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In this piece of coursework, I will be looking at the similarities and differences between jobs and pay that effect the young and old, and between men and women. I will also be looking at other aspects which could affect people’s Work and Pay, or might be caused by it. To do this, I will be looking at both Primary and Secondary information, separately and together, to make as complete a comparison between them as I possibly can. I obtained the information for my Secondary data from within “Fact File 1998”, which gave me the opportunity to choose several different types of data for comparison.

This is a publication produced every year by the Government. It provides statistical data on a wide range of categories, including jobs and pay, by gender and age. It also includes data on car and house ownership, holidays and a wide range of other information which was outside the scope of my coursework investigation. I obtained my Primary data from members of the public, who were using “The Galleries” shopping arcade in Wigan. I collected the data by means of a sample questionnaire, which was carried out on the afternoon of Wednesday 29th September 1999.

I will present my Secondary Data by means of line graphs, bar charts, pie charts, box and whisker diagrams and scatter diagrams. I will obtain Spearman’s Correlation Coefficients and Mean and Standard Deviations. My Primary Data will be represented using the same methods, together with data tables of my questionnaire results. After completing the individual sections on the Secondary and Primary Data, I will include a section where I will compare my findings, to see if there are any connections between the results I obtained in the Primary Data, and the results I found from data published by the government.

Secondary Data Aims My aims in this project, are to explore the relationship between the average gross weekly earnings for full time adult employees, by comparing them on gender and occupation basis. I will compare nine different occupational groups, and base my investigation on statistics prepared for April 1997, for Great Britain as a whole. Method I will compare managerial, professional, clerical, service, sales, factory, and other occupations. I will compare male workers of all ages, with female workers of all ages. Hypotheses

I will use data for full time employees only, because that is the data provide by the table. I will only use data for employees on full adult rates. I will not break down the data for different age groups, or consider other countries. I will use data provided from “Labour Market Trends” for August 1997. 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. Data

I chose this set of data to work with, from “Fact File 1998”. I chose to use this data, because it allowed me to use several different Statistical formats, to present my results. This is the table of data I used to find 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 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¬†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. 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.

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