Along with the times and technology have influenced the thinking person's pattern of looking at the concept of life as a career. This is apparent in the increasing education of women. Selection of a career in the early period took the form of higher education. Higher education is pursued in order to meet job objectives. With the stock owned education make women participate in the work. The dominance of the male-line lakipun eroded not least the role of women who have more. Most women prefer to work comfortably. Still, there is still discrimination in a particular job. One of the areas affected are the areas of accounting that can not be separated from gender discrimination. The length of the stages that must be achieved accounting students to obtain the desired career in accordance with applicable procedures cause many accounting students complete their higher education course only until S1 Accounting.
The goal of this research is to prove the influence of gender and the study of style preferences on student kerier S1 Accounting Faculty of Economics, Airlangga University Surabaya. The population in this study are all students of the Faculty of Economics, Airlangga University majoring in Accounting. The number of samples required 180 respondents, but to avoid errors in data processing then each categorical in every study period exceeded 10 respondents. So the total sample is 240 respondents surveyed. The analysis technique used is logistic regression, and regression equations in this study are:
The research proves there are significant gender variable and the study period together against the preference of a career with a significance level of less than 0.05. There are variable effects of gender and study period partially towards the preference of a career with a significance level of less than 0.05. The effect of this partial mnunjukkan that students will choose a job that demands / challenges. The value of Nagelkerke R2 of 0133. This means that 13.3% change in the dependent variable can be explained by the independent variables included in the model, while the remaining 86.7% is explained by other variables not included in the model (error factor).