Data Analysis and Interpretation Quiz

Drawing the Line

Data analysis is the process of measuring and observing, while interpretation attempts to explain the measurements and observations.
Validity of the interpretation depends on the data. The interpretation may change as the data changes, which is what makes science successful and progressive.

Flow of Summary of Findings and the Interpretation

Data Analysis: Focus on getting to know the data by reading and rereading the text, playing and listening to taped recordings several times and transcribing the interviews.
Focusing the Analysis:
- Focus by question or topic, time period, or event:
  - This focuses on how individuals or groups responded across a given period of time, topic, or event.
  - Organize data by question across all respondents to identify consistency and differences.
  - Consolidate all data from the question.
  - Apply the same approach to particular topics, time periods, events of interest by exploring the connections and relationships between questions.
- Focus by case, individual, or group:
  - A case could be a single family.
  - An individual can be a first-timer or teen participant.
  - A group can be categorized by age.
Annotating:
- Use symbols and notes on the transcript to analyze.
Coding:
- Predefined codes: Codes chosen by the researcher based on the literature reviewed.
- Emergent codes: Codes that become apparent as you review the data.
Patterns: Patterns can be characterized by:
- Similarity: things happen the same way.
- Difference: things happen in predictably different ways.
- Frequency: how often or seldom things happen.
- Sequence: events happen in a certain order.
- Correspondence: events happen in relation to other activities or events.
- Causation: events happen that appear to cause another.
Entering and Organizing the Data:
- Can be done manually or with a word processing program like MS Word.
Cleaning the Data:
- This is the process of checking data for errors because "dirty" data negatively influences the results of the study.
- Three common ways to clean qualitative data:
  - Spot-checking: Comparing raw data to the electronically entered data, checking for data entry and coding errors.
  - Eye-balling: Reviewing the data for errors that may have resulted from data entry or coding oversights.
  - Logic check: Reviewing the electronically raw data to make sure the answers to different questions make sense.
Identifying Meaningful Patterns and Themes:
- Content Analysis: This involves coding the data for certain words or content by going through all text and labeling words, phrases, and sections.
- Thematic analysis: This involves grouping data according to themes which can be derived from the research questions, or can naturally emerge from the data.
Interpreting the Data:
- After identifying and analyzing, coding, organizing, and identifying themes and patterns, the next step is to interpret the results.
- This involves synthesizing the results into a coherent whole.
- Meaning and significance are attached to the analysis of data.
- Themes and patterns serve to explain the findings.
- The implications of the study are highlighted in this section.
- Points or important findings should be listed.
- Lessons learned and new things should be noted.
- Quotes or descriptive examples given by the participants should be included.
- Newfound knowledge from other settings, programs, or reviewed literature should be applied.
Four Levels of Interpretation:
- Level 1: Data collected are compared and contrasted with unexpected results included (address any shortcomings of the study but do not emphasize any flaws).
- Level 2: The internal validity of the results, their consistency and reliability are explained. The causes or factors influencing the results are described.
- Level 3: The external validity of the results, their generality, and applicability to external conditions are explained.
- Level 4: The data is related to theoretical research or with related literature.

Guide Questions in Analyzing Data

What patterns or common themes emerged around specific items in the data?
How do these patterns help shed light on the broader study question/s?
Is there any deviation from these patterns? If there is, what factors could explain these atypical responses?
What interesting stories emerged from the data? How can these stories help shed light on the broader study question?
Do any of the patterns or common themes suggest that additional data needs to be collected? Do any of the study questions need to be revised?
Do the patterns support the findings of other qualitative analyses?

Suggested Templates for Results and Discussion

Discuss findings by presenting, explaining sources, and defending them.
Version 1:
- Results:
  - Introductory Paragraph.
  - Results for research question/objective 1.
  - Results for research question/objective 2.
  - Results for research question/objective 3.
- Discussion:
  - Introductory Paragraph.
  - Discussion of results for research question/objective 1 (link results to related literature and studies, link results to existing theories, and provide alternative explanations).
  - Discussion of results for research question/objective 2 (link results to related literature and studies, link results to existing theories, and provide alternative explanations).
  - Discussion of results for research question/objective 3 (link results to related literature and studies, link results to existing theories, and provide alternative explanations).
  - Discussion of overall results (link results to related literature and studies, link results to existing theories, and provide alternative explanations).
Version 2:
- Results and Discussion:
  - Introductory paragraph.
  - Research question/objective 1:
    - Results.
    - Discussion: discuss, defend, link to studies, and provide alternative explanations.
  - Research question/objective 2-3, etc:
    - Results
    - Discussion: discuss, defend, link to studies, and provide alternative explanations.