ANOVA Factor Analysis Cluster Analysis
Method Dependence method → causal relationship. Interdependence method →find underlying Interdependence method → group rows,
construct. based on predefined characteristics.
Treatment variables: Nominal scale
Outcome: Metric scale Variables: should be (treatable as) metric Method can deal with all types of data.
Sample size ≥20 per cell/subgroup ≥5 per variable in the dataset (preferably 10) No strict rules, but check for outliers!
Extra Covariates (max 3) can be metric. Exploratory factor analysis: data → theory Order of magnitude should be similar for
variables, measured in comparable units.
Assumptions 1. Observations independent → each Multicollinearity is absolutely required! No ‘hard’ statistical assumptions.
observation only one combination of
treatment variables (between-subjects Method 1: Visual inspection of the correlation 1. Is the sample representative for the
design). matrix → sufficient number of high (>0.3) whole set of customers?
correlations?
2. Homoscedasticity → Levene’s Test of 2. Collinearity → not that big of an issue.
Equality of Error Variances (α > 0.05). Method 2: Bartlett’s Test of Sphericity →
reject H0 that all correlations are equal to 0 (α But keeping both variables in the analysis
3. Normality of dependent variable → Test < 0.05). implicitly place more weight on these
of Normality (α > 0.05). variables
Method 3: Measure of Sampling Adequacy
(MSA) → rule of thumb: MSA-value <0.5 is
unacceptable.
If homoscedasticity is rejected: Common variance: variance that variables Deriving the clusters:
share/have in common.
1. Check sample size → similar across Specific variance: aspect that doesn’t come Hierarchical approach:
treatment groups, don’t worry. back in other variables. • Agglomerative → starting with all objects
Error variance: errors that could cause in separate clusters, then subsequently
2. Take logarithm of dependent variable and variation in the data. adding them together until every object is
redo analysis → if it solves the problem, don’t in the same group.
worry. Option 1: Common Factor Analysis (CFA) →
how many and which groups of variables exist • Divisive → starting with one cluster, then
3. Adjust cut-off for significance: in the data (summarize data). split up until every object is a separate
• Variance in larger subsample higher → cluster.
use lower cut-off; α=0.03 or α=0.01. Option 2: Principal Component Analysis (PCA)
• Variance in larger subsample lower → → representing as much information as Non-hierarchical approach:
use higher cut-off; α=0.10. possible by a minimum number of factors • K-means → start off with a fixed number
(data reduction). of clusters.
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 marketingmanagement01. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $3.23. You're not tied to anything after your purchase.
