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HOW TO RUN
SIMPLE LOGISTIC REGRESSION
TEST IN SPSS

What is the Binary Logistic Regression?

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.

How to Run Logistic Regression Test in SPSS: Explanation Step by Step

From SPSS menu, choose Analyze – Regression – Binary Logistic

Binary Logistic SPSS Output

A new window will open. From the left box transfer categorical variable work into the dependent box and continuous variable sleep into the Covariates box.

Logistic Regression SPSS

Click on the Options button and a new window will open.

Binary Logistic Analysis SPSS

Results of Simple logistic regression will appear in the output window.

Simple logistic regression SPSS

How to report the Binary Logistic Regression results: Explanation Step by Step

How to Report Case Processing Summary Table in SPSS Output?

Table Case processing summary shows the number and percent of selected cases, missing cases, unselected cases, and total.

Logistic regression SPSS Output

How to Report Dependent variable Table in SPSS Output?

Dependent table variable encoding shows how we code the dependent variable.

1 – Yes, 0 – No.

Dependent Variable Encoding SPSS Output

How to Report Classification Table SPSS Output?

The next table is the Classification table.

The cut value is 0.500 below the table means that if the probability of a case being classified into the yes category (person like to go to work) is greater than 0.50. Therefore, that case is classified into that category; otherwise, it is classified into no category.

Classification Table SPSS Output

How to Report Model Summary Table in SPSS output?

Next table Model summary shows Cox and Snell R square and Negelkerke R square. In conclusion, the results show what percent of the variation in the dependent variable is explained by the independent variable.

Logistic Regression Model Summary

How to Report Variables in the equation Table in SPSS Output?

If the p-value is greater than 0.05.

Therefore, the independent variable did not add significantly to the model. In our example, p = 0.000 < 0.05. So, the independent variable (sleep) added significantly to the model.

Simple logistic regression SPSS Output

How to Interpret a Binary Logistic Regression Results in APA Style?

A simple logistic regression was conducted to determine the effect of the number of hours slept on the likelihood that participants like to go to work. Moreover, the number of hours slept explained 10.00% (Nagelkerke R2) of the variance in the like to go to work. To sum up, the number of hours slept was associated with the likelihood of going to work.

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