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Beaujean, A. Alexander – Practical Assessment, Research & Evaluation, 2014
A common question asked by researchers using regression models is, What sample size is needed for my study? While there are formulae to estimate sample sizes, their assumptions are often not met in the collected data. A more realistic approach to sample size determination requires more information such as the model of interest, strength of the…
Descriptors: Regression (Statistics), Sample Size, Sampling, Monte Carlo Methods
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Pek, Jolynn; Losardo, Diane; Bauer, Daniel J. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the…
Descriptors: Structural Equation Models, Sampling, Statistical Inference, Computation
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Guastella, Ivan; Fazio, Claudio; Sperandeo-Mineo, Rosa Maria – European Journal of Physics, 2012
A procedure modelling ideal classical and quantum gases is discussed. The proposed approach is mainly based on the idea that modelling and algorithm analysis can provide a deeper understanding of particularly complex physical systems. Appropriate representations and physical models able to mimic possible pseudo-mechanisms of functioning and having…
Descriptors: Predictive Validity, Quantum Mechanics, Science Education, Science Instruction
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Morio, Jerome; Pastel, Rudy; Le Gland, Francois – European Journal of Physics, 2010
Monte Carlo simulations are a classical tool to analyse physical systems. When unlikely events are to be simulated, the importance sampling technique is often used instead of Monte Carlo. Importance sampling has some drawbacks when the problem dimensionality is high or when the optimal importance sampling density is complex to obtain. In this…
Descriptors: Science Instruction, Physics, Simulation, Sampling
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Browne, William; Goldstein, Harvey – Journal of Educational and Behavioral Statistics, 2010
In this article, we discuss the effect of removing the independence assumptions between the residuals in two-level random effect models. We first consider removing the independence between the Level 2 residuals and instead assume that the vector of all residuals at the cluster level follows a general multivariate normal distribution. We…
Descriptors: Computation, Sampling, Markov Processes, Monte Carlo Methods
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Steyn, H. S., Jr.; Ellis, S. M. – Multivariate Behavioral Research, 2009
When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…
Descriptors: Effect Size, Multivariate Analysis, Computation, Monte Carlo Methods
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Bonnett, Douglas G. – Psychological Methods, 2008
Most psychology journals now require authors to report a sample value of effect size along with hypothesis testing results. The sample effect size value can be misleading because it contains sampling error. Authors often incorrectly interpret the sample effect size as if it were the population effect size. A simple solution to this problem is to…
Descriptors: Intervals, Hypothesis Testing, Effect Size, Sampling
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Raiche, Gilles; Blais, Jean-Guy – Applied Psychological Measurement, 2006
Monte Carlo methodologies are frequently applied to study the sampling distribution of the estimated proficiency level in adaptive testing. These methods eliminate real situational constraints. However, these Monte Carlo methodologies are not currently supported by the available software programs, and when these programs are available, their…
Descriptors: Computer Assisted Instruction, Computer Software, Sampling, Adaptive Testing
Brooks, Gordon P.; Barcikowski, Robert S.; Robey, Randall R. – 1999
The meaningful investigation of many problems in statistics can be solved through Monte Carlo methods. Monte Carlo studies can help solve problems that are mathematically intractable through the analysis of random samples from populations whose characteristics are known to the researcher. Using Monte Carlo simulation, the values of a statistic are…
Descriptors: Computer Simulation, Monte Carlo Methods, Research Methodology, Sampling
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Coenders, Germa; Saris, Willem E.; Batista-Foguet, Joan M.; Andreenkova, Anna – Structural Equation Modeling, 1999
Illustrates that sampling variance can be very large when a three-wave quasi simplex model is used to obtain reliability estimates. Also shows that, for the reliability parameter to be identified, the model assumes a Markov process. These problems are evaluated with both real and Monte Carlo data. (SLD)
Descriptors: Estimation (Mathematics), Markov Processes, Monte Carlo Methods, Reliability
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Kim, Jee-Seon; Bolt, Daniel M. – Educational Measurement: Issues and Practice, 2007
The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain…
Descriptors: Placement, Monte Carlo Methods, Markov Processes, Measurement
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Huitema, Bradley E.; McKean, Joseph W.; McKnight, Scott – Educational and Psychological Measurement, 1999
Clarifies several issues regarding the effects of autocorrelated errors on Type I error in ordinary least-squares models. Demonstrates through Monte Carlo simulation the conditions under which distortion in Type I error is less than predicted by asymptotic theory. Suggests a recently developed small-sample method for time-series analyses. (SLD)
Descriptors: Least Squares Statistics, Monte Carlo Methods, Sample Size, Sampling
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Maris, Gunter; Maris, Eric – Psychometrika, 2002
Introduces a new technique for estimating the parameters of models with continuous latent data. To streamline presentation of this Markov Chain Monte Carlo (MCMC) method, the Rasch model is used. Also introduces a new sampling-based Bayesian technique, the DA-T-Gibbs sampler. (SLD)
Descriptors: Bayesian Statistics, Equations (Mathematics), Estimation (Mathematics), Markov Processes
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De Corte, Wilfried – Educational and Psychological Measurement, 2004
The article describes a Windows program to estimate the expected value and sampling distribution function of the adverse impact ratio for general multistage selections. The results of the program can also be used to predict the risk that a future selection decision will result in an outcome that reflects the presence of adverse impact. The method…
Descriptors: Sampling, Measurement Techniques, Evaluation Methods, Computer Software