Suppose that you have an SAS data file called https://stats.idre.ucla.edu/wp-content/uploads/2016/02/sample.sas7bdat that you would like to convert to Mplus for analyzing there. Here is a listing of the first 10 observations from this file.
proc print data="c:mplusfaqsample"(obs=10); run;
Obs FEMALE RACE SES SCHTYP READ WRITE 1 . 3 1 1 34 35 2 0 A 2 1 44 41 3 0 4 B 1 55 39 4 1 2 3 C 60 59 5 0 4 1 1 D 37 6 0 4 2 1 34 E 7 0 3 2 1 34 37 8 1 4 1 1 35 35 9 0 4 3 1 44 33 10 1 4 3 2 36 57
Note that female has at least one missing value (denoted by .). Also, the other variables have user defined missing values defined as follows — race has missing values coded as .A and ses has missing values coded as .B and sctyp has missing values as .C and read has .D as missing and finally write has .E as a missing value. You can use the following steps to ease the process of converting your file into an Mplus file. We do this in these steps.
- Get descriptive statistics for your current file,
- Convert all of the missing values to a single missing value code
- Write out the names of the variables to a file
- Modify the data file and make your Mplus program
1. Get descriptive statistics for your current file
We can get the descriptive statistics for our file like this. Normally we would use proc means, however we want to get descriptive statistics with listwise deletion (for comparability with Mplus results) so we will use proc corr with the nomiss option and that will give us descriptive statistics with listwise deletion of missing data, as illustrated below.
proc corr data="c:mplusfaqsample" nomiss; run;
Here are our results.
Simple Statistics Variable N Mean Std Dev Sum Minimum Maximum FEMALE 194 0.55155 0.49862 107.00000 0 1.00000 RACE 194 3.41237 1.05056 662.00000 1.00000 4.00000 SES 194 2.06186 0.72433 400.00000 1.00000 3.00000 SCHTYP 194 1.16495 0.37209 226.00000 1.00000 2.00000 READ 194 52.48454 10.14773 10182 28.00000 76.00000 WRITE 194 53.14948 9.25423 10311 31.00000 67.00000
2. Convert all of the missing values to a single missing value code
This step converts all of the missing values (the system missings and the user missings) into a single code, -1234. You can pick any integer value you wish since that was a value that was easy to remember and not a valid value for any of our variables.
data sample2;
set "c:mplusfaqsample";
array allvars _numeric_ ;
do over allvars;
if missing(allvars) then allvars = -1234 ;
end;
run;
proc print data=sample2(obs=10);
run;
We show some of the cases below and see that it appears the missings were converted to -1234.
Obs FEMALE RACE SES SCHTYP READ WRITE 1 -1234 3 1 1 34 35 2 0 -1234 2 1 44 41 3 0 4 -1234 1 55 39 4 1 2 3 -1234 60 59 5 0 4 1 1 -1234 37 6 0 4 2 1 34 -1234 7 0 3 2 1 34 37 8 1 4 1 1 35 35 9 0 4 3 1 44 33 10 1 4 3 2 36 57
We also use proc means to examine our data and see that our N is now 200, indicating that SAS does not see any missing data (i.e. all of the missing values have been converted into a non missing value).
options nolabel ; proc means data=sample2; run;
The MEANS Procedure Variable N Mean Std Dev Minimum Maximum ------------------------------------------------------------------------------- FEMALE 200 -5.6300000 87.2967739 -1234.00 1.0000000 RACE 200 -2.7750000 87.5044543 -1234.00 4.0000000 SES 200 -4.1250000 87.4053077 -1234.00 3.0000000 SCHTYP 200 -5.0150000 87.3398306 -1234.00 2.0000000 READ 200 45.8750000 91.5252629 -1234.00 76.0000000 WRITE 200 46.4400000 91.4773026 -1234.00 67.0000000 -------------------------------------------------------------------------------
3. Write out the data to a file
In this next step, we write out the data to a file c:sample.dat .
proc export data=sample2 outfile='c:sample.dat' dbms=dlm replace ; run;
Note that this file has the variable names on line 1 and then the data below that. Here are the first few lines of this file.
FEMALE RACE SES SCHTYP READ WRITE -1234 3 1 1 34 35 0 -1234 2 1 44 41 0 4 -1234 1 55 39 1 2 3 -1234 60 59 0 4 1 1 -1234 37 0 4 2 1 34 -1234 0 3 2 1 34 37 1 4 1 1 35 35 0 4 3 1 44 33 1 4 3 2 36 57
4. Modify the data file and make your Mplus program
Now we will edit the file and at the same time create our Mplus program for reading the data. We want to create an Mplus program that looks like the one below, so we will edit the data file (c:sample.dat) and cut the variable names from line 1 and then paste them into the Mplus template program below. When editing the data file, you can use notepad to edit the file if your file is small, but if it is larger then you will need to use something like wordpad, but be careful to save the file as a text only file.
Title:
Data:
File is c:sample.dat ;
Variable:
Names are
FEMALE RACE SES SCHTYP READ WRITE ;
Missing = all(-1234) ;
Analysis:
Type = basic meanstructure ;
Output:
sampstat;
Note that we use Type = basic meanstructure ; to get listwise deletion of missing data to match the results of step 1. And here are excepts of the results below. You can see that the means correspond to the means from step 1, and the variances along the diagonal of the covariance matrix match the variances (the standard deviations squared) from step 1. This would suggest that the transfer was successful. You can now modify the Mplus program as you wish to run whatever analysis you like.
Means
FEMALE RACE SES SCHTYP READ
________ ________ ________ ________ ________
0.552 3.412 2.062 1.165 52.485
WRITE
_______
53.149
Covariances
FEMALE RACE SES SCHTYP READ
________ ________ ________ ________ ________
FEMALE 0.249
RACE 0.020 1.104
SES -0.050 0.161 0.525
SCHTYP 0.002 0.046 0.036 0.138
READ -0.367 2.623 2.017 0.293 102.976
WRITE 1.104 2.301 1.239 0.395 54.357
Covariances
WRITE
________
WRITE 85.641