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Measurement and data quality in longitudinal research

L R Bergman1

  • 1Department of Psychology, University of Stockholm, Sweden.

European Child & Adolescent Psychiatry
|January 1, 1996
PubMed
Summary
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This study highlights the limitations of Steven's scale hierarchy in measurement, emphasizing that scale properties and sampling considerations are crucial for robust longitudinal research quality.

Area of Science:

  • Measurement theory
  • Longitudinal research methodology
  • Statistical analysis

Background:

  • Traditional measurement scales (nominal, ordinal, interval, ratio) may not capture full data richness.
  • Existing frameworks for measurement in longitudinal studies are often insufficient.
  • Sampling considerations are frequently overlooked in longitudinal research.

Purpose of the Study:

  • To critically evaluate the adequacy of existing measurement scale hierarchies.
  • To emphasize the importance of considering scale properties beyond hierarchical levels.
  • To underscore the necessity of incorporating sampling theory in longitudinal studies.

Main Methods:

  • Conceptual analysis of measurement scales and their properties.
  • Discussion of direct vs. indirect and relative vs. absolute measurement.

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  • Examination of measurement error effects.
  • Review of sampling considerations in longitudinal research.

    • Main Results:

    • Steven's scale hierarchy is deemed too narrow; other scale properties are vital.
    • Well-constructed lower-level scales can yield more information than higher-level ones.
    • Measurement errors can significantly impact study outcomes.
    • Ignoring sampling considerations can severely compromise longitudinal study quality.

    Conclusions:

    • Measurement scale selection requires nuanced consideration of properties beyond hierarchy.
    • Integrating modern sampling theory can substantially improve longitudinal study quality.
    • Recommendations are provided for best practices in longitudinal research measurement.