1. Using the data matrix in Table 19.1 draw a frequency distribution for the variable Working. Do the same for Age, recoding it into three broad categories. Draw a bar chart of these distributions. Calculate the mean, median and mode for each variable.

Construct contingency tables that show the relationship between: Sex and Working; Sex and Jobsat; Working and Jobsat. Ensure that each cell contains a count and column and row percentages. Describe the character of the relationships which you find.

Draw a scattergram, plotting Age against Jobsat. Describe the character of this relationship.

Using the recoded version of Age construct contingency tables showing the relationship between this variable and each of the other three variables. Describe the character of the relationships you find.

If you are learning SPSS or another statistical package try inputting these data. You will find it easier to get the computer to do the analyses specified above. You can also generate tests of association and significance and consider the meaning of these. Try using the software to produce output in the form of graphs (e.g., pie charts, histograms).

Table 19.1. A data matrix

Variables or questions
	Sex		Age	Working	Jobsat
Case 1		Male	66	No	Missing
Case 2		Female	34	Full time	1
Case 3		Female	25	Part time	2
Case 4		Female	44	Full time	5
Case 5		Male	78	No	Missing
Case 6		Male	40	Full time	2
Case 7		Male	33	Full time	1
Case 8		Male	16	No	Missing
Case 9		Female	35	Full time	1
Case 10		Female	45	Full time	2
Case 11		Male	30	No	Missing
Case 12		Female	56	Part time	4
Case 13		Male	79	No	Missing
Case 14		Male	60	Part time	4
Case 15		Female	55	Part time	4
Case 16		Female	54	Part time	5
Case 17		Male	55	Full time	1
Case 18		Male	17	No	Missing
Case 19		Female	23	Full time	3
Case 20		Female	20	No	Missing

2. Table 19.2 consists of four contingency tables demonstrating different types of relationship between the two variables of social class and home ownership. Below each is a p-value and the result of a test of association (Q). For each table, describe the character of the relationship and explain why the p-values and tests of association vary.

Table19.2. Tables showing different relationships between social class and home ownership (column %)

(a)                                                          (b)
                               Social class                                            Social class

               p <0.01, Q = 0.6                        p <0.01, Q = –0.8

(c)                                                           (d)
                               Social class                                            Social class

               p <0.05, Q = 0.04                       p <0.01, Q = –0.02

Table 20.1. Demonstration of the elaboration paradigm: the relationship between education and income, as affected by gender

(a)(i) Zero-order table showing an association

p = 0.00006; phi = 0.20; gamma = 0.38

(a)(ii) Replication
Men                                                       Women

p = 0.01481; phi = 0.21; gamma = 0.41   p = 0.00149; phi = 0.19; gamma = 0.37

(a)(iii) Spurious or intervening
Men                                                       Women

p = 0.78290; phi = 0.02; gamma = 0.04   p = 0.93038; phi = 0.01; gamma = 0.02

(a) (iv) Specification
Men                                                       Women

p <0.00000; phi = 0.27; gamma = 0.50    p = 1.00000; phi =0.00; gamma = 0.00

(b)(i) Zero-order table showing no association

p = 1.00000; phi = 0.00; gamma = 0.00

(b) (ii) Suppressor
Men                                                        Women

p < 0.00000; phi = 0.43; gamma = 0.78   p <0.00000; phi = -0.45; gamma = -1.00

3. This is a structured exercise in reading a statistical table that aims to give you a general strategy for perceiving the main messages of such tables. You could apply this approach to Table 17.1 in the book, or find tables as suggested in the fourth workshop and discussion exercise associated with Chapter 17.You will find that not all of the questions are relevant to every table, but experience has shown that these steps, if followed carefully, enable a deeper understanding of any statistical table.

(a) Read the title before you look at any numbers. What does this reveal about the content of the table?

(b) Look at the source: Who produced the data, with what purpose? Was it a census or a sample?

(c) Look at any notes above or below the table. How will they influence its scope and your interpretation?

(d) Read the column and row titles. They indicate which variables are applied to the data.

(e) How many variables are there and what are they? Can any be considered independent or dependent?

(f) How are the variables measured? Are there any omissions or peculiarities in the measurement scale? How else might such a measure have been constructed?

(g) What units are used – percentages; thousands; millions? If you are dealing with percentages, then which way adds up to 100%?

(h) Look at the 'All' or 'Total' column. These are usually found on the right-hand column and/ or the bottom row (the 'margins' of a table). What do variations in the row or column tell you about the variables concerned?

(i) Now look at some rows and/or columns inside the table. What do these tell you about the relationships between variables? What social processes might have generated the trends you find?

(j) Is it possible to make causal statements about the relationship between variables? If so, do any of these involve the interaction of more than two variables?

(k) What are the shortcomings of the data in drawing conclusions about social processes?

(l) What other enquiries could be conducted to take this analysis further?

(m) Finally, consider the issue of whether the table reveals something about social reality, or creates a particular way of thinking about reality.