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Fox, Jean-Paul; Marianti, Sukaesi – Journal of Educational Measurement, 2017
Response accuracy and response time data can be analyzed with a joint model to measure ability and speed of working, while accounting for relationships between item and person characteristics. In this study, person-fit statistics are proposed for joint models to detect aberrant response accuracy and/or response time patterns. The person-fit tests…
Descriptors: Accuracy, Reaction Time, Statistics, Test Items
Aydin, Burak; Leite, Walter L.; Algina, James – Educational and Psychological Measurement, 2016
We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte…
Descriptors: Error of Measurement, Predictor Variables, Randomized Controlled Trials, Experimental Groups
Murphy, Daniel L.; Beretvas, S. Natasha; Pituch, Keenan A. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, 2 new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a…
Descriptors: Sample Size, Simulation, Factor Structure, Statistical Analysis
Finch, Holmes; Habing, Brian – Applied Psychological Measurement, 2007
This Monte Carlo study compares the ability of the parametric bootstrap version of DIMTEST with three goodness-of-fit tests calculated from a fitted NOHARM model to detect violations of the assumption of unidimensionality in testing data. The effectiveness of the procedures was evaluated for different numbers of items, numbers of examinees,…
Descriptors: Guessing (Tests), Testing, Statistics, Monte Carlo Methods
Hummel, Thomas J.; Feltovich, Paul J. – 1974
In some correlational studies it is not reasonable to assume that bivariate observations are uncorrelated. An example would be a configural analysis in which two individuals are correlated across several variables (e.g., Q-technique). The present study was a Monte Carlo investigation of the robustness of techniques used in judging the magnitude of…
Descriptors: Computer Programs, Correlation, Hypothesis Testing, Matrices
Silver, N. Clayton; Hittner, James B.; May, Kim – Journal of Experimental Education, 2004
The authors conducted a Monte Carlo simulation of 4 test statistics or comparing dependent correlations with no variables in common. Empirical Type 1 error rates and power estimates were determined for K. Pearson and L. N. G. Filon's (1898) z, O. J. Dunn and V. A. Clark's (1969) z, J. H. Steiger's (1980) original modification of Dunn and Clark's…
Descriptors: Monte Carlo Methods, Simulation, Effect Size, Sample Size
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

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