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Yan Xia; Selim Havan – Educational and Psychological Measurement, 2024
Although parallel analysis has been found to be an accurate method for determining the number of factors in many conditions with complete data, its application under missing data is limited. The existing literature recommends that, after using an appropriate multiple imputation method, researchers either apply parallel analysis to every imputed…
Descriptors: Data Interpretation, Factor Analysis, Statistical Inference, Research Problems
García-Pérez, Miguel A. – Educational and Psychological Measurement, 2017
Null hypothesis significance testing (NHST) has been the subject of debate for decades and alternative approaches to data analysis have been proposed. This article addresses this debate from the perspective of scientific inquiry and inference. Inference is an inverse problem and application of statistical methods cannot reveal whether effects…
Descriptors: Hypothesis Testing, Statistical Inference, Effect Size, Bayesian Statistics
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

Bass, Roger F. – Psychology in the Schools, 1987
Evaluates arguments concerning the generality and statistical analysis of single-subject data. Considers the role of philosophy of science in evaluating research methodology. Single-subject data have special relevance for psychologists and educators who focus on the individual but statistical techniques for analyzing data have numerous…
Descriptors: Data Interpretation, Generalizability Theory, Generalization, Philosophy
Thompson, Bruce – 1987
This paper evaluates the logic underlying various criticisms of statistical significance testing and makes specific recommendations for scientific and editorial practice that might better increase the knowledge base. Reliance on the traditional hypothesis testing model has led to a major bias against nonsignificant results and to misinterpretation…
Descriptors: Analysis of Variance, Data Interpretation, Editors, Effect Size