The Multiple Bar Chart shows that men earn most as Managers and Administrators followed by Professional and Associate Professional Occupations. All other male occupations earn similar amounts; approximately i?? 300 per week. Women earn most in Professional Occupations, followed by Managers and Associated Professionals. All other female occupations earn about the same amount; approximately i?? 200 per week. On average women earn less than men in all occupational groups.
The Component Bar Chart shows the same information as the multiple bar chart, but with the data for men and women overlaid on each other. The size of the bars show the relative size of men’s weekly earnings compared to women’s and makes the differences between them more obvious. The Multiple Percentage Bar Chart shows that women generally earn between 60% and 70% of the amount earned by men in similar occupations. This applies for both low paid jobs, and for managers. Women in professions earn approximately 80% of male earnings.
Women in Clerical and Secretarial occupations, are the closest to men’s salaries for the same job; even though I know there are far fewer men doing this work. Even so, women still only earn 87% of men’s earnings. The Percentage Bar Chart shows how much more or less than the average that men and women earn. Men are shown to earn in excess of the average for each occupational group. The Comparative Pie Chart shows exactly the same information as the Multiple Bar chart, but presents it in a different way.
In this form it is not possible to directly compare male and female earnings for the same occupation. It is not possible to easily work out the wages for each individual occupation with this method of presentation. The Box & Whisker Diagram shows that men earn consistently more than women. For male earnings, the Range is greater, the Upper and Lower Quartiles are greater, and the Inter-Quartile Range is greater as well. For both men and women, the median is only slightly greater that the Lower Quartile; and this is particularly noticeable for women’s earnings.
The figures used are not completely accurate as I do not know how many people there are in each group. From the diagram, I can find these different Averages: To help people answer in accordance with our pre-determined categories, we will show them ‘prompt cards’ detailing the possible answers they could give. This will speed up the questioning process and avoid the need to record every detail that they provide. Prediction I predict that for all occupational groups, men will earn more than women.
I also predict that women will generally earn only about two – thirds of the amount that men do, apart from in Secretarial occupations, where the gap should be much closer. I also predict that, because of the time of day and place that the survey will be carried out, we will find that most people in Wigan at that time are either non-working women or students. I think that people who are in full time employment will not generally be in a shopping arcade on a Wednesday afternoon. This will mean that our actual results will not be statistically accurate for the population of Wigan as a whole.
I predict that most people who are working will be in lower paid, part-time occupations, and will live in smaller houses. The results will not show many highly paid or professional people. The results differ from my predictions in several ways. There was a higher proportion of young people than I expected. I think this was because of the number of students interviewed, and it may have been just after the end of their lectures for the day. There were more people in full time employment than I expected.
I think this was because most of the men we interviewed were in professional occupations, and many of the women also worked full time. The range of people’s house sizes was greater than I predicted, and was also not related to earnings. This could be because many young people live in large shared houses with other young people, and better off people also have larger houses, so that there was a wide variety of results. The biggest difference to my predictions was the relatively low earnings of women compared to men. This was due to the high proportion of women students in the sample.
The data for men showed a higher proportion of professional people and consequently higher earnings and more people in full time employment. Generally my results were approximately in accordance with my predictions. The variations were mainly caused by the small sample size and the short time the survey was carried out over. If I was able to carry out a survey for a longer period, over several days, and at different locations, then my results would reflect more accurately the type of people who use Wigan Town Centre, their occupations, earnings and house size.