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Evaluation of systematic literature reviews in built environment research
Published in Emmanuel Manu, Julius Akotia, Secondary Research Methods in the Built Environment, 2021
Vijayan Chelliah, Nicola Thounaojam, Ganesh Devkar, Boeing Laishram
Secondary research is conducted to gather information about a particular domain from a variety of relevant sources. The information is gathered for numerous purposes, particularly to identify the gaps and deficiencies in an area of research, to find out additional information that must be collected, and to form a basis for developing standards and guidelines for practice and policies. This type of research has been used extensively by researchers in the built environment. Moreover, systematic literature review (SLR), also known as systematic review, has demonstrated much attention as a prime tool for carrying out secondary research. A SLR is “a means to identify, analyse and interpret reported evidence related to a set of specific research questions in a way that is unbiased and (to a degree) repeatable” (Kuhrmann et al., 2017). Bias can impact the validity of the results, lead to over-estimation of findings and also make the review untrustworthy for decision-making (Hall et al., 2017). Unlike traditional literature reviews, explicit and rigorous criteria are adopted for SLR, which is used to evaluate critically and synthesise all the literature in a particular domain (Cronin et al., 2008). In addition, SLR differs from other literature reviews because of its “distinct” and “exacting” principles (Denyer and Tranfield, 2009).
Exposure Assessment in Environmental Epidemiology
Published in Roberto Bertollini, Michael D. Lebowitz, Rodolfo Saracci, David A. Savitz, Environmental Epidemiology, 2019
Bias in epidemiology refers to the difference between the estimated association between exposure and disease, and the true association. Three general types of bias can be identified: selection bias, information bias, and confounding bias.3 For the purpose of this chapter, the discussion can be restricted to information bias, which occurs when the information about exposure and/or disease is wrong to the extent that the relationship between the two is no longer correctly being estimated. Errors in exposure assessment can lead to information bias. In addition to the notions of validity and precision of measures of exposure, the concepts of differential misclassification and nondifferential misclassification are often used in epidemiology to put these notions in the context of classification of study subjects into various categories of exposure. Differential misclassification means that the extent of misclassification is different for cases and controls. Nondifferential misclassification refers to a misclassification of exposure which is not different between cases and controls. The extent of nondifferential misclassification is related to both the precision of the exposure measurement and the magnitude of the differences in exposure within the population. If the differences in exposure are substantial, even a fairly imprecise exposure measurement would not lead to much misclassification of exposure. For this reason, it is important to judge the precision of exposure measurements always relative to the magnitude of the exposure variability within the population.
Introduction
Published in William M. Mendenhall, Terry L. Sincich, Statistics for Engineering and the Sciences, 2016
William M. Mendenhall, Terry L. Sincich
No matter what type of sampling design you employ to collect the data for your study, be careful to avoid selection bias. Selection bias occurs when some experimental units in the population have less chance of being included in the sample than others. This results in samples that are not representative of the population. Consider an opinion poll on whether a device to prevent cell phone use while driving should be installed in all cars. Suppose the poll employs either a telephone survey or mail survey. After collecting a random sample of phone numbers or mailing addresses, each person in the sample is contacted via telephone or the mail and a survey conducted. Unfortunately, these types of surveys often suffer from selection bias due to nonresponse. Some individuals may not be home when the phone rings, or others may refuse to answer the questions or mail back the questionnaire. As a consequence, no data is obtained for the nonrespondents in the sample. If the nonrespondents and respondents differ greatly on an issue, then nonresponse bias exits. For example, those who choose to answer the question on cell phone usage while driving may have a vested interest in the outcome of the survey—say, parents of teenagers with cell phones, or employees of a company that produces cell phones. Others with no vested interest may have an opinion on the issue but might not take the time to respond. Finally, we caution that you may encounter a biased sample that was intentional, with the sole purpose of misleading the public. Such a researcher would be guilty of unethical statistical practice.
Using naturalistic driving study data to explore the association between horizontal curve safety and operation on rural two-lane highways
Published in Journal of Transportation Safety & Security, 2021
Lingtao Wu, Bahar Dadashova, Srinivas Geedipally, Michael P. Pratt, Mohammadali Shirazi
Nevertheless, there are a few limitations that need to be addressed in the future. First, the comparison between curve severity and crash rates was conducted based on cross-sectional data. It may suffer from the common problems observed in cross-sectional analyses, such as the confounding bias (also known as omitted variable bias; Wu, Lord, & Zou, 2015; Wu & Lord, 2017). Second, the sample size used in the study is relatively small. Based on the method used, about 64% to 81% of 252 curves included in the study belong to curve severity category A. The small sample size and the biased distribution of crashes among curves cause inconsistent results. It is recommended to include more curves that belong to higher severity categories to validate the results of this study in the future.
A Worker’s Fitness-for-Duty Status Identification Based on Biosignals to Reduce Human Error in Nuclear Power Plants
Published in Nuclear Technology, 2020
First, grouping of subjects for experiments has limitations. They were categorized by self-evaluation. The use of self-evaluation to classify the subjects’ FFD status with respect to stress, depression, and anxiety leaves the issue of subjectivity. Kendall and Watson98 emphasized the limitations associated with the existing self-report scales for both anxiety and depression. A more objective way to select these groups needs to be considered in future work. The method of collecting biosignal data in this study was based on using subjects who were volunteers and college students that were all males. While the use of male college students reduces potential confounding effects, it has a potential selection bias and limits the applicability of the findings for the general population. The selection bias can be defined as an experimental error that occurs when the participant pool, or the subsequent data, is not representative of the target population. To address the representativeness issue of the subjects, our sample size was 114, which is relatively large. Based on post hoc power analysis,99 our collected subjects had 79.9% power. This power represents the ability of a trial to detect a difference between two different groups. This means that our sample size was large enough to represent the populations. But, the workers in NPPs are expected to be older. Thus, the possible effects of age on the findings may not be captured in the study. Using older people as part of the experimental subjects needs to be considered in future studies.
Occupational tuberculosis in healthcare workers in sub-Saharan Africa: A systematic review
Published in Archives of Environmental & Occupational Health, 2019
Faith O Alele, Richard C. Franklin, Theophilus I. Emeto, Peter Leggat
Bias refers to the systematic sources of error which can affect the internal and external validity of a study.23 Eleven of the studies used hospital records as the data source. This has an increased risk of inaccuracy and incompleteness and could have introduced information bias into the studies.24–32,41,42 There was an increased risk of selection bias in eight of the included studies.33–38,40,43 The samples in the studies did not fully represent the target population. Three of the eight studies lost participants to follow-up which could have led to attrition bias.33–35 In addition, one study identified the healthy worker effect as a potential source of bias34. Measurement bias was identified in twelve studies.26,27,29–32,35,36,39,40,44,45 HIV related respiratory disease in some HCWs may have been misdiagnosed as TB which may have introduced misclassification bias.26 Other sources of misclassification bias was HIV associated immunosuppression. HIV-infected HCWs may have been anergic resulting in negative TST results and under diagnosis of TB. This could have underestimated the incidence of LTBI/ TB in HCWs.26,27,29–32,35,39,40,44