Perturbation by multiplicative noise and the Simulation Extrapolation method

Biewen, Elena; Nolte, Sandra; Rosemann, Martin

doi:10.1007/s10182-008-0089-7

Cited by 6 publications

(10 citation statements)

References 12 publications

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“…These results are consistent with Biewen et al (2008) who, in a simulated probit model with one predictor, find an upward bias in the constant and a downward bias in the slope induced by classical multiplicative ME. These results obtained here serve to reinforce these findings.…”

Section: The Impact Of Classical Multiplicative Measurement Error In ...supporting

confidence: 91%

“…In this paper I will use simulated data to study the effectiveness of multiplicative Simulation Extrapolation Method (SIMEX) (Carroll et al 2006;and Biewen, Nolte & Rosemann, 2008). This is an extension of the standard SIMEX method (Cook & Stefanski, 1994) capable of adjusting for the recall errors that are typically observed in the retrospective reports of life-course events.…”

Section: Introductionmentioning

confidence: 99%

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Adjustment of recall errors in duration data using SIMEX

Pina-Sánchez¹

2024

Adv Meth Stat

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It is widely accepted that due to memory failures retrospective survey questions tend to be prone to measurement error. However, the proportion of studies using such data that attempt to adjust for the measurement problem is shockingly low. Arguably, to a great extent this is due to both the complexity of the methods available and the need to access a subsample containing either a gold standard or replicated values. Here I suggest the implementation of a version of SIMEX capable of adjusting for the types of multiplicative measurement errors associated with memory failures in the retrospective report of durations of life-course events. SIMEX is a method relatively simple to implement and it does not require the use of replicated or validation data so long as the error process can be adequately specified. To assess the effectiveness of the method I use simulated data. I create twelve scenarios based on the combinations of three outcome models (linear, logit and Poisson) and four types of multiplicative errors (non-systematic, systematic negative, systematic positive and heteroscedastic) affecting one of the explanatory variables. I show that SIMEX can be satisfactorily implemented in each of these scenarios. Furthermore, the method can also achieve partial adjustments even in scenarios where the actual distribution and prevalence of the measurement error differs substantially from what is assumed in the adjustment, which makes it an interesting sensitivity tool in those cases where all that is known about the error process is reduced to an educated guess.

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Section: The Impact Of Classical Multiplicative Measurement Error In ...supporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Adjustment of recall errors in duration data using SIMEX

Pina-Sánchez¹

2024

Adv Meth Stat

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“…In the UK there are multiple examples of secure portals like the Ministry of Justice Data Lab, as well as repositories managing access of sensitive data like the UK Data Service. Other options that could be explored are robust data anonymisation techniques, such as those based on the incorporation of simulated errors (Biewen, Nolte and Rosemann, 2008). All of these approaches have been successfully applied to various other fields of research, and could be equally well applied to sentencing research.…”

Section: A Final Note On the Need For Better Sentencing Datamentioning

confidence: 99%

Exploring the origin of sentencing disparities in the Crown Court: Using text mining techniques to differentiate between court and judge disparities

Pina-Sánchez

Grech

Brunton‐Smith

et al. 2019

Social Science Research

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“…Further work is needed to explore the impact of measurement error in those instances. Even if the impact happens to be difficult to anticipate, and regardless of the complexity of the outcome model, we could still rely on flexible methods such as Bayesian measurement models (Gustafson, 2003;Pina-Sánchez et al, 2019), or simulation-extrapolation (Biewen et al, 2008;Pina-Sánchez, 2016) to adjust for the impact of measurement error.…”

Section: Caveats and Future Avenues Of Researchmentioning

confidence: 99%

The impact of measurement error in models using police recorded crime rates

Pina-Sánchez¹,

Buil‐Gil²,

Brunton‐Smith³

et al. 2021

Preprint

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Objectives: Assess the extent to which measurement error in police recorded crime rates impact the estimates of regression models exploring the causes and consequences of crime.Methods: We focus on linear models where crime rates are included either as the response or as an explanatory variable, in their original scale, or log-transformed. Two measurement error mechanisms are considered, systematic errors in the form of under-recorded crime, and random errors in the form of recording inconsistencies across areas. The extent to which such measurement error mechanisms impact model parameters is demonstrated algebraically, using formal notation, and graphically, using simulations.Results: Most coefficients and measures of uncertainty from models where crime rates are included in their original scale are severely biased. However, in many cases, this problem could be minimised, or altogether eliminated by log-transforming crime rates. This transforms the multiplicative measurement error observed in police recorded crime rates into a less harmful additive mechanism.Conclusions: The validity of findings from regression models where police recorded crime rates are used in their original scale is put into question. In interpreting the large evidence base exploring the effects and consequences of crime using police statistics we urge researchers to consider the biasing effects shown here. Equally, we urge researchers to log-transform crime rates before they are introduced in statistical models.

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Perturbation by multiplicative noise and the Simulation Extrapolation method

Cited by 6 publications

References 12 publications

Adjustment of recall errors in duration data using SIMEX

Adjustment of recall errors in duration data using SIMEX

Exploring the origin of sentencing disparities in the Crown Court: Using text mining techniques to differentiate between court and judge disparities

The impact of measurement error in models using police recorded crime rates

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