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Multisource Assessment for Development Purposes: Revisiting the Methodology of Data Analysis
Joan Manuel Batista-Foguet1, Willem Saris2, Richard E Boyatzis3
1ESADE BS, Universitat Ramon Llull, Barcelona, Spain.
Frontiers in Psychology
|January 22, 2019
Summary
Multisource assessment (MSA) for development requires analyzing data from diverse raters. This study proposes an Aggregate factor model and scalar invariance testing for more effective leadership development insights.
Area of Science:
- Psychological assessment
- Organizational behavior
- Leadership development
Background:
- Multisource assessment (MSA) relies on valid inferences of individual behavior.
- Performance management MSA prioritizes rater convergence and factor invariance.
- Developmental MSA involves diverse raters, necessitating alternative data analysis.
Purpose of the Study:
- To enhance Multisource Assessment (MSA) data analysis for developmental purposes.
- To propose a new analytical approach for MSA when raters originate from varied contexts.
- To improve the effectiveness of MSA in leadership development programs.
Main Methods:
- Revisiting MSA data analysis methodology for developmental contexts.
- Specifying an Aggregate multidimensional factor model, treating trait-context dyads as latent variables.
- Proposing scalar invariance testing alongside factor invariance for accurate mean comparisons.
Main Results:
- An Aggregate factor model is proposed as more suitable for developmental MSA.
- Scalar invariance testing is identified as crucial for comparing aggregated rater scores with self-ratings.
- A 360° survey on over 1100 MBA students demonstrated the methodology's application.
Conclusions:
- The proposed methodology enhances MSA effectiveness for leadership development.
- Customizing surveys for each rater group optimizes the MSA process.
- The study advocates for an Aggregate factor model and scalar invariance in developmental MSA.
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