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

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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

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










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Institution
Tilburg University (UVT)
Study
Marketing Management
Course
Introduction to Research in Marketing (328049M6)
All documents for this subject (21)

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Uploaded on
January 10, 2023
Number of pages
30
Written in
2022/2023
Type
Summary

Subjects

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

Content preview

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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