## Table of Contents

## 1 Week 2 Lab

### 1.1 An ordinal view

Re-generate this table in Stata, using the method from last week's lab:

| qual class | Univ 2nd level Incomplet | Total -------------------+---------------------------------+---------- Prof/Man | 1025 1566 767 | 3358 Routine non-manual | 124 687 713 | 1524 Skilled manual | 31 483 464 | 978 Semi/unskilled | 18 361 716 | 1095 -------------------+---------------------------------+---------- Total | 1198 3097 2660 | 6955 Source: British Household Panel Survey 2001

Note that both variables have an ordinal interpretation.

- Calculate the correlation and the Spearman Rank Correlation:

corr class qual spearman class qual

- Run and interpret the gamma test
- What does it tell you? Compare with the pattern of association shown in the adjusted residuals with the gamma, and consider which gives you the better summary.

### 1.2 Spurious association and suppression

Use the scouting example to explore an association that differs when you take account of more variables.

do http://teaching.sociology.ul.ie/so5032/labs/church.do tab s d [freq=n], row tab3way s d c [freq=n]

(Note that `tab3way`

will need to be installed the first time: `ssc install tab3way`

.)

Start by calculating a measure of association for the scouting by delinquency table, then for each of the sub-panels (Odds Ratios would be a good idea, since this is 2X2). Then do the same for the three subtables in the church by scouting by delinquency table. Finally, figure out how the three subtables without association add up into a 2-way table with association.

### 1.3 Tables and complex association

do http://teaching.sociology.ul.ie/so5032/labs/dpbig.do

Agresti uses data on race and the death penalty (use code above) to illustrate the possible complexity of a three-way relationship. The data classifies the sentence handed down in murder trials in Florida, by defendent's race and victim's race.

Look first at the defendent/penalty table, then at the three-way table (ie, defendent/penalty controlling for victim's race). Calculating odds ratios would also be useful here. What is going on with this data set?

### 1.4 Maths and Height

Load the following data:

use http://teaching.sociology.ul.ie/so5032/mathsheight.dta

This is the maths/height example considered yesterday. Examine the
correlations between the variables, numerically and graphically (`corr`

*varlist* and `scatter`

*yvar xvar*). Then regress maths on height: ```
reg
maths height
```

. Interpret the output, and relate it to the scatter plot.

Consider controlling for year. First, compare the maths/height scatterplot across year:

scatter maths height, by(year)

What does this tell you? Does this command make it clearer?

bysort year: pwcorr height maths, sig

Then fit the regression including year as well as height as
explanatory variables: `reg maths height year`

. Interpret the
output.