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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
Li, Ying; Rupp, Andre A. – Educational and Psychological Measurement, 2011
This study investigated the Type I error rate and power of the multivariate extension of the S - [chi][squared] statistic using unidimensional and multidimensional item response theory (UIRT and MIRT, respectively) models as well as full-information bifactor (FI-bifactor) models through simulation. Manipulated factors included test length, sample…
Descriptors: Test Length, Item Response Theory, Statistical Analysis, Error Patterns
Chou, Yeh-Tai; Wang, Wen-Chung – Educational and Psychological Measurement, 2010
Dimensionality is an important assumption in item response theory (IRT). Principal component analysis on standardized residuals has been used to check dimensionality, especially under the family of Rasch models. It has been suggested that an eigenvalue greater than 1.5 for the first eigenvalue signifies a violation of unidimensionality when there…
Descriptors: Test Length, Sample Size, Correlation, Item Response Theory
Jamshidian, Mortaza; Jalal, Siavash – Psychometrika, 2010
Test of homogeneity of covariances (or homoscedasticity) among several groups has many applications in statistical analysis. In the context of incomplete data analysis, tests of homoscedasticity among groups of cases with identical missing data patterns have been proposed to test whether data are missing completely at random (MCAR). These tests of…
Descriptors: Sample Size, Statistical Analysis, Nonparametric Statistics, Simulation
Keiffer, Elizabeth Ann – ProQuest LLC, 2011
A differential item functioning (DIF) simulation study was conducted to explore the type and level of impact that contamination had on type I error and power rates in DIF analyses when the suspect item favored the same or opposite group as the DIF items in the matching subtest. Type I error and power rates were displayed separately for the…
Descriptors: Test Items, Sample Size, Simulation, Identification
Coffman, Donna L. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Mediation is usually assessed by a regression-based or structural equation modeling (SEM) approach that we refer to as the classical approach. This approach relies on the assumption that there are no confounders that influence both the mediator, "M", and the outcome, "Y". This assumption holds if individuals are randomly…
Descriptors: Structural Equation Models, Simulation, Regression (Statistics), Probability
de la Torre, Jimmy; Hong, Yuan; Deng, Weiling – Journal of Educational Measurement, 2010
To better understand the statistical properties of the deterministic inputs, noisy "and" gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the…
Descriptors: Classification, Computation, Models, Simulation
Finch, Holmes – Journal of Experimental Education, 2010
Discriminant Analysis (DA) is a tool commonly used for differentiating among 2 or more groups based on 2 or more predictor variables. DA works by finding 1 or more linear combinations of the predictors that yield maximal difference among the groups. One common goal of researchers using DA is to characterize the nature of group difference by…
Descriptors: Simulation, Predictor Variables, Discriminant Analysis, Comparative Analysis
Wu, Huey-Min; Kuo, Bor-Chen; Yang, Jinn-Min – Educational Technology & Society, 2012
In recent years, many computerized test systems have been developed for diagnosing students' learning profiles. Nevertheless, it remains a challenging issue to find an adaptive testing algorithm to both shorten testing time and precisely diagnose the knowledge status of students. In order to find a suitable algorithm, four adaptive testing…
Descriptors: Adaptive Testing, Test Items, Computer Assisted Testing, Mathematics
Kubinger, Klaus D.; Rasch, Dieter; Yanagida, Takuya – Educational Research and Evaluation, 2011
Though calibration of an achievement test within psychological and educational context is very often carried out by the Rasch model, data sampling is hardly designed according to statistical foundations. However, Kubinger, Rasch, and Yanagida (2009) recently suggested an approach for the determination of sample size according to a given Type I and…
Descriptors: Sample Size, Simulation, Testing, Achievement Tests
Dawson, Robert – Journal of Statistics Education, 2011
It is common to consider Tukey's schematic ("full") boxplot as an informal test for the existence of outliers. While the procedure is useful, it should be used with caution, as at least 30% of samples from a normally-distributed population of any size will be flagged as containing an outlier, while for small samples (N less than 10) even extreme…
Descriptors: Spreadsheets, Educational Technology, Simulation, Mathematics Activities
Cui, Zhongmin; Kolen, Michael J. – Journal of Educational Measurement, 2009
This article considers two new smoothing methods in equipercentile equating, the cubic B-spline presmoothing method and the direct presmoothing method. Using a simulation study, these two methods are compared with established methods, the beta-4 method, the polynomial loglinear method, and the cubic spline postsmoothing method, under three sample…
Descriptors: Equated Scores, Methods, Sample Size, Test Content
Kozak, Marcin – Teaching Statistics: An International Journal for Teachers, 2009
This article suggests how to explain a problem of small sample size when considering correlation between two Normal variables. Two techniques are shown: one based on graphs and the other on simulation. (Contains 3 figures and 1 table.)
Descriptors: Sample Size, Correlation, Predictor Variables, Simulation
Doolen, Jessica – ProQuest LLC, 2012
High fidelity simulation has become a widespread and costly learning strategy in nursing education because it can fill the gap left by a shortage of clinical sites. In addition, high fidelity simulation is an active learning strategy that is thought to increase higher order thinking such as clinical reasoning and judgment skills in nursing…
Descriptors: Simulation, Nursing Education, Simulated Environment, Psychometrics
Sunnassee, Devdass – ProQuest LLC, 2011
Small sample equating remains a largely unexplored area of research. This study attempts to fill in some of the research gaps via a large-scale, IRT-based simulation study that evaluates the performance of seven small-sample equating methods under various test characteristic and sampling conditions. The equating methods considered are typically…
Descriptors: Test Length, Test Format, Sample Size, Simulation

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