Highlights
Statistics Assessment
o Assumption #1:
Your dependent variable should be measured on a dichotomous scale. Examples of dichotomous variables include gender, body composition and so forth. However, if your dependent variable was not measured on a dichotomous scale, but a continuous scale instead, you will need to carry out multiple regression, whereas if your dependent variable was measured on an ordinal scale, ordinal regression would be a more appropriate starting point.
o Assumption #2:
You have one or more independent variables, which can be either continuous (i.e., an interval or ratio variable) or categorical (i.e., an ordinal or nominal variable). Examples of continuous variables include revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg), and so forth. Examples of ordinal variables include Likert items, amongst other ways of ranking categories (e.g., a 3-point scale explaining how much a customer liked a product, ranging from Examples of nominal variables include gender , ethnicity , profession (e.g., 5 groups: surgeon, doctor, nurse, dentist, therapist), and so forth. You can learn more about variables in our article: Types of Variable.
o Assumption #3:
You should have independence of observations and the dependent variable should have mutually exclusive and exhaustive categories.
o Assumption #4:
There needs to be a linear relationship between any continuous independent variables and the logit transformation of the dependent variable. In our enhanced binomial logistic regression guide, we show you how to: (a) use the Box-Tidwell (1962) procedure to test for linearity; and (b) interpret the SPSS Statistics output from this test and report the results.
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