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Project Control System
Published in Adedeji B. Badiru, Project Management, 2019
All the units in each cluster may be included in the overall sample or a subsample of the units in each cluster may be used. If all the units of the selected clusters are included in the overall sample, the procedure is referred to as single-stage sampling. If a subsample is taken at random from each selected cluster and all units of each subsample are included in the overall sample, then the sampling procedure is called two-stage sampling. If the sampling procedure involves more than two stages of subsampling, then the procedure is referred to as multistage sampling. Cluster sampling is typically less expensive to implement than stratified sampling. For example, the cost of taking a random sample of 2,000 managers from different industry types may be reduced by first selecting a sample, or cluster, of 25 industries and then selecting 80 managers from each of the 25 industries. This represents a two-stage sampling that will be considerably cheaper than trying to survey 2,000 individuals in several industries in a single-stage procedure.
What Do My Customers Really Want?
Published in Chris Hook, Ryan Burge, James Bagg, Routines for Results, 2017
Chris Hook, Ryan Burge, James Bagg
Probability sampling methods include simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. These methods make it easier to compose a sample that is representative of the population.
Exploring the determinants affecting the usage of blockchain-based remittance services: an empirical study on the banking sector
Published in Behaviour & Information Technology, 2023
Muhammad Mansoor, Amir Zaib Abbasi, Ghazanfar Ali Abbasi, Sheraz Ahmad, Yujong Hwang
The current study is done in the context of Pakistan, and data were collected from 240 respondents of banks from Islamabad, Rawalpindi, Peshawar, Mardan, and Nowshera. The multistage sampling is used in the current study. The data were collected during the Coronavirus pandemic. Future research can be done in different contexts with different population orders. Future research can be done in normal conditions because the current situation was a much-stressed situation for the whole planet. The unified theory of acceptance and use of technology is used in the current study; in future research the researcher can use the extended form of the unified theory of acceptance and use of technology (UTAUT2). If they add some additional variables, that may be a good future contribution to the adoptability of blockchain. Future research can use the unified theory of acceptance and use of technology 2 (UTAUT 2), which was presented by Venkatesh, Thong, and Xu (2012), adding some more variable to the unified theory. The literature on BT is very limited so future research can enrich the literature about BT in new technologies such as Metaverse.
Temporal relationship between attitude toward mathematics and mathematics achievement
Published in International Journal of Mathematical Education in Science and Technology, 2022
Henry Nsubuga Kiwanuka, Jan Van Damme, Wim Van den Noortgate, Chandra Reynolds
Our study used a multistage sampling design. At the first stage, four districts in Central Uganda were purposively chosen. These were: Kampala and Wakiso, which are urban and populated with people from different parts of the country, and Mpigi and Mukono, which are semi-rural but reachable. At the second stage, 60schools were selected from a total of 376 schools in the four districts, with 25 semi-rural and 35 urban schools, thus forming two strata. These schools followed the same curriculum, but with a variety of learning climates and teacher practices. At the third stage, classes were selected according to the number of 7th gradeclasses at a school. Four, three and two classes were randomly selected from schools with five, four and three classes, respectively. In schools with only one and two classes, all classes were selected. The participating sample consisted of 4819 first-year secondary school students (grade 7; about 14–15 years old) who participated in at least one measurement occasion. They were grouped in 78 classes of 49 schools. Data came from student information of Wave 1 (N = 4768), Wave 2 (N = 4531) and Wave 3 (N = 4244). Table 1 describes numbers of sampled schools and classes in the target and participating sample. Out of the 60 targeted schools, 11 did not cooperate or comply with our research procedures. Table 2 describes the categories of the participating schools.
Modeling pesticide use intention in Pakistani farmers using expanded versions of the theory of planned behavior
Published in Human and Ecological Risk Assessment: An International Journal, 2021
Fawad Zafar Ahmad Khan, Syed Amir Manzoor, Muhammad Akmal, Muhammad Usama Imran, Muhammad Taqi, Syed Asad Manzoor, Martin Lukac, Hafiza Tahira Gul, Shimat V. Joseph
The case study area is located in the Multan division in the southern region of Punjab province, Pakistan. Multan division is one of the largest cotton-producing regions of the Punjab and consists of four districts, i.e., Multan, Vehari, Lodhran, and Khanewal. Each district is further divided into Tehsils. We used the multistage sampling technique to sample the cotton-growers in the study area. This technique is used to obtain cross-sectional data in which larger groups are further subdivided into smaller, better-targeted clusters, thus creating a more representative sample of the population without the cost of a large-scale survey (Bagheri et al. 2019). In the first stage, two districts (Multan and Lodhran, Figure 2) were randomly selected, followed by a second stage selection of random Tehsils from the two districts (Table 1). Farmers were then randomly sampled from within selected tehsils. District Multan and Lodhran extend over an area of 3721 km2 and 1790 km2 respectively and have a total population of nearly 5.5 million. The study area has an arid tropical climate characterized by long and hot summers (Manzoor et al. 2019). The region is part of the Indus plain where fertile soils support the production of cotton, wheat, and rice—among many other crops. The selected districts have a pesticide-use history of more than 50 years (Khan et al. 2015).