Using Simple Logistic Regression in Research
This easy tutorial will show you how to run Simple Logistic Regression Test in SPSS, and how to interpret the result.
We use the Logistic regression to predict a categorical (usually dichotomous) variable from a set of predictor variables. In addition, Logistic regression is especially popular with medical research in which the dependent variable is whether or not a patient has a disease. (Source)
We use the binary logistic regression to describe data and to explain the relationship between one dependent binary variable and one or more continuous-level (interval or ratio scale) independent variables.
That is to say, we model the log of odds of the dependent variable as a linear combination of the independent variables. So, Log odds are an alternate way of expressing probabilities, which simplifies the process of updating them with new evidence.
Assumptions of the Logistic Regression:
When performing a Logistic regression Test procedure the following assumptions are required:
- one categorical dichotomous dependent variable (0 or 1)
- one or more continuous or categorical independent variables
- independence of observations
- a linear relationship between any continuous independent variables and the logit transformation of the dependent variable
- no outliers
An Example: Logistic Regression Test
This guide will explain, step by step, how to run the Logistic Regression Test in SPSS statistical software by using an example.
We want to know whether a number of hours slept predicts the probability that someone likes to go to work. Therefore, we have one independent continuous variable (number of hours slept) and one dependent dichotomous variable (work, takes value one if a person to go to work, 0 otherwise).
This easy tutorial will show you how to run the Logistic Regression Test in SPSS, and how to interpret the result.