```Date: Fri, 9 Sep 2005 12:21:49 -1000 Reply-To: Bob Schacht Sender: "SPSSX(r) Discussion" From: Bob Schacht Subject: Re: Stratified Sampling In-Reply-To: <20050909210047.5944.qmail@web31301.mail.mud.yahoo.com> Content-type: text/plain; charset=us-ascii; format=flowed At 11:00 AM 9/9/2005, Chao Yawo wrote: >Hi, > >I am teaching a sophomore social statistics course. >I've been covering sampling, especially stratified >sampling this week. > >The students need some assistance in explaining the >weighting procedures associated with disproportionate >stratified sampling. How can I demonstrate this in a >class with a concrete example. > >Also, is there any guidelines as to how to oversample >a particular stratum? Assuming i have 2 groups (males >are 20% and Females=80%). If I am drawing a sample of >100 students - it means I would end up with 20 males >and 80 females. If i need to oversample the males, >what values should i chose - 30, 40, 50? - is the >choice really arbitrary or is guided by theory or >calculations? > >I will appreciate your thoughts on this. Usually the purpose of over-sampling is to generate a sufficient sample size for population subsamples so that one can calculate prevalence estimates or conduct statistical tests, etc. An example is that information on American Indians in National surveys is seldom sufficient to provide prevalence estimates for almost anything. Find almost any national health survey that advertises information about minorities, and you'll discover that "minorities" usually refer only to Black and Hispanic (which are over-sampled, BTW) but if there is any information on American Indians at all, there will usually be an asterisk that leads you to a statement that sample size was insufficient. What is sufficient? Unfortunately, that depends on what you're looking at. If, for example, you want to know the prevalence rate of American Indian Males with HIV, you're looking at a subset of a subset of a subset, and even the National Health Interview Survey (NHIS) is probably not going to have enough cases to produce reliable statistics. So what you need to do is to start with the real target population, and then use some of the standard Sample Size estimators to tell you how many cases you'll need to obtain useful conclusions. Then compare the needed n (call it n1) with the sample you would come up with in a straightforward random stratified sample of N cases (call it n2). This will give you some idea of how much over-sampling will be needed. I'm not sure that this will work well with the kind of examples you are seeking for classroom use, but maybe someone else can help you think of something. Bob Robert M. Schacht, Ph.D. Pacific Basin Rehabilitation Research & Training Center 1268 Young Street, Suite #204 Research Center, University of Hawaii Honolulu, HI 96814 ```

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