# Binary Logistic Regression using SPSS

## What is it All About?

Logistic regression analysis is **a method to determine the reason-result relationship of independent variable(s) with dependent variable**

### Learn to Analyze Data using Binary Logistic Regression using SPSS

The tutorial is a step by step guide on how to perform Binary Logistic Regression using SPSS.

###### Do Like and Share the Video

## The Concept of Binary Logistic Regression

#### "What to do when we have a binary/Dichotomous outcome variable in Research"

Regression technique is used to assess the strength of a relationship between one dependent and independent variable(s). It helps in predicting value of a dependent variable from one or more independent variable. Regression analysis helps in predicting how much variance is being accounted in a single response (dependent variable) by a set of independent variables.

Linear regression analysis requires the outcome/criterion variable to be measured as a continuous variable. (To Learn More About Regression Analysis, Click Here). However, there may be situations when the researcher would like to predict an outcome that is dichotomous/binary.

In such situation, a scholar can use Binary Logistic Regression to assess the impact of one of more predictor variables on the outcomes. Logistic regression analysis is **a method to determine the reason-result relationship of independent variable(s) with dependent variable**

The logistic regression predicts group membership

- Since logistic regression calculates the probability of success over the probability of failure, the results of the analysis are in the form of an odds ratio.
- Logistic regression determines the impact of multiple independent variables presented simultaneously to predict membership of one or other of the two dependent variable categories.
- In logistic regression, the expected outcome is represented by 1 while the other is coded as 0.

## Examples of Binary Logistic Regression

#### A scholar may utilize binary logistic regression in following situations

- A store would like to assess factors that lead to return/no return of the customer.
- A college would like to assess admission (admit/Do not admit) of a student based on Age, Grade, Aptitude Test Results.
- Assess if a particular candidate wins/loses an election based on the time spent in constituency, previously elected, no. of issues resolved.
- A HR researcher would like to ascertain how factors like experience, years of education, previous salary, university ranking affect the selection of a candidate in a job interview.
**A scholar would like to predict****the choice of bank (Public or Private) based on****independent****variables that include****Technology, Interest Rates, Value Added Services, Perceived Risks, Reputation, and others.**

## Assumptions

- Logistic regression does not assume a linear relationship between the dependent and independent variables.
- The independent variables need not be interval, nor normally distributed, nor linearly related, nor of equal variance within each group
- Homoscedasticity is not required. The error terms (residuals) do not need to be normally distributed.
- The dependent variable in logistic regression is not measured on an interval or ratio scale. The dependent variable must be a dichotomous ( 2 categories) for the binary logistic regression.
- The categories (groups) as a dependent variable must be mutually exclusive and exhaustive; a case can only be in one group and every case must be a member of one of the groups.
- Larger samples are needed than for linear regression because maximum coefficients using a ML method are large sample estimates. A minimum of 50 cases per predictor is recommended (Field, 2013)
- Hosmer, Lemeshow, and Sturdivant (2013) suggest a minimum sample of 10 observations per independent variable in the model, but caution that 20 observations per variable should be sought if possible.
- Leblanc and Fitzgerald (2000) suggest a minimum of 30 observations per independent variable.

## Example Problem

For the purpose of this tutorial, i am considering the following example. The IVs are on interval scale while the DV is binary (Public or Private Bank)

**A scholar would like to predict****the choice of bank (Public or Private) based on****independent****variables that includeÂ****Technology, Interest Rates, Value Added Services, Perceived Risk, Reputation, Attractiveness, and Perceived Costs**.

## How to Run Binary Logistic Regression

**Step 1: In SPSS, Go to Analyze -> Regression -> Binary Logistic**

**Step 2: **Next, The Logistic Regression Dialog Box will Appear

**Step 3: **Add **Preferred Choice of Bank [Choice] **in the Dependent Box and Add IVs, **Technology, Interest Rates, Value Added Services, Perceived Risk, Reputation, Attractiveness, and Perceived Costs** in the **Covariates** list box.The Dialog box should now look like

**Step 4: **Next, Select Options, Check, **Hosmer-Lemeshow goodness-of-fit and CI for exp(B)**

**Step 5: **Press, Continue, and then Press OK.

## Interpreting Binary Logistic Regression

### Case Processing Summary and Encoding

The first section of the output shows Case Processing Summary highlighting the cases included in the analysis. In this example we have a total of 341 respondents.

The Dependent variable encoding table shows the coding for the criterion variable, in this case those who will encourage are classified as 1 while those who will not encourage to take up the Islamic Banking are classified as 0.

### Block 0

The next section of the output, headed **Block 0**, is the results of the analysis without any of our independent variables used in the model. This will serve as a baseline later for comparing the model with our predictor variables included.