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J. Vincent Nix; Yi-Chin Wu; Lan Misty Song; Joseph D. Levy – Research & Practice in Assessment, 2024
Traditionally, assessment professionals use analyses relying upon null hypothesis significance testing (NHST), but those tools have limitations when analyzing small samples or disaggregated data. This study used common NHST analytical techniques, compared their results, and then explored an alternative technique that perhaps allows for a more…
Descriptors: Sample Size, Statistical Significance, MOOCs, Geographic Location
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Simsek, Ahmet Salih – International Journal of Assessment Tools in Education, 2023
Likert-type item is the most popular response format for collecting data in social, educational, and psychological studies through scales or questionnaires. However, there is no consensus on whether parametric or non-parametric tests should be preferred when analyzing Likert-type data. This study examined the statistical power of parametric and…
Descriptors: Error of Measurement, Likert Scales, Nonparametric Statistics, Statistical Analysis
Maryellen Brunson McClain; Tiffany L. Otero; Jillian Haut; Rochelle B. Schatz – Sage Research Methods Cases, 2014
With growing popularity of single subject design as a method to evaluate the efficacy of interventions, it is important to ensure that the analyses of these methods are rigorous and reliable. The purpose of this case study is to discuss the measures used to evaluate the efficacy of interventions in single subject design studies in the fields of…
Descriptors: Educational Research, Effect Size, Data Analysis, Data Interpretation
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Citkowicz, Martyna; Hedges, Larry V. – Society for Research on Educational Effectiveness, 2013
In some instances, intentionally or not, study designs are such that there is clustering in one group but not in the other. This paper describes methods for computing effect size estimates and their variances when there is clustering in only one group and the analysis has not taken that clustering into account. The authors provide the effect size…
Descriptors: Multivariate Analysis, Effect Size, Sampling, Sample Size
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Dunst, Carl J.; Hamby, Deborah W. – Journal of Intellectual & Developmental Disability, 2012
This paper includes a nontechnical description of methods for calculating effect sizes in intellectual and developmental disability studies. Different hypothetical studies are used to illustrate how null hypothesis significance testing (NHST) and effect size findings can result in quite different outcomes and therefore conflicting results. Whereas…
Descriptors: Intervals, Developmental Disabilities, Statistical Significance, Effect Size
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Vincent, Claudia G.; Sprague, Jeffrey R.; Tobin, Tary J. – Education and Treatment of Children, 2012
We examined 2009-2010 data on exclusionary discipline practices from one state in the Pacific Northwest of the United States across students' racial/ethnic backgrounds and disability status. Our focus was on proportionate representation in exclusionary discipline actions and in the duration of those disciplinary actions. Descriptive outcomes…
Descriptors: Disabilities, Race, Alaska Natives, Nontraditional Education
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What Works Clearinghouse, 2014
This "What Works Clearinghouse Procedures and Standards Handbook (Version 3.0)" provides a detailed description of the standards and procedures of the What Works Clearinghouse (WWC). The remaining chapters of this Handbook are organized to take the reader through the basic steps that the WWC uses to develop a review protocol, identify…
Descriptors: Educational Research, Guides, Intervention, Classification
Moses, Tim; Miao, Jing; Dorans, Neil – Educational Testing Service, 2010
This study compared the accuracies of four differential item functioning (DIF) estimation methods, where each method makes use of only one of the following: raw data, logistic regression, loglinear models, or kernel smoothing. The major focus was on the estimation strategies' potential for estimating score-level, conditional DIF. A secondary focus…
Descriptors: Test Bias, Statistical Analysis, Computation, Scores
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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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Huberty, Carl J.; Holmes, Susan E. – Educational and Psychological Measurement, 1983
An alternative analysis of the two-group single response variable design is proposed. It involves the classification of experimental units to populations represented by the two groups. Three real data sets are provided to illustrate the utility of the classification analysis. A table of sample sizes required for the analysis is presented.…
Descriptors: Classification, Data Analysis, Hypothesis Testing, Research Design
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Woolley, Thomas W.; Dawson, George O. – Journal of Research in Science Teaching, 1983
Examines what power-related changes occured in science education research over the past decade as a result of an earlier survey. Previous recommendations are expanded/expounded upon within the context of more recent work in the area. Proposes guidelines for reporting minimal amount of information for clear/independent evaluation of research…
Descriptors: Data Analysis, Effect Size, Guidelines, Power (Statistics)
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Barbella, Peter; And Others – Mathematics Teacher, 1990
Demonstrates a statistically valid method allowing students to explore randomization. Described are two examples: counting techniques for a small set of data and simulation for a large sample. (YP)
Descriptors: Data Analysis, Data Interpretation, Mathematical Concepts, Mathematical Logic