Collecting and Analyzing Data

How do you test a hypothesis to determine if it is supported or refuted? You need to collect information, using one of the research designs described later in the chapter. The research design guides the researcher in collecting and analyzing data.

Selecting the Sample. In most studies, social scientists must carefully select what is known as a sample. A sample is a selection from a larger population that is statistically representative of that population. There are many kinds of samples, but the one social scientists use most frequently is the random sample. In a random sample, every member of an entire population being studied has the same chance of being selected. Thus, if researchers want to examine the opinions of people listed in a city directory, they might use a computer to randomly select names from the directory. The results would constitute a random sample. The advantage of using specialized sampling techniques is that sociologists do not need to question everyone in a population. It is all too easy to confuse the careful scientific techniques used in representative sampling with the many nonscientific polls that receive much more media attention. For example, television viewers and radio listeners are often encouraged to e-mail their views on headline news or political contests. Such polls reflect nothing more than the views of those who happened to see the television program (or hear the radio broadcast) and took the time, perhaps at some cost, to register their opinions. These data do not necessarily reflect (and indeed may distort) the views of the broader population. Not everyone has access to a television or radio, time to watch or listen to a program, or the means and/or inclination to send e-mail. Similar problems are raised by the "mail-back" questionnaires found in many magazines and by "mall intercepts," in which shoppers are asked about some issue. Even when these techniques include answers from tens of thousands of people, they will be far less accurate than a carefully selected representative sample of 1,500 respondents.

Ensuring Validity and Reliability. The scientific method requires that research results be both valid and reliable. Validity refers to the degree to which a measure or scale truly reflects the phenomenon under study. A valid measure of income depends on the gathering of accurate data. Various studies show that people are reasonably accurate in reporting how much money they earned in the most recent year. Reliability refers to the extent to which a measure produces consistent results. Some people may not disclose accurate information, but most do. In the General Social Survey, only 5 percent of the respondents refused to give their income or indicated they did not know what their income was. That means 95 percent of the respondents gave their income, which we can assume is reasonably accurate (given their other responses about occupation and years in the labor force).

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