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Thompson, W. Burt – Teaching of Psychology, 2019
When a psychologist announces a new research finding, it is often based on a rejected null hypothesis. However, if that hypothesis is true, the claim is a false alarm. Many students mistakenly believe that the probability of committing a false alarm equals alpha, the criterion for statistical significance, which is typically set at 5%. Instructors…
Descriptors: Statistical Analysis, Hypothesis Testing, Misconceptions, Data Interpretation
Kano, Megumi; Franke, Todd; Afifi, Abdelmonem A.; Bourque, Linda B. – Educational Researcher, 2008
To ensure accurate interpretation of research findings, researchers should report details about their research design, data collection method, and response rates when presenting findings from survey research. A review of 100 peer-reviewed articles reporting the results of survey research on K-12 schools with principals as the designated…
Descriptors: Research Methodology, School Surveys, Mail Surveys, Case Studies
Erosheva, Elena A. – Psychometrika, 2005
This paper focuses on model interpretation issues and employs a geometric approach to compare the potential value of using the Grade of Membership (GoM) model in representing population heterogeneity. We consider population heterogeneity manifolds generated by letting subject specific parameters vary over their natural range, while keeping other…
Descriptors: Mathematical Formulas, Research Methodology, Models, Comparative Analysis
Thompson, Bruce – 1994
Too few researchers understand what statistical significance testing does and does not do, and consequently their results are misinterpreted. This Digest explains the concept of statistical significance testing and discusses the meaning of probabilities, the concept of statistical significance, arguments against significance testing,…
Descriptors: Data Analysis, Data Interpretation, Decision Making, Effect Size

Beck, E. M.; Tolnay, Stewart E. – Historical Methods, 1995
Asserts that traditional approaches to multivariate analysis, including standard linear regression techniques, ignore the special character of count data. Explicates three suitable alternatives to standard regression techniques, a simple Poisson regression, a modified Poisson regression, and a negative binomial model. (MJP)
Descriptors: Data Interpretation, Evaluation Criteria, Higher Education, Multivariate Analysis