How can I limit the number of observations used by SUDAAN?

How can I limit the number of observations used by SUDAAN?

SUDAAN is a software package used for analyzing complex survey data. It allows researchers to account for the complex sampling designs commonly used in surveys and provides accurate estimates of population parameters. In order to improve the efficiency and speed of data analysis, the option to limit the number of observations used by SUDAAN is available. This feature allows researchers to specify a subset of the data to be used in the analysis, reducing the computational burden and potentially improving the speed of the analysis. By limiting the number of observations, researchers can also focus on specific subgroups of the population and obtain more precise estimates for these groups. This flexibility in data selection can help researchers tailor their analysis to their specific research questions and improve the accuracy of their findings.

How can I limit the number of observations used by SUDAAN? | SUDAAN FAQ

If you are working with a very large data set and you find that running
procedures takes a while, you can use the maxobs = option on the proc
statement of all analysis procedures to limit the number of observations that
are read in.  This can be very useful when you are debugging a program. 
Just remember to delete that option when you have the programming working
correctly.  Compare the results of the two proc reg calls below.

proc regress data=temp1 filetype=sas design = jackknife maxobs = 1000;
weight rakedw0;
jackwgts rakedw1--rakedw80 / adjjack=1;
model ae13 = ae14;
run;
Number of observations read       :   1000    Weighted count:   431947
Observations used in the analysis :    591    Weighted count:   242364
Denominator degrees of freedom    :     80

Maximum number of estimable parameters for the model is  2
Weighted mean response is 2.262239

Multiple R-Square for the dependent variable AE13: 0.216196
Variance Estimation Method: Replicate Weight Jackknife
Working Correlations: Independent
Link Function: Identity
Response variable AE13: AE13

----------------------------------------------------------------------
Independent                                                   P-value
  Variables and        Beta                                   T-Test
  Effects              Coeff.          SE Beta   T-Test B=0   B=0
----------------------------------------------------------------------
Intercept                    1.96         0.11        17.83     0.0000
AE14                         0.32         0.08         3.78     0.0003
----------------------------------------------------------------------

-------------------------------------------------------

Contrast               Degrees
                       of                      P-value
                       Freedom        Wald F   Wald F
-------------------------------------------------------
OVERALL MODEL                 2       197.90     0.0000
MODEL MINUS
  INTERCEPT                   1        14.29     0.0003
INTERCEPT                     1       317.85     0.0000
AE14                          1        14.29     0.0003
-------------------------------------------------------
proc regress data=temp1 filetype=sas design = jackknife;
weight rakedw0;
jackwgts rakedw1--rakedw80 / adjjack=1;
model ae13 = ae14;
run;
Number of observations read       :  55428    Weighted count: 23847415
Observations used in the analysis :  32538    Weighted count: 13783845
Denominator degrees of freedom    :     80

Maximum number of estimable parameters for the model is  2
Weighted mean response is 2.188590

Multiple R-Square for the dependent variable AE13: 0.241897
Variance Estimation Method: Replicate Weight Jackknife
Working Correlations: Independent
Link Function: Identity
Response variable AE13: AE13

----------------------------------------------------------------------
Independent                                                   P-value
  Variables and        Beta                                   T-Test
  Effects              Coeff.          SE Beta   T-Test B=0   B=0
----------------------------------------------------------------------
Intercept                    1.88         0.01       152.15     0.0000
AE14                         0.34         0.01        25.47     0.0000
----------------------------------------------------------------------
-------------------------------------------------------

Contrast               Degrees
                       of                      P-value
                       Freedom        Wald F   Wald F
-------------------------------------------------------
OVERALL MODEL                 2     12818.28     0.0000
MODEL MINUS
  INTERCEPT                   1       648.71     0.0000
INTERCEPT                     1     23150.59     0.0000
AE14                          1       648.71     0.0000
-------------------------------------------------------

Cite this article

stats writer (2024). How can I limit the number of observations used by SUDAAN?. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/stats/how-can-i-limit-the-number-of-observations-used-by-sudaan/

stats writer. "How can I limit the number of observations used by SUDAAN?." PSYCHOLOGICAL SCALES, 1 Jul. 2024, https://scales.arabpsychology.com/stats/how-can-i-limit-the-number-of-observations-used-by-sudaan/.

stats writer. "How can I limit the number of observations used by SUDAAN?." PSYCHOLOGICAL SCALES, 2024. https://scales.arabpsychology.com/stats/how-can-i-limit-the-number-of-observations-used-by-sudaan/.

stats writer (2024) 'How can I limit the number of observations used by SUDAAN?', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/stats/how-can-i-limit-the-number-of-observations-used-by-sudaan/.

[1] stats writer, "How can I limit the number of observations used by SUDAAN?," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, July, 2024.

stats writer. How can I limit the number of observations used by SUDAAN?. PSYCHOLOGICAL SCALES. 2024;vol(issue):pages.

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