Comparability of Jackknife and Bootstrap Results: An Investigation for a Case of Canonical Correlation Analysis

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Abstract
The jackknife and bootstrap methods are becoming increasingly popular in research. Although the two approaches have similar goals and use similar strategies, information is lacking with regard to the comparability of their results. In the present study, this issue was systematically investigated for a case of canonical correlation analysis. Bootstrap, jackknife, and Monte Carlo experiments were carried out for 4 sample sizes (n = 200, 100, 50, 20). The jackknife analyses were also varied as regards the number of jackknife observations deleted in each analysis. Some meaningful discrepancies were observed between the bootstrap and jackknife results, especially under small sample-size conditions. Based on the comparisons made with Monte Carlo estimates, the empirical results suggest that the bootstrap technique provides less biased and more consistent results than the jackknife technique does.