Resampling-Based Inference Methods for Comparing Two Coefficients Alpha

Resampling-Based Inference Methods for Comparing Two Coefficients Alpha The two-sample problem for Cronbach’s coefficient $$\alpha _C$$ α C , as an estimate of test or composite score reliability, has attracted little attention compared to the extensive treatment of the one-sample case. It is necessary to compare the reliability of a test for different subgroups, for different tests or the short and long forms of a test. In this paper, we study statistical procedures of comparing two coefficients $$\alpha _{C,1}$$ α C , 1 and $$\alpha _{C,2}$$ α C , 2 . The null hypothesis of interest is $$H_0 : \alpha _{C,1} = \alpha _{C,2}$$ H 0 : α C , 1 = α C , 2 , which we test against one-or two-sided alternatives. For this purpose, resampling-based permutation and bootstrap tests are proposed for two-group multivariate non-normal models under the general asymptotically distribution-free (ADF) setting. These statistical tests ensure a better control of the type-I error, in finite or very small sample sizes, when the state-of-affairs ADF large-sample test may fail to properly attain the nominal significance level. By proper choice of a studentized test statistic, the resampling tests are modified in order to be valid asymptotically even in non-exchangeable data frameworks. Moreover, extensions of this approach to other designs and reliability measures are discussed as well. Finally, the usefulness of the proposed resampling-based testing strategies is demonstrated in an extensive simulation study and illustrated by real data applications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Psychometrika Springer Journals

Resampling-Based Inference Methods for Comparing Two Coefficients Alpha

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Publisher
Springer US
Copyright
Copyright © 2017 by The Psychometric Society
Subject
Psychology; Psychometrics; Assessment, Testing and Evaluation; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law; Statistical Theory and Methods
ISSN
0033-3123
eISSN
1860-0980
D.O.I.
10.1007/s11336-017-9601-x
Publisher site
See Article on Publisher Site

Abstract

The two-sample problem for Cronbach’s coefficient $$\alpha _C$$ α C , as an estimate of test or composite score reliability, has attracted little attention compared to the extensive treatment of the one-sample case. It is necessary to compare the reliability of a test for different subgroups, for different tests or the short and long forms of a test. In this paper, we study statistical procedures of comparing two coefficients $$\alpha _{C,1}$$ α C , 1 and $$\alpha _{C,2}$$ α C , 2 . The null hypothesis of interest is $$H_0 : \alpha _{C,1} = \alpha _{C,2}$$ H 0 : α C , 1 = α C , 2 , which we test against one-or two-sided alternatives. For this purpose, resampling-based permutation and bootstrap tests are proposed for two-group multivariate non-normal models under the general asymptotically distribution-free (ADF) setting. These statistical tests ensure a better control of the type-I error, in finite or very small sample sizes, when the state-of-affairs ADF large-sample test may fail to properly attain the nominal significance level. By proper choice of a studentized test statistic, the resampling tests are modified in order to be valid asymptotically even in non-exchangeable data frameworks. Moreover, extensions of this approach to other designs and reliability measures are discussed as well. Finally, the usefulness of the proposed resampling-based testing strategies is demonstrated in an extensive simulation study and illustrated by real data applications.

Journal

PsychometrikaSpringer Journals

Published: Jan 2, 2018

References

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