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ERIC Number: EJ792650
Record Type: Journal
Publication Date: 2008
Pages: 12
Abstractor: Author
Reference Count: 19
ISSN: ISSN-0013-1644
Sample Sizes when Using Multiple Linear Regression for Prediction
Knofczynski, Gregory T.; Mundfrom, Daniel
Educational and Psychological Measurement, v68 n3 p431-442 2008
When using multiple regression for prediction purposes, the issue of minimum required sample size often needs to be addressed. Using a Monte Carlo simulation, models with varying numbers of independent variables were examined and minimum sample sizes were determined for multiple scenarios at each number of independent variables. The scenarios arrive from varying the levels of correlations between the criterion variable and predictor variables as well as among predictor variables. Two minimum sample sizes were determined for each scenario, a good and an excellent prediction level. The relationship between the squared multiple correlation coefficients and minimum necessary sample sizes were examined. A definite relationship, similar to a negative exponential relationship, was found between the squared multiple correlation coefficient and the minimum sample size. As the squared multiple correlation coefficient decreased, the sample size increased at an increasing rate. This study provides guidelines for sample size needed for accurate predictions. (Contains 3 figures and 2 tables.)
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A