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 genderspecific 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 agespecific
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 unitweighted 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 regressionweighted 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 ttest or 1way ANOVA to determine
whether there are different mean selfratings for the different
resilience factors. Does it make a difference whether you use
the unitweighted scores or the regressionweighted scores?
(Usually no  there is usually a high correlation between
unitweighted and regressionweighted scores, but in some analysis,
the regressionweighted scores may prove to be more valid/accurate)
Factor Analysis Quiz 2 Writeup
Please prepare 2 x 250 word (max) writeups 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 betweensubjects variable
