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Stanley, T. D.; Doucouliagos, Hristos – Research Synthesis Methods, 2014
Publication selection bias is a serious challenge to the integrity of all empirical sciences. We derive meta-regression approximations to reduce this bias. Our approach employs Taylor polynomial approximations to the conditional mean of a truncated distribution. A quadratic approximation without a linear term, precision-effect estimate with…
Descriptors: Regression (Statistics), Bias, Algebra, Mathematical Formulas
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Huitema, Bradley E.; McKean, Joseph W. – Educational and Psychological Measurement, 1994
Effectiveness of jackknife methods in reducing bias in estimation of the log-1 autocorrelation parameter p1 was evaluated through a Monte Carlo study using sample sizes ranging from 6 to 500. These estimates appear less biased in the small sample case than many that have been investigated recently. (SLD)
Descriptors: Computer Simulation, Estimation (Mathematics), Monte Carlo Methods, Sample Size
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Hanges, Paul J.; And Others – Educational and Psychological Measurement, 1991
Whether it is possible to develop a classification function that identifies the underlying range restriction from sample information alone was investigated in a simulation. Results indicate that such a function is possible. The procedure was found to be relatively accurate, robust, and powerful. (SLD)
Descriptors: Classification, Computer Simulation, Equations (Mathematics), Mathematical Models
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Milligan, Glenn W. – Educational and Psychological Measurement, 1987
The use of the arc-sine transformation in analysis of variance can lead to difficult inference situations and pose problems in interpretation. It can also produce tests of noticeably lower power when the null hypothesis is false, and is not recommended as a standard tool. Simulated illustrations are provided. (Author/GDC)
Descriptors: Analysis of Variance, Computer Simulation, Monte Carlo Methods, Statistical Bias
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Law, Kenneth S. – Journal of Educational and Behavioral Statistics, 1995
Two new methods of estimating the mean population correlation (M) and the standard deviation of population correlations (SD) were suggested and tested by Monte Carlo simulations. Results show no consistent advantage to using the Pearson correlation or Fisher's Z in estimating M or SD; estimates from all methods are similar. (SLD)
Descriptors: Computer Simulation, Correlation, Effect Size, Estimation (Mathematics)
Thompson, Bruce – 1988
Canonical correlation analysis is a powerful statistical method subsuming other parametric significance tests as special cases, and which can often best honor the complex reality to which most researchers wish to generalize. However, it has been suggested that the canonical correlation coefficient is positively biased. A Monte Carlo study…
Descriptors: Computer Simulation, Correlation, Error of Measurement, Monte Carlo Methods
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Chen, Ru San; Dunlap, William P. – Journal of Educational Statistics, 1994
The present simulation study confirms that the corrected epsilon approximate test of B. Lecoutre yields a less biased estimation of population epsilon and reduces Type I error rates when compared to the epsilon approximate test of H. Huynh and L. S. Feldt. (SLD)
Descriptors: Computer Simulation, Estimation (Mathematics), Evaluation Methods, Monte Carlo Methods
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Buja, Andreas; Eyuboglu, Nermin – Multivariate Behavioral Research, 1992
Use of parallel analysis (PA), a selection rule for the number-of-factors problem, is investigated from the viewpoint of permutation assessment through a Monte Carlo simulation. Results reveal advantages and limitations of PA. Tables of sample eigenvalues are included. (SLD)
Descriptors: Computer Simulation, Correlation, Factor Structure, Mathematical Models
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Stanley, T. D. – Evaluation Review, 1991
W. M. K. Trochim and others defend the record of the regression-discontinuity (RD) design and blur the statistical tests for treatment effect. Their Monte Carlo results show the problematic nature of RD and its potential bias. New testing strategies and restrictions for the application of RD are proposed. (SLD)
Descriptors: Computer Simulation, Equations (Mathematics), Error of Measurement, Estimation (Mathematics)
Tryon, Warren W. – 1984
A normally distributed data set of 1,000 values--ranging from 50 to 150, with a mean of 50 and a standard deviation of 20--was created in order to evaluate the bootstrap method of repeated random sampling. Nine bootstrap samples of N=10 and nine more bootstrap samples of N=25 were randomly selected. One thousand random samples were selected from…
Descriptors: Computer Simulation, Estimation (Mathematics), Higher Education, Monte Carlo Methods
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Trochim, William M. K.; And Others – Evaluation Review, 1991
The regression-discontinuity design involving a treatment interaction effect (TIE), pretest-posttest functional form specification, and choice of point-of-estimation of the TIE are examined. Formulas for controlling the magnitude of TIE in simulations can be used for simulating the randomized experimental case where estimation is not at the…
Descriptors: Computer Simulation, Control Groups, Equations (Mathematics), Error of Measurement
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Farley, John U.; Reddy, Srinivas K. – Multivariate Behavioral Research, 1987
In an experiment manipulating artificial data in a factorial design, model misspecification and varying levels of error in measurement and in model structure are shown to have significant effects on LISREL parameter estimates in a modified peer influence model. (Author/LMO)
Descriptors: Analysis of Variance, Computer Simulation, Error of Measurement, Estimation (Mathematics)
Samejima, Fumiko – 1986
Item analysis data fitting the normal ogive model were simulated in order to investigate the problems encountered when applying the three-parameter logistic model. Binary item tests containing 10 and 35 items were created, and Monte Carlo methods simulated the responses of 2,000 and 500 examinees. Item parameters were obtained using Logist 5.…
Descriptors: Computer Simulation, Difficulty Level, Guessing (Tests), Item Analysis
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Cappelleri, Joseph C.; And Others – Evaluation Review, 1991
A conceptual approach and a set of computer simulations are presented to demonstrate that random measurement error in the pretest does not bias the estimate of the treatment effect in the regression-discontinuity design. Focus is on the case of no interaction between pretest and treatment on posttest. (SLD)
Descriptors: Analysis of Covariance, Computer Simulation, Equations (Mathematics), Error of Measurement
Du Bose, Pansy; Kromrey, Jeffrey D. – 1993
Empirical evidence is presented of the relative efficiency of two potential linkage plans to be used when equivalent test forms are being administered. Equating is a process by which scores on one form of a test are converted to scores on another form of the same test. A Monte Carlo study was conducted to examine equating stability and statistical…
Descriptors: Art Education, Comparative Testing, Computer Simulation, Equated Scores
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