## Introduction

There are lots of potential outcome variables that can be used when conducting a driver analysis,

from five-point rating scales through to utilities from conjoint studies. To conduct a valid driver analysis

we need to select an appropriate regression type or generalized linear model (GLM) which is consistent with our data.

This article describes how to select the Regression Type suitable for your Driver Analysis.

## Requirements

A Driver Analysis output (See: How to Do Driver Analysis).

## Method

- The type of Regression type required will depend on the outcome variable used in your Regression model. To find the
**Outcome**variable select the output and go**Inputs > Linear Regression > Outcome**. - OPTIONAL: To view all potential outcomes of the dependent variable go to the
**Data Sets**tree, select the Outcome variable and go to the**object inspector > Properties > DATA VALUES > Values**. Alternatively, you can also drag and drop the variable onto the**Page**to create a table containing all outputs. - Go to
**Inputs > Regression Type**and select the appropriate model depending on the number of categories of the**Outcome variable**.

**Outcome variable****Example****Regression Type**Two categories 1. Yes / Selected

2. No / Not selected

Binary logit (also known as

logistic regression)Three to 11 ordered

categories1. Hate

2. Dislike

3. Acceptable

4. Like

5. LoveOrdered logit 12 or more ordered

categoriesHow would you rate your

happiness on a scale of 0 to

100Linear regression Net Promoter Score

(NPS)-100: Detractor

0: Passive/Neutral

100: PromoterLinear regression Purchase or usage

quantitiesNumber of cans of coke

consumed per weekNBD (or, if you get a weird

message, quasi-Poisson

regression)Utilities from a conjoint

or MaxDiff studyThat is, a variable that

contains the estimated utilities

for each respondentLinear regression Probabilities or shares

from conjoint and

MaxDiff studiesThat is, a variable that

contains the probabilities for

each respondentUse the utilities instead of the preference shares, and then use Linear regression.

## See Also

## Comments

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