1. Methodological Issues in Mixed Methods Data:
The Use of Qualitative and Quantitative Data in Health Services Research.
Susan Zickmund, PhD
Director, Qualitative Research Core
CHERP, VA Pittsburgh
Division of General Internal Medicine
University of Pittsburgh

2. Goal for the Cyber Seminar
Briefly describe the ABCs of qualitative research.
Provide an introductory guide to mixed methods designs.
Suggest practical ideas on how to best transform qualitative themes into numerical information and then to integrate that into a final analysis plan.

3. Organization of the Seminar
Traditional qualitative: description, data types, methods, sample size, recruitment, coding.
Mixed methods: traditional designs, an “Integrated Mixed Methods” approach (with methods, sample size, recruitment, coding), and conclude with examples of integrated mixed statistical models.

4. Traditional Qualitative Approach

5. Description of Qualitative
The focus is on the participants’ subjective viewpoints.
Words/images are the primary data elements.
The approach is inductive.
Theory development may be a main outcome of the analysis.

6. Central Characteristics
Having an iterative /open ended approach to the data.
Observations or coding schemes emerge directly from the text.
The researcher strives to avoid bias when interpreting the data.

7. Classic Components
Types of data, qualitative methods, determining sample size, recruitment, and coding philosophies.

8. Data Types
Interviews/Focus Groups.
Observational.

9. Interviews / Focus Groups
Interviews: Allow in-depth discussion with one participant; effective for sensitive topics; interviewer controls the discussion.
Focus Group: Allows participants to interact; group dynamics provide unique insight; moderator has less control over discussion with one participant.

10. Example: Hand washing study.
Observational
A complex situation where a researcher would need to observe what is occurring in order to best understand the situation.
Example: Hand washing study.
Participants may have reasons for dishonesty.
The activity is open and observable.

11. Qualitative Methodologies
Important to have one to guide data collection and analysis.
Types include:
Grounded Theory.
Ethnography.

12. Grounded Theory Most prominent method in medicine.
Uses constant comparisons between cases.
Can change recruitment goals based on previous findings.
The goal is an emerging theory.

13. Ethnography
Method of anthropologists.
Involves field notes; goal to observe and understand a culture.
Effective for an unique or unknown cultural dimension of medicine.
Requires a research question best fitted for this method.

14. Sample Size
Uses “thematic saturation” – idea that once no new themes arise, data collection is complete.
Minimum sample size for saturation is around
Maximum sample size is any size interfering with “case oriented thrust” of qualitative research (60-100).

15. Recruitment: Purposeful Sampling
The goal of recruitment is to purposefully section special cases.
It does not seek “generalizability.”
Types of sampling used include:
Extreme / maximum variation.
Snowball sampling.

16. Coding Philosophies Single investigator. Research team approach.
Independent coders.

17. Mixed Methods Approach

18. Mixed Methods Terminology
Multiple types of qualitative data or using experts with different academic backgrounds (“triangulation”).
Newer: Integrating qualitative and quantitative data collection together.

19. The Qualitative-Quantitative Divide
To some, qualitative is seen as incommensurate with empirical data.
Thus, there is a need to conduct the qualitative study in a mixed methods design so as to best overcome this divide.

20. QUANT  qual qual follow-up
Mixed Methods Designs
Time and emphasis (in CAPS).
qual  QUANT qual preliminary
quant  QUAL quant preliminary
QUANT  qual qual follow-up
QUAL  quant
quant follow-up

21. QUANT  qual qual follow-up
Mixed Methods Designs
A smaller qualitative study designed to provide data for a larger quantitative one (often survey based).
qual  QUANT qual preliminary
quant  QUAL quant preliminary
QUANT  qual qual follow-up
QUAL  quant
quant follow-up

22. QUANT  qual qual follow-up
Mixed Methods Designs
A small quantitative study that is the set- up for the major qualitative study to follow.
qual  QUANT qual preliminary
quant  QUAL quant preliminary
QUANT  qual qual follow-up
QUAL  quant
quant follow-up

23. QUANT  qual qual follow-up
Mixed Methods Designs
A major quantitative study that uses qualitative data to gain insight into its findings.
qual  QUANT qual preliminary
quant  QUAL quant preliminary
QUANT  qual qual follow-up
QUAL  quant
quant follow-up

24. QUANT  qual qual follow-up
Mixed Methods Designs
A major qualitative study that uses a follow-up quantitative study at the end.
qual  QUANT qual preliminary
quant  QUAL quant preliminary
QUANT  qual qual follow-up
QUAL  quant
quant follow-up

25. Rarely Integrated at Analytic Level
These studies are sequential.
Participants infrequently complete all portions of the data.
These cases do not lend themselves readily to analytic integration.

26. QUANT  qual qual follow-up
Simultaneous Design
Where qualitative and quantitative methods reinforce simultaneously.
qual  QUANT qual preliminary
quant  QUAL quant preliminary
QUANT  qual qual follow-up
QUAL  QUANT
same time

27. Simultaneous: Gold Standard
Quantitative (demographics, surveys, clinical) and qualitative data is collected from all participants.
Analysis plan integrates the quantitative / qualitative data together.
Few examples, but is the best method for fully interpreting data in an empirical study.

28. Integrating Mixed Methods (IMM): Overview
Provide practical approach to:
Research design.
Analytic strategies that best
facilitate integrating qualitative
data into empirical studies.

29. Data Type, Qualitative Methodology
The data type used would be dictated by the research question and may not differ.
One qualitative method that lends itself well is the quasi-statistical method by Crabtree and Miller*, a methodological approach developed for health research.
Doing Qualitative Research, 1992.

30. Sample Size and Recruitment
Sample size, rather than using “thematic saturation,” would be determined by sample size calculation.
Recruitment, rather than using “purposeful sampling,” would be consistent with clinical research, using inclusion/exclusion criteria.
Goal is generalizability.

31. Codebook Construction
Use well-defined methods:
Inclusion/exclusion criteria for codes.
Sample ~20%-100% of cases for the codebook construction.
Audit trail, time/date stamping.
Goal is transparency of method.

32. Coding Philosophy Independent coders (two).
Agreement model to adjudicate differences (need final master file).
Inter-coder kappa statistic to measure reliability.
Goal is reliability.

33. Interpreting Kappa Statistics
0.00 = poor
= slight
= fair
= moderate
= substantial
= almost perfect
Rule of thumb: shoot for 0.70 and above

34. Data Transformation
Convert thematic analysis into present / absent (0, 1).
Convert thematic analysis into Likert scales.

35. Computer Data Management
Software programs (Atlas.ti, Nudist) allow for computerized management of:
Interview/focus group files.
Codebooks.
Codes.
Enables a level of textual complexity not possible with notes alone.

36. Computer Data Management

37. Computerized Data Output
Atlas includes the ability to output data to tables and spreadsheets (Excel, SPSS).
Atlas uses 0/1 for presence and absence of codes (Likert scales require adaptation).

38. Computerized Data Output
Useful for master files for integration with other data, and facilitates intercoder reliability files.
MST
VIEW
BETTER
WORSE
NO CHANGE
SEC 1
C436 2
C470 3
C107 4
C69 5 C1 6
C477

39. IMM Summary *Sample size calculation over “saturation.”
*Simultaneous mixed methods, where the data is collected from all participants.
*Sample size calculation over “saturation.”
*Recruitment: generalizability over “purposive sampling.”
Transparency of codebook/coding methods.
Intercoder reliability kappa statistics.
Computerized management spreadsheets.

40. Examples of Using Qualitative Data in Statistical Models
Qualitative data as a predictor variable.
Qualitative data as an outcome variable.

41. The impact of chronic disease on cancer patients' self conception
Patient Narrative Study
The impact of chronic disease on cancer patients' self conception
IMM: Simultaneous mixed methods design: 1 hour semi-structured interview, survey data, demographics, clinical data.

42. Qualitative Interview
“View of Self” Code:
“As you go through this experience, have you begun to think about yourself differently?”
Prompts used to guide beyond yes/no.

43. Additional Quantitative Data
Mortality data (current).
Charleston Comorbidity data.
Cancer Staging (time of interview).
Demographics (self report).
Sickness Impact Profile (sub-scales).
Hospital Anxiety Depression Scale (Anxiety/Depression scores).

44. Distribution of Answers
Data available on 825 participants for
“View of Self” code:
Better View %
Unchanged View %
Worse View %

45. Example of Better View of Self:
“I'm back to realizing that I do have an internal strength; that it will take me wherever I need to go in this journey. And it will be a good journey, whatever the end outcome is.”

46. Example of Worse View of Self:
“I am not the person that I was (cries). Just to grasp the concept that at a young age you're disabled, just like overnight, is very hard to swallow. That's a very hard thing to tell someone: `Too bad, your life is ruined, you just better learn to go on.’ And at 37, you're thinking: `Oh my gosh, I just had a baby.’ ”

47. Univariate Results for Predictors of Mortality
“View of Self” <0.001
Age <0.001
Employed
> High School education
Cancer staging <0.001
Sickness Impact Profile
Ambulation
Appetite
Physical Sub-scales

48. Final Multivariable Model for Predictors of Mortality
Variables p-value
View of self
Age <0.001
Cancer Stage <0.001
SIP-Appetite
SIP-Ambulation

49. Racial Disparities QI Project
Racial Disparities in Satisfaction with VA Care
Zickmund SL, Burkitt KH, Rodriguez KL, Switzer GE, Stone RA, Shea JA, Gao S, Bayliss N, Meiksin R, McClenney LM, Powell CT, Newsome ES, Allen R, Fine MJ.
Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System and Philadelphia VA Medical Center; VA Center for Minority Veterans (CMV); VHA Office of the Assistant Deputy Undersecretary for Health

50. Background
The 2008 VHA Hospital Report Card revealed racial disparities in veteran satisfaction with VA health care.
CHERP and the Center for Minority Veterans were commissioned to identify reasons for the disparity between African Americans and whites.

51. Objectives
To determine whether racial differences in satisfaction existed in overall, outpatient, and inpatient VA care.
To describe racial differences in satisfaction in eight domains of health care quality.

52. Design
Multi-site QI of 30 white/30 African American veterans (20 per site).
Telephone interviews with Likert scale and open-ended questions.
Demographic data collected.

53. Qualitative Methods
Interviews were coded by 2 coders using an iteratively developed codebook.
Intercoder reliability statistic was Kappa=0.99.
Coded themes were then aggregated within the 8 health care domains, with a distinction between “satisfied” and “dissatisfied.”

54. 8 Health Care Quality Domains
Trust in provider
Pain management
Feelings of respect
Access to medical care
Communication with providers
Coordination of care
Involvement of family and friends.
Role of race

55. Domain: Access to Care
“One of the things [that’s] a concern for me individually right now is that I’m trying to get a primary care doctor now, and that’s like, well…I haven’t had one, and I’ve been attending the VA off and on for six, seven years.”

56. Domain: Role of Race
Veteran: “A lot of times, they, especially people of color and black, Hispanics, Latino, et cetera like that, they [the providers] have a tendency to act like we’re lying, or you want to get high, or you’re trying—you know, it’s almost—you’ve got to either act out or cry or some kind of way to validate…”
Q: “That you’re really in pain.”
Veteran: “Yes, yes.”

57. Statistical Analysis of Qualitative Data
1. Using Chi Square statistics on codes.

58. Total
(N=60)
African American (N=30)
White
(N=30)
p-value n %
Domain 1: Trust in Medical Providers
Dissatisfied
Primary care provider 20 12
40.0 8
26.7
0.40
Provider(s) not giving enough information 18 11
36.7 7
23.3

59. Statistical Analysis of Qualitative Data
Using statistical modeling.
Item response theory approach (the Rasch model).
Fit random intercept logistic models were used to assess the differences between African American and white veterans accounting for domain and dissatisfaction/satisfaction themes.

60. Themes of Satisfaction and Dissatisfaction by Domain
Odds Ratio
for African Americans
(95% CI )
P-value
Trust in Medical Providers
Dissatisfied
1.40 (0.63, 3.09)
0.41
Satisfied
0.36 (0.18, 0.73)
0.005

61. Conclusion
Mixed methods allow the richness of qualitative themes to be used along with quantitative data.
Sequential designs facilitate combining qualitative and quantitative work, but do so in a segmented way.
IMM approach enables the integration of the qualitative data at the level of the statistical analysis.

62. Director, Qualitative Research Core
Questions?
Susan Zickmund, PhD
Director, Qualitative Research Core
CHERP, VA Pittsburgh