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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 1 to 15 of 18 results
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Peng, Chao-Ying Joanne; Long, Haiying; Abaci, Serdar – Journal of Experimental Education, 2012
Given the importance of statistical power analysis in quantitative research and the repeated emphasis on it by American Educational Research Association/American Psychological Association journals, the authors examined the reporting practice of power analysis by the quantitative studies published in 12 education/psychology journals between 2005…
Descriptors: Educational Research, Computer Software, Hypothesis Testing, Statistical Analysis
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Liu, Xiaofeng Steven – Journal of Experimental Education, 2010
The noncentrality parameter for a contrast test in a one-way analysis of variance is based on the dot product of 2 vectors whose geometric meaning in a Euclidian space offers mnemonic hints about its constituents. Additionally, the noncentrality parameters for a set of orthogonal contrasts sum up to the noncentrality parameter for the omnibus "F"…
Descriptors: Statistical Analysis, Geometric Concepts, Mnemonics, Sample Size
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Pituch, Keenan A.; Murphy, Daniel L.; Tate, Richard L. – Journal of Experimental Education, 2009
Due to the clustered nature of field data, multi-level modeling has become commonly used to analyze data arising from educational field experiments. While recent methodological literature has focused on multi-level mediation analysis, relatively little attention has been devoted to mediation analysis when three levels (e.g., student, class,…
Descriptors: Research Design, Educational Experiments, Models, Mediation Theory
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Harwell, Michael; Maeda, Yukiko – Journal of Experimental Education, 2008
There is general agreement that meta-analysis is an important tool for synthesizing study results in quantitative educational research. Yet, a shared feature of many meta-analyses is a failure to report sufficient information for readers to fully judge the reported findings, such as the populations to which generalizations are to be made,…
Descriptors: Educational Research, Meta Analysis, Research Methodology, Statistical Analysis
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Tate, Richard L.; Pituch, Keenan A. – Journal of Experimental Education, 2007
The hierarchical linear model (HLM) is now commonly accepted as a useful modeling approach for multilevel data resulting from randomized field experiments. When multiple outcomes of interest exist, a multivariate extension of the conventional univariate HLM offers advantages over the usual application of separate HLM analyses for each of the…
Descriptors: Statistical Analysis, Hypothesis Testing, Student Motivation, Academic Achievement
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Goodwin, Laura D.; Leech, Nancy L. – Journal of Experimental Education, 2006
The authors describe and illustrate 6 factors that affect the size of a Pearson correlation: (a) the amount of variability in the data, (b) differences in the shapes of the 2 distributions, (c) lack of linearity, (d) the presence of 1 or more "outliers," (e) characteristics of the sample, and (f) measurement error. Also discussed are ways to…
Descriptors: Effect Size, Correlation, Influences, Error of Measurement
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Hahs-Vaughn, Debbie L.; Onwuegbuzi, Anthony J. – Journal of Experimental Education, 2006
Propensity score analysis is one statistical technique that can be applied to observational data to mimic randomization and thus can be used to estimate causal effects in studies in which the researchers have not applied randomization. In this article the authors (a) describe propensity score methodology and (b) demonstrate its application using…
Descriptors: Researchers, Research Methodology, Private Schools, Public Schools
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Ferron, John; Jones, Peggy K. – Journal of Experimental Education, 2006
The authors present a method that ensures control over the Type I error rate for those who visually analyze the data from response-guided multiple-baseline designs. The method can be seen as a modification of visual analysis methods to incorporate a mechanism to control Type I errors or as a modification of randomization test methods to allow…
Descriptors: Multivariate Analysis, Data Analysis, Inferences, Monte Carlo Methods
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Bahr, G. Sussane; Dansereau, Donald F. – Journal of Experimental Education, 2005
The use of bilingual graphic organizers (bilingual knowledge maps [BiK maps]) as a presentation format was investigated for the acquisition of foreign language vocabulary. Participants were assigned to 1 of 4 conditions for the task of studying 32 German-English word pairs. Participants in each condition were trained on either lists or BiK maps…
Descriptors: Maps, Bilingualism, Second Language Learning, Vocabulary
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Sierra, Vicenta; Solanas, Antonio; Quera, Vicenc – Journal of Experimental Education, 2005
The authors used a Monte Carlo simulation to examine how the violation of the exchangeability assumption affects empirical Type I error rates of the LMH randomization test (J. R. Levin, L. A. Marascuilo, & L. J. Hubert, 1978). Simulation results showed that the LMH test is not always an appropriate technique for analyzing systematic designs when…
Descriptors: Monte Carlo Methods, Statistical Analysis, Item Response Theory, Error of Measurement
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Kelly, Sean – Journal of Experimental Education, 2004
In this event history analysis of the 1990-1991 Schools and Staffing Survey and the 1992 Teacher Follow-up Survey, a retrospective person-year database was constructed to examine teacher attrition over the course of the teaching career. Consistent with prior research, higher teacher salaries reduced attrition, but only slightly so. Teacher…
Descriptors: Faculty Mobility, Disadvantaged Schools, Teacher Salaries, Job Satisfaction
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Tate, Richard – Journal of Experimental Education, 2004
Current descriptions of results from hierarchical linear models (HLM) and hierarchical generalized linear models (HGLM), usually based only on interpretations of individual model parameters, are incomplete in the presence of statistically significant and practically important "slopes as outcomes" terms in the models. For complete description of…
Descriptors: Computation, Models, Measurement Techniques, Statistical Analysis
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Pituch, Keenan A. – Journal of Experimental Education, 2001
Shows how one can combine a large-scale planned variation experimental design and multilevel analysis to address research questions that go beyond the issue of overall treatment effectiveness. Uses data from a simulated field experiment to describe the multilevel models that can be used. (SLD)
Descriptors: Educational Research, Evaluation Methods, Experiments, Intervention
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Knapp, Thomas R.; Sawilowsky, Shlomo S. – Journal of Experimental Education, 2001
Critiques some of the strong positions taken by Bruce Thompson on a variety of methodological issues. Provides alternative views, and discusses the role of editorial policy in research reporting. Focuses on: (1) stepwise methods; (2) context specificity; (3) weights and structure coefficients; (4) reliability as a characteristic of scores and not…
Descriptors: Editing, Effect Size, Reliability, Research Methodology
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Riniolo, Todd C. – Journal of Experimental Education, 1999
Presents an alternative statistical test, BOOT(subscript)med for the two-group situation when a small experimental group is being compared with a large control group. BOOTmed is a between-groups median test derived through bootstrapping techniques. Empirical validation indicates that BOOTmed maintains relatively robust error rates under a variety…
Descriptors: Comparative Analysis, Control Groups, Error of Measurement, Statistical Analysis
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