Statistical procedures most used in the analysis of measures repeated in time in the agricultural sector
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In the agricultural research, situations are presented where it is difficult to use the classical linear models of analysis of variance, because the assumptions of independence, equality of variances and linearity are not fulfilled by making measures repeated in time. This paper had as object to review the statistical procedures used to analyze the designs of measures repeated in time, and determine which analytical
strategies are more appropriate for each purpose. In this study, three types of traditionally used analyses are described: univariate variance (ANOVA), multivariate variance (MANOVA), and the recent one, the approach of mixed models. At present, it has been agreed that the latter is the most adequate and versatile, because it provides the possibility of examining data with structures of dependence, unbalance, and lack of normality. Besides, it provides a solution to the limitation of the multivariate analysis of variance in respect to the number of individuals and variables. Also, the model of random effects is described, another member of the wide spectrum of the mixed models that is used in numerous studies in the agricultural field. This approach is strengthened by the use of selection criteria of models, due to the estimation of parameters is based on methods of maximum likelihood or restricted maximum likelihood. The Akaike information criterion (AIC) and the
Bayesian information criterion (BIC) are described, permitting the optimum selection of competing mixed models.
Key words: repeated measures, univariate analysis, multivariate analysis, mixed models, information criteria.
strategies are more appropriate for each purpose. In this study, three types of traditionally used analyses are described: univariate variance (ANOVA), multivariate variance (MANOVA), and the recent one, the approach of mixed models. At present, it has been agreed that the latter is the most adequate and versatile, because it provides the possibility of examining data with structures of dependence, unbalance, and lack of normality. Besides, it provides a solution to the limitation of the multivariate analysis of variance in respect to the number of individuals and variables. Also, the model of random effects is described, another member of the wide spectrum of the mixed models that is used in numerous studies in the agricultural field. This approach is strengthened by the use of selection criteria of models, due to the estimation of parameters is based on methods of maximum likelihood or restricted maximum likelihood. The Akaike information criterion (AIC) and the
Bayesian information criterion (BIC) are described, permitting the optimum selection of competing mixed models.
Key words: repeated measures, univariate analysis, multivariate analysis, mixed models, information criteria.
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G?mez, S., Torres, V., Garc?a, Y., & Navarro, J. A. (2013). Statistical procedures most used in the analysis of measures repeated in time in the agricultural sector. Cuban Journal of Agricultural Science, 46(1). Recuperado a partir de https://cjascience.com/index.php/CJAS/article/view/74
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