Cluster Sampling and Basic Principle of Polling - Research Paper Example

Beyond a certain minimum number, the actual number of respondents interviewed is less important to the accuracy of a poll than the process by which a random probability sample is generated.

Most polls conducted today use telephone interviewing. Interviewers are not usually permitted to vary the question in any way, and no extemporaneous explanations are allowed. In-home interviews can also be conducted but these are more expensive. Surveys can be conducted by mail or online, though there are concerns that samples will not be representative (although for targeted populations with an incentive to reply, these methods can be efficient and cost-effective). For telephone surveys, the usual method is random digit dialing (RDD) or the use of the most recently published telephone listings. If using the latter method, one usually chooses telephone numbers randomly, but then changes the last digit to ensure that unlisted numbers and those who recently got telephones have an equal chance of being selected.

Cluster sampling is usually used for in-home interviewing, whereby representative clusters in particular neighborhoods are selected (the costs of traveling hundreds of kilometers to conduct one interview make a truly random sample impossible for in-home interviews). Quota sampling can also be used. With this method, the percentage of the population that falls into a given group is known (for example, women represent 52% of the population) and therefore once the quota is filled (520 women in a sample of 1000) one ceases to interview women, instead filling one’s male quota. Quota sampling is used less frequently because of the simplicity of techniques, and because of the possibility to weight the data afterwards. Weighting the data is a process that ensures that the sample is numerically representative of various groups in the population. For example, to be able to make reasonable extrapolations about, say, Atlantic Canadians, in a survey, it may be necessary to “oversample” Atlantic Canada.

However, when looking at the national results, each Atlantic Canadian respondent would be counted as less than a full respondent, otherwise, Atlantic Canadians would be overrepresented in the sample. This is what “weighting the data” means, and it is customary to weight the data by region, gender, and age to ensure representativity. Polling firms now tend to use the CATI system (computer-assisted telephone interviewing) in which the interview process is streamlined. Answers are automatically submitted into a data bank, question filters can be used that ask different respondents different questions depending on their previous answers, and the wording of questions can be randomly altered.

Understanding error: margin and otherwise

We have all heard poll results in news stories described as being, “accurate to plus or minus three percentage points, 19 times out of 20,” but what does this mean? This statement, and the figures that it contains, refer to the “sampling error” (3%) and “confidence interval” (95%, or 19 out of 20) of the poll that has been taken. This means that 95 percent of all samples taken from the same population using the same question at the same time will be +/- the sampling error (usually referred to as the “margin of error”). The reported margin of error assumes two things: that the sample was properly collected (and therefore represents the cross-section of the target population), and that the questions are properly designed and measure the underlying concepts that are of interest. If either of these two assumptions is violated, the margin of error underestimates the real difference between the survey results and what the population “thinks.” The reporting of the margin of error may therefore create an aura of precision about the results that is lacking. If the sample is not truly random, or the questions are poorly worded, then the margin of error underestimates the degree of uncertainty in the results. Read More