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Date:         Fri, 26 Jun 1998 14:06:28 -0400
Reply-To:     "Susan C. Underhill" <susan.underhill@UTORONTO.CA>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@UGA.CC.UGA.EDU>
From:         "Susan C. Underhill" <susan.underhill@UTORONTO.CA>
Subject:      Re: T-tests and small samples?
Comments: To: stanky73@EMAIL.MSN.COM
Content-Type: TEXT/PLAIN; CHARSET=US-ASCII

> I am currently under pressure to compare two groups (white and nonwhite) > using independent samples t-tests which I am comfortable with. However, the > nonwhite group comprises just 11 to 12% of the total response group (N=452). > This percentage is representative of the population. Am I wrong to be > uncomfortable comparing these two groups? >

Ideally, when you are designing a study, you make sure that you would sample enough people to to make this comparison. Check your sampling procedure to see if you are okay. Do you have the minimum number of people to make this comparison? My gut feeling is that you should have at least 50 people in the non-white group so you might be okay but check your sampling.

If you do not have enough people in the non-white group (as we realize real-life data sometimes doesn't work as nicely as we hope), you could weight but you would have to recognize the weakness in your design.

The best resource I've read in recent years for sampling and weighting is Kervin (1992) Harper Collins Publishers.

To weight you would weight them so that they each represented 50% of the target population. The weighting formula would be:

Weight factor=.5/achieved proportion of non-whites and then repeat this for whites Weight factor=.5/achieved proportion of whites

To apply this weight simply compute a variable:

compute weight=0. if (race=1) weight=(put results of calculation). if (race=2) weight=(put results of calculation). execute.

Then,

weight by weight.

This would effectively upweight the non-whites and downweight the whites. Your total N should remain the same (check this). Before you weight you should be sure that there was no sampling bias (e.g, whites were more likely to answer than non-whites. Since you said that the proportion of non-whites was represenative of the population, I'm assuming this was not problem.

Good luck.

Susan

************************************************************ Susan C. Underhill, Senior Research Officer Institute for Human Development, Life Course & Aging 222 College St., Ste.106 Toronto, ON M5T 3J1 PH: (416) 978-5968 FAX: (416) 978-4771 ************************************************************


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