Bootstrap Standard Error and Confidence Intervals for the Difference Between Two Squared Multiple Correlation Coefficients  

Oleh:

Wai, Chan

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Educational and Psychological Measurement vol. 69 no. 04 (Aug. 2009), page 566-584.
Topik: bootstrap standard error; confidence interval; squared multiple correlation
Fulltext: 566.pdf (180.4KB)

Isi artikel

A typical question in multiple regression analysis is to determine if a set of predictors gives the same degree of predictor power in two different populations. Olkin and Finn (1995) proposed two asymptotic-based methods for testing the equality of two population squared multiple correlations, r21 and r22 . Simulation results indicated that these methods failed to perform accurately under certain model conditions (Algina & Keselman, 1999). In the present study, a unified bootstrap procedure is proposed for estimating the standard error of R21 -R22 and constructing the confidence interval for r21 -r22 . A simulation study was conducted to examine the empirical performance of the proposed procedure under different levels of r2, sample sizes, numbers of predictors, and types of data distribution. Results indicated that the asymptotic method, in general, can only work well with normal data. The bootstrap procedure, on the other hand, performs satisfactorily with both normal and nonnormal data. However, both methods fail when r21 and r22are zero.

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