# Ordinal Logistic Regression using SPSS

## What is it All About?

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

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

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

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## The Concept of Ordinal Logistic Regression

#### "What to do when we have a Likert Scale 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.

Linear regression analysis requires the outcome/criterion variable to be measured as a continuous variable. However, there may be situations when the researcher would like to predict an outcome that is Ordinal (for instance Strongly Disagree to Strongly Agree).

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

## Examples of Ordinal Logistic Regression

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

- A restaurant would like to assess factors that lead to higher customer satisfaction ratings (Strongly Dis-satisfied (1) to Strongly Satisfied(5)).
- A college would like to assess student confidence level (Low (1) to High (5)) of a student based on Age, Grade, Aptitude Test Results.
- A HR researcher would like to ascertain how factors like experience, years of education, previous salary, university ranking affect the selection chances of a candidate in a job interview (Low (1) to High (5).
- A scholar would like to predict interest of University Students in their studies (Low (1) to High (5)) based on independent variables that include Assignments, Co-curricular activities, Gender, and Age.

## 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.

**A scholar would like to predict interest of University Students in their studies (Low (1) to High (5)) based on****independent variables that include Assignments, Co-curricular activities, Gender, and Age.**

## How to Run Ordinal Logistic Regression

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

** Step 2: Next, The Ordinal Logistic Regression Dialog Box will Appear.Â **Add

**Interest**in the Dependent Box and Add IVs,

**Assignments, CCA, and Gender in the Factor(s) List Box and Age**in the

**Covariates**list box.

**Step 3: **Next, Select Options, make sure **Logit** is selected from **Link** combo box.

**Step 4: **Press, **Output**, and Select **Test of Parallel Lines**.

## Interpreting Ordinal 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 144 respondents.