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| Psychometrika | 15 |
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| Journal Articles | 11 |
| Reports - Evaluative | 7 |
| Reports - Research | 3 |
| Book/Product Reviews | 1 |
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Peer reviewedYung, Yiu-Fai – Psychometrika, 1997
Various types of finite mixtures of confirmatory factor analysis models are proposed for handling data heterogeneity. Proposed classes of mixture models differ in their unique representations of data heterogeneity, and three sampling schemes for these mixtures are distinguished. Advantages of the Approximate Scoring method are outlined. (SLD)
Descriptors: Data Analysis, Mathematical Models, Sampling, Scoring
Peer reviewedThomas, Hoben – Psychometrika, 1990
It is contended that this book's conceptually rigorous and complete treatment of meta-analysis is written with the consumer in mind. Some of the models presented are not appropriate for studies that meta-analyses commonly consider. Sampling issues are not covered adequately, and much of the supporting theory is omitted. (SLD)
Descriptors: Book Reviews, Mathematical Models, Meta Analysis, Research Methodology
Peer reviewedClarkson, Douglas B. – Psychometrika, 1979
The jackknife by groups and modifications of the jackknife by groups are used to estimate standard errors of rotated factor loadings for selected populations in common factor model maximum likelihood factor analysis. Simulations are performed in which t-statistics based upon these jackknife estimates of the standard errors are computed.…
Descriptors: Error of Measurement, Factor Analysis, Factor Structure, Mathematical Models
Peer reviewedAlf, Edward F., Jr.; Abrahams, Norman M. – Psychometrika, 1975
In applied and experimental research, it has been demonstrated that the extreme groups procedure is more powerful than the standard correlational approach for some values of the correlation and extreme group size. Methods are provided for using the covariance information that is usually discarded in the classical extreme groups approach.…
Descriptors: Comparative Analysis, Correlation, Experimental Groups, Mathematical Models
Peer reviewedTucker, Ledyard R. – Psychometrika, 1972
Descriptors: Factor Analysis, Factor Structure, Mathematical Models, Mathematics
Peer reviewedBerger, Martijn, P. F. – Psychometrika, 1992
A generalized variance criterion is used for sequential sampling in the two-parameter item response theory model. Some principles are offered to enable the researcher to select the best sampling design for efficient estimation of item parameters. Topics include the choice of an optimality criterion, two-stage designs, and sequential designs. (SLD)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Evaluation Criteria, Graphs
Peer reviewedNovick, Melvin R.; And Others – Psychometrika, 1973
This paper develops theory and methods for the application of the Bayesian Model II method to the estimation of binomial proportions and demonstrates its application to educational data. (Author/RK)
Descriptors: Bayesian Statistics, Educational Testing, Mathematical Models, Measurement
Peer reviewedRosenbaum, Paul R. – Psychometrika, 1989
Single construct validity is examined, and appropriate statistical tests are described when criterion information is available for: (1) examinees selected at random; (2) examinees selected on the basis of the current test; and (3) examinees selected using other measures of the latent construct. Discriminant validity is also discussed. (SLD)
Descriptors: College Entrance Examinations, Construct Validity, Equations (Mathematics), Item Response Theory
Peer reviewedSanders, Piet F. – Psychometrika, 1992
Presents solutions for the problem of maximizing the generalizability coefficient under a budget constraint. Shows that the Cauchy-Schwarz inequality can be applied to derive optimal continuous solutions for the number of conditions of each facet. Illustrates the formal similarity between optimization problems in survey sampling and…
Descriptors: Budgeting, Cost Effectiveness, Equations (Mathematics), Error of Measurement
Peer reviewedAnderson, James C.; Gerbing, David W. – Psychometrika, 1984
This study of maximum likelihood confirmatory factor analysis found effects of practical significance due to sample size, the number of indicators per factor, and the number of factors for Joreskog and Sorbom's (1981) goodness-of-fit index (GFI), GFI adjusted for degrees of freedom, and the root mean square residual. (Author/BW)
Descriptors: Factor Analysis, Factor Structure, Goodness of Fit, Mathematical Models
Peer reviewedKristof, Walter – Psychometrika, 1971
Descriptors: Cognitive Measurement, Error of Measurement, Mathematical Models, Psychological Testing
Peer reviewedLin, Miao-Hsiang; Hsiung, Chao A. – Psychometrika, 1992
Four bootstrap methods are identified for constructing confidence intervals for the binomial-error model. The extent to which similar results are obtained and the theoretical foundation of each method and its relevance and ranges of modeling the true score uncertainty are discussed. (SLD)
Descriptors: Bayesian Statistics, Computer Simulation, Equations (Mathematics), Estimation (Mathematics)
Peer reviewedZwinderman, Aeilko H. – Psychometrika, 1991
A method is suggested to estimate the relationship between a latent trait and one or more manifest predictors without estimating subject parameters. The method, developed for the Rasch model, can be generalized to two-parameter and three-parameter logistic latent trait models. The model is illustrated with simulated and empirical data. (SLD)
Descriptors: Computer Simulation, Equations (Mathematics), Estimation (Mathematics), Generalization
Peer reviewedHakstian, A. Ralph; And Others – Psychometrika, 1988
A model and computation procedure based on classical test score theory are presented for determination of a correlation coefficient corrected for attenuation due to unreliability. Delta and Monte Carlo method applications are discussed. A power analysis revealed no serious loss in efficiency resulting from correction for attentuation. (TJH)
Descriptors: Correlation, Equations (Mathematics), Hypothesis Testing, Mathematical Models
Peer reviewedCudeck, Robert; Browne, Michael W. – Psychometrika, 1992
A method is proposed for constructing a population covariance matrix as the sum of a particular model plus a nonstochastic residual matrix, with the stipulation that the model holds with a prespecified lack of fit. The procedure is considered promising for Monte Carlo studies. (SLD)
Descriptors: Algorithms, Equations (Mathematics), Estimation (Mathematics), Factor Analysis


