CBR 302 A HandsOn Approach to Qualitative Methods

CBR 302 A Hands-On Approach to Qualitative Methods and Analysis

Introduction About making the world a better place via Community Based Research (CBR) : ü ü We’re all part of solution No one way Use dialogue Deep learning to get at root of problem (Aha)

Learning Objectives Upon completion of this workshop, you will be able to: Describe qualitative research concepts Explain some of the important terms associated with qualitative research List misconceptions and limitations of ‘qualitative’ methods Follow the steps to develop a research project Differentiate between Methods, Methodology and Research Develop strategies and skill for gathering and managing 'good' data (e. g. recording, transcribing, note-taking, qualitative software, etc) Participate in the analysis of data and develop strategies for coding data Learn effective approaches for presenting qualitative data

Qualitative Research: Prevalent Concepts “seek … how social experience is created and given meaning” (Denzin & Lincoln, 2000, p. 8) Produce findings without statistical procedures or quantification Accept different ways of making sense of the world Seek participant interpretation Emphasize participant more than researcher view

Qualitative Research: Prevalent Concepts Researcher primary instrument Focus on subjective (or lived) experience Obtain descriptive, rich, in-depth accounts Allow ‘from the ground up’ theory building Can use open, exploratory, ‘unstructured’ means

One Definition of Qualitative Research Understanding actions & meanings in their social context (or ‘natural settings’) § § Focus on subjective (or lived) experience Descriptive, rich, in-depth Allows for ‘from the ground up’ theory building Open ended, exploratory, ‘unstructured’

What is Qualitative Data? Data in the form of words Language in the form of texts such as : interviews, questionnaire responses & long text Observation of settings & events (researcher’s notes) Personal document such as letter, diaries, etc

Aspects of QR Methods Increases understanding of context, content Increases understanding of what is affected & how Analyzes why particular impacts are occurring Assesses how policy can be improved

Qualitative Research: Common Misconceptions Easier than quantitative research Not methodologically rigorous Doesn’t reveal anything about ‘populations’ Data more manageable Only experts can do it Only a first step (for survey development)

Limitations of Qualitative Methods Can be difficult to focus (holistic) Researcher may have difficulty reconciling differences in the data & assessing how representative they are Cannot make predictions Cannot be objective since objectivity rejected on principle Cannot be replicated

Qualitative Research Stages State Research Purpose and Problem Design Research Project - Method Conduct Research Project Data Collection & Analysis Disseminate Results

Exercise 1: Define Qualitative Research in your own words & discuss its application in the context of your work?

Methods, Methodology & Research Method: The practice of research The specific techniques that are employed in collecting, analyzing and disseminating the research Methodology: The experience of research The situations, contexts and social factors that informs the researcher’s and participants’ involvement in the project

Method A set of procedures and techniques for gathering and analyzing data (Strauss & Corbin, 1998, p. 3) • Observation - Participant - Nonparticipant • • • Interview Focus Group Case study Short-answer Questionnaire Case study Life history, Narrative,

Methods, Methodology & Research Methodology – Process issues that can significantly impact on the quality and validity of the research data § § Relationships to the community Power and research Nature of the questions Role of prior knowledge in developing the project and analyzing the data

Research Design and Methods … there is no one design or method appropriate for all Instead, each partnership has to decide what works best for its research question and intervention goal in its particular community context. P. 70 Reference: Minkler & Wallerstein, 2003

Data Rich Description Captures sufficient data (e. g. , quotations) so reader can follow process, results, themes, conclusions Captures sufficient detail to capture themes or patterns to summarize essence of data

Method Exercise 1: Continued Working with your table mates please discuss: What factors should you consider when making the decision about which method to use to collect the data? Be prepared to discuss your recommendations with the class.

Stages of Research Identifying the Research Project – Collaborative Venture with community partners Clearly identify the focus and objectives of the research project Designing the Research Project – Method Conducting the Research Project Data Collection & Analysis Dissemination of the Data

Develop Interview Guide Using Standard Open–Ended Questions Sub-questions should help answer main research question Ask only what you need to know Ask easier questions first Do not ask more than one question within a sentence Seeking more than a yes or no answer Use exploratory words e. g. , “describe, ” “tell me”, “how, ” “where, ” ‘imagine, ” “think back”

Develop Interview Guide Using Standard Open–Ended Questions Create dialogue, encouraging participant to talk Ask factual before controversial ones Ask demographical questions as required Last question for additional comments, interview impressions No leading questions

Develop Interview Guide Unstructured Open-Ended Interview Ask one question and allow questions to emerge Good for initial explorations of topic when not much known

Develop Interview Guide Closed fixed questions Participants choose from possible answers Useful for non-practiced interviewers

Group Exercise 2: Using the scenarios that you have been given, please identify the following: 1. A specific focus of the research project 2. Two primary goals of the research project 3. The method that you would use to conduct the research 4. Four research / interview questions Be prepared to present your group’s results to the class

Considerations for Sampling Exclusion: Decide if, why, how will exclude certain members who want to be in the study Collaboration: Decide norms, decision process, how to make change, procedures to encourage follow principles (e. g. , member checks; data collection/analysis personnel)

Data Analysis The process through which data are organized so that comparisons can be made and conclusions drawn

Analyzing the Data Manifest Content – the actual ideas that are present and observable in the data Latent Content – the ideas and themes that emerge as you analyze and interpret the data

Data Analysis: Levels of Exploration Need to look below the surface (Described from Interview) Events Trends, Patterns Themes/Structure (Forces/pressures at play) Reference: Hagland, (1997), see also Argyris, C. & SchÖn (1974)

Group Exercise: Data Collection Using a copy of the newspaper provided, please conduct a research analysis of the document and explore the following: What messages does the media provide you about issues of gender? In what way are those messages evidenced? What are the potential benefits and drawbacks of providing those messages? Record your findings and make notes of your observations and responses – verbal, non-verbal and emotional.

Data Analysis Coding § a word or short phrase that serves to organize or categorize your data (community; equitable coverage; stereotyping) Themes § clusters or patterns of codes that thematically ‘hang together’ (positive portrayal of women; equitable portrayal; )

Coding means going through your data systematically (i. e. , read each transcript carefully) Decide ahead of time how you are going to approach analyzing the data You get to make up the names for the codes Remember – not everything is important Choose what is important to study by going back to your goals & objectives.

Categories • Codes are grouped to see where patterns occur (observations over time causing event) • From your exercise, examine both sets of data to see if there any codes that could be grouped together to form a category • Tip: look at definitions of codes as well E. g. , some codes might be all about “process” • Try to see if any other categories could be formed

Themes or Structure After categories are formed ………. . look for themes or structure (reasons why you might be getting the particular pattern or trend)

Exercise 3: Data Analysis Using the data you have collected, try to develop a simple ‘coding scheme’ of main ideas (maximum 10 codes) Pick 1 code to apply: Look for all the times that this idea is mentioned in your data. Analyze your code: What are the range of experiences here? What patterns/themes do you see here? What does this mean?

Discussion Questions What are themes that emerged? What are the factors that determined the choice of the particular themes? How did your prior knowledge about the issue impact on themes that you identified? How would that knowledge impact on the analysis of the data?

Collaborative Data Analysis Seeing the world through different eyes – § Staff, clients & researchers all come with different perspectives § The more people you involve in developing your coding scheme – the better. Different eyes lends credibility to data analysis

Collaborative Data Analysis Members of the team can examine specific parts of the data E. g. , stratified data: executive interviews by some members; low-income interviews coded by other team members Some members may code only for a certain period of time due to their availability Code because of knowledge and expertise of certain member Code because of lack of knowledge and expertise of certain members

Advocacy Look for those persistent patterns that block social change Once you identify the patterns, you can start to do something about them

Analysis QR and CBR seeks “authenticity” or ‘truthfulness” Because of research rigor, results are credible, demonstrates authenticity, truthfulness, provides voice Reference: Lincoln & Guba, 2000

Strategies for Optimizing Validity Triangulation: - the process of using a variety of data sources and approaches to understanding a problem. Thick description: Rich, thick descriptions of the participants allow readers to assess ‘transferability’ to other research settings and context. Member checks: Asking participants to reflect on preliminary analyses and read rough drafts of chapters/reports to ensure accuracy. These “checks” can take place individually or in a group setting. Clear audit trail: Keeping a complete and accurate record of the steps taken to come to the conclusions. This includes keeping organized notes, a clear and consistent coding scheme and a ‘transparent’ data analysis process in a reflective journal.

What about Software? PROs: § manages large amounts of data, gives an ‘air’ of legitimacy, makes organization and retrieval easy, provides structure especially for teams not colocated § Helps easily look at various slices of the data § Helps track decisions over time CONs § steep learning curve (may not be worth it), too much structure, expense Remember. . . software manages. . . YOU DO ANALYSIS!

Types of Software § § NUDIST- N 6 NVivo Atlas. ti SPSS

Dissemination Approaches for presenting qualitative data effectively § Predefine who your audience will be § How do they receive their communications now? § What will help them hear the message? § Write in a language that is easily understood § Can depict environment in which research took place

Critical Questions to Examine Who owns the data? What commitment have you made to share the findings/results with the community? How do you ensure that the results are accessible to the community? What role will the community play in dissemination, and any resulting action outcomes?

Presenting Your Data Describe your methods – tell your audience exactly what it is that you did to come up with your results. Be descriptive & analytical (so conclusions are accepted) Use lots of quotes (as evidence, let people see them) Consider tables, charts, figures, models & diagrams Contextualize – where does this data come from, who does it apply to (particularity, applicability). . . Speak with confidence about your data

Final Phase What are the future indications of your research? Are there other areas that have been highlighted for future research? Are there any other steps to be taken?

Workshop Reflections Any surprises? What are the most important learnings/messages that you will take away from today? Is there anything that you will plan to do differently?

Learning Objectives Having completed this workshop you should now be able to: Describe qualitative research concepts Explain some of the important terms associated with qualitative research List misconceptions and limitations of ‘qualitative’ methods Follow the steps to develop a research project Differentiate between Methods, Methodology and Research Develop strategies and skill for gathering and managing 'good' data (e. g. recording, transcribing, note-taking, qualitative software, etc) Participate in the analysis of data and develop strategies for coding data Learn effective approaches for presenting qualitative data

CBR 302 A Hands-On Approach to Qualitative Methods and Analysis