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Schreiber, James B.; Nora, Amaury; Stage, Frances K.; Barlow, Elizabeth A.; King, Jamie – Journal of Educational Research, 2006
The authors provide a basic set of guidelines and recommendations for information that should be included in any manuscript that has confirmatory factor analysis or structural equation modeling as the primary statistical analysis technique. The authors provide an introduction to both techniques, along with sample analyses, recommendations for…
Descriptors: Structural Equation Models, Guidelines, Factor Analysis, Statistical Analysis
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Sivo, Stephen A.; Xitao, Fan; Witta, E. Lea; Willse, John T. – Journal of Experimental Education, 2006
This study is a partial replication of L. Hu and P. M. Bentler's (1999) fit criteria work. The purpose of this study was twofold: (a) to determine whether cut-off values vary according to which model is the true population model for a dataset and (b) to identify which of 13 fit indexes behave optimally by retaining all of the correct models while…
Descriptors: Structural Equation Models, Goodness of Fit, Criteria, Sample Size
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Gionta, Dana A.; Harlow, Lisa L.; Loitman, Jane E.; Leeman, Joanne M. – Structural Equation Modeling, 2005
Three structural equation models of communication between family members and medical staff were examined to understand relations among staff accessibility, inhibitory family attitudes, getting communication needs met, perceived stress, and satisfaction with communication. Compared to full and direct models, a mediational model fit best in which…
Descriptors: Patients, Family Attitudes, Family Needs, Structural Equation Models
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Hazlett-Stevens, Holly; Ullman, Jodie B.; Craske, Michelle G. – Assessment, 2004
The Penn State Worry Questionnaire (PSWQ) was originally designed as a unifactorial measure of pathological trait worry. However, recent studies supported a two-factor solution with positively worded items loading on the first factor and reverse-scored items loading on a second factor. The current study compared this two-factor model to a negative…
Descriptors: Measures (Individuals), Psychometrics, Factor Structure, Questionnaires
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Nevitt, Jonathan; Hancock, Gregory R. – Multivariate Behavioral Research, 2004
Through Monte Carlo simulation, small sample methods for evaluating overall data-model fit in structural equation modeling were explored. Type I error behavior and power were examined using maximum likelihood (ML), Satorra-Bentler scaled and adjusted (SB; Satorra & Bentler, 1988, 1994), residual-based (Browne, 1984), and asymptotically…
Descriptors: Statistical Data, Sample Size, Monte Carlo Methods, Structural Equation Models
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Lee, Sik-Yum; Song, Xin-Yuan – Multivariate Behavioral Research, 2004
The main objective of this article is to investigate the empirical performances of the Bayesian approach in analyzing structural equation models with small sample sizes. The traditional maximum likelihood (ML) is also included for comparison. In the context of a confirmatory factor analysis model and a structural equation model, simulation studies…
Descriptors: Sample Size, Factor Analysis, Structural Equation Models, Comparative Analysis
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Mamon, Rogemar S. – International Journal of Mathematical Education in Science and Technology, 2004
Within the general framework of a multifactor term structure model, the fundamental partial differential equation (PDE) satisfied by a default-free zero-coupon bond price is derived via a martingale-oriented approach. Using this PDE, a result characterizing a model belonging to an exponential affine class is established using only a system of…
Descriptors: Factor Analysis, Structural Equation Models, Bond Issues, Computation
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Lincoln, Karen D.; Chatters, Linda M.; Taylor, Robert Joseph – Journal of Marriage and Family, 2005
Structural equation modeling was used to examine the relationships among stress, social support, negative interaction, and mental health in a sample of African American men and women between ages 18 and 54 (N = 591) from the National Comorbidity Study. The study findings indicated that social support decreased the number of depressive symptoms,…
Descriptors: African Americans, Interpersonal Relationship, Interaction, Structural Equation Models
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Csizer, Kata; Dornyei, Zoltan – Modern Language Journal, 2005
Language learning motivation is a complex, composite construct, and although past research has identified a number of its key components, the interrelationship of these components has often been subject to debate. Similarly, the exact contribution of the various motivational components to learning behaviors and learning achievement has also been…
Descriptors: Foreign Countries, Structural Equation Models, Learning Motivation, Second Language Learning
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Kahn, Jeffrey H. – Counseling Psychologist, 2005
The author reacts to the three core articles in the Scientific Forum of the May 2005 issue of "The Counseling Psychologist" about institutional research productivity, the use of theory-driven research, and the application of structural equation modeling to research in counseling psychology. To have a research base that maximizes divergent…
Descriptors: Structural Equation Models, Productivity, Institutional Research, Counseling Psychology
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Ma Prieto, Isabel; Revilla, Elena – Learning Organization, 2006
Purpose: There has been little research that includes reliable deductions about the positive influence of learning capability on business performance. For this reason, the main objective of the present study is to empirically explore the link between learning capability in organizations and business performance evaluated in both financial and…
Descriptors: Foreign Countries, Business, Money Management, Performance Factors
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Raykov, Tenko – Structural Equation Modeling: A Multidisciplinary Journal, 2006
A structural equation modeling based method is outlined that accomplishes interval estimation of individual optimal scores resulting from multiple-component measuring instruments evaluating single underlying latent dimensions. The procedure capitalizes on the linear combination of a prespecified set of measures that is associated with maximal…
Descriptors: Scores, Structural Equation Models, Reliability, Validity
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Schweizer, Karl; Moosbrugger, Helfried – Intelligence, 2004
The paper reports on an investigation of attention and working memory as sources of intelligence. The investigation was concentrated on the relatedness of attention and working memory as predictors of intelligence and on the structure underlying the prediction. In a sample of 120 participants, intelligence was assessed by the Advanced Progressive…
Descriptors: Intelligence, Memory, Predictor Variables, Attention
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Rodriguez, Michael C.; Ooms, Ann; Montañez, Marcel – Journal of Interactive Online Learning, 2008
Understanding factors in successful online course experiences can provide suggestions for instructors and students to promote improved learning experiences. A survey of 700 students regarding perceptions of online-learning quality was analyzed with a structural equation model. For students with online-learning experience, comfort with technology…
Descriptors: College Students, Student Attitudes, Student Surveys, Electronic Learning
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Smith, Leann E.; Borkowski, John G.; Whitman, Thomas L. – Scientific Studies of Reading, 2008
This study investigated how self-regulation contributes to the development of reading competence in an at-risk sample of 157 children born to adolescent mothers. It was hypothesized that reading readiness at age 5 would shape self-regulation at age 10, which in turn would influence reading competence at age 14. Based on structural equation…
Descriptors: Reading Readiness, Early Reading, Structural Equation Models, Reading Skills
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