You would have to study the effect across time, between groups and the interaction effect. At least 2 different times: Within-Factor Basically the same as the 2-Way ANOVA, but imagine instead of types of job you have 3 different times: 2009,20. One categorical variable: Fixed Factor(at least 2 groups)= Between Factor. We have an interaction effect here because the decrease from Data Scientist to Software Engineer is much bigger in Seattle than it is for San Francisco or New-York.ġ8 Mixed ANOVA: Continuous variable/measurement: Diet group. We change the colour of San Francisco to Orange.ġ5 More colourful graph… For Seattle, I can put the points and line in Green.ġ6 More colourful graph… Modifying the titles is easy.ġ7 Interaction Effect: When the lines are not parallelĪn interaction effect occurs if basically your salary would evolve differently across jobs when you are in a city compared to another. You would have to re-run the 2-way ANOVA if you want both.ġ3 Graph illustrating the 2-Way ANOVA: very important!ġ4 More colourful graph… By Clicking on “Change”, symbol “histograms”, you can change the design. If you want a comparison between Job types, you can do so by Comparing the row means. The columns will be the cities (3 cities).ħ Select the measurement of the cross-category “Data Scientist” x “Seattle”.Ĩ You can copy paste as “transpose” in Prism.ĩ City: 1st categorical variable Job type: 2nd categorical variableġ1 Multiple Comparison: You need to choose what you would like to compare.įor example here, if you want to do a pairwise comparison between Cities, you can do so by comparing column means. The rows will be the type of Jobs (3 jobs). Download the file in a folder on your computer.ĥ We need to enter 20 replicate values per cross-category.Ħ The rows will be the type of Jobs (3 jobs) Pearson/Spearman correlation coefficient.ģ 2 Way ANOVA City: 1st categorical variable Job type: 2nd categorical variable Salary: Continuous Measurement (DV: Dependent Variable) Data set in: Click on Save As. 2 Session 2 2-way ANOVA Mixed ANOVA Regression (simple and multiple)
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