Quality Indicators of Rigor in Qualitative Methods & Analysis

Alignment World view Theoretical Framework Research Questions Research Design Data Generation Data Analysis

World View Predict Understand Emancipate Deconstruct Positivist Interpretive Emancipate Critical Deconstruct Poststructuralist Postmodern

Theoretical Framework Transformative Pedagogy Pedagogies where learner searches for meaning through reflecting on values, social norms, and assumptions about educational, moral, and political issues/situations (Ukpokodu, 2009). TP in PETE categorized as negotiated learning, storytelling, peer teaching, case studies, and place-based pedagogies (Ovens, 2017)

Research Questions Purpose statement is clear and precise outlining the intent of the study Clear research questions that align with the world view and theory used as the lens for the study

Research Questions The primary purpose of the study was to explore how sociocultural and social justice issues are addressed and implemented in PESP programs. What intentional pedagogies do PESP faculty employ to educate for social justice?

Methods Design Researcher subjectivity/Reflexivity Context/setting Participants Data Generation Data Analysis Trustworthiness

Design Interpretive Symbolic interactionism Ethnography Phenomenology Case study Auto-ethnography Grounded theory

Design Critical interpretive design Social constructivist and transformative world views of the research team (Creswell, 2014) Explore and understand the intentional pedagogies employed by 72 PESP/PETE faculty to educate for social justice

Reflexivity Ways that research is shaped by the particular perspectives, interests, and biography of the researcher. Reflect on how own biography and assumptions influence the entire research process Important to state this for the reader – help them to understand where you are coming from and what assumptions, experience, or influence you bring to the study

Reflexivity All members of the research team are current PESP faculty Range from 1-20+ years in PETE/PESP program From 3 different countries Social justice (in PE/PETE/PESP) has been a strong influence for all members of the research team All strong advocates for social justice Published and presented on SJE

Context Detailed description of where the study took place. Help the reader understand the nature of the environment. Country, geographic region, Urban, suburban, rural Location – e.g. school, childcare, youth sport Other relevant information such as socio- cultural factors

Context PETE/PESP programs from 7 different countries

Methods Sampling Participants Detailed description of participants age, gender, ethnicity, income level, sports experience, home environment etc Description of characteristics relevant to study Sampling What sampling procedures are used in the study Purposive, case study, snowball, etc.

Methods Sampling Participants Purposive sampling 72 PETE/PESP faculty members More than 48 different PETE/PESP programs 7 different countries Time in profession (1- 30+years) Range of institutions All had a terminal degree Sampling Purposive sampling Participants who identified as PETE or Health Education faculty in an ITE program Recruited by research team through research networks, conferences, and professional listserves. Ethics – University IRB approval Informed consent of participants

Data Generation What are the data generation tools? Do they align with the researchers’ world view, research design, research questions? Discussed in sufficient detail to fully understand what happened, with whom, and when in terms of data generation Describe each data generation method in detail How conducted When collected Who

Data Generation What are the data generation tools? Observation – field notes Interviews – semi-structured, face-to-face, focus group Video/photo recall Photo voice Documents – curriculums, policy papers, etc., Journals Reflective comments (audio, text) Researcher Journal

Data Generation Data Generation Informational survey – background information from participants Age, gender, time in profession, educational background, employment history, social identity etc. Semi-structured interviews One-on-one Face to face or skype/zoom Range from 30-90 minutes Program documents Researcher Journal

Design & Analysis Aligned with world view, theory, research design

Data Analysis Analysis Process of systematically searching and arranging all of your data to allow you to come up with findings Interpretation Developing ideas about your findings and relating back to the literature

Data Analysis Don’t wait until all your data is collected Ongoing analysis of the data will help to focus future data collection, raise new insights you want to explore, help you to realize areas that need more probing/data. Inductive or deductive process

Data Analysis Content analysis and constant comparative method (Corbin & Strauss, 2008) Transcribed, open coding or line-by-line analysis = initial codes Axial coding = collapsing initial codes into primary themes with description of theme Researchers discussed and refined/adjusted initial themes All transcripts reviewed to select salient quotes Supporting documents reviewed to support and enhance description of themes

Trustworthiness Credibility Transferability Dependability Confidence in the “truth” of the findings Transferability Findings have applicability in other contexts Dependability Showing that the findings are consistent and can be repeated Confirmability Extent to which findings are shaped by participants rather than researcher

Trustworthiness Credibility Data collection methods described in detail Entre and time at site– includes prior to the study Triangulation of data - how established Rapport with participants allows for honest answers Negative case analysis or disconfirming evidence Peer debriefing Member checking – transcripts and analysis Researcher’s reflexivity

Trustworthiness Transferability Thick rich description of Context, Setting, Participants, Data collection methods, Timeline, Interpretations Data excerpts

Trustworthiness Dependability Research design and implementation – in detail what you did and when Data generation – what you did and when to collect the data Reflective appraisal of study

Trustworthiness Confirmability Triangulation Researcher reflexivity Audit trail – trace the course of the research step by step – data collection, analysis, process, timeline etc

  RIGOR, ALIGNMENT

Beginning the Data Analysis Process Read, read, read, and read some more all of your data corpus Organize how best suits you Software packages – e.g. NVIVO Hands on with the data – use of post its, cards, print outs Hands on Print out your data with two columns and room between lines to allow for coding. One column has the data Second column allows room for your initial interpretation of the data

Beginning the Coding Process In the data, words, phrases, behavior, etc., repeat or stand out Begin to find a label of phrase that represents these piles to separate them from other piles Develop a list of coding categories – this can be driven by your questions, theoretical approach etc Constantly revisit data chunks when new codes arise

Beginning the Coding Process Disconfirming evidence or negative case As codes develop begin to look for codes that fit together into larger concepts/thoughts – begin transforming codes into initial theme Constantly revisit your themes as you move through the process

Coding Family Kinds of codes – broad areas to consider in your coding Testing theory use codes from the theory (deductive) – apply the theory to the data Setting/context codes Information on the setting, participants etc Definition of the situation codes How subjects define setting or topics etc Perspectives held by subjects Ways of thinking of some or all of participants

Coding Family Subject’s ways of thinking about people and objects Subjects understanding of each other, outsiders, objects that make up their world Process codes Categorize sequencing of events, changes over time, etc Activity codes Regularly occurring kinds of behavior – student smoking, joking, lunch, warm up, game etc,.

Themes Themes develop from your coding process Could be related to RQ’s, theory, what you see in the data Use of themes and sub-themes Must be related to the purpose of your study!

Rigor Outline what you did in your data analysis Research journal for reflexivity on the whole research process Coding book - codes and possible initial interpretation Audit trail!