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CHAPTER 5: Survey Research 145sample typically comprised of students registered for the introductory psychologycourse.Crossen (1994) describes the drawbacks of another variation of conveniencesampling, call-in surveys. Call-in surveys are used by TV and radio shows topoll the views of their audience. Those who happen to be “tuned in” and whoare willing to call (and sometimes to pay the charge for calling a 900 number)make up the sample for these call-in surveys. People who make calls in responseto a call-in request differ from the general population not only because they arepart of the particular show’s audience, but because they are motivated enoughto make a call. Similarly, online computer users who respond to a “pop up” surveyquestion displayed on their home page will differ from those who choosenot to respond (or are not regular computer users).A prime-time TV news show once conducted a call-in survey with a questionconcerning whether the United Nations (UN) headquarters should remainin the United States (Crossen, 1994). It turns out that another surveyresearch study involving about 500 randomly selected respondents alsoasked the same question. Of the 186,000 callers who responded, a solid majority(67%) wanted the UN out of the United States. Of the 500 respondentsto the survey research study, a clear majority (72%) wanted the UN to stayin the United States. How could these two surveys yield such different—evenopposite— results? Should we put more confidence in the results of the call-insurvey because of the massive sample size? Absolutely not! A large conveniencesample is just as likely to be an unrepresentative sample as is any otherconvenience sample. As a general rule, you should consider that conveniencesampling will result in a biased sample unless you have strong evidence confirmingthe representativeness of the sample.Key ConceptProbability Sampling The distinguishing characteristic of probability samplingis that the researcher can specify, for each element of the population, the probabilitythat it will be included in the sample. Two common types of probabilitysampling are simple random sampling and stratified random sampling. Simplerandom sampling is the basic technique of probability sampling. The most commondefinition of simple random sampling is that every element has an equalchance of being included in the sample. The procedures for simple randomsampling are outlined in Box 5.1.One critical decision that must be made in selecting a random sample is howlarge it should be. For now, we will simply note that the size of a random sampleneeded to represent a population depends on the degree of variability inthe population. For example, college students in Ivy League schools representa more homogeneous population than college students in all U.S. colleges interms of their academic abilities. At one extreme, the most homogeneous populationwould be one in which all members of the population are identical. Asample of one element would be representative of this population regardless ofthe size of the population. At the other extreme, the most heterogeneous populationwould be one in which each member was completely different from allother members on all characteristics. No sample, regardless of its size, could berepresentative of this population. Every individual would have to be included