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1. Create a data table

From the Welcome or New Table dialog, choose to create a multiple variables data table. If you are just getting started, you can choose to use the sample data for multiple logistic regression

2. Enter data

Each row represents a different observational unit, for example an individual, animal, or experimental replicate

Each column represents a different variable. Continuous variables must be entered as numbers, while Categorical variables can be entered using text for the names of the levels of the variable (i.e. you can enter "Male" and "Female" instead of "1" and "0"). For multiple logistic regression, the dependent (Y) variable must contain only two values: this can either be a continuous variable with values 0 and 1, or it can be a categorical variable with only two levels. For example, if studying mice, you could simply enter "Male" and "Female" directly into the data table, or you could enter “0” for male and “1” for female.

Other variables (predictor variables) can also be continuous or categorical. There's no need for you to encode categorical variables manually, and you can simply enter the (text) levels of the categorical variable directly into the data table. Be sure that the variable type is correctly set to "Categorical".

Another way of entering information for a categorical variable with only two possible levels is to enter the values of 0 and 1 (representing each level) and setting the variable type to "Continuous". This process is known as "dummy encoding"and is what Prism does behind the scenes for categorical variables entered into the model.

Earlier, it was mentioned that a categorical variable with two levels (i.e. "Male" and "Female") could be entered as a continuous variable using "1" and "0". This process is known as "dummy coding". If a categorical variable has three or more possible values, you could manually do the extra work to encode this variable yourself. One good source to learn about these coding methods is Glantz and Slinker, cited below. However, Prism will handle all of this automatically for categorical variables entered into the model; simply enter the names (text) of the levels for the categorical variable directly into the data table.

Note that there is no need to code interactions manually. Prism will allow you to add interactions automatically in the parameters dialog

3. Run the multiple logistic regression

Click Analyze, and then choose multiple logistic regression from the list of analyses for multiple variables tables. The multiple logistic regression dialog has five tabs:

Model. Choose which variable is the dependent (Y) variable. Add labels for the dependent variable outcomes 1 and 0. Specify which other variables to include as dependent variables, and choose any interactions or transforms you wish to include in the model.

Reference level. Set a reference level for any categorical variable in the specified model. The reference level generally indicates a “baseline” or “usual” level of the categorical variable and is important for results interpretation.

Compare. Choose a second model and specify how the fit of the two models should be compared.

Options. Specify which results Prism should report (note, additional results options on Goodness-of-fit tab).

Goodness-of-fit. Select from a range of various metrics that provide some insight into how well the model fits the entered data.

Graphs. Select which visualizations you would like Prism to generate from the fit.


Glantz and Slinker, Primer of Applied Regression and Analysis of Variance, 3rd edition, Chapter “Using linear regression to do one-way analysis of variance with any number of treatments”, page 391


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