Accounting for measurement error in human life history trade‐offs using structural equation modeling

Accounting for measurement error in human life history trade‐offs using structural equation... INTRODUCTIONIt is a commonly held view in evolutionary biology that causal inference is tied to experimental approach and that correlative data cannot be used to draw causal conclusions (Roff, ; Stearns, ). This problem concerns particularly human life history studies, because the manipulation of life history traits in humans is not feasible owing to ethical reasons. Most researchers in our field recognize that such inferential problems are due to the nonrandom selection of study subjects or to missing confounding variables not included in the analysis (Gagnon et al., ; Sear, ). But few have seemed to recognize that measurement error in independent variables, defined as the difference between a value measured and the true value of scientific interest, has also detrimental consequences on causal inference (Antonakis, Bendahan, Jacquart, & Lalive, ; Antonakis, Bendahan, Jacquart, & Lalive, ; Pearl, ). Measurement error, non‐random selection, and omitted variables all undermine causal inference because they all introduce a correlation between independent variables and the model errors (e.g., of measurement error, please see the Appendix), thus violating a key assumption for any regression modeling (Antonakis et al., ; Pearl, ). Of these, the problem of missing variables is, however, the most severe in http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Human Biology Wiley

Accounting for measurement error in human life history trade‐offs using structural equation modeling

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
© 2018 Wiley Periodicals, Inc.
ISSN
1042-0533
eISSN
1520-6300
D.O.I.
10.1002/ajhb.23075
Publisher site
See Article on Publisher Site

Abstract

INTRODUCTIONIt is a commonly held view in evolutionary biology that causal inference is tied to experimental approach and that correlative data cannot be used to draw causal conclusions (Roff, ; Stearns, ). This problem concerns particularly human life history studies, because the manipulation of life history traits in humans is not feasible owing to ethical reasons. Most researchers in our field recognize that such inferential problems are due to the nonrandom selection of study subjects or to missing confounding variables not included in the analysis (Gagnon et al., ; Sear, ). But few have seemed to recognize that measurement error in independent variables, defined as the difference between a value measured and the true value of scientific interest, has also detrimental consequences on causal inference (Antonakis, Bendahan, Jacquart, & Lalive, ; Antonakis, Bendahan, Jacquart, & Lalive, ; Pearl, ). Measurement error, non‐random selection, and omitted variables all undermine causal inference because they all introduce a correlation between independent variables and the model errors (e.g., of measurement error, please see the Appendix), thus violating a key assumption for any regression modeling (Antonakis et al., ; Pearl, ). Of these, the problem of missing variables is, however, the most severe in

Journal

American Journal of Human BiologyWiley

Published: Jan 1, 2018

References

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