Extensive SPSS Exam Summary for Multivariate Data Analysis (MVDA)
46 views 1 purchase
Course
Multivariate Data Analysis - SPSS
Institution
Universiteit Leiden (UL)
This extensive exam document covers almost every single possible questions an SPSS MVDA exam can give. With this summary, I personally received a 9 for my SPSS final exam grade.
Week 1: Multiple Regression Analysis (MRA)
MRA = if A is regressed on B, A is the dependent variable and B the predictor/independent
→ Y is regressed on X
Does it make sense to perform a linear regression?
If there is a relationship = yes!
Which variable is likely to be a good predictor?
Highest significance = good predictor
Perform Linear Regression
Analyse → regression → linear → add what is needed (also if they ask
for extras)
Can the null hypothesis be rejected?
Look at the ANOVA table → it if is significant (p < 0.05) reject the
H0
What predictor explains the most unique variance?
, Look at the coefficients table → look at "part" → do each X 2 and check the
highest value
Example: 0.4872 = 0.237 → do for each: if this is the highest, then this
is the most unique
Is there evidence of multicollinearity in the predictors?
Look at VIF ( should be lower than 10) and Tolerance (should be higher than 0.10)
→ if they are lower than 10 and higher than 0.10 = no
multicollinearity (good thing!)
Do Cook's distances and Leverage values suggest the presence of outliers?
Look at Cook's (should be lower than 1) and Leverage (should be less than 3(k+1)/N)
● K = number of predictors/variables
Compare these values to the maximum in the table
Get rid of outliers (if needed)
Data → select cases → "if condition is satisfied" → put in what needs
to be gone (so this could be Cooks < 1 and/or Leverage < 3(k+1)/N)
How much variance is explained by..?
R2 = total variance explained, given in model summary table → unique
variance explained is part2
→ R2 is given under one of the tables, do not have to calculate it
Is there evidence of non-linearity, heteroscedasticity or non-normality of the residuals?
Scatterplot of standardised predicted values vs standardised residuals
Within linear regression: plots → full in Y: ZRESID and X: ZPRED →
check normal probability plot
What is the estimated regression equation? Interpret the regression coefficient
Yi = b0 + b1x1 + b2x2 + … → fill in the data and interpret the significance
(p < 0.05)
Week 2: Analysis of Variance (ANOVA)
Make a cross tabulation of X and Y
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 evalindekuyper. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $8.12. You're not tied to anything after your purchase.
