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

• Understand the kinds of research questions that can be answered through observational (cross-sectional and case study) designs;
  
• Identify inappropriate or inadequate uses of the cross-sectionaldesigns, including multi-point in time cross-sectional studies which do have a time component but are not equivalent to longitudinal designs;
• Take steps to reduce the threats to internal and external validity inherent in cross-sectional designs, particularly the threats that result from limited ability to anticipate unanticipated or accounted for differences among groups that threaten the validity of results;
• Assess the explanatory power, and internal and external validity of cross-sectional designs;
• Evaluate the quality of sampling strategies used in these designs and develop sampling strategies to help ensure that samples are adequate and that the samples for comparison groups are reasonable;
  
• Create cross-sectional designs to answer research and evaluation questions.
  

Topic 1: Cross-Sectional Designs

We are at a point in this class where you should be able to understand and apply the concepts in these last modules well. They should make sense to you now. If they do not, come to class prepared to ask questions. Anything that is not clear to you is probably not clear to others as well.

This piece by the Institute for Work & Health provides a very short explanation of the differences between cross-sectional and longitudinal designs. What Researchers Mean by Cross-Sectional vs. Longitudinal Studies

Slide show Cross Sectional Designs My comments, for what they are worth -- which may not be very much

Have a copy of Comparative Characteristics of Design Groups in class

Some Clarifications about Multiple Comparison Populations in Cross-Sectional Designs This is my cheat sheet about the use of multiple comparison groups. Do NOT confuse these comparison groups with the treatment and control groups that are central to experimental designs. The comparison groups typically differ in regard to critical traits -- like race or communities or political perspectives. These are "naturally occuring" groups -- not groups formed by the researcher administering a treatment to some people and not to others.. I think the overview of cross-sectional designs from Johns Hopkins (just a few slides) is better. They use an example of disease prevalence in multiple populations, but the same principles apply to any outcome variable, and the outcome variable typically defines the populations of interest in an observational design.

Improving the Internal Validity of Cross-Sectional Designs This document gives some good ideas about how to improve the internal validity (the critical confidence issues) in cross sectional designs. Internal validity rather than external validity is often the problem in cross sectional designs, although of coruse generalizability is critical.

This is a much-maligned group. Some researchers call anything a case study and there is a general tendency to fail to distinguish between the purely descriptive "case study as a story" and explanatory case studies. Explanatory case studies require multiple cases and they also often require that those cases represent comparison groups, just as samples for cross-sectional designs do. R.K. Yin is the absolute guru on case studies -- please read this chapter from his old, but still continuously quoted book on case studies.

Yin, R.K. (2009) Case Study Research. 4th Edition. Chapter 2, Designing Case Studies: Identifying Your Cases and Establishing the Logic of Your Case Study, pp. 24-65. E-reserve 

Additional Materials - Cross-Sectional Designs: Sampling is critical to the internal validity, external validity, and explanatory power of conclusions reached through cross-sectional designs. Consult the appropriate materials from this list for Assignment 2. You will need to understand clearly what kind of sample is taken in each article. The quality of the sample is absolutely critical

Almutairi, A.F., Gardner, G.E. & McCarthy, A. (2014) Practical guidance for the use of pattern-matching technique in case-study research: A case presentation. Nursing & Health Sciences 16(2), 239-244. DOI: 10.1111/nhs.12096.

Bennett, C., Khangura, S., Brehaut, J.C. et al. (2011) Reporting guidelines for survey research: An analysis of published guidance and reporting practices. PLoS Medicine 8(8), 1-11. DOI: 10.1371/journal.pmed.1001069.

Bethlemem, J. (2016) Solving the nonresponse problem with sample matching? Social Science Computer Review 34(1), 59-77. DOI: 10.1177/0894439315573926.

Cronin, C. (2014) Using case study research as a rigorous form of inquiry. Nurse Researcher 21(5), 19-27.

DeBoni, R., Do Nascimento Silva, P.L. Bastos, F.I. et al. (2012) Reaching the hard-to-reach: A probability sampling method for assessing prevalence of driving under the influence of drinking in alcohol outlets. PLoS ONE 7(4), 1-9. DOI: 10.1371/journal.pone.0034104.

Dragulis, J.R. & Plaza, C.M. (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.

Elman, C., Gerring, J. & Mahoney, J. (2016) Case study research: Putting the quant into the qual. Sociological Methods & Research. 45(3), 375-391. DOI: 10.1177/0049124116644273.

Freeman Herreid, C., Prod'homme-Genereaux, A., Schiller, A. et al. (2016) What makaes a good case, revisited: The Survey Monkey tells all. Journal of College Science Teaching 45(1), 60-65.

Hayward, M.W., Boitani, L., Burrows, N.D. et al. (2015) Ecologists need robust survey designs, sampling and analytical methods. Journal of Applied Ecology 52(2), 286-290. DOI: 10.1111/1365-2664.12408.

Houghton, C., Casey, D., Shaw, D. & Murphy, K. (2013). Rigour in qualitative case-study research. Nurse Researcher 20(4), 12-17.

Kamholz, B.W., Gulliver, S.B., Helstrom, A. et al. (2009) Implications of participant self-selection for generalizability: Who participates in smoking laboratory research. Substance Use and Misuse 44(3), 343-356. DOI: 10.1080/10826080802345051.

McInroy, L.B. (2016) Pitfalls, potentials and ethics of online survey research: LGBTQ and other marginalized and hard-to-access youths. Social Work Research 40(2), 83-93. DOI: 10.1093/swr/svw005

Miller, P.G., Johnston, J., Dunn, M. et al. (2010) Comparing probabiity and non-probability sampling methods in ecstasy research: Implications for the Internet as a research tool. Substance Use & Misuse 45(3), 437-450. DOI: 10.3109/10826080903452470.

Rule, P., John, V.M. (2015) A necessary dialogue: Theory in case study research. International Journal of Qualitative Methods 14(4), 1-11. DOI: 10.1177/1609406915611575.

Tingley, D. (2014) Survey research in international political economy: Motivations, designs, methods. International Interactions 40(3), 443-451. DOI: 10.1080/03050629.2014.900614.

Unicomb, R., Colyvas, K., Harrison, E. & Hewat, S. (2015) Assessment of reliable change usingn 95% credible intervals for the differences in proportions: A statistical analysis for case-study methodology. Journal of Speech, Language & Hearing Research 58(3), 728-739. DOI: 10.1044/2015_JSLHR-S-14-0158.

Walia, R., Bhansali, A. Ravikiran, M. et al. (2014) Self weighing and non-probability samples. Indian Journal of Medical Research 140(1), 150-151.

West, B.T., Sakshaug, J.W. & Aurelien, G.A.S. (2016) How big of a problem is analytic error in secondary analyses of survey data? PLoS ONE 11(6). DOI: : e0158120. doi:10.1371/journal.pone.0158120

Yeager, D.S., Krosnick, J.A., Chang, L.C. et al. (2011) Comparing the accuracy of RDD telephone surveys and internet surveys conducted with probability and non-probability samples. Public Opinion Quarterly 75(4), 709-747.

Additional Materials -- Case Study Designs -- including good information on sampling and data analysis for case studies

Additional Materials Almutairi, A.F., Gardner, G.E. & McCarthy, A. (2014) Practical guidance for the use of pattern-matching technique in case-study research: A case presentation. Nursing & Health Sciences 16(2), 239-244. DOI: 10.1111/nhs.12096.