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Published in Qingyan Chen, Zhiqiang Zhai, Xueyi You, Tengfei Zhang, Inverse Design Methods for the Built Environment, 2017
Qingyan Chen, Zhiqiang Zhai, Xueyi You, Tengfei Zhang
Because simple random sampling can be inefficient and time-consuming, statisticians have turned to other methods, such as systematic sampling. Systematic sampling is a statistical method in which individuals are selected from an ordered sampling frame. The most common form of systematic sampling is an equal-probability method. In this approach, the individuals of the population are put into a list, and then every individual in the list is chosen for inclusion in the sample. Progression through the list is treated circularly, with a return to the top once the end of the list is reached. Systematic sampling consists of the following steps: (a) The populations are coded from 1 to Np.(b) The sampling interval, Is, is determined by dividing the population size Np by the sample size Ns and rounding to the nearest integer.(c) To ensure against any possible human bias in this method, the simple random sampling method is used to generate the code for the first sample (Nf).(d) Subsequent samples are selected according to a specific rule, for example, Ndd = Nf + (dd- 1) × Is.
Designing a Defensible Sampling Program
Published in Mark Edward Byrnes, Field Sampling Methods for Remedial Investigations, 2023
Systematic sampling may introduce a certain type of sampling bias. Because sampling occurs at the nodes, small areas of contamination may be missed if they are entirely within the grid. This could result in underestimating the contamination of the site. Conversely, if the spread of the contamination is very similar to the grid pattern, overestimation of the contamination could occur. Because of these factors, care must be taken in choosing both the size and type of sampling grid to be used. Sampling on a triangular grid pattern is often preferred because it reduces the possibility of sampling bias.
Data Collection and Analysis
Published in James William Martin, Lean Six Sigma for the Office, 2021
Systematic sampling draws samples from a process at equal intervals or every nth unit. A common example is a time series chart that displays variation of a metric by time. In cluster sampling, a population is divided into naturally occurring groups from which random samples are drawn. An example is dividing customers into naturally occurring market segments, sampling from each segment, and measuring a characteristic such as value, time, percentage customer satisfaction, or others.
Understanding Consumers’ Post-Adoption Behavior in Sharing Economy Services
Published in Journal of Computer Information Systems, 2021
Xuequn Wang, Xiaolin Lin, Zilong Liu
A bicycle-sharing service was chosen for our study because it is not only a popular sharing economy phenomenon in China but also represents a huge market.2http://news.ikanchai.com/2017/0710/144237.shtml. Participants were recruited by a survey company which maintains national panels and has access to consumers of bicycle-sharing services from a variety of backgrounds. Invitations to participate in the survey were sent using systematic sampling. Participants received credits (which can be converted to money) through participation. All participants were existing customers of the leading bicycle-sharing service companies in China, including OFO, Mobike, and Youon. They were asked questions about perceptions of bicycle-sharing services and usage behavior. A total of 460 valid responses were received. 30.0% of participants were female, and 80.0% were 30 years old or under. 78.5% had bachelor degrees. This percentage is consistent with a recent industrial report by iResearch,3http://www.iresearchchina.com/content/details8_32338.html, accessed in June, 2019. which shows that 71.7% of Chinese bicycle-sharing consumers have bachelor degrees. The detailed demographic information of the participants is shown in Table 1 and their usage behaviors are presented in Table 2.