Summary

Brief summary of all IRM lectures at Tilburg University

57 views 5 purchases

Course
Introduction to Research in Marketing (328049M6)

Institution
Tilburg University (UVT)

Brief summary of the Introduction to Research in Marketing lectures. The topics covered are (in order): data exploration & visualization, ANOVA, linear regression, logistic regression, factor analysis and cluster analysis. The structure of the lectures has been maintained to make it easier to study...

[Show more]

Preview 3 out of 24 pages

View example

Uploaded on October 22, 2023
Number of pages 24
Written in 2023/2024
Type Summary

marketing
management
analytics
anova
linear regression
factor analyis
cluster analysis
logistic regression
research methods
tilburg
samenva
introduction to research in marketing
uvt
tilburg university

Institution
Tilburg University (UVT)
Education
Marketing Management & Marketing Analytics
Course
Introduction to Research in Marketing (328049M6)

marliesnoeijen

Member since 3 year 51 documents sold

CA$10.07

Added

Add to cart

Add to wishlist

100% satisfaction guarantee
Immediately available after payment
Both online and in PDF
No strings attached

Introduction to Research in Marketing
Hoofdstuk 1 - Data exploration and visualization

Total error framework: Next to true value, chances are that there are several types of errors present:
 Sampling error
 Measurement error
 Statistical error

Note: take these errors into account, otherwise your results will be biased and your
recommendations will be wrong!

Sampling error
Population: the whole group of people/companies/events/object over which you want to conclude
something
Sample: a subgroup of the population, in order to generalize the results to the population.

There are different types of sampling errors:
 Coverage error: frame population ≠ population
Population: voters of a certain election
Frame population: everyone with a telephone

 Sample error: the method of sampling may cause errors, for example choosing random digits
 Non-response error: not everyone in the sample population accepts the call (big error!)

Eventually, chances are that the sample respondents differ significantly from the population

How to deal with this in practice?
Post-stratification weights: make your sample closer to your population by using the correct weights
Population is 50% female, but your sample is 80% female.
 Simple average: 0.2 x 4.2 + 0.8 x 3.4 = 3.56
Females are overrepresented

 Weighted average: 0.5 x 4.2 + 0.5 x 3.4 = 3.8

Example: 20% of the population is highly educated, while 70% of the sample is highly educated

It is important to compare the characteristics (gender/education/etc) of the population to those of
the sample

Measurement error

Measurement scales
Non-metric Metric (continuous)
Nomina  Numbers only serve as a Interva  Relative positions are
l label/tag l comparable
 Mutually exclusive: not at the  No natural zero point
same time → Likert scale, temperature (Celsius)
 Collectively exhaustive: at least Ratio  Most precise scale

1

, one of the categories  Absolute zero point (cannot
→ Gender, SNR be negative)
Ordinal  Numbers are assigned to → Weight, height, age, temperature
indicate relative position, but (Kelvin)
not the magnitude
→ Gold, Silver, Bronze
Outcomes measure direction, and intensity as well
(strongly agree/somewhat agree)
Outcomes can only measure the direction of the
response (e.g. yes/no)

Note: The right statistical technique depends on what scale is used (metric/non-metric)

Summated scales: abstract concepts (attitudes/beliefs) are measured using more than one question.

Validity: Does it measure what it is supposed to?
→ Do the 8 questions measure the concept of frugality?
Reliability: Do you get the same results every time?

Ideally, you want a measure that is both reliable and valid.

How to deal with this in practice?
Face validity: Do the coefficients make sense? (Effect sizes and signs)

Reliability: How much do these results change if:
 Additional control variables are added
 Some observations are taken away (outliers)
 The model is estimated on a new dataset

Statistical error

Two possible outcomes of hypothesis testing:
 H0 is true
 Alternative is true

Example:
H0: Not pregnant
H1: Pregnant

Two types of errors:
 Type 1: false positive (H0 is incorrectly rejected)
→ You’re pregnant (to a man)
 Type 2: false negative (H0 is incorrectly accepted)
→ You’re not pregnant (to a pregnant woman)

P-value: probability of observed data/statistic, given that the null hypothesis is true
 Low p-value: data are unlikely according to the null → Reject H0 (Low chance of Type 1 error)
 Typically, threshold (α) is set at 0.05 → Reject H0 if p-value < α

2

, In practice: P-value is misused and overused.
→ Consider p-values along with interpretation, power, measurement, sampling and summaries

Exploratory data analysis
 Preparation
- Always explore your data before running any model!
→ Missing observations, mutually consistent, valid, etcetera

 Visualization
Purpose: exploration of data, understanding data and communicating the results

Hoofdstuk 2 – ANOVA

Steps:
1. Defining the objectives

Objective: to test if there are differences in the mean of a metric (interval/ratio) dependent variable
across different levels of one/more non-metric (nominal/ordinal) independent variables (factors).

One-way ANOVA: 1 factor, 2+ levels
→ The difference of the type of ad (skinny/average/plussize) on the appeal

T-test: a special case of ANOVA, when there are only 2 levels within 1 IV.
→ The difference of the type of ad (naked/not naked) on the appeal

Two-way ANOVA: When there is >1 independent variable
→ The difference of the type of poster (1/2/3) on the appeal, and to what extent does the difference
depend on gender (male/female)?
 2 factors, so two-way ANOVA
 3x2 ANOVA

2. Designing the ANOVA

2.1 Interactions
Interaction: the effect of one variable on the DV is dependent on
another (moderator)
→ Interaction between poster and gender?

2.2 Covariates
Covariates/control variables: affect DV separately from the
treatment variables
 If not included → biased results!

Requirements control variables:
1. Pre-measure: Independent of treatment
2. Limited number

3

The benefits of buying summaries with Stuvia:

Guaranteed quality through customer reviews

Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.

Quick and easy check-out

You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.

Focus on what matters

Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!

Frequently asked questions

What do I get when I buy this document?

You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.

Satisfaction guarantee: how does it work?

Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.

Who am I buying these notes from?

Stuvia is a marketplace, so you are not buying this document from us, but from seller marliesnoeijen. Stuvia facilitates payment to the seller.

Will I be stuck with a subscription?

No, you only buy these notes for CA$10.07. You're not tied to anything after your purchase.

Can Stuvia be trusted?

4.6 stars on Google & Trustpilot (+1000 reviews)

78600 documents were sold in the last 30 days

Founded in 2010, the go-to place to buy study notes for 14 years now

Start selling

Summary

Brief summary of all IRM lectures at Tilburg University

Document information

Subjects

Written for

Seller

Reviews received

Content preview