these multilevel mediational models with that of singlelevel mediational models in clustered data with individual or grouplevel initial independent variables, individual or grouplevel mediators, and individual level outcomes. The standard errors of mediated effects from the multilevel solution were generally accurate, while those from the singlelevel procedure were downwardly biased, often by 20 % or more. The multilevel advantage was greatest in those situations involving grouplevel variables, larger group sizes, and higher intraclass correlations in mediator and outcome variables. Multilevel mediational modeling methods were also applied to data from a preventive intervention designed to reduce intentions to use steroids among players on high school football teams. This example illustrates differences between singlelevel and multilevel mediational modeling in realworld clustered data and shows how the multilevel technique may lead to more accurate results. Mediational analysis is a method that can help researchers understand
Application of covariance structure modeling in psychology: cause for concern? Psychol
 Bull
, 1990
"... Methods of covariance structure modeling are frequently applied in psychological research. These methods merge the logic of confirmatory factor analysis, multiple regression, and path analysis within a single data analytic framework. Among the many applications are estimation of disattenuated corre ..."
Abstract

Cited by 31 (0 self)
 Add to MetaCart
(Show Context)
Methods of covariance structure modeling are frequently applied in psychological research. These methods merge the logic of confirmatory factor analysis, multiple regression, and path analysis within a single data analytic framework. Among the many applications are estimation of disattenuated correlation and regression coefficients, evaluation of multitraitmultimethod matrices, and assessment of hypothesized causal structures. Shortcomings of these methods are commonly acknowledged in the mathematical literature and in textbooks. Nevertheless, serious flaws remain in many published applications. For example, it is rarely noted that the fit of a favored model is identical for a potentially large number of equivalent models. A review of the personality and social psychology literature illustrates the nature of this and other problems in reported applications of covariance structure models. A principal goal of experimentation in psychology is to provide a basis for inferring causation. Among the tools used to achieve this goal are the active manipulation and control of independent variables, random assignment to experimental treatments, and appropriate methods of data analysis. Causal infer
The relation between adolescent alcohol use and peer alcohol use: A longitudinal random coefficients model
 Journal of Consulting and Clinical Psychology
, 1997
"... Longitudinal latent growth models were used to examine the relation between changes in adolescent alcohol use and changes in peer alcohol use over a 3year period in a communitybased sample of 363 Hispanic and Caucasian adolescents. Both adolescent alcohol use and peer alcohol use were characterize ..."
Abstract

Cited by 18 (0 self)
 Add to MetaCart
(Show Context)
Longitudinal latent growth models were used to examine the relation between changes in adolescent alcohol use and changes in peer alcohol use over a 3year period in a communitybased sample of 363 Hispanic and Caucasian adolescents. Both adolescent alcohol use and peer alcohol use were characterized by positive linear growth over time. Not only were changes in adolescent alcohol use closely related to changes in peer alcohol use, but the initial status on peer alcohol use was predictive of later increases in adolescent alcohol use and the initial status on adolescent alcohol use was predictive of later increases in peer alcohol use. These results are inconsistent with models positing solely unidirectional effects between adolescent alcohol use and peer alcohol use. Of the variables found to be related to adolescent substance use, peer substance use is consistently one of the strongest pre
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 ..."
Abstract

Cited by 15 (0 self)
 Add to MetaCart
(Show Context)
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
The TETRAD Project: Constraint Based Aids to Causal Model Specification
 MULTIVARIATE BEHAVIORAL RESEARCH
"... ..."
Peer exclusion and victimization: Processes that mediate the relation between peer group rejection and children's classroom engagement and achievement
 Journal of Educational Psychology
, 2006
"... Longitudinal data from a study of kindergarten through 5th graders were used to estimate a structural model in which chronic peer exclusion and chronic peer abuse were hypothesized to mediate the link between children’s early peer rejection, later classroom engagement, and achievement. Peer exclusio ..."
Abstract

Cited by 11 (0 self)
 Add to MetaCart
(Show Context)
Longitudinal data from a study of kindergarten through 5th graders were used to estimate a structural model in which chronic peer exclusion and chronic peer abuse were hypothesized to mediate the link between children’s early peer rejection, later classroom engagement, and achievement. Peer exclusion and abuse were expected to predict changes in 2 forms of school engagement (classroom participation and school avoidance), and changes in both forms of engagement were expected to predict changes in achievement. The model fit the data well and lent support to the premise that distinct forms of peer maltreatment and classroom engagement mediate the link between early peer rejection and changes in children’s achievement. Early peer rejection was associated with declining classroom participation and increasing school avoidance, but different forms of chronic peer maltreatment mediated these relations. Whereas chronic peer exclusion principally mediated the link between peer rejection and classroom participation, chronic peer abuse primarily mediated the link between rejection and school avoidance. Children’s reduced classroom participation, more than gains in school avoidance, anteceded decrements in children’s achievement.
Boundary Role Ambiguity: Facets, Determinants, and Impact
 Journal of Marketing
, 1993
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at