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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

Griffin, Dale; Tversky, Amos – Cognitive Psychology, 1992
Tests the hypotheses that overconfidence occurs when strength is high and weight is low and that underconfidence occurs when weight is high and strength is low in 4 experiments with 153 students. Discusses weighing evidence, overconfidence/underconfidence in intuitive judgments, and item difficulty's effect on overconfidence. Relates the…
Descriptors: Data Interpretation, Difficulty Level, Hypothesis Testing, Reliability

Warin, Jo; Simco, Neil – British Educational Research Journal, 1997
Addresses concerns about the trustworthiness of research results derived from image-based data, particularly questions of validity assessment. Compares examples of such research to provide an illustration of these issues. Presents a tentative strategy for ensuring trustworthiness based on five criteria: completeness, interpretive adequacy,…
Descriptors: Data Interpretation, Higher Education, Imagery, Reliability

Merenda, Peter F. – Measurement and Evaluation in Counseling and Development, 1997
Offers suggestions for proper procedures for authors to use--and some pitfalls to avoid--when writing studies using factor analysis methods. Discusses distinctions among different methods of analysis, the adequacy of factor structure, and other notes of caution. Encourages authors to ensure that their research is statistically sound. (RJM)
Descriptors: Data Interpretation, Factor Analysis, Factor Structure, Reliability
Wilkinson, Rebecca L. – 1992
Problems inherent in relying solely on statistical significance testing as a means of data interpretation are reviewed. The biggest problem with statistical significance testing is that researchers have used the results of this testing to ascribe importance or meaning to their studies where such meaning often does not exist. Often researchers…
Descriptors: Data Interpretation, Effect Size, Power (Statistics), Reliability
Guess, Doug; Roberts, Sally; Behrens, Gene Ann; Rues, Jane – American Journal on Mental Retardation, 1998
Responds to a critique by Mudford, Hogg, and Roberts (1997) that raised concerns about the observation code used in a longitudinal research project to assess emerging behavior state patterns in young children with disabilities. Concerns about the thoroughness of the reliability data collected by Mudford are discussed. (Author/CR)
Descriptors: Behavior Patterns, Data Collection, Data Interpretation, Disabilities
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
Rose, Michael – 1999
Data from the Work History and Attitudes survey of the Social Change and Economic Life research initiative (SCELI) enquiry of 1986-1989 and the first wave of the British Household Panel Survey (BHPS) were analyzed to determine their continuity and comparability with regard to employee attitudes in general and job satisfaction and work centrality…
Descriptors: Attitude Measures, Comparative Analysis, Data Analysis, Data Collection