Dear Statalist,
To predict the impact of gender egalitarianism on life satisfaction (7-scale ordinal variable), I wanted to create a factor score from a relevant group of variables (mothers should work: agree to disagree - 5 scale; men should have the right to work when jobs are scarce: agree to disagree - 7 scale etc., five variables in total). As these are all ordinal, I decided to go for polychoric factor analysis. So, one factor score was created as a result (please see the analysis below: hope I did it right!), I looked the factor score up in the data browser, and the factor score looks like a continuous variable.
Thank you very much in advance.
Best regards,
A.
-----
polychoric motherworks menbusiness housewifebetter menrightjob wommoreinc
Polychoric correlation matrix
motherworks menbusiness housewifebetter menrightjob wommoreinc
motherworks 1
menbusiness .28425957 1
housewifebetter .19026393 .22742278 1
menrightjob .28492466 .58025867 .21089833 1
wommoreinc .33885352 .33572039 .140892 .39764391 1
. display r(sum_w)
65408
. matrix r = r(R)
. factormat r, n(65146) factors(1) (THIS IS THE N OF THE DATA SET, hope I understood it correctly)
(obs=65,146)
Factor analysis/correlation Number of obs = 65,146
Method: principal factors Retained factors = 1
Rotation: (unrotated) Number of params = 5
--------------------------------------------------------------------------
Factor | Eigenvalue Difference Proportion Cumulative
-------------+------------------------------------------------------------
Factor1 | 1.53825 1.47170 1.2592 1.2592
Factor2 | 0.06654 0.07187 0.0545 1.3137
Factor3 | -0.00533 0.15521 -0.0044 1.3093
Factor4 | -0.16053 0.05682 -0.1314 1.1779
Factor5 | -0.21735 . -0.1779 1.0000
--------------------------------------------------------------------------
LR test: independent vs. saturated: chi2(10) = 5.5e+04 Prob>chi2 = 0.0000
Factor loadings (pattern matrix) and unique variances
---------------------------------------
Variable | Factor1 | Uniqueness
-------------+----------+--------------
motherworks | 0.4601 | 0.7883
menbusiness | 0.6726 | 0.5476
housewifeb~r | 0.3166 | 0.8998
menrightjob | 0.6983 | 0.5124
wommoreinc | 0.5351 | 0.7137
---------------------------------------
. predict Factor1
(regression scoring assumed)
Scoring coefficients (method = regression)
------------------------
Variable | Factor1
-------------+----------
motherworks | 0.17576
menbusiness | 0.32150
housewifeb~r | 0.10528
menrightjob | 0.35539
wommoreinc | 0.21144
------------------------
(variable means assumed 0; use means() option of factormat for nonzero means)
(variable std. deviations assumed 1; use sds() option of factormat to change)
To predict the impact of gender egalitarianism on life satisfaction (7-scale ordinal variable), I wanted to create a factor score from a relevant group of variables (mothers should work: agree to disagree - 5 scale; men should have the right to work when jobs are scarce: agree to disagree - 7 scale etc., five variables in total). As these are all ordinal, I decided to go for polychoric factor analysis. So, one factor score was created as a result (please see the analysis below: hope I did it right!), I looked the factor score up in the data browser, and the factor score looks like a continuous variable.
- Can I directly (without putting an i. in front) add it to my model s an independent/control variable?
- Can I interpret this as below?
Thank you very much in advance.
Best regards,
A.
-----
polychoric motherworks menbusiness housewifebetter menrightjob wommoreinc
Polychoric correlation matrix
motherworks menbusiness housewifebetter menrightjob wommoreinc
motherworks 1
menbusiness .28425957 1
housewifebetter .19026393 .22742278 1
menrightjob .28492466 .58025867 .21089833 1
wommoreinc .33885352 .33572039 .140892 .39764391 1
. display r(sum_w)
65408
. matrix r = r(R)
. factormat r, n(65146) factors(1) (THIS IS THE N OF THE DATA SET, hope I understood it correctly)
(obs=65,146)
Factor analysis/correlation Number of obs = 65,146
Method: principal factors Retained factors = 1
Rotation: (unrotated) Number of params = 5
--------------------------------------------------------------------------
Factor | Eigenvalue Difference Proportion Cumulative
-------------+------------------------------------------------------------
Factor1 | 1.53825 1.47170 1.2592 1.2592
Factor2 | 0.06654 0.07187 0.0545 1.3137
Factor3 | -0.00533 0.15521 -0.0044 1.3093
Factor4 | -0.16053 0.05682 -0.1314 1.1779
Factor5 | -0.21735 . -0.1779 1.0000
--------------------------------------------------------------------------
LR test: independent vs. saturated: chi2(10) = 5.5e+04 Prob>chi2 = 0.0000
Factor loadings (pattern matrix) and unique variances
---------------------------------------
Variable | Factor1 | Uniqueness
-------------+----------+--------------
motherworks | 0.4601 | 0.7883
menbusiness | 0.6726 | 0.5476
housewifeb~r | 0.3166 | 0.8998
menrightjob | 0.6983 | 0.5124
wommoreinc | 0.5351 | 0.7137
---------------------------------------
. predict Factor1
(regression scoring assumed)
Scoring coefficients (method = regression)
------------------------
Variable | Factor1
-------------+----------
motherworks | 0.17576
menbusiness | 0.32150
housewifeb~r | 0.10528
menrightjob | 0.35539
wommoreinc | 0.21144
------------------------
(variable means assumed 0; use means() option of factormat for nonzero means)
(variable std. deviations assumed 1; use sds() option of factormat to change)
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