Qualitative Papers

Qualitative Papers

Literature Review:Sensitizing ConceptsContextual InformationBaseline of what reader should knowEstablish in prior research:

FlawsGapsPotential new directions

Set up your research Support your subsequent research question

Qualitative PapersFocus on:Question to address or interest in something

Methods:Collection of Data

Why and how data were collectedDescription of persons and contexts

Data analysis:Coding (putting identifiers on what you found)Looking for themes (main ideas)Putting it all together (what you learned)

Data Analysis is often written as data are analyzed and then edited into the results

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 researchershuns discreet stages, making sense of the information begins as the first data are collectedinvolves 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 deepensinvolves being very selective in the topics one chooses to address using the data (there always are multiple possibilities)

Analysis

Throughout, the analyst should ask and re-ask 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?

Qualitative PapersData Analysis Process:1. Read Data, develop ideas and

feel2. Code Data, tag items with same

meaning using a unique code3. Search and extract instances of

codes4. Identify patterns among codes

(pattern coding)5. Create figures, tables, or

descriptions of patterns

ANALYSIS

THEMES

Analysis

Process of Qualitative Analysis:Data Reduction

Data Display

Conclusion Drawing and Verification

Analysis

Data ReductionRefers 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 focusDetermine relevance of strings of data for your study at hand (fascinating does not make relevance)

Analysis

Data Reduction ProcessRead all dataMark data that are relevant to your questions or issuesCode the data

Reduce the data to short descriptionsCategorize the descriptionsNote links between codes (pattern coding)

The next step is to create data displays

Analysis

Data DisplaysData displays are an organized way of compressing information and assembling it in ways that help you draw conclusionsCan be text, diagrams, charts, matricesThey show systematic patterns and interrelationships of the “chunks of meaning” (codes) in the dataDisplaying will often reveal new connections and themes in the data beyond those already noticedCan display intra-case analysis and/or cross-case analysis

AnalysisConclusion 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

Analysis

Conclusion Drawing and VerificationThe 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

AnalysisConclusion Drawing and Verification

Tips for accurate description and causal statementsBe very attentive to patterns and themes—how do specific items form a general idea?Make contrasts and comparisonsTry weighing the prevalence of events, themes, concepts in your dataSearch for disconfirming information or negative evidence

Resolve disconfirmationsAccount 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

AnalysisConclusion Drawing and Verification

Tips for accurate description and causal statementsBuild a logical chain of evidenceSet up “if-then” models and see if they holdThink theoretically, metaphoricallyTriangulateReflect on how your biases may alter interpretationsInvolve others in the analysisGenerate and check rival explanations or meaningsGet feedback from informantsGive it the old “smell test”Can you go back to the data or notes to document how you came to your conclusions

Qualitative Papers

ResultsWrite what the reader should know about the data.

Description

Causal Logic

Writing should have been occurring during analysis

Document statements with exemplary quotes

Qualitative Papers

ConclusionsAnswer your research question

Discuss how you added to our knowledge

Find areas of dis/agreement with literature

Make suggestions for future research