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Desjardins, Christopher David – Journal of Experimental Education, 2016
The purpose of this article is to develop a statistical model that best explains variability in the number of school days suspended. Number of school days suspended is a count variable that may be zero-inflated and overdispersed relative to a Poisson model. Four models were examined: Poisson, negative binomial, Poisson hurdle, and negative…
Descriptors: Suspension, Statistical Analysis, Models, Data
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Hembry, Ian; Bunuan, Rommel; Beretvas, S. Natasha; Ferron, John M.; Van den Noortgate, Wim – Journal of Experimental Education, 2015
A multilevel logistic model for estimating a nonlinear trajectory in a multiple-baseline design is introduced. The model is applied to data from a real multiple-baseline design study to demonstrate interpretation of relevant parameters. A simple change-in-levels (?"Levels") model and a model involving a quadratic function…
Descriptors: Computation, Research Design, Data, Intervention
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Lai, Mark H. C.; Kwok, Oi-man – Journal of Experimental Education, 2015
Educational researchers commonly use the rule of thumb of "design effect smaller than 2" as the justification of not accounting for the multilevel or clustered structure in their data. The rule, however, has not yet been systematically studied in previous research. In the present study, we generated data from three different models…
Descriptors: Educational Research, Research Design, Cluster Grouping, Statistical Data
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Li, Jian; Lomax, Richard G. – Journal of Experimental Education, 2011
Users assume statistical software packages produce accurate results. In this article, the authors systematically examined Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS) for 3 analysis of variance (ANOVA) designs, mixed-effects ANOVA, fixed-effects analysis of covariance (ANCOVA), and nested ANOVA. For each…
Descriptors: Social Sciences, Computer Software, Statistical Analysis, Models
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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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Katz, Barry M.; McSweeney, Maryellen – Journal of Experimental Education, 1979
Errors of misclassification and their effects on categorical data analysis are discussed. The chi-square test for equality of two proportions is examined in the context of errorful categorical data. The effects of such errors are illustrated. A correction procedure is developed and discussed. (Author/MH)
Descriptors: Classification, Data Analysis, Data Collection, Error Patterns
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Engel, Barney M.; Cooper, Martin – Journal of Experimental Education, 1971
A study designed to compare the academic achievement of pupils in graded and non-graded schools using an Index of Non-gradedness to determine the validity of the term non-graded" as applied to some schools. (Author/RY)
Descriptors: Academic Achievement, Data Analysis, Elementary Schools, Nongraded Instructional Grouping
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Holdaway, Edward A. – Journal of Experimental Education, 1971
To assess whether different response patterns were associated with differences in the naming and placement of response categories, 1000 undergraduate students in educational administration completed a 10-item personal-values questionnaire. (Author)
Descriptors: Attitudes, Behavior Rating Scales, Data Analysis, Questionnaires
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McNeil, Keith A.; Kelly, Francis J. – Journal of Experimental Education, 1970
Descriptors: Data Analysis, Psychometrics, Statistical Analysis
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Hahs-Vaughn, Debbie L. – Journal of Experimental Education, 2005
Using data from the National Study of Postsecondary Faculty and the Early Childhood Longitudinal Study--Kindergarten Class of 1998-99, the author provides guidelines for incorporating weights and design effects in single-level analysis using Windows-based SPSS and AM software. Examples of analyses that do and do not employ weights and design…
Descriptors: Statistical Analysis, Data, Guidelines, Sampling
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Huck, Schuyler W.; Sutton, Cary O. – Journal of Experimental Education, 1974
Considered the statistical comparison of groups under the condition of equal sample sizes. (Author)
Descriptors: Educational Research, Sampling, Statistical Analysis, Tables (Data)
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Veitch, William R.; Roscoe, John T. – Journal of Experimental Education, 1974
A Monte Carlo technique was employed in order to compare the relative power and robustness of the Bartlett, Cochran, Hartley, and Levene tests for homogeniety of variance. (Editor)
Descriptors: Research Methodology, Statistical Analysis, Statistical Data, Test Reliability
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Williams, John D. – Journal of Experimental Education, 1974
The importance of the method given in the body of this article is that it presents Tukey's test in a greatly simplified form for the applied researcher. (Author)
Descriptors: Educational Research, Multiple Regression Analysis, Statistical Analysis, Tables (Data)
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Leung, Shing On; Sachs, John – Journal of Experimental Education, 2005
Quite often in data reduction, it is more meaningful and economical to select a subset of variables instead of reducing the dimensionality of the variable space with principal components analysis. The authors present a neglected method for variable selection called the BI-method (R. P. Bhargava & T. Ishizuka, 1981). It is a direct, simple method…
Descriptors: Statistical Analysis, Statistical Data, Selection, Psychological Studies
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Hopkins, Kenneth D. – Journal of Experimental Education, 1976
Illustrates how expected mean squares needed in the analysis of variance can be arrived at via the use of only one rule: the expected mean square E(MS) for any source of variation for any ANOVA model is specified effect plus the specified effect in combination with any random effect. (Editor/RK)
Descriptors: Analysis of Variance, Charts, Correlation, Methods
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