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50 Years of ERIC
50 Years of ERIC
The Education Resources Information Center (ERIC) is celebrating its 50th Birthday! First opened on May 15th, 1964 ERIC continues the long tradition of ongoing innovation and enhancement.

Learn more about the history of ERIC here. PDF icon

Showing 151 to 165 of 463 results
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Sinharay, Sandip; Dorans, Neil J.; Grant, Mary C.; Blew, Edwin O. – Journal of Educational and Behavioral Statistics, 2009
Test administrators often face the challenge of detecting differential item functioning (DIF) with samples of size smaller than that recommended by experts. A Bayesian approach can incorporate, in the form of a prior distribution, existing information on the inference problem at hand, which yields more stable estimation, especially for small…
Descriptors: Test Bias, Computation, Bayesian Statistics, Data
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Bauer, Daniel J.; Cai, Li – Journal of Educational and Behavioral Statistics, 2009
Applications of multilevel models have increased markedly during the past decade. In incorporating lower-level predictors into multilevel models, a key interest is often whether or not a given predictor requires a random slope, that is, whether the effect of the predictor varies over upper-level units. If the variance of a random slope…
Descriptors: Models, Predictor Variables, Statistical Analysis, Regression (Statistics)
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DeMars, Christine E. – Journal of Educational and Behavioral Statistics, 2009
The Mantel-Haenszel (MH) and logistic regression (LR) differential item functioning (DIF) procedures have inflated Type I error rates when there are large mean group differences, short tests, and large sample sizes.When there are large group differences in mean score, groups matched on the observed number-correct score differ on true score,…
Descriptors: Regression (Statistics), Test Bias, Error of Measurement, True Scores
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Tryon, Warren W.; Lewis, Charles – Journal of Educational and Behavioral Statistics, 2009
Tryon presented a graphic inferential confidence interval (ICI) approach to analyzing two independent and dependent means for statistical difference, equivalence, replication, indeterminacy, and trivial difference. Tryon and Lewis corrected the reduction factor used to adjust descriptive confidence intervals (DCIs) to create ICIs and introduced…
Descriptors: Statistical Analysis, Intervals, Differences, Computation
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Zwick, Rebecca; Lenaburg, Lubella – Journal of Educational and Behavioral Statistics, 2009
In certain data analyses (e.g., multiple discriminant analysis and multinomial log-linear modeling), classification decisions are made based on the estimated posterior probabilities that individuals belong to each of several distinct categories. In the Bayesian network literature, this type of classification is often accomplished by assigning…
Descriptors: Classification, Bayesian Statistics, Network Analysis, Probability
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Ho, Andrew Dean – Journal of Educational and Behavioral Statistics, 2009
Problems of scale typically arise when comparing test score trends, gaps, and gap trends across different tests. To overcome some of these difficulties, test score distributions on the same score scale can be represented by nonparametric graphs or statistics that are invariant under monotone scale transformations. This article motivates and then…
Descriptors: Nonparametric Statistics, Comparative Analysis, Trend Analysis, Scores
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Rotondi, Michael A.; Donner, Allan – Journal of Educational and Behavioral Statistics, 2009
The educational field has now accumulated an extensive literature reporting on values of the intraclass correlation coefficient, a parameter essential to determining the required size of a planned cluster randomized trial. We propose here a simple simulation-based approach including all relevant information that can facilitate this task. An…
Descriptors: Sample Size, Computation, Correlation, Bayesian Statistics
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Ostapczuk, Martin; Moshagen, Morten; Zhao, Zengmei; Musch, Jochen – Journal of Educational and Behavioral Statistics, 2009
Randomized response techniques (RRTs) aim to reduce social desirability bias in the assessment of sensitive attributes but differ regarding privacy protection. The less protection a design offers, the more likely respondents cheat by disobeying the instructions. In asymmetric RRT designs, respondents can play safe by giving a response that is…
Descriptors: Response Style (Tests), Social Desirability, Attitude Measures, Privacy
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Harring, Jeffrey R. – Journal of Educational and Behavioral Statistics, 2009
The nonlinear mixed effects model for continuous repeated measures data has become an increasingly popular and versatile tool for investigating nonlinear longitudinal change in observed variables. In practice, for each individual subject, multiple measurements are obtained on a single response variable over time or condition. This structure can be…
Descriptors: Regression (Statistics), Computation, Measurement, Models
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Azen, Razia; Traxel, Nicole – Journal of Educational and Behavioral Statistics, 2009
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Descriptors: Regression (Statistics), Predictor Variables, Measurement, Simulation
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Soares, Tufi M.; Goncalves, Flavio B.; Gamerman, Dani – Journal of Educational and Behavioral Statistics, 2009
In this article, an integrated Bayesian model for differential item functioning (DIF) analysis is proposed. The model is integrated in the sense of modeling the responses along with the DIF analysis. This approach allows DIF detection and explanation in a simultaneous setup. Previous empirical studies and/or subjective beliefs about the item…
Descriptors: Test Bias, Bayesian Statistics, Models, Item Response Theory
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van der Linden, Wim J. – Journal of Educational and Behavioral Statistics, 2009
A bivariate lognormal model for the distribution of the response times on a test by a pair of test takers is presented. As the model has parameters for the item effects on the response times, its correlation parameter automatically corrects for the spuriousness in the observed correlation between the response times of different test takers because…
Descriptors: Cheating, Models, Reaction Time, Correlation
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Wainer, Howard; Robinson, Daniel H. – Journal of Educational and Behavioral Statistics, 2009
This article presents an interview with Linda Gottfredson (nee Howarth), who obtained her BA (psychology, Phi Beta Kappa) from UC Berkeley in 1969, served in the Peace Corps in the Malaysian Health Service from 1969 to 1972, and received her PhD (sociology) from Johns Hopkins University (JHU) in 1976. She was Research Scientist at JHU's Center for…
Descriptors: Scientific Research, Intelligence, Barriers, Conflict
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Li, Deping; Oranje, Andreas; Jiang, Yanlin – Journal of Educational and Behavioral Statistics, 2009
To find population proficiency distributions, a two-level hierarchical linear model may be applied to large-scale survey assessments such as the National Assessment of Educational Progress (NAEP). The model and parameter estimation are developed and a simulation was carried out to evaluate parameter recovery. Subsequently, both a hierarchical and…
Descriptors: Computation, National Competency Tests, Measurement, Regression (Statistics)
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Hedges, Larry V. – Journal of Educational and Behavioral Statistics, 2009
A common mistake in analysis of cluster randomized experiments is to ignore the effect of clustering and analyze the data as if each treatment group were a simple random sample. This typically leads to an overstatement of the precision of results and anticonservative conclusions about precision and statistical significance of treatment effects.…
Descriptors: Data Analysis, Statistical Significance, Statistics, Experiments
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