Hello all,

I'm examining a hypothetical scenario to determine how living alone and the mechanism of feedback affects a person's willingness to turn themselves into police for a crime. Mechanism of feedback refers to the person being told the positive or negative consequences of turning themselves in (e.g. a positive mechanism would tell the person all of the good things that come with turning him or herself in while a negative mechanism would tell the person all the bad things that come with turning him or herself in - a neutral mechanism doesn't mention any good or bad things).

I'm running a hierarchical logistic regression model using two steps: the main effects on the first step and the interaction on the second step where v1 is the dichotomous variable "Lives alone" (yes/no) and v2 is the categorical variable "Feedback mechanism" with three categories (positive/negative/neutral)

Code:
nestreg, lr: logistic turnselfin (i.livesalone ib0.feedbackmech) (i.livesalone#ib0.feedbackmech)
Which spits out:

Code:
note: 0.livesalone omitted because of estimability.
note: 0.feedbackmech omitted because of estimability.
note: 0.livesalone#0.feedbackmech omitted because of estimability.
note: 0.livesalone#1.feedbackmech omitted because of estimability.
note: 0.livesalone#2.feedbackmech omitted because of estimability.
note: 1.livesalone#0.feedbackmech omitted because of estimability.

Block 1: 1.livesalone 1.feedbackmech 2.feedbackmech

Logistic regression                                     Number of obs =    308
                                                        LR chi2(3)    =  31.75
                                                        Prob > chi2   = 0.0000
Log likelihood = -186.56561                             Pseudo R2     = 0.0784

------------------------------------------------------------------------------
  turnselfin | Odds ratio   Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
  livesalone |
Lives alone  |   1.935901    .499876     2.56   0.011     1.167055    3.211256
             |
feedbackmech |
   Positive  |   4.574841   1.510175     4.61   0.000      2.39547    8.736979
    Neutral  |   3.450492    1.14231     3.74   0.000     1.803369     6.60203
             |
       _cons |    .142871   .0454746    -6.11   0.000     .0765622    .2666085
------------------------------------------------------------------------------
Note: _cons estimates baseline odds.

Block 2: 1.livesalone#1.feedbackmech 1.livesalone#2.feedbackmech

Logistic regression                                     Number of obs =    308
                                                        LR chi2(5)    =  33.36
                                                        Prob > chi2   = 0.0000
Log likelihood = -185.76036                             Pseudo R2     = 0.0824

-----------------------------------------------------------------------------------------
             turnselfin | Odds ratio   Std. err.      z    P>|z|     [95% conf. interval]
------------------------+----------------------------------------------------------------
             livesalone |
           Lives alone  |    1.93617    1.10679     1.16   0.248     .6314858    5.936405
                        |
           feedbackmech |
              Positive  |   5.727273   3.287981     3.04   0.002     1.859002    17.64476
               Neutral  |   2.757576   1.597961     1.75   0.080     .8856718    8.585826
                        |
livesalone#feedbackmech |
  Lives alone#Positive  |   .6943834    .486771    -0.52   0.603     .1757507    2.743479
   Lives alone#Neutral  |   1.451546   1.028417     0.53   0.599     .3620396    5.819764
                        |
                  _cons |   .1428571   .0682988    -4.07   0.000     .0559693    .3646315
-----------------------------------------------------------------------------------------
Note: _cons estimates baseline odds.


  +----------------------------------------------------------------+
  | Block |        LL       LR     df  Pr > LR       AIC       BIC |
  |-------+--------------------------------------------------------|
  |     1 | -186.5656    31.75      3   0.0000  381.1312  396.0516 |
  |     2 | -185.7604     1.61      2   0.4470  383.5207  405.9013 |
  +----------------------------------------------------------------+
My question is how to interpret and explain this in lay terms. My two real struggles are:
1. Living alone loses its significance when controlling for the interaction between living alone and the mechanism of feedback.
2. Positive mechanism of feedback retains significance after the interaction, but the interactions themselves are not significant.

And I still struggle to understand exactly how to interpret interaction effects in this way. Is it correct to say that living alone does not significantly impact someone turning themselves in when they live alone and are presented with feedback (Block 2)? But, living alone without considering feedback does significantly increase turning themselves in (Block 1)?

Asking how to interpret and discuss these results for the lay person might be elementary, but I've looked everywhere and virtually all threads, videos, etc. interpret hierarchical regression with interactions in a statistical way. I suppose I'm asking how I would explain this result to my grandmother, as the saying goes.

Thanks for all the help.