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Summary Overview Marketing Research Methods 2024

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Rijksuniversiteit Groningen (RuG)

Marketing Research Methods - Summary Overview. Perfect for the exam preparation, to get to know the basic definitions and interpretation of the research methods. Features: - 7 Key Topics: Covers essential methods per lecture week: PCA, factor analysis, ANOVA, and ANCOVA. - Simple Definition...

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Week Lecture Focus Areas Definitions & Things to Know
Theme
1 Academic • Basics of Research Design: Framework for
Research, research design conducting research to answer specific
Experimental (hypothesis questions; includes methods like
Design, Data testing, experimental, observational, and survey
Preparation research research.
methods) Experimental Design: Design for testing
• Types of causal hypotheses by manipulating
experimental independent variables and measuring
design effects on dependent variables.
• Data cleaning Internal Validity: Degree to which
and preparation results are due to the treatment and not
external factors; high in controlled lab
settings.
Data Preparation: Involves cleaning,
handling missing values, and identifying
outliers.
Listwise vs. Pairwise Deletion:
Listwise removes entire cases with
missing data; pairwise only excludes
missing values for specific analyses.

2 PCA / Factor • Principal Principal Component Analysis (PCA):
Analysis / Component Data reduction technique that creates
Reliability Analysis (PCA) uncorrelated components from
Analysis • Factor Analysis correlated variables.
(FA) Factor Analysis: Method to identify
• Reliability underlying dimensions (factors) from
analysis and observed variables, often used in survey
Cronbach’s data.
Alpha Eigenvalue: Amount of variance
explained by a factor; factors with
eigenvalues >1 are typically retained.
KMO (Kaiser-Meyer-Olkin): Measure of
sample adequacy; values >0.5 indicate
suitability for FA.
Cronbach's Alpha: Reliability measure;
values >0.7 indicate good internal
consistency for a scale.

3 ANOVA and • Understanding ANOVA (Analysis of Variance):
ANCOVA and applying Statistical test for comparing means
ANOVA across multiple groups; identifies if any
• ANCOVA with group means differ significantly.
covariates ANCOVA (Analysis of Covariance):
• Main and ANOVA that controls for covariates,
interaction reducing unexplained variance.
effects Main Effect: The effect of a single
independent variable on the dependent
variable.
Interaction Effect: The combined effect
of two or more independent variables on
the dependent variable, showing if one

, variable’s effect depends on another
variable.

4 Regression • Basics of linear Regression Analysis: Predictive
Analysis regression modeling technique to understand the
• Interpreting relationship between dependent and
regression independent variables.
coefficients Coefficient: Represents the change in
• Assumptions the dependent variable for each unit
and diagnostics increase in the independent variable.
R-squared: Proportion of variance in the
dependent variable explained by the
model; values closer to 1 indicate better
fit.
Multicollinearity: Occurs when
independent variables are highly
correlated, potentially distorting the
analysis.
Binary regression: A statistical method
used to model outcomes with two
possible values (like yes/no or 0/1). It
predicts the probability of one outcome
occurring based on one or more
predictor variables, commonly using
models like logistic regression to ensure
predicted probabilities stay within the 0-1
range.
5 Moderation • Identifying Moderation: Occurs when the
Analysis moderators relationship between an independent
• Interaction variable and a dependent variable
terms in changes depending on the level of a
regression third variable (the moderator).
• Interpreting Interaction Term: Created in regression
moderation to test moderation; shows if the effect of
effects one variable varies across levels of the
moderator.
Simple Slope Analysis: Analyzes the
effect of the independent variable at
specific levels of the moderator, often
used for interpreting significant
interaction terms.

6 Mediation • Mediators in Mediation: Process where an
Analysis causal models independent variable influences a
• Indirect effects dependent variable through a third
and mediation variable (the mediator).
• Baron and Direct Effect: The effect of the
Kenny’s steps independent variable on the dependent
for mediation variable without considering the
mediator.
Indirect Effect: The portion of the effect
of the independent variable on the
dependent variable that occurs through
the mediator.

, Baron and Kenny Steps: Four steps to
establish mediation, including showing
that the IV affects the mediator and that
the mediator affects the DV.

7 Cluster • Hierarchical vs. Cluster Analysis: Technique to group
Analysis non-hierarchical similar cases into clusters, aiming for
clustering high similarity within clusters and high
• Dendrograms dissimilarity between clusters.
and cluster Dendrogram: A tree diagram that shows
interpretation the arrangement of clusters formed by
• Standardization hierarchical clustering.
Hierarchical Clustering: Type of
clustering that builds clusters step-by-
step, either by merging or splitting
clusters.
K-means Clustering: Non-hierarchical
method that partitions data into a set
number of clusters by minimizing within-
cluster variance.
Standardization: Converts variables to
a common scale to ensure each has
equal weight in clustering.

Inhoudsopgave
Inhoudsopgave ..................................................................................................................................... 3

Lecture 1: Academic Research, Experimental Design, and Data Preparation ................................ 4

Lecture 2: Principal Component Analysis (PCA), Factor Analysis (FA), and Reliability Analysis 7

Lecture 3: ANOVA and ANCOVA ........................................................................................................ 10

Lecture 4: Regression Analysis......................................................................................................... 13

Lecture 5: Moderation Analysis ......................................................................................................... 16

Lecture 6: Mediation Analysis ........................................................................................................... 19

Lecture 7: Cluster Analysis ................................................................................................................ 22

Output interpretation .......................................................................................................................... 25

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