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How to test the homogeneity of slopes by spss version 25 how to#
On page 2 of this introductory guide, we set out the example we use to illustrate the two-way ANCOVA using SPSS Statistics, before showing how to set up your data in the Variable View and Data View of SPSS Statistics. Making sure that your study design, variables and data pass these assumptions is critical because if they do not, the two-way ANCOVA is likely to be the incorrect statistical test to use. Next, we set out the assumptions of the two-way ANCOVA. In this introductory guide to the two-way ANCOVA, we first set out a couple of study designs where the two-way ANCOVA is most often used. Depending on whether you find a statistically significant two-way interaction effect, and the type of interaction you have, will determine which effects in the two-way ANCOVA you should interpret and any post hoc tests you may want to run (i.e., where "post hoc tests" are follow-up analyses that are carried out after running a two-way ANCOVA analysis to learn more about your results). Assuming that a statistically significant two-way interaction effect is found, this indicates that the two drugs have different effects in low and high risk elderly patients (i.e., the effect of drug on cholesterol depends on level of risk), after adjusting/controlling for age. This is usually given first priority in a two-way ANCOVA analysis because its result will determine whether the researchers' first aim is misleading or incomplete. Importantly, the second aim is answered by determining whether there is a statistically significant two-way interaction effect. These two aims are entirely typical of a two-way ANCOVA analysis.
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The researchers wanted to know: (a) whether the experimental drug was better or worse than the current drug at lowering cholesterol and (b) whether the effect of the two drugs was different depending on whether elderly patients are classified as at low risk or high risk.
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Therefore, in this experiment the two independent variables are drug (with two groups: "Current" and "Experimental") and risk (with two levels: "Low" and "High"), the dependent variable was cholesterol (i.e., cholesterol concentration in the blood) and the continuous covariate was age. The patients were of varying ages and the researchers wanted to control for these differences in age. The goal was for the drugs to lower cholesterol concentration in the blood. The researchers also wanted to understand how the drugs compared in low and high risk elderly patients. One of the drugs was the current drug being used to treat heart disease and the other was an experimental drug that the researchers wanted to compare against the current drug. In many ways, the two-way ANCOVA can be considered an extension of the one-way ANCOVA, which has just one independent variable (rather than two independent variables), or an extension of the two-way ANOVA to incorporate one or more continuous covariates.Ī two-way ANCOVA can be used in a number of situations. For example, consider an experiment where two drugs were being given to elderly patients to treat heart disease. The two-way ANCOVA (also referred to as a "factorial ANCOVA") is used to determine whether there is an interaction effect between two independent variables in terms of a continuous dependent variable (i.e., if a two-way interaction effect exists), after adjusting/controlling for one or more continuous covariates. Two-way ANCOVA in SPSS Statistics Introduction