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Summary: Lectures IRM - Introduction to research in marketing (passed the first time!) CA$7.58
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  5. Introduction to Research in Marketing (328049M6)

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Summary: Lectures IRM - Introduction to research in marketing (passed the first time!)

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  • Course
  • Introduction to Research in Marketing (328049M6)
  • Institution
  • Tilburg University (UVT)

Summary of all the lectures for the course introduction to research in marketing Aims to provide a clear overview of the most important topic in a comprehensive way

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Document information

  • January 10, 2023
  • 30
  • 2022/2023
  • Summary

Subjects

  • irm
  • research
  • anova
  • cluster analysis
  • logistic regression
  • linear regression
  • rstudio
  • factor analysis
  • conjoint analysis
  • introduction to research in marketing
  • r

Written for

  • Tilburg University (UVT)
  • Marketing Management
  • Introduction to Research in Marketing (328049M6)
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2023
SUMMARY:
Introduction to Research in Marketing




Tilburg University
Course Number: 328049-M-6

,Table of Contents
Lecture 1 ................................................................................................................................................1
Lecture 2 - ANOVA..................................................................................................................................4
Lecture 3 - Linear Regression..................................................................................................................8
Lecture 4 - Logistic Regression..............................................................................................................12
Lecture 5 - Factor analysis....................................................................................................................17
Lecture 6 - Conjoint Analysis................................................................................................................22
Lecture 7 - Cluster analysis...................................................................................................................26




Lecture 1


1

, Total error framework
What you observe = True value + Sampling error + Measurement error + STATISTICAL




ERROR (What you don’t observe)
! If you mess up Sampling error/Measurement error/STATISTICAL ERROR your results will
be biased & your recommendations will be wrong




Statistics
Characteristics of the sample
→ Estimate the parameters


Parameters
Characteristics of the population

Sampling error
Error that is due to a sample that is not representative of the population
> Sample differs (significantly) from the population
FE: Non-response error (Sample of respondents differs (significantly) from the population

Solution
Stratification weights
Make your sample closer to your population by using post-stratification weights




Measurement Error
Measurement Scales


2

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