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Cross-sectional Versus Longitudinal Surveys, Limitations Of Survey Research And Problems With Interpretations, Major Recent Surveys

Most surveys have several common characteristics (Fowler). Their purpose is to generate information that statistically summarizes issues of interest in the study population. This information is collected by asking people (respondents) questions, either in person or over the telephone. In most cases, a sampling strategy is used to select only a fraction of the population that is actually interviewed. Those interviews are highly structured and standardized such that each respondent is asked the same questions in the same way and is provided a predetermined set of response categories.

Explaining survey mechanics is beyond the scope of this entry. Instead, we suggest two survey methods books geared toward nonmethodologists. Aday approaches designing good surveys by building on a reporter’s stock questions: who do you want to study, what do you want to know about them, where will the data be collected, when do you want to do the field work, why is this information needed, and how will the questions be asked. Fowler pragmatically focuses on enhancing the quality of collected data by identifying the best practices for question design, interviewing procedures and skills, and achieving high response rates. Two user-friendly electronic resources are also recommended. One is a methodology reference tool (Trochim), and the other is a statistics reference tool (Statsoft).

Using good survey design and best practices helps to minimize survey error. Survey errors are deviations of the observed findings from their ‘‘true’’ values (Groves), and come in two categories. Sampling errors result from the fact that when a sample is drawn, there is a chance that it may not be representative of the population from which it is taken. If probability-sampling methods are used, sampling errors can be calculated, and confidence intervals can be established. Confidence intervals are often expressed using statements like ‘‘these results have a margin of error of +/- 5 percent.’’ The best way to reduce sampling error in a probability sample usually involves increasing sample size. When nonprobability sampling methods are used, such as convenience samples of people approached on street corners or in shopping malls, it is not possible to determine the accuracy of the findings, or to know what broader population the sample represents.

The second category of survey errors involves nonsampling errors. Three sources contribute to this problem: the interviewer, the questions, and the respondent (Aday). Good interviewers can increase response rates (the percent of people who participate), minimize the number of questions that are not answered (missing data), and increase the consistency of the measurement process (reliability). Well-designed and crafted questions are easy for interviewers to ask and for respondents to answer. Such questions are brief, use simple and familiar words that do not have multiple meanings, avoid technical jargon, use the active voice and good grammar, and do not involve compound sentences or double negatives.

When using survey methods with older adults, one wonders whether there will be more nonsampling errors than usual. Older adults are more likely to experience health and cognitive problems than younger adults, and these may prevent older adults from participating or diminish the quality of the information that they provide (Herzog and Rodgers). Although more research is needed, the literature has generally not identified disproportionately larger nonsampling errors among older adults. For the oldest old and the least healthy, however, there is some evidence that both willingness to participate and the quality of the information provided may be compromised (Herzog and Rodgers). Therefore, when designing surveys for those subgroups, special emphasis on minimizing respondent burden and providing greater flexibility is warranted.

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