By Albert R. Wildt
This e-book offers a strategy for interpreting the consequences of variables, teams, and coverings in either experimental and observational settings. It considers not just the most results of 1 variable upon one other, but additionally the results of team situations.
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Additional info for Analysis of Covariance (Quantitative Applications in the Social Sciences)
Second, the use of analysis of covariance in these situations may result in the same benefit as in a completely randomized experiment, that is, increase the precision of the experiment by reducing the error variance. While the use of the analysis of covariance to adjust for preexisting differences among intact groups is very useful in certain instances, it should be used with caution. This caution stems from the fact that when random assignment is not used in forming experimental groups, the results may be subject to difficulties in interpretation.
One such approach is described below. If one wishes to predict the value of a particular observation on the dependent variable with knowledge of the sample mean for the dependent variable and the value of the associated covariate, but with no knowledge of which group or treatment is associated with each observation, how would one proceed? A reasonable approach would be to use the value of the sample mean adjusted by the value of the covariate. Specifically, the model used to yield this estimate would be, Page 21 where; Y'ij= the predicted score for observation j in group i, Y * = the grand (sample) mean of the dependent variable, Xij = the value of the covariate for observation j in group i, X* = the grand (sample) mean of covariate, and bT = the estimated coefficient for the covariate.
One of the following formats can be adapted (depending on the style manual used): (1) WILDT, ALBERT R. and AHTOLA, OLLI T. (1978) Analysis of covariance. Sage University Paper Series on Quantitative Applications in the Social Sciences, 07-012. Newbury Park, CA: Sage. OR (2) Wildt, A. , & Ahtola, O. T. (1978). Analysis of covariance (Sage University Paper Series on Quantitative Applications in the Social Sciences, series no. 07-012). Newbury Park, CA: Sage. Page 3 Contents Editor's Introduction 5 1.