Brooklyn College, CUNYSTAT MISC

TRUE/FALSE QUESTIONS

CHAPTER 9

1)The process by which the factors are determined from a larger set of variables is called extraction. -

 

2. A general rule of thumb is to retain the factors that account for at least 70% of the total variability. -

 

3. Factor scores are estimates of the scores participants would have received on each of the factors had they been measured directly. -

 

4. An index provided inn the results of a factor analysis is the list of communalities for each variable. -

5. In factor analysis, unique, shared, and error variability is analyzed for each observed variable. [only shared] -

 

6. Interpretation of components or factors involves much subjective decision making on the part of the researcher. [p.252] -

 

7. A final criterion for retaining components is the assessment of model fit. [p.249] -

 

8. Factor analysis analyzes variance. [covariance - pg.248] -

 

9. Principal components analysis may be used as a variable reducing scheme for further analysis. [p.254] -

 

10. Kaiser’s rule states that only those components in principal components analysis whose eigenvalues are greater than 1 should be retained. [p.248] -

 

11. Rotation is a process by which a factor solution is made more interpretable by altering the underlying mathematical structure. [without altering - p.252] -

 

12. Varimax is the most commonly used oblique rotation procedure. [orthogonal - pg 252] -

 

13. When interpreting or naming components, one should pay particular attention to the size and direction of each loading. [p.254] -

 

14. Factor analysis is used to describe the underlying structure that explains a set of variables. [pg 247] -

 

15. Oblique rotation results in factors being uncorrelated with each other. [correlated - p.252] -

 

16. A factor correlation matrix is produced from an orthogonal rotation. [oblique - p.252] -

 

17. The underlying hypothetical (unobservable) variables in factor analysis are called factors. -

 

18. Principal components analysis is usually the preferred method of factor extraction, especially when the focus of an analysis searching for an underlying structure is explanatory. [exploratory - p.248] -

 

19. Principle components analysis analyzes covariance. [variance - p.248] -

 

20. An eigenvalue is defined as the amount of total variance explained by each factor, with the total amount of variability in the analysis equal to the number of original variables in the analysis. [p.248] -

 

21. A bipolar factor refers to a component that contains both high positive and high negative loadings. [p.254] -

 

22. Orthogonal rotation is a rotation of factors that results in factors being correlated with each other. [uncorrelated - p.252] -

 

23. A scree plot is a graph of the magnitude of each eigenvalue (vertical axis) plotted against its ordinal numbers (horizontal axis). -

 

24. The main set of results obtained from a factor analysis consists of factor loadings. -

 

25. In principal components analysis, only unique variability is analyzed for each observed variable. [all sources of variability - unique, shared, and error variability - p.248] -