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Mixed ANOVA×Analyse av kovarians (ANCOVA)×T-test for parede prøver×
FagfeltStatistikkStatistikkStatistikk
FamilieHypothesis testHypothesis testHypothesis test
Opprinnelsesår192519321908
OpphavspersonR. A. Fisher (ANOVA framework); split-plot design formalised in agricultural experimentationRonald A. FisherStudent (W. S. Gosset)
TypeParametric factorial ANOVAParametric group comparison with covariate controlParametric mean comparison (paired)
Opprinnelig kildeField, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE. ISBN: 978-1526419521Tabachnick, B.G. & Fidell, L.S. (2013). Using Multivariate Statistics (6th ed.). Pearson. ISBN: 978-0205849574Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE. ISBN: 978-1446249185
Aliassplit-plot ANOVA, mixed-design ANOVA, between-within ANOVA, Karma ANOVA (Mixed ANOVA — Gruplar Arası × Tekrarlı)analysis of covariance, covariance analysis, ANCOVA (Kovaryans Analizi)dependent samples t-test, repeated measures t-test, matched-pairs t-test, eşleştirilmiş örneklem t-testi
Relaterte644
SammendragMixed ANOVA is a parametric factorial analysis of variance that simultaneously examines at least one between-subjects factor and at least one within-subjects (repeated-measures) factor. Rooted in R. A. Fisher's ANOVA framework formalised in 1925, it is the standard method for experimental and longitudinal designs in which different groups are each measured across multiple time points or conditions.ANCOVA is a parametric hypothesis test that compares the adjusted means of two or more independent groups while statistically controlling for one or more continuous covariates. By removing the portion of outcome variance explained by the covariate, ANCOVA increases statistical precision and produces fairer group comparisons. The method builds on the general linear model framework consolidated by Fisher in the early 1930s and is described comprehensively by Tabachnick and Fidell (2013).The paired samples t-test is a parametric hypothesis test that compares two measurements taken on the same subjects — such as a before and after reading — to decide whether the average change differs from zero. It rests on the t-distribution introduced by Student (W. S. Gosset) in 1908 and works on the within-subject difference scores rather than the raw measurements.
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ScholarGateSammenlign metoder: Mixed ANOVA · ANCOVA · Paired t-test. Hentet 2026-06-20 fra https://scholargate.app/no/compare