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Analyzing Qualitative Data. What does that mean?. Analysis. Qualitative analysis refers to ways of examining, comparing and contrasting, discerning, and interpreting meaningful patterns or themes in your data. Descriptive Analysis Causal Analysis

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analyzing qualitative data

Analyzing Qualitative Data

What does that mean?

  • Qualitative analysis refers to ways of examining, comparing and contrasting, discerning, and interpreting meaningful patterns or themes in your data.
    • Descriptive Analysis
    • Causal Analysis
  • Qualitative analysis is not standardized as statistical analysis, but there are general tactics that often prove useful for the purposes of your analysis
  • Qualitative analysis is typically systematic and intensely disciplined—not “purely subjective.” The tactics we cover will help us be systematic.
  • Qualitative analysis:
    • is documented in ways that others could come to the same conclusions as the researcher
    • shuns discreet stages, making sense of the information begins as the first data are collected
    • involves loop-like patterns of revisiting the data over and over to address additional questions, uncover new connections in the data, and draw out more complex formulations as understanding of the data deepens
    • involves being very selective in the topics one chooses to address using the data (there always are multiple possibilities)
  • Throughout, the analyst should ask and reask these questions:
    • What patterns and common themes keep popping up? How do these patterns help me answer my research questions or assess the issues of focus?
    • Are there deviations from these patterns? What factors may explain the atypical?
    • What interesting stories are emerging? How do these help me answer my research questions or assess the issues of focus?
    • Does anything call for additional data?
    • Do any study questions or issues need revision?
    • Do my findings corroborate other research? If not, what might explain the differences?
  • The analysis is typically intra-case analysis and/or cross-case analysis
    • A case can be a person, a focus group, a program site, etc.
    • Cross-case analysis involves comparing and contrasting types of cases such as contrasting five program sites.
  • Process of Qualitative Analysis:
    • Data Reduction
    • Data Display
    • Conclusion Drawing and Verification
  • Data Reduction
    • Refers to the process of selecting, focusing, simplifying, abstracting and transforming data that appear in notes, transcripts, documents, etc.
    • Choices must be made on exactly what to describe, what to code, etc.
    • Choices are guided by study questions and issues, but researcher is open to broadening or narrowing focus
    • Determine relevance of strings of data for your study at hand (fascinating does not make relevance)
  • Data Reduction Process
    • Read all data
    • Mark data that are relevant to your questions or issues
    • Code the data
      • Reduce the data to short descriptions
      • Categorize the descriptions
      • Note links between codes (pattern coding)
    • The next step is to create data displays
  • Data Displays
    • Data displays are an organized way of compressing information and assembling it in ways that help you draw conclusions
    • Can be text, diagrams, charts, matrices
    • They show systematic patterns and interrelationships of the “chunks of meaning” (codes) in the data
    • Displaying will often reveal new connections and themes in the data beyond those already noticed
    • Can display intra-case analysis and/or cross-case analysis

Select Types of Data Displays

  • Partially ordered displays—some but not too much internal order aiming to uncover and describe what is happening in the local setting no matter how messy or surprising
    • Context chart—a network, mapping in graphic form the interrelationships among the roles and groups that go to make up the context of individual behavior
    • Checklist Matrix—format for analyzing field data on a major variable or general domain of interest
    • Transcript as Poem—make a poem

Select Types of Data Displays

  • Time-ordered Displays—orders data by time and sequence, preserving the historical chronological flow and permitting a good look at what led to what and when
    • Event listing—a matrix that arranges a series of concrete event by chronological time periods, sorting them into several categories
      • Critical incident chart—limited representation of critical elements of a process
      • Event state network—centers on general states linked to specific events
      • Activity record—sequencing of routine events
      • Decision modeling—steps in decision-making spelled out
    • Time-ordered matrix—column arranged by time period in sequence so that you can see when particular phenomena occurred; the rows are what else you are studying

Select Types of Data Displays

  • Role-ordered Displays—Orders information according to people’s roles in a formal or informal setting.
    • Role-ordered matrix—sorts data in its rows and columns that have been gathered from or about a certain set of “role occupants”
    • Role-by-Time Matrix—sorting role information over time

Select Types of Data Displays

  • Conceptually ordered Displays—displays the concepts or variables.
    • Conceptually clustered Matrix—rows and columns arranged to bring together items that belong together. A prior derivation or empirically driven. May be ordered by persons or themes or both.
    • Folk Taxonomy—displaying concepts in network form
    • Cognitive maps—displays the person’s representation of concepts about a particular domain, showing the relationships among them. Descriptive text is associated with it.
    • Effects matrix—displays data on one or more outcomes, in as differentiated a form as the study requires. Focus on dependent variables.

Conclusion Drawing and Verification

  • As one creates and views displays, the salient components of meaning and activities become apparent.
    • In descriptive analysis, the researcher tries to represent the data (meanings, observations) to readers in such a way that they will “understand” what the researcher “sees” in the data.
    • In causal analysis, the researcher tries to link concepts in the data together to explain observed meanings or phenomena, and to represent that in such a way that readers will “understand” what the researcher “sees.”
  • This stage relies very heavily on logical evaluation and systematic description

Conclusion Drawing and Verification

  • Remember criteria for establishing causality:

One thing causes another when there is:

a) Association—things must vary together

b) Time Order—the thing that causes the other must

occur prior to the other

c) Nonspuriousness—relationship between two things is

not coincidental and caused by a third thing

d) A Mechanism—a plausible reason that one should cause the other

e) A Context—specification of what conditions permit or favor the causal relationship


Conclusion Drawing and Verification

  • The researcher must describe what he or she sees in the data.
  • The researcher always refers back to the data displays and raw data as descriptions or causal statements are made.
    • Systematic, organized, and good coding and notes will really pay off at this point, allowing efficient, accurate access to data
  • Conclusions are made through the process of writing up (describing) what is in the data

Conclusion Drawing and Verification

  • Tips for accurate description and causal statements
    • Be very attentive to patterns and themes—how do specific items form a general idea?
    • Make contrasts and comparisons
    • Try weighing the prevalence of events, themes, concepts in your data
    • Search for disconfirming information or negative evidence
      • Resolve disconfirmations
      • Account for the exceptions to your explanations
    • Look for clustering
    • Think of information like you would variables
      • Search for systematic relationships, causality (as one thing goes up, the other goes down)
      • Search for intervening variables

Conclusion Drawing and Verification

  • Tips for accurate description and causal statements
    • Build a logical chain of evidence
    • Set up “if-then” models and see if they hold
    • Think theoretically, metaphorically
    • Triangulate
    • Reflect on how your biases may alter interpretations
    • Involve others in the analysis
    • Generate and check rival explanations or meanings
    • Get feedback from informants
    • Give it the old “smell test”
    • Can you go back to the data or notes to document how you came to your conclusions