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Kang, Yoonjeong; McNeish, Daniel M.; Hancock, Gregory R. – Educational and Psychological Measurement, 2016
Although differences in goodness-of-fit indices (?GOFs) have been advocated for assessing measurement invariance, studies that advanced recommended differential cutoffs for adjudicating invariance actually utilized a very limited range of values representing the quality of indicator variables (i.e., magnitude of loadings). Because quality of…
Descriptors: Measurement, Goodness of Fit, Guidelines, Models
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Mao, Xiulin; Harring, Jeffrey R.; Hancock, Gregory R. – Educational and Psychological Measurement, 2015
Latent interaction models have motivated a great deal of methodological research, mainly in the area of estimating such models. Product-indicator methods have been shown to be competitive with other methods of estimation in terms of parameter bias and standard error accuracy, and their continued popularity in empirical studies is due, in part, to…
Descriptors: Structural Equation Models, Error of Measurement, Algebra, Statistical Analysis
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Liu, Min; Hancock, Gregory R. – Educational and Psychological Measurement, 2014
Growth mixture modeling has gained much attention in applied and methodological social science research recently, but the selection of the number of latent classes for such models remains a challenging issue, especially when the assumption of proper model specification is violated. The current simulation study compared the performance of a linear…
Descriptors: Models, Classification, Simulation, Comparative Analysis
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Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R. – Educational and Psychological Measurement, 2013
Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…
Descriptors: Models, Statistical Analysis, Goodness of Fit, Change
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Hancock, Gregory R.; Mueller, Ralph O. – Educational and Psychological Measurement, 2011
A two-step process is commonly used to evaluate data-model fit of latent variable path models, the first step addressing the measurement portion of the model and the second addressing the structural portion of the model. Unfortunately, even if the fit of the measurement portion of the model is perfect, the ability to assess the fit within the…
Descriptors: Reliability, Structural Equation Models, Goodness of Fit, Measurement
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Mann, Heather M.; Rutstein, Daisy W.; Hancock, Gregory R. – Educational and Psychological Measurement, 2009
Multisample measured variable path analysis is used to test whether causal/structural relations among measured variables differ across populations. Several invariance testing approaches are available for assessing cross-group equality of such relations, but the associated test statistics may vary considerably across methods. This study is a…
Descriptors: Path Analysis, Inferences, Sampling, Measurement
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Myers, Nicholas D.; Feltz, Deborah L.; Chase, Melissa A.; Reckase, Mark D.; Hancock, Gregory R. – Educational and Psychological Measurement, 2008
The purpose of this validity study was to improve measurement of coaching efficacy, an important variable in models of coaching effectiveness. A revised version of the coaching efficacy scale (CES) was developed for head coaches of high school teams (CES II-HST). Data were collected from head coaches of 14 relevant high school sports (N = 799).…
Descriptors: Factor Structure, Measures (Individuals), Factor Analysis, Athletic Coaches
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Fan, Weihua; Hancock, Gregory R. – Educational and Psychological Measurement, 2006
In the common two-step structural equation modeling process, modifications are routinely made to the measurement portion of the model prior to assessing structural relations. The effect of such measurement model modifications on the structural parameter estimates, however, is not well known and is the subject of the current investigation. For a…
Descriptors: Error of Measurement, Evaluation Methods, Monte Carlo Methods, Sample Size
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Lawrence, Frank R.; Hancock, Gregory R. – Educational and Psychological Measurement, 1999
Used simulated data to test the integrity of orthogonal factor solutions when varying sample size, factor pattern/structure coefficient magnitude, method of extraction, number of variables, number of factors, and degree of overextraction. Discusses implications of results with regard to overextraction. (SLD)
Descriptors: Factor Analysis, Factor Structure, Orthogonal Rotation, Sample Size
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Hancock, Gregory R.; And Others – Educational and Psychological Measurement, 1993
Two-option multiple-choice vocabulary test items are compared with comparably written true-false test items. Results from a study with 111 high school students suggest that multiple-choice items provide a significantly more reliable measure than the true-false format. (SLD)
Descriptors: Ability, High School Students, High Schools, Objective Tests
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Hancock, Gregory R. – Educational and Psychological Measurement, 1997
Methods are offered for conducting hypothesis testing associated with disattenuated validity coefficients to overcome limitations of some other suggested approaches. Through using classical test theory's notion of reliability in the form of structured path models, such hypothesis testing may be done with hierarchically related structural equation…
Descriptors: Correlation, Hypothesis Testing, Reliability, Scores
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Hancock, Gregory R.; Freeman, Mara J. – Educational and Psychological Measurement, 2001
Provides select power and sample size tables and interpolation strategies associated with the root mean square error of approximation test of not close fit under standard assumed conditions. The goal is to inform researchers conducting structural equation modeling about power limitations when testing a model. (SLD)
Descriptors: Goodness of Fit, Power (Statistics), Sample Size, Structural Equation Models
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Klockars, Alan J.; Hancock, Gregory R. – Educational and Psychological Measurement, 1994
Differences between per experiment (PE) and experimentwise (EW) error rates were studied through simulation for several multiple-comparison procedures for both pairwise comparisons and planned contrasts. Results suggest ways to control PE rates through new multiple-comparison procedures that maximize experimental power while controlling Type I…
Descriptors: Comparative Analysis, Computer Simulation, Research Methodology