Hello all!

I am running an ologit regression with marginal effects and need some assistance on how to interpret the results and what numbers are important to use in the reporting. Here is some background:

Research question = How do voters partisan affiliations (support for winner or loser of election) impact confidence in the Election Management Body and ultimately their perception of the quality of the election?

Dependent variable = Election Quality (Scale: 1 - Not free and fair; 2 – Somewhat free and fair; 3 – Free and fair; Leave off – Don't know)

Independent variables = Party Affiliation (Losers of election – 0; Winners of election -1) and Confidence in the Election Management Body (Scale: 1 – No confidence; 2 – Some confidence; 3 – A lot of confidence)

For my first step on the analysis, I am only interested in those voters who identify with the losing candidate/party.

These were the results (FYI – This is my first time using the code delimiters so I hope it is readable):

Code:
ologit $ylist $xlist if PartyAffiliation == 0

note: PartyAffiliation omitted because of collinearity
Iteration 0:   log likelihood = -213.40792  
Iteration 1:   log likelihood =  -207.6473  
Iteration 2:   log likelihood = -207.57952  
Iteration 3:   log likelihood = -207.57951  

Ordered logistic regression                       Number of obs   =        260
                                                  LR chi2(1)      =      11.66
                                                  Prob > chi2     =     0.0006
Log likelihood = -207.57951                       Pseudo R2       =     0.0273

------------------------------------------------------------------------------------------
ElectionQualityWithoutDK |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
         ConfidenceinEMB |   .5210839   .1523154     3.42   0.001     .2225513    .8196166
        PartyAffiliation |          0  (omitted)
-------------------------+----------------------------------------------------------------
                   /cut1 |   1.695917   .3022936                      1.103432    2.288401
                   /cut2 |   2.345522   .3208443                      1.716678    2.974365
------------------------------------------------------------------------------------------

. 
. margins, dydx(*) predict(outcome(1))

Average marginal effects                          Number of obs   =        260
Model VCE    : OIM

Expression   : Pr(ElectionQualityWithoutDK==1), predict(outcome(1))
dy/dx w.r.t. : ConfidenceinEMB PartyAffiliation

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
 ConfidenceinEMB |  -.1061178   .0285221    -3.72   0.000    -.1620201   -.0502155
PartyAffiliation |          0  (omitted)
----------------------------------------------------------------------------------

. margins, dydx(*) predict(outcome(2))

Average marginal effects                          Number of obs   =        260
Model VCE    : OIM

Expression   : Pr(ElectionQualityWithoutDK==2), predict(outcome(2))
dy/dx w.r.t. : ConfidenceinEMB PartyAffiliation

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
 ConfidenceinEMB |   .0278309   .0082481     3.37   0.001     .0116649    .0439969
PartyAffiliation |          0  (omitted)
----------------------------------------------------------------------------------

. margins, dydx(*) predict(outcome(3))

Average marginal effects                          Number of obs   =        260
Model VCE    : OIM

Expression   : Pr(ElectionQualityWithoutDK==3), predict(outcome(3))
dy/dx w.r.t. : ConfidenceinEMB PartyAffiliation

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
 ConfidenceinEMB |   .0782869   .0226371     3.46   0.001     .0339191    .1226547
PartyAffiliation |          0  (omitted)
----------------------------------------------------------------------------------
Outcome 1 – Not free and fair

Outcome 2 – Somewhat free and fair

Outcome 3 – Free and fair

Outcome 1 - Does this mean in relation to those who identify with the losing party as their confidence in the EMB increases, they are less likely to rate the elections as not free and fair? Could one say that as someone's confidence in the EMB increases a single unit, he/she is 10.97% less likely to have rated the election as free and fair? I understand that is not so much about the magnitude, but the direction - I just want to make sure that I can wrap my head around the numbers.

Outcome 2/3 - Does this means that in relation those who identify with the losing party as their confidence in the EMB increases, they are more likely to rate the elections as somewhat free and fair as well as free and fair?

What parts of the results should I use in my paper – just the ologit coefficient (with standard deviation, etc.) or each of the margin outcome results or something else that is more straightforward?

Thank you in advance for any help/insight!