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Dependant Variables Identified

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Introduction

Dependant Variables Identified Political party supported (AVOTE) would depend on a person's social class, Labour force status. Having a full-time job is the best way for a woman to be an independent person (AOPFAME) would depend on a person's sex, their marital status, their age, and their annual income. A Husbands job is to earn money and a Wife's job is to look after the home and family (AOPFAME) would depend on a person's sex, their marital status, their age, and their annual income. Housing tenure (ATENURE) would depend on a person's annual income, their marital status and their social class. Independent Variables A frequency table tells you how frequent each response occurs. Marital Status (AMASTAT) The first line identifies the variable marital status. Each line of the frequency table describes a particular code for each code, the first column, called value label is the description label you attach to the code. Marital status is called a nominal variable since the numerical code assigned to the possible responses conveys no information (Norusis 1991). ...read more.

Middle

have an order from high to low. Highest Degree code 1 First Degree code 2 Teaching Degree code 3 down to No Qualifications code 12. The cum percentage has some importance with this variable. For example: - the table shows that 20.9 percent have the equivalent or above a higher education qualification or 23.5 percent have above A level educational qualification. When looking at the bar chart the first impression it gives is that the majority have no qualifications. However, by examining the frequency table and in particular the cum percent column which gives a wider picture. You can see by looking down this column that 63.4 percent have some qualifications and 36.6 percent have none. There are 20 (.4 percent) miss cases that are given the coding value - 9. Sex (ASEX) This is a nominal variable. Numerical code assigned means nothing The frequency table shows that: - There are 2397 males - 43.6 percent - value 1 3104 females - 56.4 percent - value 2 Total 5501 100 percent no missing cases. ...read more.

Conclusion

What you have to do is look at the percentages in each cell which gives a figure were by each sample adds up to 100. The numbers then turn themselves around, 43.3 percent of those with professional occupations vote Conservative. 44.3 percent of those with unskilled occupations vote Labour against 26.7 percent of those with managerial and technical occupations. If we stay with those who voted conservative and labour you can see that for those who vote conservative the percentages in the cells get lower has you move across the column from professional occupation to unskilled occupation. For those who vote labour the percentages in the cells get higher as you move across the column. This is what I would have expected. The test shows a high significance of .00000*1 Number of missing observations = 2617 By looking at the frequency table for (AJBSTAT) current labour force status you can see who these missing cases are - unemployed 273, retired 1236, family care159 FT student 159, and so on. Further Test Another variable could be added i.e. sex or class together with present occupations to discover other influences. ...read more.

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