#Statplus ordinal regression how to

You can learn more about ordinal variables in our article: Types of Variable. Examples of ordinal variables include Likert items (e.g., a 7-point scale from 'strongly agree' through to 'strongly disagree'), amongst other ways of ranking categories (e.g., a 3-point scale explaining how much a customer liked a product, ranging from 'Not very much', to 'It is OK', to 'Yes, a lot'). Assumption #1: Your dependent variable should be measured at the ordinal level.First, let's take a look at these four assumptions: Even when your data fails certain assumptions, there is often a solution to overcome this.

#Statplus ordinal regression how to

This is not uncommon when working with real-world data rather than textbook examples, which often only show you how to carry out ordinal regression when everything goes well! However, don’t worry. In practice, checking for these four assumptions just adds a little bit more time to your analysis, requiring you to click a few more buttons in SPSS Statistics when performing your analysis, as well as think a little bit more about your data, but it is not a difficult task.īefore we introduce you to these four assumptions, do not be surprised if, when analysing your own data using SPSS Statistics, one or more of these assumptions is violated (i.e., not met). You need to do this because it is only appropriate to use ordinal regression if your data 'passes' four assumptions that are required for ordinal regression to give you a valid result. When you choose to analyse your data using ordinal regression, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using ordinal regression. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for ordinal regression to give you a valid result. This 'quick start' guide shows you how to carry out ordinal regression using SPSS Statistics and explain what you need to interpret and report. You will also be able to determine how well your ordinal regression model predicts the dependent variable. For continuous independent variables (e.g., 'age', measured in years), you will be able to interpret how a single unit increase or decrease in that variable (e.g., a one year increase or decrease in age), was associated with the odds of your dependent variable having a higher or lower value (e.g., a one year increase in participants' age increasing the odds that they would consider tax to be too high). For categorical independent variables (e.g., 'Political party last voted for', which in Great Britain, has 3 groups for this example: 'Conservatives', 'Labour' and 'Liberal Democrats'), you will be able to interpret the odds that one group (e.g., 'Conservative' supporters) had a higher or lower value on your dependent variable (e.g., a higher value could be stating that they 'Strongly agree' that 'Tax is too high' rather than stating that they 'Disagree') compared to the second group (e.g., 'Labour' supporters). Having carried out ordinal regression, you will be able to determine which of your independent variables (if any) have a statistically significant effect on your dependent variable. Alternately, you could use ordinal regression to determine whether a number of independent variables, such as 'age', 'gender', 'level of physical activity' (amongst others), predict the ordinal dependent variable, 'obesity', where obesity is measured using using three ordered categories: 'normal', 'overweight' and 'obese'. As with other types of regression, ordinal regression can also use interactions between independent variables to predict the dependent variable.įor example, you could use ordinal regression to predict the belief that 'tax is too high' (your ordinal dependent variable, measured on a 4-point Likert item from 'Strongly Disagree' to 'Strongly Agree'), based on two independent variables: 'age' and 'income'. It can be considered as either a generalisation of multiple linear regression or as a generalisation of binomial logistic regression, but this guide will concentrate on the latter. Ordinal logistic regression (often just called 'ordinal regression') is used to predict an ordinal dependent variable given one or more independent variables. Ordinal Regression using SPSS Statistics Introduction