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Session 2

Housekeeping:

Variable labels, value labels, calculations and recoding

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Review

You have used Stata Largely through the menus and dialogues But also with a few commands

We hope you found it (surprisingly?) easy Discuss

what you liked And difficulties so far

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Housekeeping tasks

By housekeeping, we mean the small jobs to organise and add labels to the data They make life easier later.

This includes: labelling and adding notes to datasets; labelling variables labelling categories (or values) taken by the variable recoding variables and dealing with codes for missing

values using log files to keep a record of what you have done.

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Labels and notes

Open the file named E_HouseholdComposition.dtaUse Data Labels Label dataset

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Dialogue for labelling data set

Type in dialogue as below or use the commandlabel data “Young Lives Study……”

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Labelling variables

Or type the command:label variable relcare "What is your relationship to child?“

Use the menu sequenceData Labels Label variable as shown below

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Defining value labelsUse:

Data Labels Label values Define or modify value labels and complete the dialogue box that follows.

The corresponding commands show that two steps are needed to label the values.

• First, a label must be defined,e.g. label define sexlabel 1 "male" 2 "female"

• Then this label is attached to the variable, • e.g. for the variable called sex use the command

label values sex sexlabel

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Your turn

Work through Section 4.1of the Stata Guide

Note down any difficulties you have and clarify your difficulties with a resource person

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Recoding a variable

Data Create or change variables Other variable transformation commands Recode categorical variable

Also use options to define a new variable

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Information on the recoded variable

Always safer to recode into a new variable,e.g. seedad2.

The effect of the recoding can be seen by typingcodebook seedad2

If seedad is later no longer needed, it can be dropped.

Use File Save, to save information on the new variable in the data set.

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Your turn again

Work through Section 4.2 of the Stata Guide

Note down any difficulties you have and clarify your difficulties with a resource person

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Missing values

Symbols for missing values in Stata:

. and .a .b .c and so on, up to .z

These are used to distinguish between the different reasons for values to be missing.

When making calculations, comparisons or sorting, the following rules are observed:

all non-missing numbers are less than . . is less than .a .a is less than .b, and so on, up to .z

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Memory

The initial memory in Stata is 1 megabyte This can be changed, but first type Clear to clear memory To increase the current memory to 20 mbytes, type

set memory 20m For setting Permanent memory, use

set memory 20m, permanently For problems processing large datasets, use the compress

command.

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Log files

To keep a record of the output, while using Stata Open a log file by clicking on the Log icon.

This opens a dialogue In your working directory so you can name the log file

It suggests an extension smcl .smcl stands for Stata Markup and Control Language.

•Log files in Stata record both commands and output.

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Remarks You can change the extension to “log” to produce a

simple ASCII file

Other packages use the idea of a log file to record just the command – not the output as well

You can do this in Stata (but not from the menus) Notice that the command Stata used for its log file was . log using “name of file”

Do the same again, but using . cmdlog using “name of file”

If at a later stage you need to append or replace this file, add the option replace or append at the end of the above commands.

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Your turn

Practice the above ideas by working through Sections 4.6, 4.7, 4.8 of the Stata Guide.

Then either read your own data into Stata and perform some simple analyses using methods

covered so far Or use a dataset suggested by the resource persons.

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So if you have a dataset…

Open, within Stata, the data file in Stata formatthat you created in the previous session.

Identify the key variables in your data set and set up labels for each of these variables.

Identify any categorical variables in your data set. Then define, and set value labels that describe the levels for each categorical variable.

Finally, re-save your data file.