C532: ANOVA & Regression Modeling Nov 9, 2011 Y. Cao 1Topics: learn aboutANCOVA model.and realize it with the general linear model procedure in SPSS. Datasets: C530_clbp.sav References: lecture note and PCCR books on statistics. In general, we can say that ANCOVA, an abbreviation of analysis of covariance, is a combination of 2 types of ANOVA. In common situation, we say that the ANOVA model is where X1 represents a nominal categorical variable, for example, treatment group; in the other case, X1 can be a continuous type of variable like Age, or RMQ. Both are simple linear regressions. In the first case, so called one way ANOVA model, we have one nominal categorical variable (a fixed factor) to explain the outcome. A fixed factor, we say so because the levels of the factor, like treatment groups 1, 2, 3 and 4 (or A, B, C, D or whatever name you like), once assigned (usually randomly) to a patient, they arefixed/constant. Now, we want to include one more variable to predict the outcome, this comes out a continuous variable, called covariate, to explain more variations in the outcome. Covariate means the continuous variable vary togetherwith the fixed factor to affect the outcome. After considering this supplementarycovariate, we then want to adjust the group means (in SPSS, it is calledEstimated Marginal Means), meaning usually we would like to change it a little bit on the original group means. Then, ANCOVA is born. It is a multiple linear regression. Correspondingly, the ANOVA table for ANCOVA is a combination of the 2 ANOVA tables. See the lecture notes, the 3 tables on page 1. ANCOVA is to test/comparemean differences between factor levels (in our case, treatment groups) and adjusted forthe effect of the covariate(s). ANCOVA will generallyincrease our power to detect true meaningful group mean differences because the inclusion of the covariate will usually explain more variability in the outcome variable (the Y).
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