Data Interpretation and Analysis Overview

Study Notes

Data Interpretation

Data analysis involves measurement and observation, while interpretation seeks to explain the data.
Successful scientific progress depends on the interplay between data and interpretation.
While data analysis should not be misconstrued as manipulation, it aims to uncover ideas, concepts, and attitudes.
Interpretation involves explaining patterns and trends identified through analysis, drawing on the researcher's knowledge and experience.

Summary of Findings

Getting to know the data: Involves careful reading and rereading of the text. Playing and listening to recordings multiple times. Transcribing interviews is a useful tool.
Focusing the analysis:
- By question or topic:
  - Focuses on how individuals or groups responded to a specific question, topic, time period, or event.
  - Organizes data by question, identifying consistencies and differences in participant responses.
  - Consolidates data from multiple questions.
  - Explores connections and relationships between questions.
- By case, individual, or group:
  - Can focus on a single family, an individual (e.g., a first-timer or teen participant), or a group categorized by age.
Annotating: Involves writing symbols and notes on transcripts, especially on the margins, while reading and rereading the text.
Coding:
- Predefined codes: Formulated by the researcher based on existing literature.
- Emergent codes: Developed as data is reviewed.
Patterns and Themes
- Patterns:
  - Similarity: Things happen the same way.
  - Difference: Things happen in predictably different ways.
  - Frequency: Things occur often or seldom.
  - Sequence: Things happen in a specific order.
  - Correspondence: Things happen in relation to other activities or events.
  - Causation: One thing appears to cause another.\
- Content Analysis: Involves coding data for specific words or content, either by labeling words or phrases, or by using a matrix to categorize the text.

Data Organization and Cleaning

Entering and Organizing Data: Can be done manually or using computer programs like MS Word.
Cleaning Data: Involves checking for errors.
- Spot-checking: Comparing raw data to electronically entered data to identify data entry and coding errors.
- Eye-balling: Reviewing data for errors that may have occurred during data entry or coding.
- Logic check: Reviewing electronically entered raw data to ensure that answers to different questions are consistent and make sense.