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Yi, Zhihui; Schreiber, James B.; Paliliunas, Dana; Barron, Becky F.; Dixon, Mark R. – Journal of Behavioral Education, 2021
The recent commentary by Beaujean and Farmer (2020) on the original paper by Dixon et al. (2019) serves a cautionary tale of selective p-values, the law of small N sizes, and the type-II error. We believe these authors have crafted a somewhat questionable argument in which only 57% of the original Dixon et al. data were re-analyzed, based on a…
Descriptors: Research Problems, Data Analysis, Statistical Analysis, Probability
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Powell, Jay C. – Guidance & Counselling, 1992
This commentary praises the clustering technique of data gathering from clients described by Carney (1992). It also cautions that analyzing qualitative data by quantitative means must be done with care, so that a matrix analysis and resulting interpretation of data gathered through the clustering technique, as Carney suggests, is valid and…
Descriptors: Counseling Techniques, Data Analysis, Matrices, Probability
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Carver, Ronald P. – Harvard Educational Review, 1978
Reviews case against using traditional statistical methods in educational research. Examines "fantasies" entertained by researchers about the meaning of statistical significance. Recommends abandoning statistical significance testing and suggests other ways of evaluating research results. Advocates a return to the scientific method of examining…
Descriptors: Data Analysis, Differences, Educational Research, Hypothesis Testing
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Deal, James E.; Anderson, Edward R. – Journal of Marriage and the Family, 1995
Presentation of quantitative research on the family often suffers from a tendency to interpret findings on a statistical rather than substantive basis. Advocates the use of data analysis that lends itself to an intuitive understanding of the nature of the findings, the strength of the association, and the import of the result. (JPS)
Descriptors: Data Analysis, Effect Size, Evaluation Methods, Goodness of Fit