In my thesis I'm trying to discover which factors influence the CSR (corporate social responsibility,
GSE_RAW) behavior of companies. Two groups of possible factors / variables have been identified: company-specific and country-specific.
First, company-specific variables are (among others)
MKT_AVG_LN: the marketvalue of the company
SIGN: the number of CSR treaties the company has signed
INCID: the number of reported CSR incidents the company has been involved in
Second, each of the 4,000 companies in the dataset is headquartered in one of 35 countries. For each country, I have gathered some country-specific data, among others:
LAW_FAM: the legal family the countries' legal system stems from (either French, English, Scandinavian, or German)
LAW_SR: relative protection the countries' company law gives to shareholders (for instance, in case of company default)
LAW_LE: the relative effectiveness of the countries' legal system (higher value means more effective, thus for instance less corrupted)
COM_CLA: a measurement for the intensity of internal market competition
GCI_505: mesurement for the quality of primary education
GCI_701: measurement for the quality of secondary education
HOF_PDI: power distance (higher value means more hierarchical society)
HOF_LTO: country time orientation (higher means more long-term orientation)
DEP_AVG: the countries' GDP per capita
CON_AVG: the countries' average inflation over the 2008-2010 timeframe
In order to make an analysis on this data, I "raised" the country-level data to the company-level. For instance, if Belgium has a
COM_CLA value of 23, then all Belgian companies in the dataset have their
COM_CLA value set to 23. The variable
LAW_FAM is split up into 4 dummy variables (
LAW_GER), giving each company a 1 for one of these dummies.
This all results in a dataset like this:
COMPANY MKT_AVG_LN ... INCID ... LAW_FRA LAW_SCA ... LAW_SR LAW_LE COM_CLA ... etc ---------------------------------------------------------------------------------- 1 1.54 55 0 1 34 65 53 2 1.44 16 0 1 34 65 53 3 0.11 2 0 1 34 65 53 4 0.38 12 1 0 18 40 27 5 1.98 114 1 0 18 40 27 . . . . . . . . . . . . . . . . 4,000 0.87 9 0 1 5 14 18
Here, companies 1 to 3 are from the same country A, and 4 and 5 from country B.
First, I tried analyzing using OLS, but the model seemed very "unstable", as is shown below. The first model has a r-squared of .516:
Adding only two variables changes many of the beta's and significance levels, as well as the r-squared (.591). Of course the r-squared increases when variables are added, but this is quite an increase from .516:
Eventually, it was suggested in another post that I should not use OLS here but mixed models, because of the categorical countly-level data. However, I am confused as to how perform this in SPSS. The examples I found online are not comparable to mine, so I don't know what to fill in, amongst others, in the below mixed model dialogue:
Could somebody using SPSS please help me explain how to perform this analysis so that I may come to a regression model (CSR = b1*MKT_AVG_LN + b2*SIGN + ... + b13*CON_AVG) so that I can conclude wheter CSR is determined by company-features or country-features (or by neither or both)?
Any help is greatly appreciated!