Statalist


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: Unexpected proportions after survey commands


From   "Michael I. Lichter" <MLichter@Buffalo.EDU>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Unexpected proportions after survey commands
Date   Sat, 09 May 2009 18:26:52 -0400

Jean-Gael,

First of all, you can use Stata's svy poststratification weights (as opposed to probability weights) by creating a variable that has cell totals for each poststratification cell. E.g,

TOURIND Community #
Yes     1         10
No      1         80
Yes     2         5
No      2         92

etc. You then use the -svyset- syntax for poststratification strata and weights.

Alternatively, what you said about "true" vs. "sample" proportions sounds fine, but you might not have done it correctly in practice. You need to assign weights to both the tourism and non-tourism parts of the sample. Also, you shouldn't use falsely high levels of precision--stick with 2-4 digits.

Michael

Jean-Gael Collomb wrote:
Hello all,

I have a question about using post stratification weights and using Stata's survey commands. After setting the weights, I do not get the proportions I expected.

My overall research question is to see if tourism (TOURIND) influences quality of life in several communities in a rural province of Namibia. My aim was to conduct individual interviews in a sample of 10% of all households in each community. I obtained household census counts from key informants within the community and my own double checks during field work. This random sample yielded a random sample of 395 interviews, of which only 9 (2.3%) were conducted with individuals working in tourism. Given this very low number of respondents who worked in tourism and my interest in trying to understand the impact of tourism, I established a sampling frame restricted to individuals working in tourism and interviewed 72 individuals. [Two of those interviews were conducted with individuals not employed in tourism but living in a household where someone was]. In total, I thus interviewed 467 people, among which 79 worked in tourism. My full sample oversampled tourism employees and i think it would be wrong to derive from it that 17% (79/467*100) of the population works in tourism. I think Post stratification weights should be assigned to my data set to correct for the oversampling. In fact, the percentage of the population working in tourism varies by communities and thus different weights should be calculated for different communities. I used existing reports documenting total numbers of community residents employed by local tourism operators and total population size as a basis to calculate the "true" distribution of tourism employees (weight2). The weights were calculated by dividing the “true” percentage by the “oversampled” percentage.

The problem is that when I apply the weights in Stata, I do not get the proportion I expected. Specifically, I expected that after svyset _n [pweight = samplewt2] and svy: tab tourind, I would find that 0.84% of the population could be labeled TOURIND, but Stata returns a value of 3.25% (and similar discrepancies for each community).

I am not sure I am doing something wrong in calculating the weights, assigning the weights to my dataset, or entering the tab commands in svy mode. I’d greatly appreciate your help in helping move past this and take advantage of survey commands in Stata.

Thank you very much if you have time to give me some feedback or point me towards the best information source (textbook?).

Cheers,

Jean-Gael Collomb, jg@ufl.edu

(PS. I run Stata 10 in Mac OSX)



State code entered:

*ASSIGNING POST STRATIFICATION WEIGHTS

*-------------------------------------

gen samplewt2=0

label var samplewt2 "Post Stratification sample weight 2"

replace samplewt2=0.99975204562360500 if conservancy==1 & sample==1

replace samplewt2=0.04357333333333330 if conservancy==2 & sample==2

replace samplewt2=1.39197814207650000 if conservancy==2 & sample==1

replace samplewt2=0.10144078144078100 if conservancy==3 & sample==2

replace samplewt2=1.18320139407518000 if conservancy==3 & sample==1

replace samplewt2=0.05683908045977010 if conservancy==4 & sample==2

replace samplewt2=1.47985380116959000 if conservancy==4 & sample==1

replace samplewt2=0.01906976744186050 if conservancy==5 & sample==2

replace samplewt2=1.05030411449016000 if conservancy==5 & sample==1

tab tourind

bysort conservancy: tab tourind

*applying weight2 (those derived from IRDNC data)

svyset _n [pweight = samplewt2]

svy: tab tourind, percent



Jean-Gael "JG" Collomb

PhD candidate

School of Natural Resources and Environment / School of Forest Resources and Conservation

University of Florida

jgcollomb@gmail.com

jg@ufl.edu

+1 (352) 870 6696





*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/

--
Michael I. Lichter, Ph.D. <mlichter@buffalo.edu>
Research Assistant Professor & NRSA Fellow
UB Department of Family Medicine / Primary Care Research Institute
UB Clinical Center, 462 Grider Street, Buffalo, NY 14215
Office: CC 125 / Phone: 716-898-4751 / FAX: 716-898-3536

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/



© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index