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Barr, Dale J.; Levy, Roger; Scheepers, Christoph; Tily, Harry J. – Journal of Memory and Language, 2013
Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholinguistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the…
Descriptors: Hypothesis Testing, Psycholinguistics, Models, Monte Carlo Methods
Wu, Yi-Fang – ProQuest LLC, 2015
Item response theory (IRT) uses a family of statistical models for estimating stable characteristics of items and examinees and defining how these characteristics interact in describing item and test performance. With a focus on the three-parameter logistic IRT (Birnbaum, 1968; Lord, 1980) model, the current study examines the accuracy and…
Descriptors: Item Response Theory, Test Items, Accuracy, Computation
Wolf, Erika J.; Harrington, Kelly M.; Clark, Shaunna L.; Miller, Mark W. – Educational and Psychological Measurement, 2013
Determining sample size requirements for structural equation modeling (SEM) is a challenge often faced by investigators, peer reviewers, and grant writers. Recent years have seen a large increase in SEMs in the behavioral science literature, but consideration of sample size requirements for applied SEMs often relies on outdated rules-of-thumb.…
Descriptors: Sample Size, Structural Equation Models, Statistical Analysis, Statistical Bias
Solomon, Benjamin G.; Forsberg, Ole J. – School Psychology Quarterly, 2017
Bayesian techniques have become increasingly present in the social sciences, fueled by advances in computer speed and the development of user-friendly software. In this paper, we forward the use of Bayesian Asymmetric Regression (BAR) to monitor intervention responsiveness when using Curriculum-Based Measurement (CBM) to assess oral reading…
Descriptors: Bayesian Statistics, Regression (Statistics), Least Squares Statistics, Evaluation Methods
Kuch, Frederick H. – ProQuest LLC, 2012
Typically in calibration research, subjects perform a task and make a judgment about the success of the task. Accurate findings help subjects improve self-calibration. In addition, researchers rely on the accuracy of findings to make inferences about underlying metacognitive processes. Consequently, it is important that the measures used to assess…
Descriptors: Statistical Bias, Accuracy, Monte Carlo Methods, Statistics
Schoemann, Alexander M.; Miller, Patrick; Pornprasertmanit, Sunthud; Wu, Wei – International Journal of Behavioral Development, 2014
Planned missing data designs allow researchers to increase the amount and quality of data collected in a single study. Unfortunately, the effect of planned missing data designs on power is not straightforward. Under certain conditions using a planned missing design will increase power, whereas in other situations using a planned missing design…
Descriptors: Monte Carlo Methods, Simulation, Sample Size, Research Design
Shieh, Gwowen; Jan, Show-Li – Journal of Experimental Education, 2013
The authors examined 2 approaches for determining the required sample size of Welch's test for detecting equality of means when the greatest difference between any 2 group means is given. It is shown that the actual power obtained with the sample size of the suggested approach is consistently at least as great as the nominal power. However, the…
Descriptors: Sampling, Statistical Analysis, Computation, Research Methodology
Revilla, Melanie; Saris, Willem E. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Saris, Satorra, and Coenders (2004) proposed a new approach to estimate the quality of survey questions, combining the advantages of 2 existing approaches: the multitrait-multimethod (MTMM) and the split-ballot (SB) ones. Implemented in practice, this new approach led to frequent problems of nonconvergence and improper solutions. This article uses…
Descriptors: Multitrait Multimethod Techniques, Surveys, Monte Carlo Methods, Correlation
Padilla, Miguel A.; Divers, Jasmin – Educational and Psychological Measurement, 2013
The performance of the normal theory bootstrap (NTB), the percentile bootstrap (PB), and the bias-corrected and accelerated (BCa) bootstrap confidence intervals (CIs) for coefficient omega was assessed through a Monte Carlo simulation under conditions not previously investigated. Of particular interests were nonnormal Likert-type and binary items.…
Descriptors: Sampling, Statistical Inference, Computation, Statistical Analysis
Bellara, Aarti P. – ProQuest LLC, 2013
Propensity score analysis has been used to minimize the selection bias in observational studies to identify causal relationships. A propensity score is an estimate of an individual's probability of being placed in a treatment group given a set of covariates. Propensity score analysis aims to use the estimate to create balanced groups, akin to a…
Descriptors: Scores, Probability, Monte Carlo Methods, Statistical Analysis
Prindle, John J.; McArdle, John J. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
This study used statistical simulation to calculate differential statistical power in dynamic structural equation models with groups (as in McArdle & Prindle, 2008). Patterns of between-group differences were simulated to provide insight into how model parameters influence power approximations. Chi-square and root mean square error of…
Descriptors: Statistical Analysis, Structural Equation Models, Goodness of Fit, Monte Carlo Methods
Kim, Su-Young – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Just as growth mixture models are useful with single-phase longitudinal data, multiphase growth mixture models can be used with multiple-phase longitudinal data. One of the practically important issues in single- and multiphase growth mixture models is the sample size requirements for accurate estimation. In a Monte Carlo simulation study, the…
Descriptors: Structural Equation Models, Sample Size, Computation, Monte Carlo Methods
Tong, Xiaoxiao; Bentler, Peter M. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
Recently a new mean scaled and skewness adjusted test statistic was developed for evaluating structural equation models in small samples and with potentially nonnormal data, but this statistic has received only limited evaluation. The performance of this statistic is compared to normal theory maximum likelihood and 2 well-known robust test…
Descriptors: Structural Equation Models, Maximum Likelihood Statistics, Robustness (Statistics), Sample Size
Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
Moshagen, Morten – Structural Equation Modeling: A Multidisciplinary Journal, 2012
The size of a model has been shown to critically affect the goodness of approximation of the model fit statistic "T" to the asymptotic chi-square distribution in finite samples. It is not clear, however, whether this "model size effect" is a function of the number of manifest variables, the number of free parameters, or both. It is demonstrated by…
Descriptors: Goodness of Fit, Structural Equation Models, Statistical Analysis, Monte Carlo Methods

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