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Descriptive Research: Surveys, Interviews, and Questionnaires


This is all the resources you will need to complete the assignment Chapter 6 Chapter 8 video on youtube the assignment details The discussion post from earlier this week.

Now that we have reviewed the basic types of measured variables and considered how to evaluate their effectiveness at assessing the conceptual variables of interest, it is time to more fully discuss the use of these measures in descriptive research. In this chapter, we will discuss the use of self-report measures, and in Chapter 7, we will discuss the use of behavioral measures. Although these measures are frequently used in a qualitative sense—to draw a complete and complex picture in the form of a narrative—they can also be used quantitatively, as measured variables. As you read these chapters, keep in mind that the goal of descriptive research is to describe the current state of affairs but that it does not by itself provide direct methods for testing research hypotheses. However, both surveys (discussed in this chapter) and naturalistic methods.

A survey is a series of self-report measures administered through either an interview or a written questionnaire. Surveys are the most widely used method of collecting descriptive information about a group of people. You may have received a phone call (it usually arrives in the middle of the dinner hour when most people are home) from a survey research group asking you about your taste in music, your shopping habits, or your political preferences.

   The goal of a survey, as with all descriptive research, is to produce a “snapshot” of the opinions, attitudes, or behaviors of a group of people at a given time. Because surveys can be used to gather information about a wide variety of information in a relatively short time, they are used extensively by businesspeople, advertisers, and politicians to help them learn what people think, feel, or do.

   Surveys are usually administered in the form of an interview, in which questions are read to the respondent in person or over the telephone. One advantage of in-person interviews is that they may allow the researcher to develop a close rapport and sense of trust with the respondent. This may motivate the respondent to continue with the interview and may lead to more honest and open responding. However, face-to-face interviews are extremely expensive to conduct, and consequently telephone surveys are now more common. In a telephone interview all of the interviewers are located in one place, the telephone numbers are generated automatically, and the questions are read from computer terminals in front of the researchers. This procedure provides such efficiency and coordination among the interviewers that many surveys can be conducted in one day.


Unstructured Interviews. Interviews may use either free-format or fixed-format self-report measures. In an unstructured interview the interviewer talks freely with the person being interviewed about many topics. Although a general list of the topics of interest is prepared beforehand, the actual interview focuses on those topics that the respondent is most interested in or most knowledgeable about. Because the questions asked in an unstructured interview differ from respondent to respondent, the interviewer must be trained to ask questions in a way that gets the most information from the respondent and allows the respondent to express his or her true feelings. One type of a face-to-face unstructured interview in which a number of people are interviewed at the same time and share ideas both with the interviewer and with each other is called a focus group.

   Unstructured interviews may provide in-depth information about the particular concerns of an individual or a group of people and thus may produce ideas for future research projects or for policy decisions. It is, however, very difficult to adequately train interviewers to ask questions in an unbiased manner and to be sure that they have actually done so. And, as we have seen in Chapter 4, because the topics of conversation and the types of answers given in free-response formats vary across participants, the data are difficult to objectively quantify and analyze, and are therefore frequently treated qualitatively.

Structured Interviews. Because researchers usually want more objective data, the structured interview, which uses quantitative fixed-format items, is most common. The questions are prepared ahead of time, and the interviewer reads the questions to the respondent. The structured interview has the advantage over an unstructured interview of allowing better comparisons of the responses across different individuals because the questions, time frame, and response format are controlled to be the same for each respondent.

   A questionnaire is a set of fixed-format, self-report items that is completed by respondents at their own pace, often without supervision. Questionnaires are generally cheaper than interviews because a researcher can mail the questionnaires to many people or have them complete the questionnaires in large groups. Questionnaires may also produce more honest responses than interviews, particularly when the questions involve sensitive issues such as sexual activity or annual income, because respondents are more likely to perceive their responses as being anonymous than they are in interviews. In comparison to interviews, questionnaires are also likely to be less influenced by the characteristics of the experimenter. For instance, if the topic concerns race-related attitudes, how the respondent answers might depend on the race of the interviewer and how the respondent thinks the interviewer wants him or her to respond. Because the experimenter is not present when a questionnaire is completed, or at least is not directly asking the questions, such problems are less likely to occur.


   To make the sample representative of the population, any of several probability sampling techniques may be employed. In probability sampling, procedures are used to ensure that each person in the population has a known chance of being selected to be part of the sample. As a result, the likelihood that the sample is representative of the population is increased, as is the ability to use the sample to draw inferences about the population.

The most basic probability sample is drawn using simple random sampling. In this case, the goal is to ensure that each person in the population has an equal chance of being selected to be in the sample. To draw a simple random sample, an investigator must first have a complete list (known as a sampling frame) of all of the people in the population. For instance, voting registration lists may be used as a sampling frame, or telephone numbers of all of the households in a given geographic location may be used. The latter list will basically represent the population that lives in that area because almost all U.S. households now have a telephone. Recent advances in survey methodology allow researchers to include cell phone numbers in their sampling frame.

   Then the investigator randomly selects from the frame a sample of a given number of people. Let's say you are interested in studying volunteering behavior of the students at your college or university, and you want to collect a random sample of 100 students. You would begin by finding a list of all of the students currently enrolled at the college. Assume that there are 7,000 names on this list, numbered sequentially from 1 to 7,000. Then, as shown in the instructions for using Statistical Table A (in Appendix E), you could use a random number table (or a random number generator on a computer) to produce 100 numbers that fall between 1 and 7,000 and select those 100 students to be in your sample.

If the list of names on the sampling frame is itself known to be in a random sequence, then a probability sampling procedure known as systematic random sampling can be used. In your case, because you wish to draw a sample of 100 students from a population of 7,000 students, you will want to sample 1 out of every 70 students (100/7,000 = 1/70). To create the systematic sample, you first draw a random number between 1 and 70 and then sample the person on the list with that number. You create the rest of the sample by taking every 70th person on the list after the initial person. For instance, if the first person sampled was number 32, you would then sample number 102, 172, and so on. You can see that it is easier to use systematic sampling than simple random sampling because only one initial number has to be chosen at random.

Probability Sampling

Because in most cases sampling frames include such information about the population as sex, age, ethnicity, and region of residence, and because the variables being measured are frequently expected to differ across these subgroups, it is often useful to draw separate samples from each of these subgroups rather than to sample from the population as a whole. The subgroups are called strata, and the sampling procedure is known as stratified sampling.

   To collect a proportionate stratified sample, frames of all of the people within each strata are first located, and random samples are drawn from within each of the strata. For example, if you expected that volunteering rates would be different for students from different majors, you could first make separate lists of the students in each of the majors at your school and then randomly sample from each list. One outcome of this procedure is that the different majors are guaranteed to be represented in the sample in the same proportion that they are represented in the population, a result that might not occur if you had used random sampling. Furthermore, it can be shown mathematically that if volunteering behavior does indeed differ among the strata, a stratified sample will provide a more precise estimate of the population characteristics than will a simple random sample (Kish, 1965).

Disproportionate stratified sampling is frequently used when the strata differ in size and the researcher is interested in comparing the characteristics of the strata. For instance, in a class of 7,000 students, only 10 or so might be French majors. If a random sample of 100 students was drawn, there might not be any French majors in the sample, or at least there would be too few to allow a researcher to draw meaningful conclusions about them. In this case, the researcher draws a sample that includes a larger proportion of some strata compared with its actual representation in the population. This procedure is called oversampling and is used to provide large enough samples of the strata of interest to allow analysis. Mathematical formulas are used to determine the optimum size for each of the strata.

Although simple and stratified sampling can be used to create representative samples when there is a complete sampling frame for the population, in some cases there is no such list. For instance, there is no single list of all of the currently matriculated college students in the United States. In these cases an alternative approach known as cluster sampling can be used. The technique is to break the population into a set of smaller groups (called clusters) for which there are sampling frames and then to randomly choose some of the clusters for inclusion in the sample. At this point, every person in the cluster may be sampled, or a random sample of the cluster may be drawn.

Often, the clustering is done in stages. For instance, we might first divide the United States into regions (for instance, East, Midwest, South, Southwest, and West). Then we would randomly select states from each region, counties from each state, and colleges or universities from each county. Because there is a sampling frame of the matriculated students at each of the selected colleges, we could draw a random sample from these lists. In addition to allowing a representative sample to be drawn when there is no sampling frame, cluster sampling is convenient. Once we have selected the clusters, we need only contact the students at the selected colleges rather than having to sample from all of the colleges and universities in the United States. In cluster sampling, the selected clusters are used to draw inferences about the nonselected ones. Although this practice loses some precision, cluster sampling is frequently used because of convenience.

The advantage of probability sampling methods is that their samples will be representative and thus can be used to draw inferences about the characteristics

One approach to summarizing a quantitative variable is to combine adjacent values into a set of categories and then to examine the frequencies of each of the categories. The resulting distribution is known as a grouped frequency distribution. A grouped frequency distribution of the age variable from Table 6.1 is shown in Figure 6.2(a). In this case, the ages have been grouped into five categories (less than 21, 21–30, 31–40, 41–50, and greater than 50).

The grouped frequency distribution may be displayed visually in the form of a histogram, as shown in Figure 6.2(b). A histogram is slightly different from a bar chart because the bars are drawn so that they touch each other. This indicates that the original variable is quantitative. If the frequencies of the groups are indicated with a line, rather than bars, as shown in Figure 6.2(c), the display is called a frequency curve.

One limitation of grouped frequency distributions is that grouping the values together into categories results in the loss of some information. For instance, it is not possible to tell from the grouped frequency distribution in Figure 6.2(a) exactly how many people in the sample are 23 years old. A stem and leaf plot is a method of graphically summarizing the raw data such that the original data values can still be seen.

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