Confidence Intervals for an Effect Size Measure in Multiple Linear Regression

Dados Bibliográficos

ID20282966
AutoresJames Algina (0000-0001-6924-655X, University of Florida), H J Keselman (University of Manitoba), Randall D Penfield (University of Miami)
Ano2007
Volume67
Fascículo2
Páginas207-218
Data de publicação2007-04-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEducational and Psychological Measurement (JOURNAL)
Identificadores do periódicoISSN: 0013-1644 • E-ISSN: 1552-3888
EditoraSAGE Publications (PUBLISHER • US)
DOI10.1177/0013164406292030
OpenAlexW2083403230
IdiomaEN
Citações recebidas7
Referências citadas9

The increase in the squared multiple correlation coefficient (ΔR 2 ) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. The coverage probability that an asymptotic and percentile bootstrap confidence interval includes Δρ 2 was investigated. As expected, coverage probability for the asymptotic confidence interval was often inadequate (outside the interval .925 to .975 for a 95% confidence interval), even when sample size was quite large (i.e., 200). However, adequate coverage probability for the confidence interval based on a bootstrap interval could typically be obtained with a sample size of 200 or less, and moreover, this accuracy was obtained with relatively small sample sizes (100 or less) with six or fewer predictors

CDF-based nonparametric confidence interval · Confidence distribution · Confidence interval · Coverage probability · Linear regression · Measure (data warehouse) · Percentile · Regression · Regression analysis · Robust confidence intervals · Sample size determination · Statistics · Tolerance interval · Advanced Statistical Methods and Models · Computer Science · Mathematics · Optimal Experimental Design Methods · Statistical Methods and Applications