This is the summary of all regular Marketing Research Methods lectures of the academic year 2019/2020. Being a summary of the lectures, a large amount of screenshots from the lectures is added to provide additional visual clarity on some aspects and concepts.
2023-2024 [Marketing Research An Applied Orientation,Malhotra,6e] Test Bank: Your Pathway to A+ Grades
Overview of statistics used in Marketing Research Methods
Summary Marketing Research: An applied Orientation (Malhotra, 6th edition)
All for this textbook (8)
Written for
Rijksuniversiteit Groningen (RuG)
Marketing
Marketing Research Methods (EBM080A05)
All documents for this subject (6)
2
reviews
By: ApolloCreed34 • 2 year ago
By: jeroenvdvalk96 • 3 year ago
Seller
Follow
adriaanvschaik
Reviews received
Content preview
Marketing Research Methods – Lectures Summary
Lecture 1: Introduction, Factor Analysis
• This course is about questions & answers, NOT methods.
• Data analysis = means to an end. More important is telling the story, and being able to
interpret the outcome. Your ‘‘grandma’’ should be able to understand it; this gives you
points.
• Different scales for surveys:
o One-item; yes/no, age, gender, etc.
o Multi-item; attitudes, lifestyles, etc. Be careful not to include too many items.
• What if you have too many items?
o Factor Analysis: defining all questions of the survey in factors (price, service,
location, etc.)
o Reliability Analysis
▪ Both for data reduction, especially useful for multi-item scales.
• Data analysis basic steps:
o Inspection & preparing data for final analysis.
o Final analysis: testing hypotheses, regression, etc.
▪ Is there crap in your data initially? Then crap will come out. Crap in =
crap out.
• Factor Analysis: reduction of large quantity of data by finding common variance.
o To retrieve underlying dimensions in a dataset.
o To test if hypothesized dimensions actually exist in a dataset.
▪ No distinction will be made here at first between dependent and
independent variables = no causal relation in the first stage.
o Data reduction; n items -> summarize items in p < n factors, to achieve
parsimony: explaining a lot with little; the amount of information is the same
only with less variables. Also, there will be less multicollinearity after factor
analysis.
▪ Variables will be expressed as a linear combination of other variables,
called factors; x1 -> F1. Only choose the strongest factors. But; the
fewer factors you obtain, the less information.
▪ F1 -> x1, x2, x3. F2 -> x4, x5, x6, and so forth.
,o Factor Analysis in SPSS:
▪ 1) Research purpose: what variables to include?
• Easier if you have a certain hypothesis.
▪ 2) Is Factor Analysis appropriate?
• KMO (Kaiser-Meyer-Olkin) test; will data factor well?
If KMO < 0.5, drop variable with the lowest KMO.
• Bartlett’s test; H0; variables are uncorrelated. If we cannot
reject H0, no correlations can be established.
• Communalities; percentage of variance of a given variable
explained by all extracted factors. Should be < 1, but larger than
0.4.
▪ 3) Select factor model to achieve weights wij:
• Principal component analysis (PCA).
• Common factor analysis (not being done in this course).
▪ 4) Select best number of factors; check the output.
• Ignore factors which have less significance (less variance),
based on one or more of the following methods:
o Choose those which have eigenvalues of > 1.
o Look at total explained variance to be > 60%
(cumulative).
o Look at factors that explain > 5% each (non-
cumulative).
o Inspect scree plot, which is a graphical representation;
where is the drop in the line? Include variables before
this drop.
▪ In any case, better include too many variables
than too few variables.
▪ 5) Rotate factor matrix in SPSS.
2
,Lecture 2: Factor Analysis, Reliability Analysis, Cluster Analysis
• Step 5 of Factor Analysis: Rotate factor matrix in SPSS.
o ‘‘Cleaning the window’’; prevents that all variables load in 1 factor; minimizes
number of variables with high loadings, but does not change explained
variance.
▪ High loading = strong connection to one dimension (not more than one
dimension, otherwise this might be an indication that your factors are
not good.
• 6) Interpreting & labelling factors that have rotated loadings of > 0.5.
o Eigenvalue = how much variance is explained by a factor?
o Communality = how much variance is explained of a variable? More factors =
more communality.
▪ F1 F2
▪ X1 X1 Communality
▪ X2 X2
▪ X3 X3
Eigenvalue
o Cross-loading = variables having high loadings to more than one factor;
reduce the number of factors or variables.
• 7) Subsequent use of factors: create new variables, use obtained factors for this.
o 2 ways:
▪ Calculate factor scores for each respondent.
▪ Use reliability analysis.
• Reliability Analysis: when underlying dimensions are known, after using factor
analysis; how strong are these factors?
o Cronbach’s Alpha: measure internal consistency.
• 2 ways of reliability analysis:
o Reliability analysis of scale as found in theory.
o Reliability analysis of factors found in PCA.
• Measurement error = systematic error + random error.
o Reduce the systematic error. An analysis can still be reliable if there is a
consistent systematic error! There is only a problem if it is inconsistent.
• For step 7 of Factor Analysis, use Cronbach’s Alpha.
o CA should be higher than 0.6.
▪ Too low; delete the item.
▪ SPSS will show what CA will look like if one item is deleted.
• If it will appear low, keep the item, if higher, delete it.
• Only 2 items left? Don’t do Cronbach’s Alpha.
3
, • Data analysis with a spatial map:
o 1) Let many consumers rate n brands on m attributes.
o 2) Reduce m variables to a lower number of dimensions, preferably 2 (using
FA).
o 3) Check internal consistency with Cronbach’s Alpha.
o 4) Compute average scores on dimension or factor per brand.
o 5) Plot (in Excel).
• Cluster Analysis (STP):
o Segmenting:
▪ Identify segmentation bases.
▪ Develop profiles of resulting segments.
• 1) Rationale for segmentation (strategy): why?
• 2) Select most useful segmentation variables.
• 3) Segment market; select cluster analysis procedure.
o Divide heterogeneous sample in homogeneous groups.
Groups should internally be as similar as possible,
compared to other groups, which should among each
other be as different as possible!
o Active variables = used for clustering.
o Passive variables = used for group identification.
• 4) Group customers in segments.
• 5) Choose segments that best serve firm’s strategy, given
capabilities, skills, competitors.
o Targeting:
▪ Evaluate attractiveness of each segment.
▪ Select target segments.
o Positioning:
▪ Identify positioning concepts.
▪ Select, develop.
o Define measures for similarities of customers, based on their needs.
o Group customers with similar characteristics.
o Select number of segments using numeric & strategic criteria.
o Profile needs of segments.
o Measure -> Group -> Selection of number of segments -> Profile.
o At what moment in time do you start to combine people who are not that
similar anymore?
▪ Use the Agglomeration Schedule. Coefficient = cost of combining.
▪ Use a Dendogram; visual representation.
4
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 adriaanvschaik. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $7.85. You're not tied to anything after your purchase.
