Testing equivalence with repeated measurestests of the difference model of two-alternative forced-choice performance

  1. Alcalá-Quintana, Rocío 1
  2. Miguel Angel García Pérez 1
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

Aldizkaria:
The Spanish Journal of Psychology

ISSN: 1138-7416

Argitalpen urtea: 2011

Alea: 14

Zenbakia: 2

Orrialdeak: 1023-1049

Mota: Artikulua

DOI: 10.5209/REV_SJOP.2011.V14.N2.48 DIALNET GOOGLE SCHOLAR lock_openSarbide irekia editor

Beste argitalpen batzuk: The Spanish Journal of Psychology

Garapen Iraunkorreko Helburuak

Laburpena

Solving theoretical or empirical issues sometimes involves establishing the equality of two variables with repeated measures. This defies the logic of null hypothesis significance testing, which aims at assessing evidence against the null hypothesis of equality, not for it. In some contexts, equivalence is assessed through regression analysis by testing for zero intercept and unit slope (or simply for unit slope in case that regression is forced through the origin). This paper shows that this approach renders highly inflated Type I error rates under the most common sampling models implied in studies of equivalence. We propose an alternative approach based on omnibus tests of equality of means and variances and in subject-by-subject analyses (where applicable), and we show that these tests have adequate Type I error rates and power. The approach is illustrated with a re-analysis of published data from a signal detection theory experiment with which several hypotheses of equivalence had been tested using only regression analysis. Some further errors and inadequacies of the original analyses are described, and further scrutiny of the data contradict the conclusions raised through inadequate application of regression analyses.

Finantzaketari buruzko informazioa

Supported by grant PSI2009-08800 from Ministerio de Ciencia e Innovación (Spain). We thank Marisa Carrasco for sharing the data from their study.

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