I have a multilevel logistic regression model predicting the probability of item nonresponse, where the random intercept variance at country level takes on the following distribution for the different countries (unconditional model):
Given the country codes
Belgium (BE), Bulgaria (BG), Croatia (HR), Cyprus (CY), Czech Republic (CZ), Denmark (DK), Estonia (EE), Finland (FI), France (FR), Germany (DE), Greece (GR), Hungary (HU), Ireland (IE), Israel (IL), Lithuania (LT), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Russian Federation (RU), Slovakia (SK), Slovenia (SI), Spain (ES), Sweden (SE), Switzerland (CH), United Kingdom (GB) and Ukraine (UA) it is clear there is a trend of the Western countries having a lower odds and the Eastern countries having a higher odds compared to the average, with Portugal defying this trend. I assume this variance is due to differences in composition of the population and specific country characteristics. Entering some demographic variables to predict the odds (age, gender, education) not much of the country variance in explained, except that Portugal now follows the trend West vs East better. Could this mean that for Portugal the higher odds were due to composition effects, but less for the other countries?
Then I also add a country variable: mode of administration (PAPI = paper and pencil interview and CAPI = computer assisted interview). This explains a lot of the variance in odds between the countries, and removes a bit the West-East trend:
However, I know most Western countries were CAPI countries and most Eastern countries were PAPI countries:
Is it possible that my predictor 'mode' is not related to the odds, but actually explains away the variability because it makes a clear distinction between the Eastern and Western countries?