Multinomial Logistic Regression in SPSS

Discover Multinomial Logistic Regression in SPSS! Learn how to perform, understand SPSS output, and report results in APA style. Check out this simple, easy-to-follow guide below for a quick read!

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Welcome to a comprehensive guide on Multinomial Logistic Regression in SPSS. This advanced statistical technique holds immense value in various fields, from social sciences to marketing, allowing us to predict outcomes with more than two categories. In this blog post, we will explore the intricacies of multinomial logistic regression, offering a clear understanding of its applications, assumptions, and practical implementation. Whether you are a seasoned data analyst or new to statistical modeling, this guide will equip you with the knowledge and skills needed to harness the power of multinomial logistic regression effectively. Let’s embark on this journey to unlock the potential of predicting outcomes across multiple categories.

Types of Logistic Regression

Before delving into multinomial logistic regression, let’s take a moment to explore the broader landscape of logistic regression. There are three primary types: Binomial Logistic Regression, Multinomial Logistic Regression, and Ordinal Logistic Regression.

  • Binomial Logistic Regression deals with binary outcomes, where the dependent variable has only two possible categories, such as yes/no or pass/fail.
  • Multinomial Logistic Regression comes into play when the dependent variable has more than two unordered categories, allowing us to predict which category a case is likely to fall into.
  • Ordinal Logistic Regression is employed when the dependent variable has multiple ordered categories, like low, medium, and high, enabling us to predict the likelihood of a case falling into or above a specific category.

In this post, our focus will be on Multinomial Logistic Regression, which is widely used for binary outcomes and forms the foundation for understanding logistic regression.

Definition: Multinomial Logistic Regression

Multinomial Logistic Regression is a powerful statistical method used to predict and analyse outcomes with multiple, non-sequential categories. It extends the principles of binary logistic regression to scenarios where the dependent variable has more than two unordered categories. This technique is widely applicable in diverse fields such as political science, market research, and healthcare, where the outcome variable cannot be neatly categorized as binary.

In Multinomial Logistic Regression, the dependent variable represents the different categories or groups that cases can belong to, and the goal is to understand how a set of predictor variables influences the probability of a case falling into each category. It provides valuable insights into the relationships between independent variables and a categorical outcome, allowing researchers and analysts to make informed decisions based on the probabilities of group membership.

Multinomial Regression Equation

At the heart of Multinomial Logistic Regression lies the Multinomial Regression Equation, a mathematical representation of how predictor variables affect the log odds of belonging to each category of the dependent variable. The equation can be expressed as follows:

Ln (p_i /p_k) = b_0 + b_1X_1 + b_2X_2 + ….+ b_kX_k


  • (ln) denotes the natural logarithm.
  • (p_i) represents the probability of belonging to the ( i )-th category of the dependent variable.
  • (p_k) represents the probability of belonging to a reference category (usually the base category).
  • (b_0) is the intercept term.
  • (b_1, b_2, …, b_k) are the coefficients for the independent variables ( X_1, X_2,…., X_k).

The coefficients ( b_1, b_2, …., b_k ) represent the slope of the relationship between each predictor variable and the log odds of belonging to a specific category compared to the reference category. Multinomial Logistic Regression produces separate sets of coefficients for each category, comparing them to the reference category.

Odds ratios, calculated as (exp(b_i)), help interpret the impact of predictor variables on the odds of belonging to each category. An odds ratio greater than 1 implies an increase in the odds of belonging to a specific category, while a value less than 1 indicates a decrease compared to the reference category. Understanding this equation and the concept of odds ratios is fundamental for grasping the mechanics of multinomial logistic regression.

Logistic Regression Classification

In the context of Multinomial Logistic Regression, classification plays a vital role. Classification involves assigning cases to one of the multiple categories based on their predicted probabilities of belonging to each group. The category with the highest predicted probability is assigned to the case. Unlike binary logistic regression where the choice is between two categories, multinomial logistic regression deals with more than two categories.

Multinomial Logistic Regression not only predicts the category a case is likely to belong to but also provides insights into the factors that influence this classification. By examining the coefficients and odds ratios associated with each predictor variable for different categories, analysts can identify which factors are significant predictors of category membership and understand the direction and magnitude of their impact.

Assumptions of Multinomial Logistic Regression

Before delving into the practical application of Multinomial Logistic Regression, it’s essential to be aware of the assumptions underlying the reliability of this technique. These assumptions include:

  • Nominal Dependent Variable: The dependent variable should be measured at the nominal level with at least 3 groups. If there is an ordinal level, you can perform Ordinal Logistic Regression.
  • Independence of Observations: The observations (cases) should be independent of each other, meaning that the outcome of one case should not influence the outcome of another.
  • No Perfect Multicollinearity: There should be no perfect multicollinearity among the independent variables. Perfect multicollinearity occurs when one or more predictor variables are linear combinations of others, leading to unstable coefficient estimates.
  • Proportional Odds Assumption (for ordinal multinomial logistic regression): If you are using multinomial logistic regression for ordinal outcomes, this assumption requires that the odds ratios for each pair of outcome categories are proportional. This assumption should be checked when dealing with ordinal data.
  • Adequate Sample Size: Ensure that your sample size is sufficiently large to support the analysis, particularly when dealing with multiple categories. Some guidelines suggest a minimum number of cases per category to ensure the stability of estimates.
  • Absence of Outliers: Outliers, if present, should be minimal, as they can influence the estimation of coefficients and model fit.

Adhering to these assumptions is essential to ensure the validity and reliability of the results obtained from Multinomial Logistic Regression.

Hypothesis of Multinomial Logistic Regression

Hypotheses are fundamental to statistical analysis, guiding the research process and the interpretation of results. In Multinomial Logistic Regression, hypotheses are essential for evaluating the significance of predictor variables within the context of multiple outcome categories. Two primary hypotheses are central to multinomial logistic regression:

  • Null Hypothesis (H0): There is no significant relationship between the independent variables and the outcome categories.
  • Alternative Hypothesis (H1): At least one independent variable has a significant effect on the probability of belonging to specific categories.

Hypothesis testing in multinomial logistic regression involves examining the significance of coefficients associated with each independent variable for each category. If any coefficient has a p-value less than the chosen significance level (commonly 0.05), it implies that the corresponding variable has a significant effect on the probability of belonging to that category. Hypothesis testing is a critical step in determining which predictor variables contribute significantly to category membership and understanding their impact.

An Example of Multinomial Logistic Regression

To illustrate the practical application of Multinomial Logistic Regression, let’s consider a real-world scenario. Imagine a market research study aiming to understand factors influencing consumers’ preferences for mobile phone brands. The dependent variable, “Brand Preference,” has three categories: Apple, Samsung, and Other.

In this example, Multinomial Logistic Regression would be employed to determine which predictor variables influence the likelihood of consumers preferring one brand over the others. The predictor variables may include factors such as age, gender, and Price.

This practical application showcases how Multinomial Logistic Regression helps researchers and businesses gain a nuanced understanding of consumer choices, allowing them to tailor marketing strategies and product offerings to specific target groups effectively. It demonstrates the versatility of this technique in analysing outcomes with multiple categories, offering valuable insights for decision-making and strategy development.

How to Perform Multinomial Logistic Regression using SPSS Statistics

Step by Step: Running Multinomial Logistic in SPSS

Now, let’s delve into the step-by-step process of conducting the Multinomial Logistic Regression using SPSS Statistics.  Here’s a step-by-step guide on how to perform a Multinomial Logistic Regression in SPSS:

  1. STEP: Load Data into SPSS

Commence by launching SPSS and loading your dataset, which should encompass the variables of interest – a categorical independent variable. If your data is not already in SPSS format, you can import it by navigating to File > Open > Data and selecting your data file.

  1. STEP: Access the Analyze Menu

In the top menu, locate and click on “Analyze.” Within the “Analyze” menu, navigate to “Regression” and choose ” LinearAnalyze > Regression> Multinomial Logistic

  1. STEP: Choose Variables

In the Multinomial Logistic Regression dialog box, move the outcome variable into the “Dependent” box and the categorical predictor variables into the “Factors (s)” box. And Dummy or continuous predictors into the “Covariance (s)” box.

Click “Statistics” and check “Pseudo R-Square”, “Step Summary”, “Model Fitting Information”, “Classification Table”, “Goodness-of-fit” and “Correlation of estimates

  1. STEP: Generate SPSS Output

Once you have specified your variables and chosen options, click the “OK” button to perform the analysis. SPSS will generate a comprehensive output, including the requested frequency table and chart for your dataset.

Executing these steps initiates the Multinomial Logistic Regression in SPSS, allowing researchers to assess the impact of the teaching method on students’ test scores while considering the repeated measures. In the next section, we will delve into the interpretation of SPSS output for Multinomial Logistic Regression.


Conducting a Multinomial Logistic Regression in SPSS provides a robust foundation for understanding the key features of your data. Always ensure that you consult the documentation corresponding to your SPSS version, as steps might slightly differ based on the software version in use. This guide is tailored for SPSS version 25, and for any variations, it’s recommended to refer to the software’s documentation for accurate and updated instructions.

SPSS Output for Multinomial Logistic Regression

How to Interpret SPSS Output of Multinomial Logistic Regression

Interpreting the SPSS output of Multinomial logistic regression involves examining key tables to understand the model’s performance and the significance of predictor variables. Here are the essential tables to focus on:

Model Fitting Information

This table presents a Chi-Square test for the overall significance of the model. It compares the fit of the full model (including predictor variables) to the fit of the null model (without predictor variables). A significant p-value (typically < 0.05) suggests that the model as a whole is a better fit for the data than the null model, indicating that at least one predictor variable has a significant impact on the outcome.


Pay attention to goodness of fit tests, such as the Pearson and Deviance statistics, to assess how well the model fits the data. Lower values indicate a better fit, and non-significant p-values suggest a good model fit.

Pseudo R-Square

The Model Summary table provides information about the overall fit of the model. Look at the Cox & Snell R Square and Nagelkerke R Square values. Higher values indicate better model fit.

Likelihood Ratio Tests

This table shows which of your independent variables are statistically significant.

Parameter Estimates

Examine the Coefficients table, which lists the coefficients (Bs) for each predictor variable for each category compared to the reference category.

  • Coefficients (B) presents the coefficients associated with each independent variable for each category. These coefficients represent the change in log odds of belonging to a specific category compared to the reference category for a one-unit change in the predictor variable.
  • Exp (B): Odds ratios provide a clearer interpretation of the impact of predictor variables. An odds ratio greater than 1 suggests increased odds of belonging to the category, while a value less than 1 indicates decreased odds compared to the reference category.
  • Wald Statistics This column presents the Wald Chi-Square statistic, which tests the significance of each coefficient. A significant Wald Chi-Square value (typically indicated by a p-value < 0.05) suggests that the corresponding independent variable has a significant effect on category membership for that specific category compared to the reference category
  • t-values: Indicate how many standard errors the coefficients are from zero. Higher absolute t-values suggest greater significance.
  • P values: Test the null hypothesis that the corresponding coefficient is equal to zero. A low p-value suggests that the predictors are significantly related to the dependent variable.

Classification Table

Review the Classification Table to assess the model’s accuracy in classifying cases into their respective categories. It provides information on the percentage of cases correctly classified for each category and the overall accuracy of the model.

By thoroughly examining these output tables, you can gain a comprehensive understanding of the Multinomial logistic regression model’s performance and the significance of predictor variables. This information is essential for making informed decisions and drawing meaningful conclusions from your analysis.

How to Report Results of Multinomial Logistic Regression in APA

Reporting the results of Multinomial logistic regression in APA (American Psychological Association) format requires a structured presentation. Here’s a step-by-step guide in list format:

  • Introduction: Begin by introducing the purpose of the Multinomial logistic regression analysis and the specific research question or hypothesis you aimed to address.
  • Assumption Check: Briefly mention the verification of assumptions, ensuring the robustness of the multinomial logistic regression analysis.
  • Model Specification: Clearly state the outcome variable (e.g., “The dependent variable, Nominal Outcome,’ represented whether…”), and list the predictor variables included in the analysis.
  • Model Fit: Report the model fit statistics. Include the Cox & Snell R Square and Nagelkerke R Square values to assess how well the model explains the variation in the outcome.
  • Predictor Variables: Present a summary of the significant predictor variables included in the final model. Include the variable names, coefficients (Bs), and odds ratios with confidence intervals (e.g., “Variable A (B = 0.543, Odds Ratio = 1.721, 95% CI [1.243, 2.377])”).
  • Overall Model Significance: Indicate whether the overall model was statistically significant using the Likelihood. Include the Chi-Square value and associated p-value (e.g., “The model was statistically significant, χ²(3) = 12.456, p < 0.001”).

Goodness of Fit

  • Discuss the Pearson and Deviance Test results to assess the goodness of fit. Mention whether the model fits the data well or if there are potential issues (e.g., “Goodness of fit Test indicated a good fit, χ²(7) = 6.245, p = 0.512”).
  • Classification Accuracy: Provide information from the Classification Table, such as the overall classification accuracy percentage and the number of true positives, true negatives, false positives, and false negatives.
  • Hypothesis Testing: Address the research hypotheses by discussing the significance of each predictor variable. Emphasize whether they are associated with the nominal outcome based on their p-values.
  • Practical Implications: Conclude by discussing the practical significance of the findings and how they contribute to the broader understanding of the phenomenon under investigation.

By structuring your report following these guidelines, you ensure that your results are presented in a clear and organized manner, adhering to APA format standards. This facilitates effective communication of your multinominal logistic regression findings to both academic and non-academic audiences.

Example of Multinomial Logistic Regression in APA Style

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