Please consider the following OLS model:
As a rule of thumb, a VIF score of max. 5 is considered acceptable collinearity in most fields. So for the better part I believed this model to be quite nice and significant (R-squared .516).
However, by adding only 2 variables (at the bottom of the model), many values change significantly. For instance,
GCI-505 goes from being significant at the 10% level (p=.008) to being insignificant (p=.677).
INCID reacts similarly, whereas
HOF_LTO have a reverse change:
My question is: what does this mean for my model? It doesn't seem very robust, although I though that it should have been with a significant F-score, and not too high VIF scores. What am I missing / lacking in knowledge?
Some explaining of the variables: the model tries to explain the extent to which a company is behaving socially responsible. The dataset holds 4,000 companies from 35 countries.
First, each company has a CSR score (the DV), a marketvalue and bookvalue (
PTBV_AVG_LN), a number of treaties on responsible investment they have signed (
SIGN), and a number of incidents that have been reported in the media (
Second, each company is headquartered in one of 35 countries. These countries all have certain properties, for instance from which legal family their legal system stems and the average level of education. These country-specific values have been included as a variable for each company. For instance, a company with
LAW_FRA-dummy set to 1 is headquartered in a country with a french legal system. When a company has
LAW_SR set to
5.45, this means that the shareholder rights index for the country the company in headquartered in, is
LAW_ENG (not included in the model) are dummies for legal family. The rest of the variables are continuous.
There are possibly 2 problems with my dataset
- I have "elevated" country-variables to a company level;
- the number of companies from each country is very unequally divided (for instance, 40% comes from the USA whereas only 1% of companies is headquartered in Singapore. This means that if USA has a
5.45, 40% of companies in the dataset have a
5.45whereas only a few companies will have a
LAW_SRscore for Singapore).
In SPSS Mixed Models has the following dialogue:
I am confused about which variables are factor, covariate and residual weight. As I believe continuous company-variables, such as a companies' market value and number of signed treaties (
SIGN) are factors, and company variables that are lifted from a country-level (for instance,
LAW_SR, which is a numerical value of the relative strength of shareholders under company law of a country) are covariates. Is this correct?
Also, I have a few dummy variables. For instance, each company operates under either French, German, English or Scandinavian law. Here,
LAW_SCA are dummy variables indicating for each company the origin of company law in the country where the company is headquartered. These dummy values are derived from the variable
LAW_FAMILY, which for each company is set to either
SCA. In mixed models, should I discard the dummy variables and use
LAW_FAMILY as a covariate also?