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. 

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