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Bayesian Survey Analysis: Introduction

In: Modern Survey Analysis

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  • Walter R. Paczkowski

    (Data Analytics Corp.)

Abstract

I previously discussed and illustrated deep analysis methods for survey data when the target variable of a Core Question is measured on a continuous or discrete scale. A prominent method is OLS regression for a continuous target. The target is the dependent or left-hand-side variable, and the independent variables, or features (perhaps from Surround Questions such as demographics), are the right-hand-side variables in a linear model. A logit model is used rather than an OLS model for a discrete target because of statistical issues, the most important being that OLS can predict outside the range of the target. For example, if the target is customer satisfaction measured on a 5-point Likert scale, but the five points are encoded as 0 and 1 (i.e., B3B and T2B, respectively), then OLS could predict a value of −2 for the binary target. What is −2? A logit model is used to avoid this nonsensical result. I illustrated how this is handled in Chap. 5 .

Suggested Citation

  • Walter R. Paczkowski, 2022. "Bayesian Survey Analysis: Introduction," Springer Books, in: Modern Survey Analysis, chapter 0, pages 251-302, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-76267-4_8
    DOI: 10.1007/978-3-030-76267-4_8
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