| Abstract [eng] |
The paper presents job satisfaction analysis among Italian entrepreneurs using multivariate statistical techniques. Data are taken from Veneto region (North-East part of Italy) businessmen research started by Department of Statistics of Padova University at 2006. Sample consists of 1216 observations (real population is almost 113 000 entrepreneurs at this region). The outcome variable for the job satisfaction is measured on an ordered, categorical, four-point Likert scale – „dissatisfied‘“, „neither satisfied nor dissatisfied“ , „quite satisfied“ and „satisfied“. Explanatory variables include demographic items, firm characteristics, variables representing the reasons of having started the own business, items associated with work, leisure activities and future perspectives. Quite all variables, as well as the dependent one, beeing categorical, the main objective of this work is to select an appropriate model for such type of data among possible alternatives. Discussions are made on possibility to applicate linear regression with optimal scaling, binary logit for dichotomized dependent variable, multinomial logit for analysis of every single category and ordinal logistic regression with several link functions (logit and cloglog). The number of possible determinants of job satisfaction beeing quite large (there were more than 30 questionnaire items associated with job satisfaction) it was of a great importance choosing which explanatory variables should be included in the models, considering the multicollinearity challenge. In order to reduce the number of variables, it was considered possibility to apply factor analysis. The latter beeing applied only for a quantitative data, an alternative method to extract factors was proposed evaluating polychoric and tetrachoric correlations for ordinal and dichotomious variables instead of Pearson correlations . The results show that considering model assumptions, fit statistics and classification results, the most appropriate model for our data is ordinal logistic regression with cloglog link. Multicollinearity analysis was of a great help to detect some highly associated variables that were transformed or omitted of models. Applying factor analysis the quantity of variables was reduced detecting three factors, that according to the correlated with them variables were named as ‚work‘ ‚leisure‘ and ‚advanced firm‘ factors. Later they all were included in considered models as explanatory variables instead of the variables they represent. Job satisfaction determinants were evaluated by gender. Results show that factors representing leisure and advanced firm both increase job satisfaction for men and women, while working time positively affects women job satisfaction and has negative influence for men job satisfaction. More satisfied with job seem to be unmarried or divorced, high-educated, older men and younger, married women who have not school age children and who have graduated superiour school. It is worth mentioning that analysing reasons of becoming entrepreneur it is seen that ‚professional realization‘ affects possitively job satisfaction for both genders comparing with the reference- ‚starting the autonomous life‘. While ‚money‘ as a reason of haven started the business has positive influence for women and negative one for men comparing with ‚starting autonomous life. |
