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#PERFORMING RANDOMIZATION TEST USING MINITAB 18 COUNTS HOW TO#

Assumption #2: Your two independent variables should each consist of two or more categorical, independent (unrelated) groups.

If you are unsure whether your dependent variable is continuous (i.e., measured at the interval or ratio level), see our Types of Variable guide.

Examples of such continuous variables include height (measured in feet and inches), temperature (measured in ☌), salary (measured in US dollars), revision time (measured in hours), intelligence (measured using IQ score), reaction time (measured in milliseconds), test performance (measured from 0 to 100), sales (measured in number of transactions per month), and so forth.

Assumption #1: Your dependent variable should be measured at the continuous level.

Assumptions #1, #2 and #3 are explained below: If these assumptions are not met, there is likely to be a different statistical test that you can use instead. However, you should check whether your study meets these three assumptions before moving on. You cannot test the first three of these assumptions with Minitab because they relate to your study design and choice of variables. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a two-way ANOVA to give you a valid result.

#PERFORMING RANDOMIZATION TEST USING MINITAB 18 COUNTS HOW TO#

In this "quick start" guide, we show you how to carry out a two-way ANOVA using Minitab, as well as interpret and report the results from this test. We come back to "simple main effects" later. If you have a statistically significant interaction between your two independent variables on the dependent variable, it is possible to run "simple main effects" to determine the effect of one independent variable at each level of the other independent variable on the dependent variable (e.g., perhaps students with a PhD in the biological sciences had a higher mean salary than students with an undergraduate degree in psychology).

Alternately, you could use a two-way ANOVA to understand whether there is an interaction between physical activity level and gender on blood cholesterol concentration in children (i.e., your dependent variable would be "blood cholesterol concentration", measured on a continuous scale in mmol/L, and your independent variables would be "physical activity level", which has three groups – "low", "moderate" and "high" – and "gender", which has two groups: "males" and "females"). An interaction signifies that the effect of one of the two independent variables on the dependent variable is dependent on the other independent variable.įor example, you could use a two-way ANOVA to understand whether there is an interaction between physical activity level and gender on stress level (i.e., your dependent variable would be "stress score", measured on a continuous scale, and your independent variables would be "physical activity level", which has three groups – "low", "moderate" and "high" – and "gender", which has two groups: "males" and "females"). The common goal of a two-way ANOVA is to establish if there is an interaction between the two independent variables on the dependent variable.

In this sense, it is an extension of the one-way ANOVA.

The two-way ANOVA compares the effect of two categorical independent variables (called between-subjects factors) on a continuous dependent variable.