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Communication
Published in Walter DeGrange, Lucia Darrow, Field Guide to Compelling Analytics, 2022
When selecting participants for a focus group, it is important to consider who will be most likely to contribute useful information. A few different ways to select participants are: Random sampling: In this method, everyone in the population has an equal chance of being selected.Stratified sampling: In this method, the population is divided into groups (or strata) and people are selected from each group at random.Cluster sampling: In this method, clusters of people are randomly selected, and then individual participants are chosen from within the clusters.Quota sampling: In this method, a certain number of participants from specific groups are chosen.Convenience sampling: In this method, participants are selected based on convenience (for example, people who are available at a specific time and place).
Data Collection and Analysis
Published in James William Martin, Lean Six Sigma for the Office, 2021
After the scope is decided, the team decides on the sampling method to be used to collect data from the process. Choices range between a 100% count to various types of statistical sampling of the process. In highly automated applications the count will usually be 100% because algorithms, i.e., bots, can gather all the data, condition it, and apply various analyses designed to model big databases. Relative to sampling, there are at least four major types of sampling methods. These include random, stratified, systematic, and cluster sampling. Random sampling selects observations from a process and measures one or more attributes. An example is selecting 20 or 30 invoices and evaluating them as having a defect or not to create an invoice defect percentage. Stratified sampling divides a population into strata based on a stratification variable, and random samples are drawn from each stratum. This would divide an inventory account population into strata based on dollar value, then drawing accounts at random within a stratum to calculate the inventory value for each stratum, then adding up the valuation estimates across all the strata. There is sampling efficiency if this stratification method is used. It can potentially reduce the sample size over random sampling by more than 50%.
Discrete Outcome Models
Published in Simon Washington, Matthew Karlaftis, Fred Mannering, Panagiotis Anastasopoulos, Statistical and Econometric Methods for Transportation Data Analysis, 2020
Simon Washington, Matthew Karlaftis, Fred Mannering, Panagiotis Anastasopoulos
There are two general types of sampling strategies for collecting data to estimate discrete outcome models, random and stratified random sampling. All standard MNL model derivations assume that the data used to estimate the model are drawn randomly from a population of observations. Complications may arise in the estimation when this is not the case. Stratified sampling refers to a host of nonrandom sampling alternatives. The idea of stratified sampling is that some known population of observations is partitioned into subgroups (strata) and random sampling is conducted in each of these subgroups. This type of sampling is particularly useful when one wants to gain information on a specific group that is a small percentage of the total population of observations (such as transit riders in the choice of transportation mode or households with incomes exceeding $200,000 per year). Note that random sampling is a special case of stratified sampling in that the number of observations chosen from each strata is in exact proportion to the size of the strata in the population of observations.
Built environment transformation in Nigeria: the effects of a regenerative framework
Published in Journal of Asian Architecture and Building Engineering, 2023
Oluwagbemiga Paul Agboola, Badr Saad Alotaibi, Yakubu Aminu Dodo, Mohammed Awad Abuhussain, Maher Abuhussain
For satisfactory data gathering and removal of bias, stratified random sampling was adopted. Stratified sampling involves dividing the population into homogeneous subgroups (strata) based on certain characteristics. In this case, the strata have been determined by specific factors related to the built environment or location within southwestern Nigeria. By sampling within each stratum, we ensure that different segments of the population are represented. This sampling technique allows for a more representative and accurate analysis of the different predictors, impacts of climate change, and regenerative factors within the built environment in southwestern Nigeria. According to Creswell (2012), using stratified sampling in conjunction with probability sampling is the most effective technique for minimising bias. A total of 314 survey questionnaires were distributed, and 235 were retrieved and deemed suitable for analysis. To calculate the response rate, we divided the number of retrieved questionnaires by the number of distributed questionnaires and then multiplied by 100 to get the percentage. Therefore, the response rate stood at approximately 74.84%, which was a justifiable percentage (Moser and Kalton 1971). According to the literature, when employing the questionnaire method, a response rate exceeding 30% is commonly regarded as a satisfactory and acceptable level (Crimp and Wright 1995).
A comparison of approaches to reweighting anthropometric data
Published in Ergonomics, 2022
Madison Reddie, Matthew B. Parkinson
Stratified sampling (Parsons 2017) is a sampling method in which a number of groupings, or strata, are formed from various combinations of attributes in the study population, and a certain number of individuals belonging to each group are sampled. Stratified sampling is intended to ensure that a sufficient number of individuals belonging to various subgroups of a population are sampled such that the resulting sample is effectively representative of the population. Stratification can also be used to weight anthropometric data to match a population of interest. Harrison and Robinette (2002) weighted CAESAR data to match U.S. civilians by creating 45 strata for each gender from three race categories, three age ranges and five stature and mass groupings () determined using an earlier NHANES data set (NHANES III). The CAESAR points falling within each bin were assigned a weight equal to the sum of the NHANES III weights within the same bin divided by the number of CAESAR points in the bin.
Empirical validation of different internal superficial heat transfer models on a full-scale passive house
Published in Journal of Building Performance Simulation, 2018
Fabio Munaretto, Thomas Recht, Patrick Schalbart, Bruno Peuportier
Amongst a significant variety of sampling methods (Saltelli, Chan, and Scott 2000), three main classes might be considered: Random sampling is the only method without biased estimations on the expectation and the standard deviation (central limit theorem). However, it is also the least effective way to cover properly a space.Stratified sampling subdivides the space into equiprobable strata before making a random sampling into these subgroups. This sampling strategy is also called ‘Latin Hypercube Sampling’. The uncertainty space is covered better, accelerating the results convergence.Quasi-random sampling, using pre-established sequences of randomized numbers, is probably the most efficient technique concerning the convergence of the expectation and standard deviation estimators.