Chapter 16 - Analysis of Qualitative Data Flashcards
Why is qualitative analysis challenging?
- no universal rules for analyzing
- enormous amount of work required, LOTS of narrative data
- requires creativity, sensitivity, and strong inductive skills - must be able to discern patterns and weave them together
- challenge to reduce data for reporting purposes - maintain richness of data
Category Scheme
Coding data according to categories
careful reading of data, looking for underlying concepts and clusters of concepts
Coding Qualitative Data
Coding data for correspondence to the categories (not easy)
- categories may have been missed in initial categorizing
- materials usually are not linear (paragraphs from an interview may pertain to several different categories)
Conceptual files
Way to organize data by creating a physical file for each category and then cutting out/inserting all materials relating to that category
- labor intensive
- must include contextual information when segment is cut out
Computer-assisted qualitative data analysis software (CAQDAS)
Computer program that permit an entire data set to be entered into the computer, each portion coded, and then portions corresponding to specific code can be retrieved to be analyzed
–>can NOT do the coding, only the researcher can analyze the data
Basic Steps in Qualitative Data Management & Organization (REVIEW)
Transcribing the data
Developing a category scheme
Coding the data
Organizing the data
–>Computerized methods of organization using CAQDAS
–>Manual methods of organization (conceptual files)
Data management vs data analysis
Data Management: reductionist (making it more manageable)
Data Analysis: constructionist (putting segments together into meaning conceptual patterns)
Theme
an abstract entity that brings meaning and identity to a current experience and its variant manifestations - captures and unifies the nature or basis of the experience into a meaningful whole
-not universal (will vary amongst different groups of people)
A General Analytic Overview
- begins with a search for broad categories/themes
- seek out relationships/patterns within the data
- may make flow charts - metaphors - using figurative language to evoke a visual analogy
- validation - does the theme accurately represent the perspectives of the participants
- quasi statistics: frequency with which certain themes/insights are supported by the data - weave thematic pieces together into a cohesive whole
Qualitative Content Analysis
analyzing the content of narrative data to identify prominent themes/patterns among the themes
-breaking down content into smaller units (coding/naming units)
What are the types of units involved in data collection?
- Physical - time, length, size
- Syntactical - grammatical divisions within the data (ex. words, sentences, paragraphs)
- Categorical distinctions define units by identifying something they have in common (membership in a category)
- Thematic distinctions - delicate units according to analyses
Clustering
similarities among units of analysis and hierarchies that conceptualize the text on different levels of abstraction
Ethnographic Analysis
- ->analysis usually begins when data collection starts
- continually looking for patterns/thoughts of participants and comparing one pattern to another (maps, flowcharts, matrices
Spradley’s Research Sequence (1979)
LANGUAGE is the primary means that relates cultural meaning in a culture
Spradley’s 12 Steps
12 Steps:
- locating an informant
- interviewing an informant
- making an ethnographic record
- asking descriptive questions
- analyzing ethnographic interviews
- making a domain analysis
- asking structural questions
- making a taxonomic analysis
- asking contrast questions
- making a componential analysis
- discovering cultual themes
- writing the ethnography
Spradley: Domain Analysis
1st level of analysis
Domains: units of cultural knowledge, broad categories that encompass smaller categories
- identify relational patterns among terms in the domains that are used by members of the culture
- focuses on cultural meaning of terms/symbols and their interrelationships
Spradley: Taxonomic Analysis
2nd level of analysis
–>decide how many domains the data analysis will include
taxonomy: a system of classifying/organizing terms, developed to illustrate the internal organization of a domain and he relationship among the domain subcategories
Spradley: Componential Analysis
3rd level of analysis
–>multiple relationships among terms in the domains are examined (similarities/differences are observed)
Spradley: Theme Analysis
4th (final) level of analysis
–>cultural themes are uncovered, domains are connected in cultural themes
*want to find cultural meaning
Ethnonursing Research Method (Leininger and McFarland - 2006)
Four stage ethnonursing data analysis guide
- collect, describe, record data
- identifying and categorizing descriptors
- data analyszed to discover repetitive patterns in their context
- abstracting major themes and presenting findings