Resampling-Based Inference Methods for Comparing Two Coefficients Alpha.
Psychometrika
; 83(1): 203-222, 2018 03.
Article
em En
| MEDLINE
| ID: mdl-29297150
ABSTRACT
The two-sample problem for Cronbach's coefficient [Formula see text], 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 [Formula see text] and [Formula see text]. The null hypothesis of interest is [Formula see text], 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.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Interpretação Estatística de Dados
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Female
/
Humans
/
Male
Idioma:
En
Revista:
Psychometrika
Ano de publicação:
2018
Tipo de documento:
Article
País de afiliação:
Alemanha