Methodological Issues in Mixed Methods Data:

The Use of Qualitative and Quantitative Data in Health

Services Research.Susan Zickmund, PhD

Director, Qualitative Research CoreCHERP, VA Pittsburgh

Division of General Internal MedicineUniversity of Pittsburghsusan.zickmund@va.gov

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.

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.

Traditional Qualitative Approach

• 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.

Description of Qualitative

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.

Classic Components

• Types of data, qualitative methods, determining sample size, recruitment, and coding philosophies.

Data Types

• Interviews/Focus Groups.

• Observational.

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.

ObservationalObservational

• 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.

Qualitative Methodologies

• Important to have one to guide data collection and analysis.

• Types include:

• Grounded Theory.

• Ethnography.

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.

EthnographyMethod 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.

Sample Size• Uses “thematic saturation” – idea that once no new themes arise, data collection is complete.

• Minimum sample size for saturation is around 15-20.

• Maximum sample size is any size interfering with “case oriented thrust” of qualitative research (60-100).

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.

Coding Philosophies

• Single investigator.

• Research team approach.

• Independent coders.

Mixed Methods Approach

Mixed Methods Terminology

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

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.

Time and emphasis (in CAPS).

qual QUANT

qual preliminary

quant QUAL

quant preliminary

QUANT qual

qual follow-up

QUAL quant

quant follow-up

Mixed Methods Designs

qual QUANT

qual preliminary

quant QUAL

quant preliminary

QUANT qual

qual follow-up

QUAL quant

quant follow-up

Mixed Methods DesignsA 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

Mixed Methods DesignsA 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

Mixed Methods DesignsA 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

Mixed Methods DesignsA major qualitative study that uses a follow-up quantitative study at the end.

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.

qual QUANT

qual preliminary

quant QUAL

quant preliminary

QUANT qual

qual follow-up

QUAL QUANT

performed @ same time

Simultaneous Design

Where qualitative and quantitative methods reinforce simultaneously.

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.

Integrating Mixed Methods (IMM):

Overview

Provide practical approach to: Research design. Analytic strategies that best

facilitate integrating qualitative

data into empirical studies.

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.

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.

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.

Coding Philosophy

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

Interpreting Kappa Statistics

0.00 = poor 0.01-0.20 = slight 0.21-0.40 = fair 0.41-0.61 = moderate 0.61-0.80 = substantial 0.81-1.00 = almost perfect Rule of thumb: shoot for 0.70 and above

Data Transformation

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

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.

Computer Data Management

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).

Computerized Data Output

Useful for master files for integration with other data, and facilitates intercoder reliability files.

MST VIEW BETTER WORSENO CHANGE

SEC VIEW BETTER WORSE

NO CHANGE

1 C436 0 0 0 1 C436 0 0 0

2 C470 1 0 0 2 C470 0 1 0

3 C107 0 1 1 3 C107 0 1 1

4 C69 1 0 0 4 C69 0 0 1

5 C1 0 1 1 5 C1 0 1 1

6 C477 0 0 0 6 C477 0 0 0

IMM Summary *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.

Examples of Using Qualitative Data in Statistical Models

Qualitative data as a predictor variable.

Qualitative data as an outcome variable.

IMM: Simultaneous mixed methods design: 1 hour semi-structured interview, survey data, demographics, clinical data.

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.

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).

Distribution of Answers

Data available on 825 participants for “View of Self” code: Better View 22.5% Unchanged View 49.8% Worse View 27.6%

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.”

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.’ ”

Univariate Results for Predictors of Mortality

“View of Self” <0.001Age <0.001Employed 0.001> High School education 0.007Cancer staging <0.001Sickness Impact Profile

Ambulation 0.017Appetite 0.004Physical Sub-scales 0.011

Final Multivariable Model for Predictors of Mortality

Variables p-value

View of self 0.080

Age <0.001

Cancer Stage <0.001

SIP-Appetite 0.005

SIP-Ambulation 0.008

Racial Disparities QI Project

Racial Disparities in Satisfaction with VA CareZickmund 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

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.

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.

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.

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.”

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

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.”

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.”

Statistical Analysis of Qualitative Data

• 1. Using Chi Square statistics on codes.

Total(N=60)

African American

(N=30)

White(N=30) p-

value

n % 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

1811 36.7 7 23.3 0.40

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.

Themes of Satisfaction and Dissatisfaction by Domain

Odds Ratiofor 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

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.

Questions?Susan Zickmund, PhD

Director, Qualitative Research CoreCHERP, VA Pittsburghsusan.zickmund@va.gov

412-954-5259