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Mohammed, M. A.; Ibrahim, A. I. N.; Siri, Z.; Noor, N. F. M. – Sociological Methods & Research, 2019
In this article, a numerical method integrated with statistical data simulation technique is introduced to solve a nonlinear system of ordinary differential equations with multiple random variable coefficients. The utilization of Monte Carlo simulation with central divided difference formula of finite difference (FD) method is repeated n times to…
Descriptors: Monte Carlo Methods, Calculus, Sampling, Simulation
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Luo, Yong; Jiao, Hong – Educational and Psychological Measurement, 2018
Stan is a new Bayesian statistical software program that implements the powerful and efficient Hamiltonian Monte Carlo (HMC) algorithm. To date there is not a source that systematically provides Stan code for various item response theory (IRT) models. This article provides Stan code for three representative IRT models, including the…
Descriptors: Bayesian Statistics, Item Response Theory, Probability, Computer Software
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Morio, Jerome – European Journal of Physics, 2011
Sensitivity analysis is the study of how the different input variations of a mathematical model influence the variability of its output. In this paper, we review the principle of global and local sensitivity analyses of a complex black-box system. A simulated case of application is given at the end of this paper to compare both approaches.…
Descriptors: Mathematical Models, Models, Teaching Methods, Comparative Analysis
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Pavur, Robert; Nath, Ravinder – Multivariate Behavioral Research, 1989
A Monte Carlo simulation study compared the power and Type I errors of the Wilks lambda statistic and the statistic of M. L. Puri and P. K. Sen (1971) on transformed data in a one-way multivariate analysis of variance. Preferred test procedures, based on robustness and power, are discussed. (SLD)
Descriptors: Comparative Analysis, Mathematical Models, Monte Carlo Methods, Multivariate Analysis
Klockars, Alan J.; Hancock, Gregory R. – 1993
The challenge of multiple comparisons is to maximize the power for answering specific research questions, while still maintaining control over the rate of Type I error. Several multiple comparison procedures have been suggested to meet this challenge. The stagewise protected procedure (SPP) of A. J. Klockars and G. R. Hancock tests null hypotheses…
Descriptors: Comparative Analysis, Computer Simulation, Hypothesis Testing, Mathematical Models
Huynh, Huynh – 1977
Three techniques for estimating Kuder Richardson reliability (KR20) coefficients for incomplete data are contrasted. The methods are: (1) Henderson's Method 1 (analysis of variance, or ANOVA); (2) Henderson's Method 3 (FITCO); and (3) Koch's method of symmetric sums (SYSUM). A Monte Carlo simulation was used to assess the precision of the three…
Descriptors: Analysis of Variance, Comparative Analysis, Mathematical Models, Monte Carlo Methods
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Cornell, John E.; And Others – Journal of Educational Statistics, 1992
This Monte Carlo simulation studied the relative power of 8 tests for sphericity in randomized block designs where sample size was small (10, 15, 20, and 30) and population covariance matrices of dimension-to-sample size ratio approached 1.0. The locally best invariant test demonstrated substantial power to detect departures from sphericity. (SLD)
Descriptors: Comparative Analysis, Equations (Mathematics), Mathematical Models, Monte Carlo Methods
Martin, Gerald R. – 1976
Through Monte Carlo procedures, three different techniques for estimating the parameter theta (proportion of the "shocks" remaining in the system) in the Integrated Moving Average (0,1,1) time-series model are compared in terms of (1) the accuracy of the estimates, (2) the independence of the estimates from the true value of theta, and…
Descriptors: Comparative Analysis, Computer Programs, Data Analysis, Mathematical Models
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Kano, Yutaka – Psychometrika, 1990
Based on the usual factor analysis model, this paper investigates the relationship between improper solutions and the number of factors. The properties of the noniterative estimation method of M. Ihara and Y. Kano in exploratory factor analysis are also discussed. The estimators were compared in a Monte Carlo experiment. (TJH)
Descriptors: Comparative Analysis, Estimation (Mathematics), Factor Analysis, Mathematical Models
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Wu, Yow-wu B. – Educational and Psychological Measurement, 1984
The present study compares the robustness of two different one way fixed-effects analysis of covariance (ANCOVA) models to investigate whether the model which uses a test statistic incorporating estimates of separate unequal regression slopes is more robust than the conventional model which assumes the slopes are equal. (Author/BW)
Descriptors: Analysis of Covariance, Comparative Analysis, Computer Simulation, Hypothesis Testing
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Chou, Chih-Ping; Bentler, P. M. – Multivariate Behavioral Research, 1990
The empirical performance under null/alternative hypotheses of the likelihood ratio difference test (LRDT); Lagrange Multiplier test (evaluating the impact of model modification with a specific model); and Wald test (using a general model) were compared. The new tests for covariance structure analysis performed as well as did the LRDT. (RLC)
Descriptors: Analysis of Covariance, Comparative Analysis, Hypothesis Testing, Mathematical Models
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Bissett, Randall; Schneider, Bruce – Psychometrika, 1991
The algorithm developed by B. A. Schneider (1980) for analysis of paired comparisons of psychological intervals is replaced by one proposed by R. M. Johnson. Monte Carlo simulations of pairwise dissimilarities and pairwise conjoint effects show that Johnson's algorithm can provide good metric recovery. (SLD)
Descriptors: Algorithms, Comparative Analysis, Computer Simulation, Equations (Mathematics)
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Cudeck, Robert – Journal of Educational Statistics, 1991
Two algorithms that automatically select subsets of variables (PACE algorithm) and reference variables (Fabin estimators), respectively, used for the noniterative estimators are presented. The PACE algorithm is based on a nonsymmetric matrix sweep operator. A Monte Carlo experiment compares the relative performance of these estimators and others.…
Descriptors: Algorithms, Comparative Analysis, Equations (Mathematics), Estimation (Mathematics)
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Velicer, Wayne F.; And Others – Multivariate Behavioral Research, 1982
Factor analysis, image analysis, and principal component analysis are compared with respect to the factor patterns they would produce under various conditions. The general conclusion that is reached is that the three methods produce results that are equivalent. (Author/JKS)
Descriptors: Comparative Analysis, Data Analysis, Factor Analysis, Goodness of Fit
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Noonan, Brian W.; And Others – Applied Psychological Measurement, 1992
Studied the extent to which three appropriateness indexes, Z(sub 3), ECIZ4, and W, are well standardized in a Monte Carlo study. The ECIZ4 most closely approximated a normal distribution, and its skewness and kurtosis were more stable and less affected by test length and item response theory model than the others. (SLD)
Descriptors: Comparative Analysis, Item Response Theory, Mathematical Models, Maximum Likelihood Statistics
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