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An Alternative Approach to Analyze Ipsative Data. Revisiting Experiential Learning Theory.
Joan M Batista-Foguet1, Berta Ferrer-Rosell2, Ricard Serlavós1
1Department of People Management and Organization, ESADE, Universitat Ramon Llull Barcelona, Spain.
Statistical models are often misused with ipsative data, leading to errors. This study introduces compositional data analysis to properly analyze Kolb
Area of Science:
- Psychometrics
- Learning Sciences
- Statistical Methodology
Background:
- The ritualistic application of statistical models, termed 'type zero error,' is prevalent across disciplines, particularly when dealing with ipsative data.
- Kolb's Experiential Learning Theory (ELT) and its Learning Style Inventory (KLSI) are often analyzed using inappropriate statistical methods, neglecting underlying assumptions.
- Ipsative data, which measures relative strengths, requires specialized analytical approaches to avoid misinterpretation.
Purpose of the Study:
- To address the 'type zero error' in the analysis of ipsative data within Kolb's Experiential Learning Theory (ELT).
- To introduce and demonstrate the utility of compositional data analysis (CODA) and log ratio transformations for analyzing KLSI data.
- To uncover a potential third dimension within the KLSI and explore its theoretical and practical implications.
Main Methods:
- Application of compositional data analysis (CODA) and log ratio transformations to KLSI data.
- Utilizing a sample of international MBA students for data analysis.
- Correlating the newly identified KLSI dimension with the Philosophical Orientation Questionnaire (POQ) and the Emotional and Social Competency Inventory (ESCI-U).
Main Results:
- Compositional data analysis provides a statistically sound method for analyzing KLSI data, overcoming limitations of traditional approaches.
- A novel third dimension of the KLSI was identified, representing a preference for learning via 'prehension' versus 'transformation'.
- Significant statistical relationships were found between this new dimension and both the POQ (Intellectual Operating Philosophy vs. Pragmatic Operating Philosophy) and ESCI-U (competencies).
Conclusions:
- The study highlights the critical need for appropriate statistical methodologies, such as CODA, when analyzing ipsative data like that from the KLSI.
- The discovery of a third KLSI dimension offers new theoretical insights into learning style preferences.
- The findings suggest that an individual's operating philosophy and specific competencies are linked to their preferred learning approach (prehension vs. transformation).
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