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Deriving Expected Values of Model Parameters when Using Sum Scores in Simulation Research.

A R Georgeson1

  • 1Department of Psychology, Arizona State University.

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Summary
This summary is machine-generated.

This study provides a method for calculating bias in sum scores, which are commonly used in structural equation modeling. This helps researchers compare sum scores to factor scores, improving statistical analysis.

Keywords:
Simulation Designfactor score path analysisfactor score regressionfactor scoressum scores

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Area of Science:

  • Psychometrics
  • Structural Equation Modeling

Background:

  • Sum scores are widely used in practice despite increasing interest in factor scores.
  • Comparing sum scores and factor scores in simulations is challenging due to scale differences.

Purpose of the Study:

  • To provide guidance on computing bias for sum scores in methodological research.
  • To enable direct comparison of sum scores and factor scores in structural equation models.

Main Methods:

  • Develop a method to calculate bias for sum scores.
  • Obtain expected values of model parameters under a sum score model.

Main Results:

  • The paper presents a clear approach for methodological researchers to compute bias in sum scores.
  • This facilitates a more direct comparison between sum scores and factor scores.

Conclusions:

  • The proposed method aids applied researchers in understanding the advantages of factor scores over sum scores.
  • This research addresses a gap in comparing score types in structural equation modeling simulations.