sahar salah writes: >Due to my PHD I'm preparing a questionnaire. But the popular is >very large. I classified it into 35 categories. All I need, is to >determine the sample size. is it proportional to the popular size? >How can I determine that sample size? I presume here that you mean to use the word "population" instead of "popular". You need to provide much more information before I or anyone else can answer your question. Attached below is some general guidance that I wrote up for my web pages. It is written in the context of medical research, so I apologize if the example using sensitivity of a diagnostic test seems a bit obscure to you. It is difficult to use email to provide specific advice for such an important question. You would be much better off seeking information and guidance from your dissertation advisor and/or members of your dissertation committee. If they are not helpful, see if you can find someone at your university who can provide statistical consulting face-to-face. Steve Simon, [EMAIL PROTECTED], Standard Disclaimer. STATS - Steve's Attempt to Teach Statistics: http://www.cmh.edu/stats Three things you need for a power calculation Dear Professor Mean, I want to do research. Is forty subjects enough, or do I need more? Eager Edward Dear Eager, That reminds me of a cute joke. How many research subjects does it take to screw in a light bulb? At least 300 if you want the bulb to have adequate power. Sorry, I was digressing. Is forty subjects an adequate sample size? That depends on a lot of factors. The basic idea, though, is to select a sample size which insures that your study has adequate power. Power is the probability that your research study will successfully detect a difference, assuming that the treatment or exposure you are examining actually can cause an important difference. If you don't care whether your experiment is successful or not, then you can use just about any sample size. Power is to a research design like sensitivity is to a diagnostic test. A diagnostic test with good sensitivity is normally able to detect a disease when the disease is present. A research study with good power is normally able to detect a change when your treatment is indeed effective. The actual calculation of power requires three pieces of information: your research hypothesis, the variability of your outcome measure, and your estimate of the clinically relevant difference. Calculating power is sometimes difficult and it may require you to go to the time and expense of running a pilot study. But you should NEVER start a research project without knowing what your power is. That would be like using a diagnostic test with unknown sensitivity. =========================================================================== This list is open to everyone. Occasionally, less thoughtful people send inappropriate messages. Please DO NOT COMPLAIN TO THE POSTMASTER about these messages because the postmaster has no way of controlling them, and excessive complaints will result in termination of the list. For information about this list, including information about the problem of inappropriate messages and information about how to unsubscribe, please see the web page at http://jse.stat.ncsu.edu/ ===========================================================================
