survey analysis -- ordinal scales

My friend asked for some statistical help on a finance department
analysis questionnaire. There are 3 propositions and several
sub-hypotheses. Your input is highly appreciated.


Proposition 1:

H1a:

Dependent variable: 14 scale questions (scale 1-7) for management
orientation.

Independent variable(s): CEO/Chair duality, a binary variable.

Statistical questions: Their sample size is quite small (~28), and
initially those scales were ordinal scales, so I think the easy way is
to treat them as interval scales such that we can calculate means and
use non-parametric ways to compare them. Otherwise I have no idea how
to collapse these 14 scale questions statistically to turn them into a
single dependent variable regarding hypotheses H1a and H1b.

H1b:

Dependent variable: same as H1a.

Independent variable(s): 2 scale questions on insider/outsider.

Statistical questions: Same thins as in H1a, and I can then average the
2 independent variables, and it will turn out to be a linear
regression?


Proposition 2:

H2a: to test if there is significant difference in management
orientation between the whole cohort and individual sub-functions
(note: they are not subsets). Each of the sub- functions contains 2
categories: ACTUAL and IDEAL.

Statistical questions: Since I can average the scales of ACTUAL and
IDEAL, a Wilcoxon Rank-Sum Test would be appropriate.

H2b: to test if there is significant difference in management
orientation between every 2 sub-functions.

Statistical questions: Same thing as H2a.


Proposition 3:

H3a - H3d:

Dependent variable: a binary variable.

Independent variable: 14 scale questions as in H1a.

Statistical questions: My friend wanted to use factor analysis to
reduce the number of variables. If so, the obtained factors will then
be put into a multivariate logistic regression. However, if we start
from bivariate logistic regressions and use a cut-off p-value (say 0.2
or 0.3) to remove some initial variables, and run correlation analysis
at the same time to remove collinearity, and subsequently run a
multivariate logistic regression, we will get a
different result from what we get from the first way, because factor
analysis did not remove any variables. What do you think is a
statistically appropriate way for that?


Thanks very much.

.

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