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Jane E. Miller – Numeracy, 2023
Students often believe that statistical significance is the only determinant of whether a quantitative result is "important." In this paper, I review traditional null hypothesis statistical testing to identify what questions inferential statistics can and cannot answer, including statistical significance, effect size and direction,…
Descriptors: Statistical Significance, Holistic Approach, Statistical Inference, Effect Size
Ciorba, Charles R.; Russell, Brian E. – Journal of Research in Music Education, 2014
The purpose of this study was to test a hypothesized model that proposes a causal relationship between motivation and academic achievement on the acquisition of jazz theory knowledge. A reliability analysis of the latent variables ranged from 0.92 to 0.94. Confirmatory factor analyses of the motivation (standardized root mean square residual…
Descriptors: Music Education, Hypothesis Testing, Causal Models, Student Motivation
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research
Peer reviewedKatz, Richard S.; Eagles, Munroe – PS: Political Science and Politics, 1996
Constructs a model that explains a large fraction of the variance in political science departmental rankings. Divides the objective predictors into two sets: one reflecting faculty quality ratings of department members, the other the effects of circumstances beyond a department's control. This model works well with most social science disciplines.…
Descriptors: Achievement Rating, Analysis of Variance, Causal Models, Credentials

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