Objectives -- After completing this module, you will be able to:

• Assess the implications of sampling decisions and procedures in terms of their impact on (1) internal validity, the confidence that someone can have in research results and conclusions (internal validity); (2) on external validity, your ability to generalize research results statistically; (3) in terms of their impact on your ability to generalize research results and conclusions theoretically
• Assess the implications of sampling decisions and procedures in terms of their impact on the explanatory power of a study
• Design sampling procedures that are adequate for your own research objectives, including conducting needs assessments and evaluating programs

Class Preparation & Participation

I base the videos and readings for this module on the assumption that you have little or no prior knowledge about sampling for research purposes. If you DO have experience and knowledge, you will be able to move through the materials quickly. Feel free to do so. I lay out a specific step-by-step approach to covering the materials based on my assumption of little prior knowledge. The learning guide for this week is not specific to any one of the required materials. Use it as a guide to what you need to get from the materials as a whole. If you cannot answer a question in the learning guide, you missed something that I consider important about sampling. In short, use this learning guide as a sort of checklist and check off the questions in it one by one as you work through the materials for this week to make sure you get a good understanding of the basics of sampling. Post your answers to Questions 2, 3, 5, 6, 7, 10, 13, 16 and 17 to the Week 4 Discussion Board by 6:30 PM on Wednesday, October 05.

Required Materials

Basics of Sampling This scary cheat sheet prepares you for the rest of the materials (I hope). For now you do not need to focus on the sections that deal with types of samples (probability or random versus non-random).

Learning Guide: Basics of Sampling The learning guide poses questions. If you can answer the questions, you are probably getting everything you need from the required materials. Use the guide to identify the areas where you need more information.

The Goals of Research Design You have seen this before. Keep it at hand as you think about the role of sampling in achieving these goals.

Topic 2: The details of sampling

Blair, E. & Blair, J. (2015) Applied Survey Sampling, Sage, Thousand Oaks, CA. Ch. 1, Introduction to Sampling, pp. 3-26. e-reserve This reading is repetitive of what is in my cheat sheets to some degree. I ask you to read it because sampling is complex and because different very well qualified people do have different perspectives about some key features. For example, I am not nearly as concerned with generalizing conclusions based on non-probability samples as Blair & Blair are. We just disagree, largely due to some epistemological differences. This is a lengthy reading, BUT you do not have to read the entire chapter in detail. Here is my advice about reading this selection. Read very quickly or even skip sections 1.1 (Introduction) and 1.2 (A Brief History of Sampling). Read section 1.3 (Sampling Concepts) carefully. I think it is superior to my cheat sheet. I encourage you to work through Case Study 1.1 by yourself to see how well you understand the material covered in section 1.3. Read Section 1.4 (Guidelines for Good Sampling), which is excellent -- also see Exhibit 1.2 on page 23. This table is excellent and I encourage you to make sure you understand it clearly. It will be a critical guide for Assignment 3.

Kreamer, Helena Chmura & Blasey, Christine. (2016) How Many Subjects? Statistical Power Analysis in Research. Sage Publications, Thousand Oaks, California. Chapter 2, pp. 22-29. e-reserve This is an easy-to-read explanation of the concepts of power and precision, the key paramaters you must use to decide "how big" a sample you need. I don't expect you to actually calculate a sample size in Assignments 4 and 5. I do expect you to understand these concepts thoroughly and be able to explain how you would apply these concepts to determine sample size for the research design you create in Assigment 4 and the comparison design in Assignment 5. You should also use these concepts in your discussion of the adequacy of the sample in the article you analyze for Assignment 3.

Additional Resources about Sampling

Abrams, L.S. (2010) Sampling "hard to reach" populations in qualitative research. Qualitative Social Work 9(4), 536-550.

Ahern, K. & LeBrocque, R. (2005) Methodological issues in the effects of attrition: simple solutions for social scientists. Field Methods 17(1), 53-69.

Barratt, M.J., Ferris, J.A. & Lenton, S. (2014) Hidden populations, online purposive sampling, and external validity: Taking off the blindfold. Field Methods 27(1), 3-21.

Bethlehem, J. (2016) Solving the nonresponse problem with sample matching. Social Science Computer Review 34(1), 59-77.

Bryant, J. (2014) Using respondent-driven sampling with "hard to reach" marginalised young people: Problems with slow recruitment and small network size. International Journal of Social Research Methodology 17(6), 599-611.

De Boni, R., Do Nascimento Silva, P.L., Bastos, F.I., Pechansky, F. et al. (2012) Reading the hard-to-reach: A probability sampling method for assessing prevalence of driving under the influence after drinking in alcohol outlets. PLoS ONE 7(4), 1-9.

Draugalis, J.R. & Plaza, C. (2009) Best practices for survey research reports revisited: Implications of target population, probability sampling, and response rate. American Journal of Pharmaceutical Education 73(8), 1-3.

Ellard-Gray, A., Jeffrey, N.K., Choubak, M. & Crann, S.E. (2015) Finding the hidden participant: Solutions for recruiting hidden, hard-to-reach, and vulnerable populations. International Journal of Qualitative Methods 14(5), 1-10.

Gile, K.J. & Handcock, M.S. (2010) Respondent-driven sampling: An assessment of current methodology. Sociological Methodology 40(1):, 285-327.

Glick, P. (2008) Restating the case: The benefits of diverse samples for theory development. Psychological Inquiry 19(2), 78-83.

Griffith, D.A., Morris, E.S. & Thakar, V. (2016) Spatial autocorrelation and qualitative sampling: The case of snowball type sampling designs. Annals of the American Association of Geographers 106(4), 773-787.

Kondo, M.C., Bream, K.D.W., Barg, F.K. & Branas, C.C. (2014) A random spatial sampling method in a rural developing nation. BMC Public Health 14(1), 1-15.

Mookherji, S. & LaFond, A. (2013) Strategies to maximize generalization from multiple case studies: Lessons from the Africa Routine Immunization System Essentials (ARISE) project. Evaluation 19(3), 284-303.

Murray, G.R., Rugeley, C.R., Mitchell, D. & Mondak, J.J. (2013) Convenient yet not a convenience sample: Jury pools as experimental subject pools. Social Science Research 42(1), 246-253.

Nguyen, P. (2004) The census, sampling and African Americans. Western Journal of Black Studies 28(1), 292-302.

Noy, C. (2008) Sampling knowledge: The hermeneutics of snowball sampling in qualitative research. International Journal of Social Research Methodology 11(4), 327-344.

Polit, D.F. & Beck, C.T. (2010). Generalization in quantitative and qualitative research: Myths and strategies. International Journal of Nursing Studies. 47(11) 1451-1458.

Roy, K., Zvonkovic, A., Goldberg, A., Sharp, E. & LaRossa, R. (2015) Sampling richness and qualitative integrity: Challenges for research with families. Journal of Marriage and Family 77(1), 243-260.

Seawright, J. & Gerring, J. (2008) Case selection techniques in case study research: A menu of qualitative and quantitative options. Political Research Quarterly 61(2), 294-308.

Simmons, A.D. & Bobo, L.D. (2015) Can non-full-probability internet surveys yield useful data? A comparison with full-probability face-to-face surveys in the domain of race and social inequality attitudes. Sociological Methodology 45(1), 357-387.

Sydor, A. (2013) Conducting research into hidden or hard-to-reach populations. Nurse Researcher 20(3), 33-37.

Yin, R.K. (2013) Validity and generalization in future case study evaluations. Evaluation 19(3), 321-332.

Zhu, J.J.H., Mo, Q., Wang, F. & Lu, H. (2010) A random digit search (RDS) method for sampling of blogs and other user-generated content. Social Science Computer Review. 29(3), 327-339.