**Dependent variable**

binomial logistic regression (often referred to simply as logistic regression), predicts the probability that an observation falls into one of two categories of a dichotomous dependent variable, based on one or more independent variables that can be either continuous or categorical. (If, on the other hand, your dependent variable is a count, see our Poisson regression guide. Alternatively, if you have more than two categories of the dependent variable, see our multinomial logistic regression guide.)

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.

example:

1.use binomial logistic regression to understand whether exam performance can be predicted based on revision time, test anxiety and lecture attendance

- dependent variable--exam performance, dichotomous scale (passed or failed)
- three independent variables: "revision time", "test anxiety" and "lecture attendance"

- dependent variable--drug use, measured on a dichotomous scale (yes or no)
- five independent variables: "prior criminal convictions", "drug use amongst friends", "income", "age" and "gender"

3. n=200 high schools students

- dependent variable (dichotomous variable): female (1), male (0)
- independent variable: scores on 4 tests: science, math, reading and social studies (socst)

**Independent variable**

- 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).
- Continuous variables-- revision time (measured in hours), intelligence (measured using IQ score), exam performance (measured from 0 to 100), weight (measured in kg)
- Ordinal variables --- Likert items (e.g., a 7-point scale from "strongly agree" through to "strongly disagree"), amongst other ways of ranking categories (e.g., a 3-point scale explaining how much a customer liked a product, ranging from "Not very much" to "Yes, a lot")
- Nominal variables ---- gender (e.g., 2 groups: male and female), ethnicity (e.g., 3 groups: Caucasian, African American and Hispanic), profession (e.g., 5 groups: surgeon, doctor, nurse, dentist, therapist)
- If you have a categorical variable with more than two levels (e.g., a three-level variable: low, medium ,high), you can tell SPSS to create the dummy variables necessary to include the variable in the logistic regression.

Assumption: to run logit, there needs to be a linear relationship between any continuous independent variables and the logit transformation of the dependent variable. Use the Box-Tidwell (1962) procedure to test for linearity

if a violation to the assumption is not correctable, you will no longer be able to use a binomial logistic regression (although you may be able to run another statistical test on your data instead). Just remember that if you do not run the statistical tests on these assumptions correctly, the results you get when running binomial logistic regression might not be valid.

Testing assumptions

https://statistics.laerd.com/features-overview.php

https://statistics.laerd.com/features-assumptions.php

How to use SPSS

- SPSS -- Analyze/Regression/Binary Logistic…
- By default, SPSS does a listwise deletion of missing values. This means that only cases with non-missing values for the dependent as well as all independent variables will be used in the analysis.
- https://statistics.laerd.com/spss-tutorials/binomial-logistic-regression-using-spss-statistics.php#procedure

How to interpret report the Output of a Binomial Logistic Regression Analysis

https://statistics.laerd.com/spss-tutorials/binomial-logistic-regression-using-spss-statistics.php#procedure

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