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Millsap, Roger E. – Multivariate Behavioral Research, 1998
Two theorems are presented that describe the conditions under which intercept differences can exist under factorial invariance. In such cases, intercept differences do not result from measurement bias in either the tests or the criterion. The conditions of the theorems are testable, and the test procedures are illustrated. (SLD)
Descriptors: Factor Analysis, Factor Structure, Groups, Regression (Statistics)
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Turner, Nigel E. – Educational and Psychological Measurement, 1998
This study assessed the accuracy of parallel analysis, a technique in which observed eigenvalues are compared to eigenvalues from simulated data when no real factors are present. Three studies with manipulated sizes of real factors and sample sizes illustrate the importance of modeling the data more closely when parallel analysis is used. (SLD)
Descriptors: Comparative Analysis, Factor Analysis, Factor Structure, Sample Size
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Marsh, Herbert W.; Hau, Kit-Tai; Balla, John R.; Grayson, David – Multivariate Behavioral Research, 1998
Whether "more is ever too much" for the number of indicators per factor in confirmatory factor analysis was studied by varying sample size and indicators per factor in 35,000 Monte Carlo solutions. Results suggest that traditional rules calling for fewer indicators for smaller sample size may be inappropriate. (SLD)
Descriptors: Factor Structure, Monte Carlo Methods, Research Methodology, Sample Size
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Blaha, John; Merydith, Scott P.; Wallbrown, Fred H.; Dowd, Thomas E. – Measurement and Evaluation in Counseling and Development, 2001
A hierarchical factor solution on the Minnesota Multiphasic Personality Inventory-2 standardization sample found a general psychopathology factor and four primary factors similar to those reported by Butcher, Dahlstrom, Graham, Tellegen, and Kaemmer (1989). (Contains 29 references and 2 tables.) (Author)
Descriptors: Factor Analysis, Factor Structure, Personality Measures, Psychopathology
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Bandalos, Deborah L. – Structural Equation Modeling, 2002
Used simulation to study the effects of the practice of item parceling. Results indicate that certain types of item parceling can obfuscate a multidimensional factor structure in a way that acceptable values of fit indexes are found for a misspecified solution. Discusses why the use of parceling cannot be recommended when items are…
Descriptors: Estimation (Mathematics), Factor Structure, Goodness of Fit, Test Items
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Hayduk, Leslie A.; Glaser, Dale N. – Structural Equation Modeling, 2000
Focuses on the four-step method (four nested models) of structural equation modeling advocated by S. Mulaik (1997, 1998), discussing the limitations of the approach and considering the tests and criteria to be used in moving among the four steps. (SLD)
Descriptors: Factor Analysis, Factor Structure, Research Methodology, Structural Equation Models
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Mulaik, Stanley A.; Millsap, Roger E. – Structural Equation Modeling, 2000
Defends the four-step approach to structural equation modeling based on testing sequences of models and points out misunderstandings of opponents of the approach. The four-step approach allows the separation of respective constraints within a structural equation model. (SLD)
Descriptors: Factor Analysis, Factor Structure, Research Methodology, Structural Equation Models
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Bollen, Kenneth A. – Structural Equation Modeling, 2000
Neither the four-step model nor the one-step procedure can actually tell whether the researcher has the right number of factors in structural equation modeling. In fact, for reasons discussed, a simple formulaic approach to the correct specification of models does not yet exist. (SLD)
Descriptors: Factor Analysis, Factor Structure, Research Methodology, Structural Equation Models
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Hayduk, Leslie A.; Glaser, Dale N. – Structural Equation Modeling, 2000
Replies to commentaries on the four-step approach to structural equation modeling, pointing out the strengths and weaknesses of each argument and ultimately concluding that the four-step model is subject to criticisms that can be addressed to factor analysis as well. (SLD)
Descriptors: Factor Analysis, Factor Structure, Research Methodology, Structural Equation Models
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Ruggiero, Kenneth J.; Morris, Tracy L.; Beidel, Deborah C.; Scotti, Joseph R.; McLeer, Susan V. – Assessment, 1999
Examined the discriminant validity of the State-Trait Anxiety Inventory for Children (C. Spielberger, 1973) and the Children's Depression Inventory (M. Kovacs, 1992) using a sample of 240 clinic-referred and non-clinic-referred children aged 8 to 14 years. Factor analysis yielded distinct factors of anxiety and depression. (SLD)
Descriptors: Adolescents, Anxiety, Children, Depression (Psychology)
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Boles, James S.; Dean, Dwane H.; Ricks, Joe M.; Short, Jeremy C.; Wang, Guangping – Journal of Vocational Behavior, 2000
One-factor, three-factor, and higher-order factor structures of the Maslach Burnout Inventory were tested with 183 elementary-secondary teachers and administrators and 162 small business owners. Analyses suggested the three-factor structure was most plausible. Addition of business owners extended the generalizability of the inventory. (SK)
Descriptors: Administrators, Burnout, Factor Structure, Small Businesses
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Cokley, Kevin O.; Bernard, Naijean; Cunningham, Dana; Motoike, Janice – Measurement and Evaluation in Counseling and Development, 2001
Examines the factor structure of the Academic Motivation Scale with a United States student population. There was some support for a 7-factor structure. Evidence of construct validity examining the relationship with academic self concept and academic achievement is mixed. Discusses ethnic and gender differences in motivation. (Contains 37…
Descriptors: Academic Achievement, Construct Validity, Factor Structure, Learning Motivation
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Dumenci, Levent; Erol, Nese; Achenbach, Thomas M.; Simsek, Zeynep – Journal of Abnormal Child Psychology, 2004
The new correlated 8-factor measurement structure of the Child Behavior Checklist for ages 6-18 (CBCL/6-18; T. M. Achenbach & L. A. Rescorla, 2001) derived from an American sample was used as a benchmark to evaluate its generalizability to Turkish general population (N = 5, 195) and clinical (N = 963) samples. Item-level confirmatory factor…
Descriptors: Psychometrics, Child Behavior, Check Lists, Factor Analysis
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Archer, Robert P.; Stredny, Rebecca Vauter; Mason, John A.; Arnau, Randolph C. – Assessment, 2004
There is a high prevalence of psychological disorders among adolescents in detention facilities. The need for a simple, effective screening tool led to the development of the Massachusetts Youth Screening Instrument (MAYSI) and its successor, the MAYSI-2. This study evaluated the MAYSI-2 psychometric properties based on the records of 704 youths…
Descriptors: Psychometrics, Factor Structure, Adolescents, Delinquency
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Bishop, David I.; Hertenstein, Matthew J. – Educational and Psychological Measurement, 2004
This study examines the factor structure of scores on the English-language version of the Structure of Temperament Questionnaire. Scores from 300 college students were subjected to maximum-likelihood confirmatory factor analyses (CFA). A first-order model consisting of eight correlated factors and a second-order model consisting of two…
Descriptors: Questionnaires, Personality, Factor Analysis, Factor Structure
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