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Books on the topic 'Misclassification'

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Published: 4 June 2021
Last updated: 21 February 2023

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1

Virginia. General Assembly. Joint Legislative Audit & Review Commission. Review of employee misclassification in Virginia. Richmond: Joint Legislative Audit and Review Commission, 2012.

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2

Institute, Pennsylvania Bar. Independent contractor v. employee: Repercussions of misclassification. [Mechanicsburg, Pa.]: Pennsylvania Bar Institute, 2011.

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3

Yi, Grace Y. Statistical Analysis with Measurement Error or Misclassification. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6640-0.

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4

Lee, Peter N. Misclassification of Smoking Habits and Passive Smoking. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73822-7.

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5

New York (State). Legislature. Assembly. Standing Committee on Labor. Public hearing on tax evasion through employee misclassification. New York]: En-De Court Reporting, 2010.

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6

Hilliam, Rachel M. Statistical discrimination with disease categories subject to misclassification. Leicester: De Montfort University, 2000.

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7

Tsai, Hsien-Tang. A single screening procedure using individual misclassification error. West Lafayette, Ind: Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1986.

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8

Hausman, Jerry A. Misclassification of a dependent variable in a discrete response setting. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1994.

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9

Pavlenko, Tatijana. Asymptotic behavior of the probabilities misclassification for discriminant functions with weighting. Toronto: University of Toronto, 1997.

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10

Lee, Peter N. Misclassification of smoking habits and passive smoking: A review of the evidence. Berlin: Springer-Verlag, 1988.

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11

New York (State). Legislature. Senate. Standing Committee on Labor. New York State Senate public hearing on employee misclassification in New York's underground economy. New York: s.n., 2010.

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12

Andersson, Claes. On the use of two-phase sampling in estimation of parameters in domains where data contain misclassification and measurement errors. Uppsala: Uppsala University, 1994.

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13

United States. Congress. Senate. Committee on Health, Education, Labor, and Pensions. Leveling the playing field: Protecting workers and businesses affected by misclassification : hearing of the Committee on Health, Education, Labor, and Pensions, United States Senate, One Hundred Eleventh Congress, second session ... June 17, 2010. Washington: U.S. G.P.O., 2012.

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14

United States. Congress. House. Committee on Ways and Means. Subcommittee on Select Revenue Measures. Misclassification of employees and independent contractors for federal income tax purposes: Hearing before the Subcommittee on Select Revenue Measures of the Committee on Ways and Means, House of Representatives, One Hundred Second Congress, second session, July 23, 1992. Washington: U.S. G.P.O., 1992.

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15

United States. Congress. House. Committee on Ways and Means. Subcommittee on Select Revenue Measures. Misclassification of employees and independent contractors for federal income tax purposes: Hearing before the Subcommittee on Select Revenue Measures of the Committee on Ways and Means, House of Representatives, One Hundred Second Congress, second session, July 23, 1992. Washington: U.S. G.P.O., 1992.

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16

United States. Congress. House. Committee on Education and Labor (2007). Subcommittee on Workforce Protections., ed. The misclassification of workers as independent contractors: What policies and practices best protect workers? : joint hearing before the Subcommittee on Health, Employment, Labor and Pensions and the Subcommittee on Workforce Protections, Committee on Education and Labor, U.S. House of Representatives, One Hundred Tenth Congress, first session, hearing held in Washington, DC, July 24, 2007. Washington: U.S. G.P.O., 2008.

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17

Elwood, Mark. Error and bias in observations. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682898.003.0006.

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This chapter distinguishes error and bias, non-differential and differential misclassification distinguished. Non-differential misclassification almost always biases results toward the null, while differential misclassification can affect results in any direction. Methods to minimise observation bias include single, double and triple blind assessment. It discusses recall and other biases, with methods of assessment and avoidance, and practical issues on reducing error and bias. In part two, it shows how to measure and adjust for observational error and bias, including Kappa and adjusting for non-differential misclassification, and similar adjustments using continuous exposure measures. Effects with more than two categories of outcome or exposure, and of the misclassification of confounders are discussed. In assessing the accuracy of information, sensitivity, specificity, and predictive value are defined, and the calculation of the effects of misclassification using sensitivity and specificity are shown.
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18

Michael, Evans, ed. Bayesian analysis of binary data subject to misclassification. Toronto: University of Toronto, Dept. of Statistics, 1993.

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19

Measurement Error and Misclassification: Models, Methods and Applications. Chapman & Hall/CRC, 2009.

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20

Yi, Grace Y. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application. Springer, 2018.

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21

Yi, Grace Y. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application. Springer New York, 2017.

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22

Gustafson, Paul. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. Taylor & Francis Group, 2003.

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23

Gustafson, Paul. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. Taylor & Francis Group, 2003.

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24

Gustafson, Paul. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. Taylor & Francis Group, 2003.

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25

Misclassification of exposure: Coffee as a surrogate for caffeine and methylxanthine intake. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1999.

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26

Gustafson, Paul. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. Chapman & Hall/CRC, 2003.

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27

Lee, P. N. Misclassification of Smoking Habits and Passive Smoking: A Review of the Evidence. Springer London, Limited, 2013.

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28

Misclassification of Smoking Habits and Passive Smoking: A Review of the Evidence. Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 1988.

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29

Gustafson, Paul. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. Taylor & Francis Group, 2003.

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30

Gustafson, Paul. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. Taylor & Francis Group, 2014.

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31

Gustafson, Paul. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. Taylor & Francis Group, 2003.

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32

Gustafson, Paul. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. Taylor & Francis Group, 2003.

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33

Gustafson, Paul. Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments. Taylor & Francis Group, 2003.

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34

Committee On Education and Labor, United States House of Representatives, and United States United States Congress. Misclassification of Workers As Independent Contractors: What Policies and Practices Best Protect Workers? Independently Published, 2019.

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35

Duncan, Dustin T., Seann D. Regan, and Basile Chaix. Operationalizing Neighborhood Definitions in Health Research. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190843496.003.0002.

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Defining neighborhoods for health research continues to be challenging. This chapter discusses different methods to operationalize neighborhood boundaries, including self-report, administrative definitions, geographic information system buffers and activity spaces, including global positioning system (GPS)–defined activity spaces. It discusses the strengths and limitations of each method of examining neighborhood boundaries (e.g., spatial misclassification, technical difficulties, assumptions). Readers are provided with examples of neighborhood definitions frequently applied in the epidemiology and population health literature. In addition, the chapter provides a rigorous overview of theories for selecting neighborhood definitions, including spatial polygamy theory for GPS-defined activity space neighborhoods.
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36

Posner, Eric A. How Antitrust Failed Workers. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197507629.001.0001.

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Antitrust law has very rarely been used by workers to challenge anticompetitive employment practices. Yet recent empirical research shows that labor markets are highly concentrated, and that employers engage in practices that harm competition and suppress wages. These practices include no-poaching agreements, wage-fixing, mergers, covenants not to compete, and misclassification of gig workers as independent contractors. This failure of antitrust to challenge labor-market misbehavior is due to a range of other failures—intellectual, political, moral, and economic. And the impact of this failure has been profound for wage levels, economic growth, and inequality. In light of the recent empirical work, it is urgent for regulators, courts, lawyers, and Congress to redirect antitrust resources to labor market problems. This book offers a strategy for judicial and legislative reform.
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37

Sarath, Ed. A Consciousness-Based Look at Spontaneous Creativity. Edited by Benjamin Piekut and George E. Lewis. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199892921.013.13.

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This chapter explores improvisation from a consciousness-based standpoint. Examination of an inner mechanics for the transcendent experience frequently reported by improvisers sets the stage for consciousness-based distinctions between improvisation and composition processes, in which improvisation is extricated from common misclassification as an accelerated subspecies of composition. Temporal, cultural, and linguistic factors are considered in distinguishing between improvisatory and compositional paradigms. The intimate melding between musicians and listeners in peak improvised performance is paralleled with the deep collective communion associated with group meditation practice as indicative of a nonlocal, intersubjective field of consciousness, empirical support for which suggests that possible societal benefits may result from certain applications. An “improvisatory hermeneutics” is considered as a means for new ways of perceiving global challenges and paradigmatic change that centers intersubjectivity and other anomalous possibilities not commonly embraced in academic and public policy discourse.
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38

US GOVERNMENT. Misclassification of employees and independent contractors for federal income tax purposes: Hearing before the Subcommittee on Select Revenue Measures ... Congress, second session, July 23, 1992. For sale by the U.S. G.P.O., Supt. of Docs., Congressional Sales Office, 1992.

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39

Iskander, Natasha, and Nichola Lowe. Immigration and the Politics of Skill. Edited by Gordon L. Clark, Maryann P. Feldman, Meric S. Gertler, and Dariusz Wójcik. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198755609.013.24.

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Skill has played a central role in immigration scholarship, most notably in a protracted debate over whether ‘unskilled’ immigrants threaten job security for less or moderately educated native-born workers. In recent years, scholars have re-examined whether immigrant workers, particularly those with limited formal education, are unskilled. Extending this further, the chapter argues that immigrants are not simply individuals that possess, acquire, and apply their skill. Immigrants are also contributors to collective learning processes through which industry skills are developed, replenished, and recombined overtime. But immigrants are especially vulnerable to skill misclassification because they lack access to institutions that can protect and defend spaces for collective learning. Considering immigrant skill reproduction in the absence of institutional protections allows us to reflect on the role those institutions play in shaping the politics of skill—a role that can be strengthened as part of a growing movement in support of low-wage workers more generally.
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