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de la Torre, Jimmy; Hong, Yuan – Applied Psychological Measurement, 2010
Sample size ranks as one of the most important factors that affect the item calibration task. However, due to practical concerns (e.g., item exposure) items are typically calibrated with much smaller samples than what is desired. To address the need for a more flexible framework that can be used in small sample item calibration, this article…
Descriptors: Sample Size, Markov Processes, Tests, Data Analysis
Jiang, Depeng; Pepler, Debra; Yao, Hongxing – International Journal of Behavioral Development, 2010
Do interventions work and for whom? For this article, we examined the influence of population heterogeneity on power in designing and evaluating interventions. On the basis of Monte Carlo simulations in Study 1, we demonstrated that the power to detect the overall intervention effect is lower for a mixture of two subpopulations than for a…
Descriptors: Intervention, Evaluation, Heterogeneous Grouping, Monte Carlo Methods
McMurray, Kelly – ProQuest LLC, 2010
In educational research, students often exist in a multilevel social setting that can be identified by students within classrooms, classrooms nested in schools, schools nested in school districts, school districts nested in school counties, and school counties nested in states. These are considered hierarchical, nested, or multilevel because…
Descriptors: Sample Size, Educational Research, Monte Carlo Methods, Criteria
Peugh, James L.; Enders, Craig K. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
Cluster sampling results in response variable variation both among respondents (i.e., within-cluster or Level 1) and among clusters (i.e., between-cluster or Level 2). Properly modeling within- and between-cluster variation could be of substantive interest in numerous settings, but applied researchers typically test only within-cluster (i.e.,…
Descriptors: Structural Equation Models, Monte Carlo Methods, Multivariate Analysis, Sampling
Chun, So Yeon; Shapiro, Alexander – Multivariate Behavioral Research, 2009
The noncentral chi-square approximation of the distribution of the likelihood ratio (LR) test statistic is a critical part of the methodology in structural equation modeling. Recently, it was argued by some authors that in certain situations normal distributions may give a better approximation of the distribution of the LR test statistic. The main…
Descriptors: Statistical Analysis, Structural Equation Models, Validity, Monte Carlo Methods
Raju, Nambury S.; Fortmann-Johnson, Kristen A.; Kim, Wonsuk; Morris, Scott B.; Nering, Michael L.; Oshima, T. C. – Applied Psychological Measurement, 2009
The recent study of Oshima, Raju, and Nanda proposes the item parameter replication (IPR) method for assessing statistical significance of the noncompensatory differential item functioning (NCDIF) index within the differential functioning of items and tests (DFIT) framework. Previous Monte Carlo simulations have found that the appropriate cutoff…
Descriptors: Test Bias, Statistical Significance, Item Response Theory, Monte Carlo Methods
Shin, Seon-Hi – Practical Assessment, Research & Evaluation, 2009
This study investigated the impact of the coding scheme on IRT-based true score equating under a common-item nonequivalent groups design. Two different coding schemes under investigation were carried out by assigning either a zero or a blank to a missing item response in the equating data. The investigation involved a comparison study using actual…
Descriptors: True Scores, Equated Scores, Item Response Theory, Coding
Guo, Jiin-Huarng; Luh, Wei-Ming – Educational and Psychological Measurement, 2008
This study proposes an approach for determining appropriate sample size for Welch's F test when unequal variances are expected. Given a certain maximum deviation in population means and using the quantile of F and t distributions, there is no need to specify a noncentrality parameter and it is easy to estimate the approximate sample size needed…
Descriptors: Sample Size, Monte Carlo Methods, Statistical Analysis, Mathematical Formulas
Cribbie, Robert A.; Arpin-Cribbie, Chantal A.; Gruman, Jamie A. – Journal of Experimental Education, 2009
Researchers in education are often interested in determining whether independent groups are equivalent on a specific outcome. Equivalence tests for 2 independent populations have been widely discussed, whereas testing for equivalence with more than 2 independent groups has received little attention. The authors discuss alternatives for testing the…
Descriptors: Monte Carlo Methods, Testing, Statistical Analysis, Researchers
Shin, Tacksoo; Davison, Mark L.; Long, Jeffrey D. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
The purpose of this study is to investigate the effects of missing data techniques in longitudinal studies under diverse conditions. A Monte Carlo simulation examined the performance of 3 missing data methods in latent growth modeling: listwise deletion (LD), maximum likelihood estimation using the expectation and maximization algorithm with a…
Descriptors: Sample Size, Monte Carlo Methods, Structural Equation Models, Data Collection
Yurdugul, Halil – Applied Psychological Measurement, 2009
This article describes SIMREL, a software program designed for the simulation of alpha coefficients and the estimation of its confidence intervals. SIMREL runs on two alternatives. In the first one, if SIMREL is run for a single data file, it performs descriptive statistics, principal components analysis, and variance analysis of the item scores…
Descriptors: Intervals, Monte Carlo Methods, Computer Software, Factor Analysis
Garrett, Phyllis – ProQuest LLC, 2009
The use of polytomous items in assessments has increased over the years, and as a result, the validity of these assessments has been a concern. Differential item functioning (DIF) and missing data are two factors that may adversely affect assessment validity. Both factors have been studied separately, but DIF and missing data are likely to occur…
Descriptors: Sample Size, Monte Carlo Methods, Test Validity, Effect Size
Herzog, Walter; Boomsma, Anne – Structural Equation Modeling: A Multidisciplinary Journal, 2009
Traditional estimators of fit measures based on the noncentral chi-square distribution (root mean square error of approximation [RMSEA], Steiger's [gamma], etc.) tend to overreject acceptable models when the sample size is small. To handle this problem, it is proposed to employ Bartlett's (1950), Yuan's (2005), or Swain's (1975) correction of the…
Descriptors: Intervals, Sample Size, Monte Carlo Methods, Computation
Yurdugul, Halil – Hacettepe University Journal of Education, 2008
The coefficient alpha is the most widely used measure of internal consistency for composite scores in the educational and psychological studies. However, due to the difficulties of data gathering in psychometric studies, the minimum sample size for the sample coefficient alpha has been frequently debated. There are various suggested minimum sample…
Descriptors: Sample Size, Monte Carlo Methods, Reliability, Measurement
Price, Larry R.; Laird, Angela R.; Fox, Peter T.; Ingham, Roger J. – Structural Equation Modeling: A Multidisciplinary Journal, 2009
The aims of this study were to present a method for developing a path analytic network model using data acquired from positron emission tomography. Regions of interest within the human brain were identified through quantitative activation likelihood estimation meta-analysis. Using this information, a "true" or population path model was then…
Descriptors: Sample Size, Monte Carlo Methods, Structural Equation Models, Markov Processes

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