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2022 | OriginalPaper | Buchkapitel

8. Bayesian Survey Analysis: Introduction

verfasst von : Walter R. Paczkowski

Erschienen in: Modern Survey Analysis

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Complex Surveys

Nächstes Kapitel Bayesian Survey Analysis: Multilevel Extension

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This is actually an estimate of the probability.

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Note: the “draw” keyword is not required because it is the first argument to the function.

See the pyMC3 and ArviZ documentation.

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Titel: Bayesian Survey Analysis: Introduction
verfasst von: Walter R. Paczkowski
Verlag: Springer International Publishing
Buch: Modern Survey Analysis
Print ISBN: 978-3-030-76266-7

Electronic ISBN: 978-3-030-76267-4

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-030-76267-4_8

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