2020
DOI: 10.1080/00273171.2020.1751027
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Accounting for Latent Covariates in Average Effects from Count Regressions

Abstract: The effectiveness of a treatment on a count outcome can be assessed using a negative binomial regression, where treatment effects are defined as the difference between the expected outcome under treatment and under control. These treatment effects can to date only be estimated if all covariates are manifest (observed) variables. However, some covariates are latent variables that are measured by multiple fallible indicators. In such cases, it is important to control for measurement error of covariates in order … Show more

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Cited by 8 publications
(8 citation statements)
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References 51 publications
(54 reference statements)
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“…However, the effect of information literacy on career success was not significant in SEM. The reason may be that the SEM is used to analyse the relationship between latent variables, whereas multiple linear regression is used to analyse manifest variable (Kiefer & Mayer, 2020 ). Nurses with good information literacy are more likely to seize the opportunity in big data medical treatment and collect medical data and resources faster and more efficiently (Westra et al, 2017 ).…”
Section: Discussionmentioning
confidence: 99%
“…However, the effect of information literacy on career success was not significant in SEM. The reason may be that the SEM is used to analyse the relationship between latent variables, whereas multiple linear regression is used to analyse manifest variable (Kiefer & Mayer, 2020 ). Nurses with good information literacy are more likely to seize the opportunity in big data medical treatment and collect medical data and resources faster and more efficiently (Westra et al, 2017 ).…”
Section: Discussionmentioning
confidence: 99%
“…which is similar to the original extended moment-based approach by Kiefer and Mayer (2020), with the exception of the summation over the latent classes.…”
Section: Case 2: Factorization Of Joint Distributionmentioning
confidence: 84%
“…However, in the social and health sciences, observed variables in real data sets often deviate from the normal distribution (Bono et al, 2017;Micceri, 1989). While Kiefer and Mayer (2020) proposed a moment-based approach assuming strictly multivariate normally distributed covariates, we relaxed this assumption in this paper, offering several alternatives for non-normally distributed variables. Thus, the moment-based approach with our extension should better fit typical applications in social and health sciences.…”
Section: Discussionmentioning
confidence: 99%
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