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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.