Data Collection & Analysis
Mental ModelingAfter data has been collected through knowledge elicitation methods, one method to manage and make sense of this data is to use mental models. Mental models can make sure expertise from the past is not lost or to compare the knowledge structures of novices and experts. Once information is more appropriately organized using methods such as concept maps, conceptual analysis, and relational analysis it can be used to generate valuable tools such as training programs or decision aids.
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Relational Analysis
What is Relational Analysis?
Relational analysis is a type of content analysis where the concepts found in the text are further analyzed by how they relate to each other or for emotional content.
There are three subcategories of relational analysis:
1. Affect Extraction
- provides emotional evaluation of concepts explicit in a text.
- concepts can be assigned a number corresponding to emotional/psychological scales.
2. Proximity Analysis
- concerned with cooccurence of explicit concepts in text.
- set "window" - predetermined length of words.
- scan each window for cooccurence of concepts
- creates a "concept matrix" that may suggest certain overall meaning
- can also use clustering, grouping, and scaling.
3. Cognitive Mapping
- Takes proximity analysis further by creating visual representation.
- allows comparison across texts (map analysis)\
- Map analysis allows comparisons of "how meanings and definitions shift across
people and time."
- Map analysis reference: Palmquist et al., 1997
There are three subcategories of relational analysis:
1. Affect Extraction
- provides emotional evaluation of concepts explicit in a text.
- concepts can be assigned a number corresponding to emotional/psychological scales.
2. Proximity Analysis
- concerned with cooccurence of explicit concepts in text.
- set "window" - predetermined length of words.
- scan each window for cooccurence of concepts
- creates a "concept matrix" that may suggest certain overall meaning
- can also use clustering, grouping, and scaling.
3. Cognitive Mapping
- Takes proximity analysis further by creating visual representation.
- allows comparison across texts (map analysis)\
- Map analysis allows comparisons of "how meanings and definitions shift across
people and time."
- Map analysis reference: Palmquist et al., 1997
Why Use Relational Analysis
Relational analysis allows for more interpretation than conceptual analysis. It goes beyond frequency of individual concepts and allows for inferences to be made about overall meaning.
When Use Relational Analysis
Similar to conceptual analysis, relational analysis is useful when a large body of text needs to be analyzed. Often conducted along with conceptual analysis, it allows the examination of how concepts in the text are related to each other and provides some understanding of the overall meaning of the text.
How to Use Relational Analysis
Steps for relational analysis:
1. Identify Question
- delineates purpose, focuses interpretation
2. Choose sample or subsample
- text or speech
3. Determine type of analysis
- from subtypes above
- determine level of analysis (single world, multi-word)
- Reduce text to categories for words or patterns (existence/frequency coding)
4. Reduce the text to categories to code for words or patterns
- existence vs. frequency
- deeper levels may be necessitated by research question
5. Explore the relationship between concepts
- strength of relationship - degree to which concepts are related.
- Unhelpful as to how.
- Sign of the relationship - positive or negative relationship
- not in the correlational sense, in colloquial sense
- seems highly subjective.
- Direction of the relationship
- distinguishing causes vs. effects (i.e., if x, then y)
- can code bidirectionally for correlational models or less restrictive
exploratory models.
6. Code the relationships
- No info as to how
7. Perform statistical analyses
- No info as to which
8. Map out the representations
- represent relationships graphically
1. Identify Question
- delineates purpose, focuses interpretation
2. Choose sample or subsample
- text or speech
3. Determine type of analysis
- from subtypes above
- determine level of analysis (single world, multi-word)
- Reduce text to categories for words or patterns (existence/frequency coding)
4. Reduce the text to categories to code for words or patterns
- existence vs. frequency
- deeper levels may be necessitated by research question
5. Explore the relationship between concepts
- strength of relationship - degree to which concepts are related.
- Unhelpful as to how.
- Sign of the relationship - positive or negative relationship
- not in the correlational sense, in colloquial sense
- seems highly subjective.
- Direction of the relationship
- distinguishing causes vs. effects (i.e., if x, then y)
- can code bidirectionally for correlational models or less restrictive
exploratory models.
6. Code the relationships
- No info as to how
7. Perform statistical analyses
- No info as to which
8. Map out the representations
- represent relationships graphically