EXAMPLE 2: INTRODUCTION TO SAS AND SOME NOTES ON HOUSEKEEPING PART II - MATCHING DATA FROM RESPONDENTS AT 2 WAVES INTO WIDE FORMAT

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1 EXAMPLE 2: PART I - INTRODUCTION TO SAS AND SOME NOTES ON HOUSEKEEPING PART II - MATCHING DATA FROM RESPONDENTS AT 2 WAVES INTO WIDE FORMAT USING THESE WORKSHEETS For each of the worksheets you have a corresponding SAS program containing all the SAS code for the example. You will have to write the code into a SAS program file, or run the codes from the program files we have provided. This is the case when we use loops to repeat commands for multiple waves or multiple variables. Although these commands can be used interactively, it is usually easier to run then from a program file. In general, it is good practice to write your commands into program files, and to include comments, so that you can easily reproduce and amend your work at a later time. The worksheets include the SAS code that is necessary to solve the problem at hand, and explanations of how the commands work. The SAS commands are typed in a different font: This font means: this is a SAS command The aim of this course is to become familiar with the SAS commands necessary to set up the dataset. Some of the worksheets include some commands to perform some simple analysis. These parts of the worksheets are optional (skip them if you are lagging behind). DESCRIPTION: This example has two parts. Part I introduces SAS and is aimed at those who have not used it before, or would like to be reminded of the basics. It also includes some guidance on housekeeping when using SAS. Part II combines data on respondents from the first two waves of Understanding Society: Wave 1 ( ) and Wave 2 ( ). There are two different ways of combining the data for analysis: wide and long format. This Example shows how to combine the data into a wide format (for long format see Example 3). This Example also shows how to inspect the data, create labels, deal with missing values, and compute transitions (changes from wave to wave). It shows how to compute change in marital status and in personal income from wave 1 to wave 2. FILES: a_indresp and b_indresp WAVES: 1 and 2 STEPS: Part I: Introduction to SAS and housekeeping 1. Getting started with SAS 2. Housekeeping Part II: Matching data from respondents at 2 waves into wide format 3. Inspect the data at wave 2 and deal with missing values 4. Merge waves 1 and 2 into wide format 1 E x a m p l e 2

2 5. Inspect the data, deal with missing values 6. Identify transitions (e.g. change in marital status and change in pay) NEW COMMANDS: - Housekeeping: libname - Inspecting the data: proc print; proc contents; proc means; proc summary;proc univariate, proc freq; - Dealing with missing values: data if then statement; - Combining waves: data merge - Creating labels: data label, proc format 2 E x a m p l e 2

3 PART I INTRODUCTION TO SAS AND HOUSEKEEPING 2.1 GETTING STARTED WITH SAS To start SAS double-click on the SAS icon or select Start All Programs Applications SAS SAS Windows Output Window Displays output from SAS program. Can view, print, copy or save information in the output window Log Window Reports on progress of SAS procedures (BLUE). Displays error messages (RED) and warnings (GREEN). Results and Explorer window a. Explorer pane is browsing tool for SAS libraries b. Results pane shows a tree-like summary of the output window. You can select, delete, or edit the output before printing, saving, or copying the results Enhanced Program Editor window Used to create, edit and execute SAS programs. 3 E x a m p l e 2

4 Click here to run all the commands you have typed in a SAS program file Alternatively you can use F3 You can get help from SAS in different ways: Select Help from the drop down menu. From here it is possible to explore a range of support options. This includes: How to use the help guide SAS Help and Documentation Getting started with SAS Learning SAS programming SAS on the web About the software Many of the above options will take you to the SAS website where additional information is available to help you carry out analysis. Also note that 1. SAS is not case-sensitive. You can use capital or lowercase letters in your SAS variables. However, when you specify filenames (as you do with the include and file SAS commands), you must type it exactly as it exists in UNIX. 2. One should not abbreviate commands or variable names. In some instances SAS can recognise a typo in syntax. 4 E x a m p l e 2

5 In the program file, there are different methods of telling SAS it is a comment and should be ignored when executing the do file. * also indicates a comment; it can only be used at the beginning of a line while /* text here */ will not run any text within the forward slash-asterisk. 5 E x a m p l e 2

6 2.2 HOUSEKEEPING, REPRODUCIBILITY AND PORTABILITY We start each program file with some housekeeping: Clear the working directory from any data that might already be open. proc datasets lib=work kill nolist memtype=data; quit; In this example the library is the working directory but this could be altered by the user to be any folder they desire. By default SAS saves data files, graphs, estimation results in its working directory unless a different file path is mentioned. First, if this is a shared space we may want to change the working directory to one of our own, may be to a project specific one. We can view SAS output in the output window within the SAS environment, or alternatively we can send it to a text file. This is particularly useful if you want to share results with a collaborator quickly via . However, it means that SAS will not write the output into the output window so some people prefer to delay this step until their program is complete and correct. /* writing all output to a log file */ filename myoutput "define location.txt"; proc printto print=myoutput new; To make our program files more portable (for example from work to home computer, or between computers or collaborators), we should be able to quickly convert the program file so that it will run at different workstations. Most likely the data files will be stored in different locations at the two workstations. Suppose in the program file, we have code written to use many different data files stored at this location. When we change workstations we will have to change the file path every time it is mentioned in the program file. But there is an easier way. We assign a nickname to the file path and write that at the beginning of the program file by assigning a library name. Then throughout the program file we simply refer to this library. If we type, /* defining a library pointing to where files can be permanently stored */ libname dir REPLACE THIS WITH THE DIRECTORY YOU CAN SAVE FILES IN ; Then in this case we have created a library dir which can be used in commands later on in the program and this library name acts as a pointer as to where particular commands should be directed. In this example (and in all examples in this workshop) we will use libraries to define the file path for where we can save files. SAS does not accept paths including spaces. 6 E x a m p l e 2

7 The log file above creates a text file containing the output. We can also open a log file which contains the contents of the log window in SAS. This is not the results or estimations, but information about whether and how the program has run were there any errors for example, or how many observations are in a new dataset. When you are writing your program it is useful to be able to see both the output and log windows so that you can debug errors and see your results interactively, rather than redirecting them off into a separate files that you need to open each time you want to look at something. However, when you want to share your output and analysis with other people you will want to set up a log file, which can be more easily shared. The extension.log means that the file will be written in ascii, which can be read in Word and other programmes. /* writing to a log file */ filename mylog "define location.log"; proc printto log=mylog; If you start a log file and want to close it so that the log is displayed in the log window again, you can close a log file by running the code below. This also applied to the log we started above for the output. proc printto; Note that we have specified the directory and also invoked a proc command to tell SAS to print all the output to our log file mylog. PART II: MATCHING DATA FROM RESPONDENTS AT 2 WAVES (WIDE FORMAT) 2.3 COMBINING WAVE 1 AND WAVE 2 DATA INTO WIDE FORMAT Let s start by inspecting the a_indresp file. We open the data set for wave 1, keeping only the variables of interest: a_hidp pidp a_istrtdaty a_sex a_mastat_dv a_julkjb a_sclfsato a_paygu_dv. The data are stored in the temporary area in built to SAS known as work. /* include the filename in the path */ proc import datafile = "REPLACE THIS WITH THE FILEPATH TO A_INDRESP" out=a_indresp dbms=sav replace; data a_indresp; set a_indresp; keep a_hidp pidp a_istrtdaty a_sex a_mastat_dv a_julkjb a_paygu_dv a_sclfsato; Then we describe the data: list the first 20 observations; describe the data to find out what variables are contained in the dataset; summarise the data to get information on means and sample sizes: 7 E x a m p l e 2

8 /* prints the first 20 observations in the wave 1 dataset */ proc print data=a_indresp (OBS=20); /* describes variables and datafile of wave 1 dataset */ *Add varnum option to get variables in order as they are in the datset rather than alphabetically; proc contents data=a_indresp varnum; /* produces descriptives stats on variables in wave1 data */ proc means data=a_indresp; /* produces dataset of summary statistics */ proc summary data=a_indresp; output out=a_indrespsumm; var a_hidp pidp a_istrtdaty a_sex a_mastat_dv a_julkjb a_paygu_dv a_sclfsato; Note that the person identifier pidp is the only variable that does not include a wave prefix. That is because a respondent s pidp does not change across waves. Looking at the output of the command proc contents you will notice that some numeric variables have attached value labels to each numeric value. In Understanding Society, the convention is to name the value label the same as the variable. In Understanding Society, -1, -2, -3, -7, -8 and -9 all represent missing values the different values tell us why the variable is missing. But SAS considers these to be valid values. So, we will have to convert these into system missing which is a dot (.). There are different ways to recode missing values. For example we can generate a new variable (to avoid overwriting the original variable) and then recode the new variable using an if-then condition depending on whether it should have a valid (usually positive) value using the information in the original variable: data wave1; set a_indresp; a_marstat = a_mastat_dv; if a_marstat le 0 then a_marstat =.; le in SAS code means less than or equal to it is equivalent to the <= symbol, which is another way of writing the same thing. Alternatively one could directly recode the variable using and if-then condition and pointing SAS to original variable itself: Check that the recoding has worked. Finally we sort the data by the individual unique identifier pidp, this is done to ensure the merge (described below) is carried out successfully. proc sort data=wave1; by pidp; 8 E x a m p l e 2

9 Task: Now repeat these steps for wave 2 To combine data into a wide format we use the command "merge". You will need the wave 2 file you just created in the task for this to work. In SAS both datasets that are being merged needed to be sorted on the linking variable, pidp in this case. The one-to-one merge is used when there is a one-to-one correspondence between the observations of the two files, although not all observations may find a match. We use the one-to-one if for example both files include data on individuals. data wave12merge; merge wave1 (in=a) wave2 (in=b); by pidp; We can also check how many individuals are in only wave 1, only in wave 2 and in waves 1 and 2. /* How many interviewed in wave 1 only, wave 2 only, both */ /* creating a variable called inwave to show this */ if a=1 and b=0 then inwave=1; if a=0 and b=1 then inwave=2; if a=1 and b=1 then inwave=3; Then tabulate the variable inwave: proc freq data=wave12merge; tables inwave; Question: How many respondents are interviewed in year 1 of wave 1, and how many in year 1 of wave 2? Hint: In order to check this we must cross tabulate the year of Wave 1 and Wave 2 interviews which is stored in the variables a_istrtdaty and b_istrtdaty. We are interested in transitions across the two waves and so will keep only those people present in both waves. data wave12merge; set wave12merge; where inwave=3; 9 E x a m p l e 2

10 2.4 IDENTIFYING TRANSITIONS CHANGE IN MARITAL STATUS AND PAY Describe the data to find out what variables are in the dataset; summarise the data to get information on means and sample sizes: proc contents varnum; We want to identify all the transitions across marital statuses. For simplicity, we recode marital status into smaller number of categories. To do this we create a new variable called a_marital and b_marital which identifies an individual s marital status in wave A and B respectively. Then we will attach a short description to the variable using the command label var, and make the content of the variable more accessible by labelling its values using proc format. data wave12wide; set wave12merge; if a_mastat_dv=1 then a_marital=1; if a_mastat_dv in (2,3,10) then a_marital=2; if a_mastat_dv in (4,5,7,8) then a_marital=3; if a_mastat_dv in (6,9) then a_marital=4; if b_mastat_dv=1 then b_marital=1; if b_mastat_dv in (2,3,10) then b_marital=2; if b_mastat_dv in (4,5,7,8) then b_marital=3; if b_mastat_dv in (6,9) then b_marital=4; label a_marital='marital status wave 1'; label b_marital='marital status wave 2'; proc format; value marital 1="single" 2="married/civil partnership/living as a couple"; Now we generate a new variable which summarizes different transitions across marital statuses (below we call this variable maritaltransition), and adding labels to the levels of maritaltransition using proc format. data wave12wide; set wave12wide; if a_marital=1 and b_marital=1 then maritaltransition=1; if a_marital=1 and b_marital=2 then maritaltransition=2; if a_marital=2 and b_marital=1 then maritaltransition=3; if a_marital=2 and b_marital=2 then maritaltransition=4; label maritaltransition="change in marital status between wave 1 and 2"; proc format; value maritaltransition 1="stayed single" 2="got married/entered civil partnership/began living as a couple" 3="became single" 4="remained married/in civil partnership/living as a couple"; data wave12wide; 10 E x a m p l e 2

11 set wave12wide; format maritaltransition maritaltransition.; We can use proc univariate and proc freq to get some summary statistics and the frequency distribution, respectively, of this variable maritaltransition; /* summary statistics of the new marital status transition variable */ proc univariate data=wave12wide; var maritaltransition; /* frequency distribution of the new marital status transition variable */ proc freq data=wave12wide; tables maritaltransition; If we want to compute change in pay across the two waves we first inspect the variables a_paygu_dv and b_paygu_dv. We generate new variables which take the value reported in the original variables if it is strictly positive, otherwise it is system missing. We then compute change in pay across the two waves (changepay) and label the variable. data wave12wide; set wave12wide; if (a_paygu_dv>0) then wave1wage=a_paygu_dv; if (b_paygu_dv>0) then wave2wage=b_paygu_dv; changepay=wave2wage-wave1wage; label changepay="change in pay between waves 1 and 2"; Task: using proc univariate, examine the variable changepay. We want to see how change in pay varies by sex and how it varies by type of marital change and sex. proc means will let us do this despite the name it produces many more options than just the mean of a variable in this case we ask SAS to report the total number of observations, the number missing, the sum of these observations, the mean, standard deviation, variance, minimum and maximum values. proc means is especially useful for comparing across groups though in this case, gender and changes in marital status. proc sort data=wave12wide; by a_sex; proc means data=wave12wide maxdec=2 n nmiss sum mean std var min max; class a_sex; var changepay; title ''; title2 '*******************************************'; title3 'change in pay between waves 1 and 2 by sex'; proc means data=wave12wide maxdec=2 n nmiss sum mean std var min max; 11 E x a m p l e 2

12 class a_sex maritaltransition; var changepay; title ''; title2 '*******************************************'; title3 'change in pay between waves 1 and 2 by sex and marital status transitions'; Save the dataset in the directory specified in dir, so that it can be used again. data dir.wave12wide; set wave12wide; The final step is to clean up delete temporary file no longer needed and close the log file if you are using one: /* Clean SAS work directory */ proc datasets lib=work nolist kill memtype=all; /* stop writing to external log and output files and simply write to the SAS windows */ proc printto; 12 E x a m p l e 2