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Peer reviewedZarefsky, David; Henderson, Bill – Journal of the American Forensic Association, 1983
Defends hypothesis testing in academic debate. (PD)
Descriptors: Debate, Higher Education, Hypothesis Testing, Persuasive Discourse
Peer reviewedHollihan, Thomas A. – Journal of the American Forensic Association, 1983
Argues that the practical outcomes of hypothesis testing in academic debate should be considered. (PD)
Descriptors: Debate, Higher Education, Hypothesis Testing, Persuasive Discourse
Peer reviewedHopkins, Kenneth D. – Educational and Psychological Measurement, 1983
A general analysis strategy is proposed such that the universe of inference is increased incrementally. The strategy prevents logically incongruent findings that occasionally result when the conventional analysis strategy is employed. (Author)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Research Design
Peer reviewedKarpman, Mitchell B. – Educational and Psychological Measurement, 1983
When homogeneity of slopes is not present, the Johnson-Neyman technique has been considered as an alternative to analysis of covariance. This paper describes how to apply the Johnson-Neyman technique for one or two covariates using the Statistical Package for the Social Sciences (SPSS) or BMDP (Biomedical Computer Programs). (Author/BW)
Descriptors: Analysis of Covariance, Computer Programs, Data Analysis, Hypothesis Testing
Peer reviewedFriedman, Herbert – Educational and Psychological Measurement, 1982
A concise table is presented based on a general measure of magnitude of effect which allows direct determinations of statistical power over a practical range of values and alpha levels. The table also facilitates the setting of the research sample size needed to provide a given degree of power. (Author/CM)
Descriptors: Hypothesis Testing, Power (Statistics), Research Design, Sampling
Peer reviewedHollingsworth, Holly – Journal of Experimental Education, 1980
A solution to some problems of maximized contrasts for analysis of variance situations when the cell sizes are unequal is offered. It is demonstrated that a contrast is maximized relative to the analysis used to compute the sum of squares between groups. Interpreting a maximum contrast is discussed. (Author/GK)
Descriptors: Analysis of Variance, Hypothesis Testing, Research Design, Research Problems
Peer reviewedCohen, L. Jonathan – Cognition, 1980
Kahneman and Tversky's critique of Cohen's position on adults' probability reasoning is not valid. If they think Baconian logic is normatively unsound, the onus is on them to explain why. It is valid and useful because nature itself is full of causal processes. (Author/RD)
Descriptors: Abstract Reasoning, Deduction, Hypothesis Testing, Logical Thinking
Peer reviewedLuftig, Jeffrey T.; Norton, Willis P. – Journal of Studies in Technical Careers, 1982
This article builds on an earlier discussion of the importance of the Type II error (beta) and power to the hypothesis testing process (CE 511 484), and illustrates the methods by which sample size calculations should be employed so as to improve the research process. (Author/CT)
Descriptors: Hypothesis Testing, Research Design, Research Methodology, Research Problems
Peer reviewedEiting, Mindert H.; Mellenbergh, Gideon J. – Multivariate Behavioral Research, 1981
A commentary is made on a previously published article concerning testing the equivalence of covariance matrices. An error in the previous article (by the same authors) is pointed out and the consequences of the error are discussed. (JKS)
Descriptors: Analysis of Covariance, Data Analysis, Hypothesis Testing, Matrices
Peer reviewedBorg, Ingiver; Lingoes, James C. – Psychometrika, 1980
A method for externally constraining certain distances in multidimensional scaling configurations is introduced and illustrated. The method is described in detail and several examples are presented. (Author/JKS)
Descriptors: Algorithms, Hypothesis Testing, Mathematical Models, Multidimensional Scaling
Peer reviewedWilliams, John D. – Multiple Linear Regression Viewpoints, 1980
Multiple comparisons involve the examination of which group or groups are actually different from other group(s) in analysis of variance results. Such comparisons usually involve one-way analysis of variance. This monograph discusses designs more complex than one-way designs. (JKS)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Research Design
Peer reviewedBarton, Alan R. – Psychometrika, 1980
Learning hierarchy research has been characterized by the use of ad hoc statistical procedures to determine the validity of postulated hierarchical connections. Various data sets are analyzed using a restricted maximum likelihood estimation procedure; results are compared with those obtained using the method suggested by Dayton and Macready.…
Descriptors: Data Analysis, Hypothesis Testing, Learning Processes, Validity
Peer reviewedViana, Marlos A. G. – Journal of Educational Statistics, 1980
Statistical techniques for summarizing results from independent correlational studies are presented. The case in which only the sample correlation coefficients are available and the case in which the original paired data are available are both considered. (Author/JKS)
Descriptors: Correlation, Data Analysis, Hypothesis Testing, Research Methodology
Peer reviewedEdgington, Eugene S. – Journal of Educational Statistics, 1980
Valid statistical tests for one-subject experiments are necessary to justify statistical inferences and to ensure the acceptability of research reports to a wide range of journals and readers. The validity of randomization tests for one-subject experiments is examined. (See TM 505 800-801).(Author/JKS)
Descriptors: Experimental Groups, Hypothesis Testing, Research Design, Statistical Data
Peer reviewedEdgington, Eugene S. – Journal of Educational Statistics, 1980
Two types of problems supposedly associated with the use of randomization tests for single-subject experiments have been discussed: the random introduction of treatments and the repeated alternation of treatments. Ways to reduce the adverse effects associated with these problems are presented. (See TM 505 799-800). (Author/JKS) (Author/JKS)
Descriptors: Experimental Groups, Hypothesis Testing, Research Design, Statistical Data


