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Yan Xia; Xinchang Zhou – Educational and Psychological Measurement, 2025
Parallel analysis has been considered one of the most accurate methods for determining the number of factors in factor analysis. One major advantage of parallel analysis over traditional factor retention methods (e.g., Kaiser's rule) is that it addresses the sampling variability of eigenvalues obtained from the identity matrix, representing the…
Descriptors: Factor Analysis, Statistical Analysis, Evaluation Methods, Sampling
Doroudi, Shayan; Aleven, Vincent; Brunskill, Emma – Grantee Submission, 2017
The gold standard for identifying more effective pedagogical approaches is to perform an experiment. Unfortunately, frequently a hypothesized alternate way of teaching does not yield an improved effect. Given the expense and logistics of each experiment, and the enormous space of potential ways to improve teaching, it would be highly preferable if…
Descriptors: Teaching Methods, Matrices, Evaluation Methods, Models
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Romero, Sonia J.; Ordoñez, Xavier G.; Ponsoda, Vincente; Revuelta, Javier – Psicologica: International Journal of Methodology and Experimental Psychology, 2014
Cognitive Diagnostic Models (CDMs) aim to provide information about the degree to which individuals have mastered specific attributes that underlie the success of these individuals on test items. The Q-matrix is a key element in the application of CDMs, because contains links item-attributes representing the cognitive structure proposed for solve…
Descriptors: Evaluation Methods, Q Methodology, Matrices, Sampling
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Dumenci, Levent; Yates, Phillip D. – Educational and Psychological Measurement, 2012
Estimation problems associated with the correlated-trait correlated-method (CTCM) parameterization of a multitrait-multimethod (MTMM) matrix are widely documented: the model often fails to converge; even when convergence is achieved, one or more of the parameter estimates are outside the admissible parameter space. In this study, the authors…
Descriptors: Correlation, Models, Multitrait Multimethod Techniques, Matrices
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Humphreys, Lloyd G.; Montanelli, Richard G. – Multivariate Behavioral Research, 1975
Descriptors: Correlation, Factor Analysis, Matrices, Sampling
Spearing, Debra; Woehlke, Paula – 1989
To assess the effect on discriminant analysis in terms of correct classification into two groups, the following parameters were systematically altered using Monte Carlo techniques: sample sizes; proportions of one group to the other; number of independent variables; and covariance matrices. The pairing of the off diagonals (or covariances) with…
Descriptors: Classification, Correlation, Discriminant Analysis, Matrices
Skakun, Ernest N.; Hakstian, A. Ralph – 1974
Two population raw data matrices were constructed by computer simulation techniques. Each consisted of 10,000 subjects and 12 variables, and each was constructed according to an underlying factorial model consisting of four major common factors, eight minor common factors, and 12 unique factors. The computer simulation techniques were employed to…
Descriptors: Comparative Analysis, Factor Analysis, Least Squares Statistics, Matrices