Repeated measures analyses have become the most interesting areas In psychological, health andrnagricu ltural researches. Repeated meaSU!'es data are measurements taken several times from the samernsubject. Such data tend to be serially correlated. Measurements taken close in time are potentially highlyrncorrelated than those taken far apart in time. Hence, they require special methods of analysis. In this paper,rnfour major approaches are used to analyze repeated measurements taken from thirty three varieties ofrnensel plants each measured at four successive time points. The study provides summary statistics, resultsrnbased on repeated measures analysis of variance (Spli t-plot in time ANOVA), multivariate analysis ofrnvariance (MAN OVA), and mixed model methods. Each method is described briefly. In order to apply thernrepeated measures ANOYA, compound symmetry assumptions of covariance structures should be met.rnWhether the data fulfils this structure is tested. For those data which do not satisfy this criterion, therndegree of freedom is adjusted for F test statistics by Huynh-Feldt (H-F) or Oreenhouse-Oeisser (0-0)rnepsilons. The multi variate approach is less restrictive but lacks power given that the repeated measuresrnAN OVA assumptions are sati sfied and the sample size is small. In all the methods considered, SAS wasrnused to analyze the data. The results using uni vari ate, multivariate and mixed approaches of repeatedrnmeasurements of ensel plants show that the main effects of variety and time as well as the interactionrneffect of veri ety by time were found to be significan t. It was found that mixed model approach provides arnvery flexibl e environment in which the covariance structure can be modeled. Besides, the mixed modelrnpermits selection of the covariance structure that best fits the data at hand and enables to compute efficientrnestimates of fixed effects and valid standard erro rs of the estimates.