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The designers of the 25-item Resilience Scale (Wagnild &
Young. 1993) purported five factors.
A subset of 15 of the original items is provided from data
collected from young Australian adults by Neill & Dias
(2001).
Check whether a five factor solution holds up for the
data.
For this factor analysis, we are interested in developing a
theoretical understanding of the underlying psychological
components of resilience, so use Principal Axis Factoring (PAF),
which looks at the shared variance amongst the items, not all the
items' variance.
You should find that there are really not enough primary
loadings on 4 or 5 factors to justify their presence, therefore try
2 and 3 factors. Best approach is probably 2 factors ("taking
control" and "taking it easy"), with 3 to 5 items removed (at least
remove the three worst items 2, 8, and 17) and using an oblimin
rotation.
It seems that according to this data, psychological resilience
consists of two main underlying components. The first factor,
"Solve" is about taking control, making plans, being determined,
task oriented, active, and disciplined, and solving problems (7
items - 1, 14, 10, 15, 6, 24, 21). The second factor, "Flow"
is about a flexible approach to coping, including being able to
take things in one's stride, taking it easy, laughing things off,
and finding alternative ways through problems (5 items - 19, 7, 23,
16, 9). People who exhibit both these qualities are people
who are most likely to "psychologically resilient" to negative
consequences of experiencing risk factors.
Both factors have very good internal consistency (a
>.8)
Composite
Scores
Calculate unit-weighted composite scores for SOLVE and FLOW and
create univariate descriptives and histograms.
Both factors have reasonably normal distributions with some
negative skew.
Get descriptive statistics and histograms to examine each of
the distributional properties of the composite scores you've
created.
Compare with descriptives statistics and histograms for the
individual items - what are the differences? (The composite score
should be more normally distributed)
The designer of the Self Description Questionnaire
- II, a self-concept questionnaire, for adolescents (SDQ-II),
Prof. Herb Marsh, proposes 11 factors. This is a sample of
data pertaining to 7 of those factors, collected from Australian
adolescents. Check to see whether there are 7 factors.
Use Principle Components (assume we are doing this in order to
calculate factor scores for each self-concept factor).
Check the scree plot - it will suggest looking at 3, 5 and 8
factors. Yet, further exploration of the factor loadings
suggests that 6 or 7 factors make more sense. However, there
are some cross-loadings between the Opposite-Sex Relations and
Physical Appearance items. These can be minimised by using an
oblimin rotation. A 7 factor solution makes most sense.
Note that if 6 factors are used, that it seems that Opposite-Sex
Relations join in one factor with Physical Appearance. Whilst
understandably related, it would make more sense to keep Physical
Appearance and Opposite Sex Relations as a separate factors.
It is also important to test for structural invariance across
cohorts within the sample. Further checking of the SDQ data 6 and 7
factor solutions should take place across Gender. If the
factor analyses are done separately by Gender, it becomes apparent
that the 6 factor solution can apply to both genders, whereas the 7
factor solution seems to only apply to one gender. Thus, this
is an issue which require further thinking and investigation before
ultimately deciding on the most appropriate factor structure.
Check the screeplot - it will suggest some possibilities.
Use Principle Components, since we probably want to create factor
scores for use in further analysis.
An 8 factor solution works well. Oblimin and Varimax solutions
are both good, although Oblimin is a bit cleaner. This is a pretty
clear solution reflecting the fact that this instrument has been
carefully revised through several iterations, each with hundreds or
thousands of participants.
It is important to test for structural invariance across
cohorts within the sample. Using the LEQ data, conduct your
factor analysis separately for males and females. Is life
effectiveness structured similarly for males and
females?
The number and meaning of the factors for males and females
looks similar although the order of the factors suggests a
gender-specific emphasis about which factors are most
dominant.
Does the LEQ factor structure also hold up across participant
age?
Recode age into a dichotomous variable for 25 years and below
as "young", and 26 years and over as "old". Split the data
file by the new age category variable and conduct the factor
analysis with each cohort. The conclusion would be similar -
still the same eight meaningful factors, but there is an
age-specific emphasis on which factors are the most dominant.