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Thinking twice about sum scores.

Daniel McNeish1, Melissa Gordon Wolf2

  • 1Department of Psychology, Arizona State University, PO Box 871104, Tempe, AZ, 85287, USA. dmcneish@asu.edu.

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Summary
This summary is machine-generated.

Sum scoring, often contrasted with factor analysis, is a constrained latent variable model. Researchers must justify sum scoring constraints like any other latent variable model to ensure scale validity and reliability.

Keywords:
Factor analysisPsychometricsScale scoresScales

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Scale Development

Background:

  • Sum scoring is a common method for creating scores from multiple-item scales.
  • Factor analysis and sum scoring are both types of latent variable models.
  • Sum scoring is a constrained version of factor analysis, requiring specific justifications.

Purpose of the Study:

  • To highlight that sum scoring is a constrained latent variable model.
  • To emphasize the need for justifying sum scoring constraints.
  • To discuss the potential negative impacts of unjustified sum scoring on validity and reliability.

Main Methods:

  • Review of latent variable models.
  • Conceptual comparison of sum scoring and factor analysis.
  • Discussion of psychometric property reporting differences.

Main Results:

  • Sum scoring imposes strict constraints that require justification.
  • Unjustified sum scoring can negatively affect validity, reliability, and classification.
  • Using factor analysis for validation and sum scoring for analysis creates a model mismatch.

Conclusions:

  • Sum scoring is a statistical model, not a model-free calculation.
  • Researchers should critically evaluate the acquisition, justification, and utilization of scale scores.
  • Proper justification of sum scoring is crucial for accurate research findings.