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# QUOTA SAMPLING

Quota sampling is stratified sampling without random selection. It is used widely in election polls as well as in studies that rely on qualitative data, like in-depth interviews.

The key to quota sampling is the development of a sampling design, or sampling grid. Suppose you are studying the lived experiences of labor migrants to the United States who are back home in their community in Mexico. You want to compare the experiences of (1) people who spent time on jobs in the United States but were caught by the U.S. Border Patrol and deported with those of (2) people who managed to stay on their jobs until they’d accumulated enough money to return on their own. You also want to compare the experiences of Indians and Mestizos and of men and women. That’s three binary independent variables: deported/not deported; Indian/Mestizo; male/female. Figure 7.1 shows that there are eight cells in this design. If you want at least five informants in each cell, you’ll need to do 40 interviews.

FIGURE 7.1.

Quota sampling grid with three binary independent variables.

Tinsley et al. (2002) interviewed 437 elderly users of Lincoln Park in Chicago. They selected quota samples of about 50 men and 50 women from each of the four major ethnic groups in the area, Blacks, Whites, Hispanics, and Asian Americans. Besides gender and ethnicity, Tinsley et al. stratified on place and time. They divided the park into three zones (north, south, and middle) and three time periods (6 a.m. to 10 a.m., 11 a.m. to 3 p.m., and 4 p.m. to 8 p.m.). There were, then, nine zone-time strata in which interviewers selected respondents. The interviewers were also told to make sure they got some weekday and some weekend users of the park.

When it’s done right, quota samples often do a good job of reflecting the population parameters of interest. In other words, quota sampling is an art that often approximates the results of probability sampling at less cost and less hassle than strict probability sampling (box 7.1).

Quota samples are biased toward people you can find easily—which means, for example, that they’re biased against really poor and really rich people and against single people who aren’t home as much (Marsh and Scarborough 1990)—so quota sampling is dangerous when it comes to making predictions about close election outcomes—or estimating any population parameter, for that matter, if you need precise results.

On the other hand, quota sampling is appropriate in the study of cultural domains. If you want to know how junior sports—Little League Baseball, Pop Warner football, Youth Soccer, junior and senior high school football—function in small communities across the United States, you’d ask people who have children playing those sports. There will be some intracultural variation, but open-ended interviews with four or five really knowlBOX 7.1

FAMOUS POLLING DEBACLES FROM QUOTA SAMPLES

In 1948, pollsters predicted, on the basis of quota sampling, that Thomas Dewey would beat Harry Truman in the U.S. presidential election. The Chicago Tribune was so confident in those predictions that they printed an edition announcing Dewey's victory—while the votes were being counted that would make Truman president.

Skip to 1992. In the general election in Britain that year, four different polls published on the day of the election put the Liberal Party, on average, about 1 point ahead of the Conservative Party. All the polls were based on quota sampling. The Conservatives won by 8 points. In fact, from 1992 to 1997, political polls using quota samples in Britain systematically overestimated the support for the Liberals (Curtice and Sparrow 1997). A similar polling debacle happened in the 2002 presidential election in France. Twelve polls predicted that Jacques Chirac and Lionel Jospin would defeat the far-right candidate, Jean-Marie Le Pen in the first round of voting and face each other in a run-off. No one predicted that Le Pen would trounce Jospin and face Chirac in the run-off (Durand et al. 2004).

edgeable people will produce the relevant cultural data—including data on the range of ideas that people have about these institutions (Further Reading: quota sampling).

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