Abstract

Statistical significance is often inappropriately equated with evaluating result importance and evaluating result replicability, even though these are three somewhat different issues. The prudent researcher must separately assess each of these elements of the "research triumvirate" by using different methods. This paper focuses on two types of empirical methods for estimating research result replicability: double cross-validation, and bootstrap procedures. A commonly available statistical computer package, the Statistical Package for the Social Sciences (SPSS-X), is used to carry out the steps required for the double cross-validation procedure, and a recently developed microcomputer program package (developed by C. E. Lunneborg, 1987) is implemented to demonstrate the bootstrap logic. Both methods are applied to a heuristic data set of observed values of three independent variables and one dependent variable for a sample of 25 subjects. It is concluded that although each procedure has some shortcomings, the advantages of using either far outweigh the disadvantages. There are 5 tables of analysis data and a 19-item list of references. (SLD)

Citation

Reinhardt, B.M. Estimating Result Replicability Using Double Cross-Validation and Bootstrap Methods. Retrieved August 15, 2024 from https://www.learntechlib.org/p/145158/.

This record was imported from ERIC on March 21, 2014. [Original Record]

ERIC is sponsored by the Institute of Education Sciences (IES) of the U.S. Department of Education.

Copyright for this record is held by the content creator. For more details see ERIC's copyright policy.

Keywords

• Comparative Analysis
• Computer Uses in Education
• Estimation (Mathematics)
• Heuristics
• mathematical models
• Research Methodology
• Research Methods
• Social Science Research
• Statistical Significance