Testing Factor Analysis Across Cohorts
-
leq.sav
(based on the Life Effectiveness Questionnaire, data from
Neill, Marsh & Richards, 2003)Test the factorial structure
of the LEQ separately for males and females. Is life
effectiveness structured similarly for males and females?
(Hint: First decide on the type of factor analysis, number of factors,
etc. with the whole dataset, then test the factor structure by using
split files by gender. The number and meaning of the factors for
males and females should look similar using PC - Oblimin - 8 factors,
but the order of the factors suggests a gender-specific emphasis on
which factors are most dominant).
If you want to test across another cohort within this dataset, then
do across age, e.g., recode 25 years and below as young, and 26 years
and over as old, split by the data by the age category variable and
examine the two different solutions. Conclusion would be similar -
still the same 8 meaningful factors, but there is an age-specific
emphasis on which factors are the most dominant).
-
TUSMSQ.sav
(based on The University Students Motivation & Satisfaction
Questionnaire)
Test the factorial structure of the motivation items in the TUSMSQ separately for males and
females.
(e.g. try a 6 factor solution. Note you may need to allow for
more iterations to get a solution for the males. Compare the
male and female solutions. Does the 6 factor structure fit one
gender better? Is a better factor structure for males possible?)
Factor Analysis
- conduct factor analysis of
resilience.sav
(e.g. Try a PAF 2 factor oblimin solution and remove the three worst
items (2, 8, & 17). You should find that the first factor is
about active, disciplined, determined coping and problem solving (7
items - 1, 14, 10, 15, 6, 24, 21) and the second factor is about a
flexible approach to coping, going with the flow, taking it easy,
finding ways through, etc. (5 items - 19, 7, 23, 16, 9). For our
purposes let's call the factors SOLVE and FLOW).
(download resilience_FA.spo
- SPSS output for this resilience.sav factor analysis)
Reliability Analysis
Creating Composite Scores
- Create unit-weighted composite scores for SOLVE and FLOW
from
resilience.sav
(use Compute - its easiest to do if you PASTE rather than OK your
initial computation. Then in the syntax window you can copy and edit the syntax
to create separate commands to compute each factor - for more
info, see these notes on
Creating Composite Scores in SPSS)
(If still confused, try this syntax:
COMPUTE SOLVE =
mean.5(rst1q1,rst1q14,rst1q10,rst1q15,rst1q6,rst1q24,rst1q21) .
COMPUTE FLOW = mean.3(rst1q19,rst1q7,rst1q23,rst1q16,rst1q9) .
- Create regression-weighted factor scores for the factors you
derive from
resilience.sav
(use Factor Analysis - Factor Scores)
- Get descriptions and histograms to examine each of the composite
scores you've created.
- Conduct a paired samples t-test or 1-way ANOVA to determine
whether there are different mean self-ratings for the different
resilience factors. Does it make a difference whether you use
the unit-weighted scores or the regression-weighted scores?
(Usually no - there is usually a high correlation between
unit-weighted and regression-weighted scores, but in some analysis,
the regression-weighted scores may prove to be more valid/accurate)
Factor Analysis Quiz 2 Writeup
Please prepare 2 x 250 word (max) write-ups summarizing:
- PART 1 (2 marks - 250 words max.)
- A factor analysis of the university student satisfaction items
in TUSMSQ.sav
(satis1 to satis30), including
- type of analysis
- type of rotation
- number of factors
- items eliminated
- names and definitions of factors (e.g.,
see LEQ factors)
- PART 2 (2 marks - 250 words max.)
- reliability analysis of all factors
- creation of composite scores for all factors
- A SPANOVA (Mixed Design ANOVA) examining means of the different student satisfaction
factors by any between-subjects variable
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