Discourse Analysis: Key Concepts and Projects
Key Considerations in Discourse Analysis
1. Planning a Discourse Analysis Project
When planning a discourse analysis, consider the following:
- Actual research question: Does it contain a good, researchable idea?
- Well-focused question: This is the key to a good research project.
- Identifying information: Determine what kind of information each approach can supply.
2. Evaluating a Discourse Analysis
The following issues are crucial when evaluating a discourse analysis:
- Reliability: The consistency of the results obtained.
- Validity: The truth or accuracy of the generalizations made.
- Replicability: The extent to which another researcher could reproduce the study.
Internal vs. External Reliability
- Internal Reliability: Consistency of data collection, analysis, and interpretation.
- External Reliability (Replicability): The extent to which another researcher could reproduce the study and obtain similar results.
Internal vs. External Validity
- Internal Validity: How far claims about cause are “true” in the situation being studied.
- External Validity: The extent to which the results can be generalized to a broader population.
3. Types of Discourse Analysis Projects
Several types of discourse analysis projects can be undertaken:
- Replication of Previous Studies: Useful when there’s renewed interest in a topic.
- Using Different Data, Same Methodology: Compare findings with the original study.
- Analyzing Existing Data: Apply discourse analytic techniques to previously published data.
- Analyzing Data from a Different Perspective: Use a new theoretical lens.
- Considering the Validity of a Previous Claim: Critically examine existing research.
- Focusing on Unanalyzed Genres: Analyze new genres emerging from new technologies.
- Combining Research Techniques: Integrate qualitative and quantitative methods.
4. Key Definitions
- What is Discourse Analysis? Discourse analysis is a research method for studying written or spoken language in relation to its social context.
- Difference Between Validity and Reliability:
- Reliability refers to the consistency of a measure (reproducibility under the same conditions).
- Validity refers to the accuracy of a measure (whether results represent what they should measure).
- Using different discourse data: It is important to have the same methodology.