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Categorical variables in SUDAAN refer to variables that have discrete values or categories. These variables can be utilized in SUDAAN by specifying them as the independent or dependent variable in statistical analyses. SUDAAN allows for the inclusion of categorical variables in various procedures, such as regression, ANOVA, and chi-square tests. Additionally, SUDAAN provides options for recoding and weighting categorical variables to accurately represent the population being studied. By utilizing categorical variables in SUDAAN, researchers can obtain accurate and precise estimates for complex survey data.
How do I use categorical variables in SUDAAN? | SUDAAN FAQ
Dummy variables
Suppose that you are running a regression or a logistic regression, and
instead of getting the results that you expect, you find that SUDAAN considers a large portion of
your data to be missing. You use proc print or something to see
your data set, and it looks fine to you. What has happened? You may have
included in your model and on the subgroup statement dummy variables that
are coded 0/1. While this is the standard
way of coding dummy variables, SUDAAN considers the cases coded
as 0 to be missing for variables that are listed on the subgroup
statement. In other words, to SUDAAN, non-positive values in variables
that are used as categorical independent variables are considered to be missing. Hence, when SUDAAN does a listwise deletion of missing
data, a large portion of your cases may be deleted, possibly to the point of
making the model unestimatible. (Please see pages 165-166 of the SUDAAN
manual for a complete description regarding the use of the subgroup
statement, including valid values for subgroups, and below for the example using
the subgroup statement.) Consider the example below in which srsex is coded 1/2 and
newvar1 is coded 0/1. As you can see, an
error is printed in the log and the number of cases used in the analysis is
about 4050 fewer than there should be (the 4050 cases that are coded 0 in the
data step). You have several ways of dealing with this problem.
Perhaps the easiest is to not list the 0/1 variable on the subgroup
statement. In many ways the subgroup statement in SUDAAN is like
the class statement in SAS. In the same way that you would not list
a 0/1 variable on the class statement in SAS, you do not list a 0/1
variable on the subgroup statement in SUDAAN. Another solution is
the recode the 0/1 variable to be a 1/2 variable. If you have a variable
that is 0/1/2, then you need to recode it. You can do this in a data step
before running the procedure.
data temp01; set temp1; newvar1 = 0; if _n_ ge 4050 then newvar1 = 1; run;proc regress data=temp01 filetype=sas design = jackknife; weight rakedw0; jackwgts rakedw1--rakedw80 / adjjack=1; model ab1 = srsex newvar1 ; subgroup srsex newvar1; levels 2 2; run;
The following message is displayed in the log.
Opened SAS data file TEMP01 for reading. DATA WARNING: The matrix for estimable parameters is singular. The model may be overspecified. You should reduce the number of variables on the right-hand side and refit the model before attempting to draw any conclusions. DATA WARNING : Degrees of freedom for OVERALL contrast are less than maximum number of estimable parameters You may wish to rerun this job with a tolerance (TOL) of 1.000000e-007 and 1.000000e-005
The erroneous output is shown below.
Number of observations read : 55428 Weighted count: 23847415 Observations used in the analysis : 51339 Weighted count: 22067131 Denominator degrees of freedom : 80 Maximum number of estimable parameters for the model is 3 Weighted mean response is 2.504337 Multiple R-Square for the dependent variable AB1: 0.001315Variance Estimation Method: Replicate Weight Jackknife Working Correlations: Independent Link Function: Identity Response variable AB1: AB1 ---------------------------------------------------------------------- Independent P-value Variables and Beta T-Test Effects Coeff. SE Beta T-Test B=0 B=0 ---------------------------------------------------------------------- Intercept 2.54 0.01 323.02 0.0000 SRSEX MALE -0.08 0.01 -6.56 0.0000 FEMALE 0.00 0.00 . . NEWVAR1 1 0.00 0.00 . . 2 0.00 0.00 . . ----------------------------------------------------------------------------------------------------------------------------- Contrast Degrees of P-value Freedom Wald F Wald F ------------------------------------------------------- OVERALL MODEL 2 75483.96 0.0000 MODEL MINUS INTERCEPT 1 43.01 0.0000 INTERCEPT . . . SRSEX 1 43.01 0.0000 NEWVAR1 . . . -------------------------------------------------------
This problem is caused by the dummy variable newvar1. If you
compare the number of cases used by SUDAAN for the analysis above, 51339, you
will see that the 4050 cases coded as 0 in the data step above are missing. Although
in the example below we have recoded the problem variable in a data step, you could also use
the recode statement in SUDAAN to temporarily recode the variable. If you
have many variables that need to be recoded, you may want to use an array
in a data step. These options are perhaps most useful when you really want
to have the dummy variable listed on the subgroup statement, such as when
you are using proc crosstabs. As mentioned above, you could also list only the
categorical variables coded with non-zero values on the subgroup
statement.
data temp01a; set temp01; if newvar1 = 0 then newvar2 = 1; if newvar1 = 1 then newvar2 = 2; run; proc regress data=temp01a filetype=sas design = jackknife; weight rakedw0; jackwgts rakedw1--rakedw80 / adjjack=1; model ab1 = srsex newvar2 ; subgroup srsex newvar2; levels 2 2; run;Number of observations read : 55428 Weighted count: 23847415 Observations used in the analysis : 55383 Weighted count: 23829382 Denominator degrees of freedom : 80 Maximum number of estimable parameters for the model is 3 Weighted mean response is 2.502603 Multiple R-Square for the dependent variable AB1: 0.001221Variance Estimation Method: Replicate Weight Jackknife Working Correlations: Independent Link Function: Identity Response variable AB1: AB1 ---------------------------------------------------------------------- Independent P-value Variables and Beta T-Test Effects Coeff. SE Beta T-Test B=0 B=0 ---------------------------------------------------------------------- Intercept 2.54 0.01 326.26 0.0000 SRSEX MALE -0.07 0.01 -6.39 0.0000 FEMALE 0.00 0.00 . . NEWVAR2 1 -0.02 0.02 -0.97 0.3343 2 0.00 0.00 . . ----------------------------------------------------------------------------------------------------------------------------- Contrast Degrees of P-value Freedom Wald F Wald F ------------------------------------------------------- OVERALL MODEL 3 60042.62 0.0000 MODEL MINUS INTERCEPT 2 20.71 0.0000 INTERCEPT . . . SRSEX 1 40.85 0.0000 NEWVAR2 1 0.94 0.3343 -------------------------------------------------------
The subgroup statement
In this example we have a 0/1 variable (newvar1) and we are not
using it on the subgroup statement. If you want to have the table
broken out by the values of newvar1, then you need to recode it to be a
1/2 variable and include it on the subgroup statement and include the
number of levels on the levels statement.
proc descript data=temp01 filetype=sas design = jackknife; weight rakedw0; jackwgts rakedw1--rakedw80 / adjjack=1; var srsex racehpra newvar1; subgroup srsex racehpra; levels 2 2; run;Number of observations read : 55428 Weighted count : 23847415 Denominator degrees of freedom : 80Variance Estimation Method: Replicate Weight Jackknife by: Variable, Self-reported gender. ----------------------------------------------------------------------------------- | | | | Variable | | Self-reported gender | | | Total | MALE | FEMALE | ----------------------------------------------------------------------------------- | | | | | | | Self-reported | Sample Size | 55428 | 23002 | 32426 | | gender | Weighted Size | 23847415.32 | 11631728.37 | 12215686.95 | | | Total | 36063102.27 | 11631728.37 | 24431373.90 | | | Mean | 1.51 | 1.00 | 2.00 | | | SE Mean | 0.00 | 0.00 | 0.00 | ----------------------------------------------------------------------------------- | | | | | | | Race - UCLA | Sample Size | 9677 | 4084 | 5593 | | CHPR Definition | Weighted Size | 5705917.88 | 2866894.01 | 2839023.87 | | | Total | 5767889.98 | 2897175.85 | 2870714.13 | | | Mean | 1.01 | 1.01 | 1.01 | | | SE Mean | 0.00 | 0.00 | 0.00 | ----------------------------------------------------------------------------------- | | | | | | | NEWVAR1 | Sample Size | 55428 | 23002 | 32426 | | | Weighted Size | 23847415.32 | 11631728.37 | 12215686.95 | | | Total | 22084052.10 | 10772176.06 | 11311876.04 | | | Mean | 0.93 | 0.93 | 0.93 | | | SE Mean | 0.00 | 0.00 | 0.00 | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | | | Variable | | Race - UCLA CHPR Definition | | | Total | LATINO | PACIFIC | | | | | | ISLANDER | ----------------------------------------------------------------------------------- | | | | | | | Self-reported | Sample Size | 9677 | 9458 | 219 | | gender | Weighted Size | 5705917.88 | 5643945.79 | 61972.10 | | | Total | 8544941.75 | 8451279.40 | 93662.35 | | | Mean | 1.50 | 1.50 | 1.51 | | | SE Mean | 0.01 | 0.01 | 0.04 | ----------------------------------------------------------------------------------- | | | | | | | Race - UCLA | Sample Size | 9677 | 9458 | 219 | | CHPR Definition | Weighted Size | 5705917.88 | 5643945.79 | 61972.10 | | | Total | 5767889.98 | 5643945.79 | 123944.19 | | | Mean | 1.01 | 1.00 | 2.00 | | | SE Mean | 0.00 | 0.00 | 0.00 | ----------------------------------------------------------------------------------- | | | | | | | NEWVAR1 | Sample Size | 9677 | 9458 | 219 | | | Weighted Size | 5705917.88 | 5643945.79 | 61972.10 | | | Total | 5275702.50 | 5218781.91 | 56920.60 | | | Mean | 0.92 | 0.92 | 0.92 | | | SE Mean | 0.00 | 0.00 | 0.03 | -----------------------------------------------------------------------------------
Cite this article
stats writer (2024). How do I utilize categorical variables in SUDAAN?. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/stats/how-do-i-utilize-categorical-variables-in-sudaan/
stats writer. "How do I utilize categorical variables in SUDAAN?." PSYCHOLOGICAL SCALES, 1 Jul. 2024, https://scales.arabpsychology.com/stats/how-do-i-utilize-categorical-variables-in-sudaan/.
stats writer. "How do I utilize categorical variables in SUDAAN?." PSYCHOLOGICAL SCALES, 2024. https://scales.arabpsychology.com/stats/how-do-i-utilize-categorical-variables-in-sudaan/.
stats writer (2024) 'How do I utilize categorical variables in SUDAAN?', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/stats/how-do-i-utilize-categorical-variables-in-sudaan/.
[1] stats writer, "How do I utilize categorical variables in SUDAAN?," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, July, 2024.
stats writer. How do I utilize categorical variables in SUDAAN?. PSYCHOLOGICAL SCALES. 2024;vol(issue):pages.