We discuss the problem of random measurement error in two variables when using a cross-lagged panel design. We apply the problem to the question of the causal direction between socio-economic status and subjective health, known also as health selection versus social causation. We plot the bias of the ratio between the social causation and the health selection coefficient as a function of the degree of measurement error in subjective health and socio-economic status for different scenarios which might occur in practice. Using simulated data we give an example of a Bayesian model for the treatment of measurement error that relies on external information about the degree of measurement error."> We discuss the problem of random measurement error in two variables when using a cross-lagged panel design. We apply the problem to the question of the causal direction between socio-economic status and subjective health, known also as health selection versus social causation. We plot the bias of the ratio between the social causation and the health selection coefficient as a function of the degree of measurement error in subjective health and socio-economic status for different scenarios which might occur in practice. Using simulated data we give an example of a Bayesian model for the treatment of measurement error that relies on external information about the degree of measurement error."> We discuss the problem of random measurement error in two variables when using a cross-lagged panel design. We apply the problem to the qu">
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Consequences of measurement error for inference in cross-lagged panel design—the example of the reciprocal causal relationship between subjective health and socio-economic status

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  • Hannes Kröger
  • Rasmus Hoffmann
  • Eduwin Pakpahan
Abstract
type="main" xml:id="rssa12129-abs-0001"> We discuss the problem of random measurement error in two variables when using a cross-lagged panel design. We apply the problem to the question of the causal direction between socio-economic status and subjective health, known also as health selection versus social causation. We plot the bias of the ratio between the social causation and the health selection coefficient as a function of the degree of measurement error in subjective health and socio-economic status for different scenarios which might occur in practice. Using simulated data we give an example of a Bayesian model for the treatment of measurement error that relies on external information about the degree of measurement error.

Suggested Citation

  • Hannes Kröger & Rasmus Hoffmann & Eduwin Pakpahan, 2016. "Consequences of measurement error for inference in cross-lagged panel design—the example of the reciprocal causal relationship between subjective health and socio-economic status," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 607-628, February.
    • Handle: RePEc:bla:jorssa:v:179:y:2016:i:2:p:607-628
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    File URL: http://hdl.handle.net/10.1111/rssa.2016.179.issue-2
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    Cited by:

    1. Rasmus Hoffmann & Hannes Kröger & Siegfried Geyer, 2019. "Social Causation Versus Health Selection in the Life Course: Does Their Relative Importance Differ by Dimension of SES?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(3), pages 1341-1367, February.
    2. Rasmus Hoffmann & Hannes Kröger & Eduwin Pakpahan, 2018. "The reciprocal relationship between material factors and health in the life course: evidence from SHARE and ELSA," European Journal of Ageing, Springer, vol. 15(4), pages 379-391, December.
    3. Erhart, Raphaela & Mahlendorf, Matthias D. & Reimer, Marko & Schäffer, Utz, 2017. "Theorizing and testing bidirectional effects: The relationship between strategy formation and involvement of controllers," Accounting, Organizations and Society, Elsevier, vol. 61(C), pages 36-52.

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