Qualitative Data Analysis - Nera

Qualitative Data Analysis - Nera

QUALITATIVE DATA ANALYSIS NERA Webinar Presentation Felice D. Billups, Ed.D. GETTING STARTED? Have you just conducted a qualitative study involving Interviews Focus Groups Observations Document or artifact analysis Journal notes or reflections? Felice D. Billups, EdD., NERA Webinar Presentation WHAT TO DO WITH ALL THIS DATA? Just

as there are numerous statistical tests to run for quantitative data, there are just as many options for qualitative data analysis Felice D. Billups, EdD., NERA Webinar Presentation OVERVIEW This session is designed to provide a step-by-step guide for beginning qualitative researcherswho want to know how to apply the appropriate strategies for data analysis, interpretation, and reporting. Felice D. Billups, EdD., NERA Webinar Presentation LIKE CLEANING A CLOSET ???

Think of managing your qualitative analysis process like cleaning your closets the same basic steps apply! Felice D. Billups, EdD., NERA Webinar Presentation Its the same process 1. Take everything out of the closet 2. Sort everything out save or toss? 3. Look at what you have left and organize into sub-groupings (chunking) 4. Organize sub-groups into clusters of similar things that belong together (clusters, codes) 5. As you put things back, how would you group them to maximize functionality? How do the groups make it work together? (interpretation, presentation)

Felice D. Billups, EdD., NERA Webinar Presentation FOUR BASIC STEPS All qualitative data analysis involves the same four essential steps: 1. Raw data management- data cleaning 2. Data reduction, I, II chunking, coding 3. Data interpretation coding, clustering 4. Data representation telling the story, making sense of the data for others

Felice D. Billups, EdD., NERA Webinar Presentation DATA ANALYSIS SPIRAL #1 Felice D. Billups, EdD., NERA Webinar Presentation DATA ANALYSIS SPIRAL #2 Felice D. Billups, EdD., NERA Webinar Presentation Step 1: Raw Data Management What is raw data management? The process of preparing and organizing raw data into meaningful units of analysis: Text or audio data transformed into transcripts

Image data transformed into videos, photos, charts As you review your data, you find that some of it is not usable or relevant to your study Felice D. Billups, EdD., NERA Webinar Presentation Raw Data Sample I always wanted to get my doctorate but I never felt I had the time; then I reached a point in my career where I saw that without the credentials, I would never advance to the types of positions I aspired to..but I doubted I could do the work. I wasnt sure I could go back to school after so much time. And did I have the time, with working and a family? These were the things I struggled with as I looked for the

right program. Um, ..finally starting the program with others like me, it felt surreal. Once you switch gears from being an established administrator at a college to being a doc student, you realize you lose control over your life. You are not in charge in that classroom, like you are in your office. But also, once you say you are a doc student, people look at you differently. And people at work began to take me more seriously, ask for my opinion as if I now possessed special knowledge because I was going for the doctorate. It was the same information I had shared previously but somehow it had a special quality? Its like magic! I cant think of a particular example right now Transcript of Interview Data Are some portions of this transcript

unusable or irrelevant? (purple) Raw Data Overview Felice D. Billups, EdD., NERA Webinar Presentation Step II: Data Reduction I Get a sense of the data holistically, read several times (immersion) Classify and categorize repeatedly, allowing for deeper immersion Write notes in the margins (memoing) Preliminary classification schemes emerge, categorize raw data into groupings (chunking) Felice D. Billups, EdD., NERA Webinar Presentation Winnowing Develop an initial sense of usable data

and the general categories you will create Preliminary set of codes developed, cluster raw data into units that share similar meanings or qualities Create initial code list or master code book Felice D. Billups, EdD., NERA Webinar Presentation Chunks-Clusters Sample I always wanted to get my doctorate but I never felt I had the time; then I reached a point in my career where I saw that without the credentials, I would never advance to the types of positions I aspired to..but I doubted I could do the work. I wasnt sure I could go back to school after so much time. And did I have the time, with working and a family? These were the things I

struggled with as I looked for the right program. -finally starting the program with others like me, it felt surreal. Once you switch gears from being an established administrator at a college to being a doc student, you realize you lose control over your life. You are not in charge in that classroom, like you are in your office. But also, once you say you are a doc student, people look at you differently. And people at work began to take me more seriously, ask for my opinion as if I now possessed special knowledge because I was going for the doctorate. It was the same information Transcript ofpreviously Interview Data it I had shared but somehow

had a special quality? Its like magic! Which sections of data are broadly similar? (red for credentials, blue for personal struggles, green for shift in identity) Which chunks can be clustered together to relate to a broad coding scheme? Chunking? Clusters? Felice D. Billups, EdD., NERA Webinar Presentation

Step II: Data Reduction II The process of reducing data from chunks into clusters and codes to make meaning of that data: Chunks of data that are similar begin to lead to initial clusters and coding Clusters assigning chunks of similarly labeled data into clusters and assigning preliminary codes Codes refining, developing code books, labeling codes, creating codes through 2-3 cycles Felice D. Billups, EdD., NERA Webinar Presentation Coding Process Initial coding may include as many as 30 categories Reduce codes once, probably twice Reduce again to and refine to codes that are mutually exclusive and include all raw data that was identified as usable

Felice D. Billups, EdD., NERA Webinar Presentation A Priori or In Vivo Codes A Priori Codes derived from literature, theoretical frames In Vivo (inductive or grounded) Codes derived from the data by using code names drawn from participant quotes or interpretation of the data Its like magic is a phrase that could form the basis for a code category Felice D. Billups, EdD., NERA Webinar Presentation Coding Levels

Descriptive to Interpretative to Pattern Coding Moves from summary to meaning to explanation OR Open to Axial to Selective Coding Moves from initial theory to developing relationships between codes for emerging theory OR First cycle to second cycle coding Moving from describing the data units to inferring meaning Felice D. Billups, EdD., NERA Webinar Presentation

Coding Sample I always wanted to get my doctorate but I never felt I had the time; then I reached a point in my career where I saw that without the credentials, I would never advance to the types of positions I aspired to..but I doubted I could do the work. I wasnt sure I could go back to school after so much time. And did I have the time, with working and a family? These were the things I struggled with as I looked for the right program. -finally starting the program with others like me, it felt surreal. Once you switch gears from being an established administrator at a college to being a doc student, you realize you lose

control over your life. You are not in charge in that classroom, like you are in your office. But also, once you say you are a doc student, people look at you differently. And people at work began to take me more seriously, ask for my opinion as if I now possessed special knowledge because I was going for the doctorate. It was the same information I had shared previously but somehow it had a special quality? Its like magic! Transcript of Interview Data Chunking to coding: Red for credentials codes

include career goals CG, career advancement CA Blue for personal struggles- codes include self-doubt SD, time management TM Green for shift in identity codes include student role SR, identity at work IW, shift in control SC Chunking? Clusters? Coding? Felice D. Billups, EdD., NERA Webinar Presentation Coding Levels (revisiting) Descriptive to Interpretative to Pattern Coding Moves from summary to meaning to explanation

OR Open to Axial to Selective Coding Moves from initial theory to developing relationships between codes for emerging theory OR First cycle to second cycle coding Moving from describing the data units to inferring meaning Felice D. Billups, EdD., NERA Webinar Presentation Coding Progression Descriptive Interpretative Credentials CG,CA

need for career advancement, goals Personal PSD,PG,PWL Identity advance professionally and to meet personal goals for achievement

PG IS Self-doubt Personal growth Work-life balance IS, ISR, ISC identity shifting student role shift in control Descriptive to Interpretative Pattern CR needing a doctorate to

personal struggles evolve to address self-doubt about abilities, trying to achieve things before time runs out, balancing responsibilities with family, self, work managing the shift from student to graduate, from candidate to doctor, from non-expert to expert in work settings, from losing control to regaining control at home and work Pattern Inductive meaning Felice D. Billups, EdD., NERA Webinar Presentation Step III: Data Interpretation & Themes Chunks of related data that have similar meaning are coded in several cycles Once coded, those chunks become clustered in similar theme categories Create meaning for those clusters with labels Themes emerge from those clusters

Interpret themes to answer research questions Felice D. Billups, EdD., NERA Webinar Presentation Themes Sample I always wanted to get my doctorate but I never felt I had the time; then I reached a point in my career where I saw that without the credentials, I would never advance to the types of positions I aspired to..but I doubted I could do the work. I wasnt sure I could go back to school after so much time. And did I have the time, with working and a family? These were the things I struggled with as I looked for the right

program. -finally starting the program with others like me, it felt surreal. Once you switch gears from being an established administrator at a college to being a doc student, you realize you lose control over your life. You are not in charge in that classroom, like you are in your office. But also, once you say you are a doc student, people look at you differently. And people at work began to take me more seriously, ask for my opinion as if I now possessed special knowledge because I was going for the doctorate. It was the same information I had shared previously but somehow it had a special quality? Its like magic! Transcript of Interview Data

How do you compile the clusters into emerging themes? (red for credentials, blue for personal struggles, green for shift in identity) Begin to see themes emerge: Getting the degree, becoming a new person, personal achievement How do broad sections emerge into thematic groupings? Felice D. Billups, EdD., NERA Webinar Presentation Step IV: Data Representation Interpretation or analysis of qualitative data simultaneously occurs Researchers interpret the data as they read

and re-read the data, categorize and code the data and inductively develop a thematic analysis Themes become the story or the narrative Felice D. Billups, EdD., NERA Webinar Presentation Data Representation Types Telling the story with the data Storytelling, Narrative Chronological Flashback Critical Incidents Theater Thematic Visual representation Figures, tables, charts Felice D. Billups, EdD., NERA

Webinar Presentation How will it look in the end? EXCERPT: Jumping into the Abyss: Life After the Doctorate (Felice Billups) This qualitative phenomenological study sought to explore doctoral degree graduates perceptions of self, identity and purpose in the post-dissertation phase, seeking participant perspectives on the phenomena of transition. Considerable research has been conducted on currently enrolled doctoral students (Baird, 1997; David, 2011; Pauley, 2004; ) relative to the issues of 1) overcoming obstacles to completing the dissertation, 2) managing feelings of isolation and disengagement, 3) successfully completing dissertation research and manuscript preparation, 4) negotiating relationships with advisors and committee members, and 5) searching for teaching or scholarship positions after degree completion. Research on the doctoral degree graduate has typically been conducted on individuals in Ph.D. programs, where the post-graduation transition has focused on moving into traditional academic roles (DAndrea, 2002; Di Pierro, 2007; Johnson & Conyers, 2001; Varney, 2010); minimal research has been conducted on Ed.D. graduates who are already actively engaged as professionals and/or practitioners in their fields, and who have also balanced work-life challenges while pursuing their degrees. The issues of personal accomplishment, anxiety, isolation, loss, hopes and aspirations, identity and role clarity, and professional recognition were all examined through the lens of the lived experience of purposefully selected participants, all of whom recently graduated from a small Ed.D. program in the Northeast. By integrating the two conceptual frameworks of Neugartens (1978) adult development theory, and Lachman and James (1997) midlife development theory, the following themes emerged: 1) You are not the same person!, 2) The degree is greater than the sum of its parts!, 3) Now what do I do with all this time?, and 4) When will you crown me King/Queen of the world?. These themes reveal the

experiences of recent doctoral degree graduates perceptions of the transition from doctoral student to graduate. Felice D. Billups, EdD., NERA Webinar Presentation Theme #2 The Degree is Greater than the Sum of its Parts: From Candidate to Graduate. As one participant stated, The doctoral process is complicated!. Each individual expressed similar sentiments as they described their first impressions of their course work, and the eventual evolution to dissertation research. As separate parts of the doctoral program, they seemed manageable, but when viewed as a whole program, they seemed overwhelming. The consensus, however, was that each program component informed the next in a way that defied description, and prepared them for the dissertation process. As one participant expressed,

My understanding of what the degree meant was not clear until I stepped into my defense ..I had a moment when I realized that now it all makes sense Felice D. Billups, EdD., NERA Webinar Presentation CROSS-CASE ANALYSIS OF CULTURAL INCIDENTS AND ARTIFACTS: ORGANIZATIONAL CULTURE STUDY SCHOOL HISTORICAL INCIDENT CULTURAL ARTIFACTS A egg drop contest each spring, founders ball in fall with admin, BOT supportive founding family

female heroine created institution against odds B moving from city to country a symbol of growth, expansion old archway stolen in middle of the night from old campus to new one scholarships given in heroine's name at graduation throwing seniors into pond on rural campus a rite of passage

grads must pass thru archway on way to graduation Felice D. Billups, EdD., NERA Webinar Presentation Qualitative Data Analysis Types Most common types of analytic approaches: Domain/Content Thematic Grounded theory/Constant comparative Ethnographic/cultural Metaphorical/ hermeneutical Phenomenological Biographical/narrative analysis Case Study, Mixed Methods, Focus Groups Felice D. Billups, EdD., NERA Webinar Presentation

If you are working with a particular research design? The following expert lists are provided to help you match specific qualitative research designs with the appropriate qualitative data analysis strategies Felice D. Billups, EdD., NERA Webinar Presentation APPROACHES & EXPERTS Domain Analysis: Spradley (1979) Thematic Analysis Boyatzis (1998) Guest, MacQueen, Namey (2012) Grounded theory,

constant comparison Ethnographic analysis: Birks & Mills (2011) analysis: Charmaz (2006) Glaser (1967) Strauss & Corbin (1990) Spradley (1979) Sunstein & ChiseriStrater (2012) Wolcott (2005, 2008) Felice D. Billups, EdD., NERA Webinar Presentation APPROACHES & EXPERTS Linguistic/metaphor analysis: thematic, emotional barometer, cultural values Whitcomb & Deshler

(1983) Cultural Analysis Wolcott, 1999 Van Maanen, 1984 Phenomenological Analysis: Colaizzi (1978) Giorgi (1985, 2009) Holstein & Gubrium (2012) Moustakas (1988, 1990) Smith, Flowers, & Larkin (2009) van Manen (1990) Felice D. Billups, EdD., NERA Webinar Presentation APPROACHES & EXPERTS Auto/Biographical

analysis: Denzin (1989) Spry (2011) Narrative analysis: Holstein & Gubrium (2012) Reissman (2008) Yussen & Ozcan (1997) Case Study: Stake (1995) Focus Groups: Krueger & Casey (2009) Mixed Methods: Creswell & Plano Clark (1995) Tashakkori & Teddlie (2010)

Felice D. Billups, EdD., NERA Webinar Presentation Computer Software ATLAS/TI, HyperRESEARCH, Nvivo, MaxQDA, NUD*IST Software packages either assist with theorybuilding or with concept mapping Data-voice recognition software converts audio into text, such as Dragon Felice D. Billups, EdD., NERA Webinar Presentation References Grbich, C. (2007). Qualitative data analysis: An introduction. London, UK: Sage. Miles, M. B., & Huberman, A. M. (2013). Qualitative data analysis: An expanded sourcebook. (3rd ed.). Los Angeles, CA: Sage. Saldana, J. (2009). The coding manual for qualitative researchers. Los Angeles, CA:

Sage. Felice D. Billups, EdD., NERA Webinar Presentation Feel free to contact me Felice D. Billups, Ed.D. Professor, Educational Leadership Doctoral Program at Johnson & Wales University [email protected] Direct Line: 401-598-1924 Mailing address: 8 Abbott Park Place, Providence, RI 02903 Felice D. Billups, EdD., NERA Webinar Presentation

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