When you choose to analyse your data using multinomial logistic regression, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using multinomial logistic regression. If you would like us to add a premium version of this guide, please contact us. Note: We do not currently have a premium version of this guide in the subscription part of our website. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a multinomial logistic regression to give you a valid result.
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This "quick start" guide shows you how to carry out a multinomial logistic regression using SPSS Statistics and explain some of the tables that are generated by SPSS Statistics. Alternately, you could use multinomial logistic regression to understand whether factors such as employment duration within the firm, total employment duration, qualifications and gender affect a person's job position (i.e., the dependent variable would be "job position", with three categories – junior management, middle management and senior management – and the independent variables would be the continuous variables, "employment duration within the firm" and "total employment duration", both measured in years, the nominal variables, "qualifications", with four categories – no degree, undergraduate degree, master's degree and PhD – "gender", which has two categories: "males" and "females"). As with other types of regression, multinomial logistic regression can have nominal and/or continuous independent variables and can have interactions between independent variables to predict the dependent variable.įor example, you could use multinomial logistic regression to understand which type of drink consumers prefer based on location in the UK and age (i.e., the dependent variable would be "type of drink", with four categories – Coffee, Soft Drink, Tea and Water – and your independent variables would be the nominal variable, "location in UK", assessed using three categories – London, South UK and North UK – and the continuous variable, "age", measured in years). It is sometimes considered an extension of binomial logistic regression to allow for a dependent variable with more than two categories. Multinomial logistic regression (often just called 'multinomial regression') is used to predict a nominal dependent variable given one or more independent variables. Multinomial Logistic Regression using SPSS Statistics Introduction
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