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- W3161654112 abstract "Accurate measurement of a latent variable requires maximising the correlation between true scores and measured scores. Classical psychometric indices such as convergent/discriminant validity and reliability are practically difficult to apply. To overcome this challenge, experiment-based calibration has recently been suggested as a complementary framework. In this approach, a calibration experiment is performed to impact the latent variable in question. The a priori predicted true scores, termed standard scores, can then serve as criterion. Their correlation with measured scores, termed retrodictive validity, is used to compare measurement methods. It has been shown that under plausible assumptions, increasing retrodictive validity is guaranteed to increase measurement accuracy (i.e., combined trueness and precision. Since calibration experiments will be performed in finite samples, it is desirable to design them in a way that minimises the sample variance of retrodictive validity estimators. This is the topic of the current note. For arbitrary distributions of standard, true, and measured scores, I analytically derive the asymptotic variance of the sample estimator of retrodictive validity. I analyse qualitatively how different distribution features impact on estimator variance. Then, I numerically simulate asymptotic and finite-sample estimator variance for various distributions with combinations of feature values. I find that it is preferable to use uniformly distributed (if possible discrete) experimental treatments for calibration. Secondly, inverse sigmoid systematic aberration has a large impact on estimator variance. Finally, reducing imprecision aberration decreases estimator variance in many but not all scenarios. In all of these scenarios, sample size linearly relates to estimator variance. From these findings, I derive recommendations for the design and for resource investment in calibration experiments." @default.
- W3161654112 created "2021-05-24" @default.
- W3161654112 creator A5044989397 @default.
- W3161654112 date "2021-05-06" @default.
- W3161654112 modified "2023-10-16" @default.
- W3161654112 title "Experiment-based calibration in psychology: optimal design considerations" @default.
- W3161654112 doi "https://doi.org/10.31234/osf.io/xa8mv" @default.
- W3161654112 hasPublicationYear "2021" @default.
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