Electronic Resources for Research Methods

Quantitative research methods

Sampling

• Blair, J. et al. (2000)Sample design for household telephone surveys: a bibliography 1949-2000. College Park, MD: University of Maryland, Survey Research Centre.

  A comprehensive bibliography on the subject, but no electronic sources listed.

• Brick, J. Michael, Collins, Mary & Chandler, Kathryn (1998)  An experiment in Random-Digit-Dial screening. Washington, DC: US Department of Education, Office of Educational Research and Improvement.

  "Much of the literature on RDD response rates focuses on the benefits and drawbacks of various screening and sampling procedures. The common assumption is that enumeration is more invasive, leading to lower response rates, but there are concerns about population coverage and self-selection with other methods. When the target population is found in a subset of households, a screen-out question may be used to eliminate ineligible households prior to employing sampling methods. This experiment examined the impact on screening response rates of (1) full enumeration of all households (no screen-out) versus a screen-out question and (2) mailing an advance letter. An RDD sample was divided into four quarter-samples: screen-out and letter; screen-out and no letter; no screen-out and letter; no screen-out and no letter. Response rates were significantly higher in the screen-out condition. The advance mailing increased cooperation in the no screen-out condition, but not in the screen-out condition. The no screen-out condition consumed substantially more resources than a screen-out sample of the same size, but also provided more data for each completed case. Implications of this experiment for the design of future surveys are discussed."

• Dereshiwsky, M. (1998)  Module 5: Population and sampling. Flagstaff, AZ: Northern Arizona University.

  A module in an on-line course, EDR610 - Introduction to research.

• Dobson, Annette et al.  (2000) Statistical inference: populations, samples, estimates, and repeated sampling.  Canberra: Australian National University.

  Part of the SurfStat electronic textbook prepared over a number of years. "The SurfStat project started in 1994 with the brave but naive intent of making an existing set of course notes available online as hypertext. It has since grown to include an extensive glossary, interactive exercises, JavaScript functions replacing statistical probability tables, and the beginnings of a set of Java applets demonstrating statistical concepts through dynamic graphics. It is the primary learning resource for students taking STAT101 at the University of Newcastle, Australia."

• DSS Research  (2000) Sampling error in survey research.  Arlington, TX: DSS Research.

  A brief, but clear account: "Every survey contains some form of error. Even a complete census of all known members of a population is subject to random error or potential measurement error. There are 2 major forms of sampling error that might be encountered in a survey: Random Error ... Systematic Error..." Note: this site also has a sample error calculator.

• Israel, Glen D. (1992)  Determining sample size. Gainesville, FL: Florida State University, Cooperative Extension Service.

  "Perhaps the most frequently asked question concerning sampling is, "What size sample do I need?" The answer to this question is influenced by a number of factors, including the purpose of the study, population size, the risk of selecting a "bad" sample, and the allowable sampling error. This paper reviews criteria for specifying a sample size and presents several strategies for determining the sample size."

• Israel, Glenn D.  (n.d.) Sampling issues: nonresponse.  Gainsville, FL: Institute of Food and Agricultural Sciences, Extension Digital Information Source (EDIS).

  "A number of issues related to sampling are reviewed by the author in Sampling The Evidence Of Extension Program Impact and Determining Sample Size. During the discussion of sampling, it was noted that sample size referred to the number of responses that need to be obtained. But no matter how well the sampling design is planned, a poor response rate to a mail or telephone survey or to interviews can render a study virtually useless. In an effort to obtain enough data for the analysis, many researchers commonly add 10% to the sample size to compensate for persons that the researcher is unable to contact. The sample size also is often increased by 30% to compensate for nonresponse. Thus, the number of mailed surveys or planned interviews can be substantially larger than the number required for a desired level of confidence and precision. However, inflating the sample size does not necessarily address potential bias from nonresponse. The following section discusses strategies for addressing nonresponse."

• Galloway, Alison (1997)  Sampling: a workbook. Edinburgh: Kate Galloway.

  "When undertaking any survey, it is essential that you obtain data from people that are as representative as possible of the group that you are studying. Even with the perfect questionnaire (if such a thing exists), your survey data will only be regarded as useful if it is considered that your respondents are typical of the population as a whole. For this reason, an awareness of the principles of sampling is essential to the implementation of most methods of research, both quantitative and qualitative." {Note: This site has had problems since October 2002 - it may be back online at some point.]

• Kennedy, John M. (1993)  A Comparison of telephone survey respondent selection procedures. Bloomington, IN: Indiana University, Center for Survey Research.

  "In this paper, I present comparisons of various respondent selection procedures. I determined that the various procedures have differential impacts on survey estimates. Some procedures are more effective than others. By comparing the data from the Indiana Poll with decennial census data collected at the same time, I am able to measure the differences. "

• Kennedy, John M. (1994)  An odyssey through RDD sampling procedures. Bloomington, IN: University of Indiana, Center for Survey Research.

  "This article was published in the NNSP Newsletter in Winter, 1994... This is an article about my experiences with various RDD [Random-Digit-Dial] sampling procedures. It describes my wanderings through a variety of sampling techniques and my observations of each. I wrote this article from the point of view of a practitioner, not a scientist, so I won't claim that I have systematically observed the techniques I describe. My continuing goal has been to find an RDD sampling procedure that provides accuracy and efficiency. Accuracy means that I feel the sample is representative; efficiency means that the sampling technique produces accuracy in a cost-effective manner."

• NCS Pearson Inc.  (2000)  Sample size and confidence interval calculator. Bloomington, MN: NCS Pearson Inc.

  Answers to two frequently asked questions: "This calculator will help you answer two questions.
  (1) How many completed surveys do I need to have a reasonably accurate view of the entire population?
  (2) How confident can I be that the information I collected is representative?" Note that the site now requires registration.

• Niles, Robert  (2000)  So how come a survey of 1,600 people can tell me what 250 million are thinking?.  Los Angeles, CA: RobertNiles.com.

  A painless introduction to sampling error - more interesting stuff at the same site. Example of style: "The best way to figure this one out is to think about it backwards. Let's say you picked a specific number of people in the United States at random. What then is the chance that the people you picked do not accurately represent the U.S. population as a whole? For example, what is the chance that the percentage of those people you picked who said their favorite color was blue does not match the percentage of people in the entire U.S. who like blue best?"

• O’Neill, Edward T., McClain, Patrick D. & Lavoie, Brian F. (1998)   A methodology for sampling the World Wide Web. Dublin, OH: Online Computer Library Center, Inc. (OCLC)

  "The rapid growth in the number of libraries providing Web access services has created a need for reliable, timely statistics characterizing the content of Web-accessible information. The size of the Web makes it impractical to develop descriptive statistics based on an exhaustive survey. An alternative approach is to collect a representative sample of Web pages. This report describes a methodology for sampling the content of the Web through the use of randomly generated IP addresses."

• Piazza, Tom (1996)  Telephone sampling questions and answers   Berkeley, CA: University of California, Berkeley, Survey Research Center.

  "These questions arose during various telephone surveys conducted by SRC-Berkeley...
  Many of the questions concern the appropriate outcome or disposition code to assign to a case. Some outcomes mean that a case is excluded from the sample for purposes of calculating response rates – unlike refusals or respondents who can never be found at home. See the discussion of basic rules for some general guidance on this matter."

• Trochim, William M.K. (2000)  Probability sampling. Ithaca, NY: Cornell University, College of Human Ecology.

  "A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. Humans have long practiced various forms of random selection, such as picking a name out of a hat, or choosing the short straw. These days, we tend to use computers as the mechanism for generating random numbers as the basis for random selection."

• University of Minnesota. Minnesota Population Center. (2000)  IPUMS - Integrated public use microdata series: census microdata for social and economic research. Minneapolis, MN: University of Minnesota, Minnesota Population Center.

  "The IPUMS consists of twenty-five high-precision samples of the American population drawn from thirteen federal censuses. Some of these samples have existed for years, and others were created specifically for this database. The twenty-five samples, which span the censuses of 1850 to 1990, collectively comprise our richest source of quantitative information on long-term changes in the American population. However, because different investigators created these samples at different times, they employed a wide variety of record layouts, coding schemes, and documentation. This has complicated efforts to use them to study change over time. The IPUMS assigns uniform codes across all the samples and brings relevant documentation into a coherent form to facilitate analysis of social and economic change. "

• U.S. Department of Commerce. Bureau of the Census. (1992)  1990 sample design and estimation. Minneapolis, MN: University of Minnesota, Minnesota Population Center.

  Describes the procedures adopted for sampling households in the 1990 Census of Population and Housing. Descriptions of earlier procedures exist at the same site. [Originally published as "Chapter 4, Sample Design and Estimation," 1990 Census of Population and Housing: Public-use Microdata Samples Technical Documentation, U.S. Department of Commerce, Bureau of the Census, Washington, DC, 1992, pp. 4-1 to 4-7.]

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