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Kulinskaya, Elena; Hoaglin, David C. – Research Synthesis Methods, 2023
For estimation of heterogeneity variance T[superscript 2] in meta-analysis of log-odds-ratio, we derive new mean- and median-unbiased point estimators and new interval estimators based on a generalized Q statistic, Q[subscript F], in which the weights depend on only the studies' effective sample sizes. We compare them with familiar estimators…
Descriptors: Q Methodology, Statistical Analysis, Meta Analysis, Intervals
Peabody, Michael R. – Applied Measurement in Education, 2020
The purpose of the current article is to introduce the equating and evaluation methods used in this special issue. Although a comprehensive review of all existing models and methodologies would be impractical given the format, a brief introduction to some of the more popular models will be provided. A brief discussion of the conditions required…
Descriptors: Evaluation Methods, Equated Scores, Sample Size, Item Response Theory
Liu, Chunyan; Kolen, Michael J. – Journal of Educational Measurement, 2018
Smoothing techniques are designed to improve the accuracy of equating functions. The main purpose of this study is to compare seven model selection strategies for choosing the smoothing parameter (C) for polynomial loglinear presmoothing and one procedure for model selection in cubic spline postsmoothing for mixed-format pseudo tests under the…
Descriptors: Comparative Analysis, Accuracy, Models, Sample Size
McCoach, D. Betsy; Rifenbark, Graham G.; Newton, Sarah D.; Li, Xiaoran; Kooken, Janice; Yomtov, Dani; Gambino, Anthony J.; Bellara, Aarti – Journal of Educational and Behavioral Statistics, 2018
This study compared five common multilevel software packages via Monte Carlo simulation: HLM 7, M"plus" 7.4, R (lme4 V1.1-12), Stata 14.1, and SAS 9.4 to determine how the programs differ in estimation accuracy and speed, as well as convergence, when modeling multiple randomly varying slopes of different magnitudes. Simulated data…
Descriptors: Hierarchical Linear Modeling, Computer Software, Comparative Analysis, Monte Carlo Methods
Sweet, Tracy M.; Junker, Brian W. – Journal of Educational and Behavioral Statistics, 2016
The hierarchical network model (HNM) is a framework introduced by Sweet, Thomas, and Junker for modeling interventions and other covariate effects on ensembles of social networks, such as what would be found in randomized controlled trials in education research. In this article, we develop calculations for the power to detect an intervention…
Descriptors: Intervention, Social Networks, Statistical Analysis, Computation
Lane, David M. – Journal of Statistics Education, 2015
Recently Watkins, Bargagliotti, and Franklin (2014) discovered that simulations of the sampling distribution of the mean can mislead students into concluding that the mean of the sampling distribution of the mean depends on sample size. This potential error arises from the fact that the mean of a simulated sampling distribution will tend to be…
Descriptors: Statistical Distributions, Sampling, Sample Size, Misconceptions
de la Torre, Jimmy; Lee, Young-Sun – Journal of Educational Measurement, 2013
This article used the Wald test to evaluate the item-level fit of a saturated cognitive diagnosis model (CDM) relative to the fits of the reduced models it subsumes. A simulation study was carried out to examine the Type I error and power of the Wald test in the context of the G-DINA model. Results show that when the sample size is small and a…
Descriptors: Statistical Analysis, Test Items, Goodness of Fit, Error of Measurement
Zu, Jiyun; Yuan, Ke-Hai – Journal of Educational Measurement, 2012
In the nonequivalent groups with anchor test (NEAT) design, the standard error of linear observed-score equating is commonly estimated by an estimator derived assuming multivariate normality. However, real data are seldom normally distributed, causing this normal estimator to be inconsistent. A general estimator, which does not rely on the…
Descriptors: Sample Size, Equated Scores, Test Items, Error of Measurement
Puhan, Gautam – Journal of Educational Measurement, 2011
The impact of log-linear presmoothing on the accuracy of small sample chained equipercentile equating was evaluated under two conditions. In the first condition the small samples differed randomly in ability from the target population. In the second condition the small samples were systematically different from the target population. Results…
Descriptors: Equated Scores, Data Analysis, Sample Size, Accuracy
Fan, Weihua; Hancock, Gregory R. – Journal of Educational and Behavioral Statistics, 2012
This study proposes robust means modeling (RMM) approaches for hypothesis testing of mean differences for between-subjects designs in order to control the biasing effects of nonnormality and variance inequality. Drawing from structural equation modeling (SEM), the RMM approaches make no assumption of variance homogeneity and employ robust…
Descriptors: Robustness (Statistics), Hypothesis Testing, Monte Carlo Methods, Simulation
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
Zhang, Jinming – Journal of Educational and Behavioral Statistics, 2012
The impact of uncertainty about item parameters on test information functions is investigated. The information function of a test is one of the most important tools in item response theory (IRT). Inaccuracy in the estimation of test information can have substantial consequences on data analyses based on IRT. In this article, the major part (called…
Descriptors: Item Response Theory, Tests, Accuracy, Data Analysis
Draxler, Clemens – Psychometrika, 2010
This paper is concerned with supplementing statistical tests for the Rasch model so that additionally to the probability of the error of the first kind (Type I probability) the probability of the error of the second kind (Type II probability) can be controlled at a predetermined level by basing the test on the appropriate number of observations.…
Descriptors: Statistical Analysis, Probability, Sample Size, Error of Measurement
Paek, Insu – Applied Psychological Measurement, 2010
Conservative bias in rejection of a null hypothesis from using the continuity correction in the Mantel-Haenszel (MH) procedure was examined through simulation in a differential item functioning (DIF) investigation context in which statistical testing uses a prespecified level [alpha] for the decision on an item with respect to DIF. The standard MH…
Descriptors: Test Bias, Statistical Analysis, Sample Size, Error of Measurement
Osborne, Jason W. – Practical Assessment, Research & Evaluation, 2011
Large surveys often use probability sampling in order to obtain representative samples, and these data sets are valuable tools for researchers in all areas of science. Yet many researchers are not formally prepared to appropriately utilize these resources. Indeed, users of one popular dataset were generally found "not" to have modeled…
Descriptors: Best Practices, Sampling, Sample Size, Data Analysis