Research Methods

1. Research Methods Observations Interviews Case Studies Surveys Quasi Experiments

2. Classes of Research Methods • Experimental • Non-Experimental • Observations • Interviews • Case studies • Surveys • Quasi-Experiments

3. Intervention Outcomes Target system

4. Approach Comparison Adapted from Yin (1994)

5. Experiments

6. Experiments • Most common method of evaluation • Primary components • Desired intervention or change • Inputs (what you vary or keep the same) • Outcomes (what you are looking for) • Vary one or more inputs and look for differences in the outcomes. • Randomize

7. Strengths Can be used to show cause and effect Quantitative techniques can be used to show strength of relationships Accepted across a wide-range of disciplines Limitations In the “real world” in may be difficult to have random assignment May not be able to create realistic conditions in a controlled setting Experiments

8. Observations

9. Observations • Used to capture the ways in which people act and interact within a particular environment/setting • Observations may be either qualitative or quantitative • Researcher will either use detailed field notes, audio or video recording to capture data • Focus may be on general aspects of behavior/phenomenon for qualitative; for quantitative, typically will focus on a particular aspect of behavior that can be quntified

10. Strengths • Extremely flexible – researcher can shift focus as new data come to light • Good method to study behaviors and other aspects of a system that may be difficult to quantify • Enables researcher to study the richness and complexity of human behavior • Data may be taken at multiple points in time

11. Limitations • Behavior to be studied must be defined in a precise, concrete manner to be recognizable • Typically takes multiple researchers • Researcher’s presence may impact participants’ behaviors • Requires meticulous attention to detail in planning, data collection, and data analysis • Results are limited to the system studied

12. Reliability How reproducible is the data? How consistent is the data? • Data collection protocol must exist (but may be modified as research progresses) • Data analysis training to assure consistency between researchers • Data analysis reliability assessment if multiple coders are used

13. Validity How well does the data collected address the characteristics of interest? • Face validity • Content validity • Criterion validity • Construct validity

14. Interviews

15. Interviews • Can produce a great deal of useful information • Can be structured, open-ended, or semi-structured • Can be used for individuals or groups of individuals (focus groups) • IRB – confidentiality and informed consent must be planned well in advance

16. Interview tips • Make sure interviewees are representative • Find a suitable location • Take a few minutes to establish rapport • Don’t put words in people’s mouths • Record responses verbatim • Keep your reactions to yourself • Use contingency questions to avoid irrelevant questions

17. Choosing Interviewees • Interviewees must be competent to answer • Interviewees must be willing to answer • Questions should be relevant to the Interviewees

18. Strengths • Rich and complete information • Question complexity can be greater than survey • Flexible (particularly if open-ended)

19. Limitations • Can produce information that is not related to research topic and/or not comparable to other data collected • More expensive than written surveys • Time consuming • Sensitive questions may not be answered honestly

20. Case studies

21. Case Studies • An evaluation method that • “investigates a contemporary phenomenon within its real-life context, • especially when the boundaries between phenomenon and context are not clearly evident” (Yin 1994, p. 13)

22. Case studies • Used to study an individual, program, event, or organization in depth for a pre-defined period of time • Researcher must know what he/she is looking for • Use multiple sources of data, e.g. interviews, direct observation, document analysis, or surveys

23. Strengths Can be done after the fact Valuable technique to highlight learnings from a project Data collection methods are relatively straight-forward Limitations Difficult to generalize the results to other parts of the organization or to other organizations. Will not show cause and effect relationships Time consuming Case Studies

24. Quality Digest Case Study Example Taking SPC to the production line • Target System: Organizations that have front line workers using SPC tools • Types of data: Direct observation, interviews, and documents • Findings: Lessons learned, road blocks, and best practices were highlighted

25. Surveys

26. Why use surveys? • A method for collecting data to describe some specific characteristics of individuals or groups of people. • A method for measuring some specific attitudes, perceptions, and beliefs of individuals or groups of people.

27. Survey design issues • Sample selection • Sample size • Data collection method • Questionnaire format • Question construction

28. Surveys • Understand how people view a particular topic without asking every person • Good method to quantify otherwise qualitative information • Data may be taken at multiple points in time • Use questionnaires or structured interviews to collect information Adapted from Creswell (1994)

29. New Program Evaluation Example SWE e-mail mentoring program • Target System: Program participants • Intervention: Mentors/mentees exchange e-mails about careers in engineering • Outcomes: • Are mentees more likely to consider engineering careers than before? • Do mentors and mentees feel more positive about themselves?

30. Strengths Can be used to create quantitative measures of “softer” types of data. Can be done at many points in time if needed Methods for evaluating the data exist Limitations Results are limited to the overall group represented in the survey sample Does not give you a way to follow up if results are inconclusive. Surveys

31. Quasi-Experiments

32. Quasi-Experiments • Used in social science, education, policy, and management systems • Use when randomization of target system and or intervention is not possible • Multiple-designs exist • Primary components • Study group • Comparison group • Intervention

33. Quasi-Experimental Approach

34. Nonequivalent Control Group Example O1 X O2 O1 O2

35. Autonomous Team Example Implementing teams in a mineral plant • Study group: Autonomous teams in a startup plant • Comparison group: Existing shift workers in the original plant • Intervention: Creation of teams • Outcomes: Job satisfaction, trust, and productivity

36. “Ideal Outcomes”

37. Trends in Opposite Direction

38. Trends with Differing Growth Rates

39. Strengths Can be used in the “real world” Does not require changes to existing organization Designs and techniques for evaluation exist Limitations Can not be used to show cause and effect May not be able to generalize the results Results can potentially be explained by other factors Quasi-Experiments