Qualitative Studies: Case Studies

1. Qualitative Studies:Case Studies

2. Introduction • In this presentation we will examine the use of case studies in testing research hypotheses: • Validity; • Quality; • Analysis.

3. Case study methodology • Case studies are used as a research tool to test hypotheses in an ex post facto manner ie when the researcher CAN NOT control ANY of the variables under investigation. • Case studies are therefore usually concerned with investigating how or why events occurred.

4. Case study methodology • Case studies involve examining what has occurred to identify reasons for the occurrence. • The lack of control leads to a number of potential problems with case study based research which, if not controlled, can invalidate any conclusions drawn from the research.

5. Case study methodology • There are three typical criticisms of the case study methodology: • Problems of bias; • Lack of generalisability; • They are only suitable for explanatory investigations.

6. Problems of bias • Many researchers are concerned with the apparent lack of rigour involved in case study research (as compared to experimental studies) bought about by investigator bias. • Relationships may appear simply because you are looking for them.

7. Generalisability • Because case studies allow no control over any of the variables many question whether conclusions drawn from one specific example can be applied generally.

8. Exploratory studies • Many argue that only experimental research can be used to establish causal relationships with case studies being limited in use to the explanatory stage of a research project. • This view misses the most important aspect of a case study, its holistic and real-life nature.

9. Design of a case study • Many of the concerns associated with the use of case studies can be minimised by careful design. • Validity is the most important issue to be addressed in the design of a case study methodology.

10. Concepts and indicators • Most research involves measuring concepts (eg intent). • Unfortunately concepts are normally abstract and thus not directly observable. • Thus indicators (variables) are specified which operationalise the concept.

11. Concepts and indicators • Unfortunately not all concepts can be easily operationalised and thus the validity of the indicator is drawn into question. • One way around this problem is to use multiple indicators each of which are a partial operationalisation of the concept.

12. Validity of indicators • The next question is how to evaluate the validity and reliability of the indicators: does it measure what it is supposed to measure.

13. Validity of indicators • There are various types of validity: • Face validity; • Criterion validity; • Construct validity; • Internal validity; • External validity.

14. Face validity • Face validity involves a subjective evaluation of the indicator using: • Logic; • Common sense; • Previous reported studies; • Jury/Expert opinion.

15. Criterion validity • Criterion validity uses a well established indicator to compare the performance of a newly defined indicator. • The new indicator behaves in a similar manner to the established one then validity is assumed.

16. Construct validity • Construct validity involves examining one indicator and its relationships to other presumed indicators of the same concept. • High correlation between the indicators implies they are valid measures of the concept. • Perfect correlation implies redundancy.

17. Construct validity • In case studies three strategies can be used to increase construct validity: • multiple sources of evidence; • chains of evidence; • informants comments.

18. Internal validity • Internal validity examines the extent to which a research finding (eg A lead to B) is valid. • Internal validity involves eliminating all other relationships between other variables (eg C) and B.

19. Internal validity Independent Variable Dependent Variable

20. Internal validity Other possible Variables X Independent Variable Dependent Variable

21. Internal validity • There are numerous factors that can effect internal validity. • History/Maturity • a long time between observations can cause problems. • Testing • people behave differently under test conditions.

22. Internal validity • Selection • observed differences could be the result of differences within the group. • Mortality • systematic dropout from the study.

23. Internal validity in case studies • The nature of case studies require inferences to be made during data collection. • Is the inference correct? • Have all possibilities been considered? • Is the evidence convergent? • The overall quality depends on the quality of the investigator.

24. Internal validity in case studies

25. External validity • External validity is concerned with the applicability of the research results to other (non examined) populations. • Testing • attitudes may change as a result of the questioning and the sample thus be unrepresentative.

26. External validity • Sample selection • self-selecting samples could be biased - include details of those who refused to cooperate. • In case studies the use of multiple case studies usually satisfies the requirements for external validity

27. Reliability • Reliability is concerned with the reproducibility of measurements. • When attitudes are examined how do you know that each respondents ratings are the same? • Interview structure, telephone protocols, standard letters, rigour in data collection and pilot studies all help in improving reliability.

28. Analysing case study data • Each case study must be reported in detail: • the visit; • interviews; • conversations; • facts; • evidence in support/rejection of hypotheses

29. Analysing case study data • conclusions; • outstanding issues: • further investigation; • other case studies. • Across case studies: • replication; • rigourous thinking.

30. Summary • A good case study must be: • Significant; • Complete; • Consider all perspectives; • Display sufficient evidence; • Compelling.