Detecting a lack of association: an equivalence testing approach.
Br J Math Stat Psychol
; 63(Pt 3): 527-37, 2010 Nov.
Article
em En
| MEDLINE
| ID: mdl-20030968
ABSTRACT
Researchers often test for a lack of association between variables. A lack of association is usually established by demonstrating a non-significant relationship with a traditional test (e.g., Pearson's r). However, for logical as well as statistical reasons, such conclusions are problematic. In this paper, we discuss and compare the empirical Type I error and power rates of three lack of association tests. The results indicate that large, sometimes very large, sample sizes are required for the test statistics to be appropriate. What is especially problematic is that the required sample sizes may exceed what is practically feasible for the conditions that are expected to be common among researchers in psychology. This paper highlights the importance of using available lack of association tests, instead of traditional tests of association, for demonstrating the independence of variables, and qualifies the conditions under which these tests are appropriate.
Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Psicologia
/
Estatística como Assunto
/
Modelos Estatísticos
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2010
Tipo de documento:
Article