Advantages And Disadvantages Of Sampling Methods Pdf
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Sampling may be defined as the procedure in which a sample is selected from an individual or a group of people of certain kind for research purpose. In sampling, the population is divided into a number of parts called sampling units. Sampling ensures convenience, collection of intensive and exhaustive data, suitability in limited resources and better rapport.
- Advantages and Disadvantages of Sampling
- Sampling in Developmental Science: Situations, Shortcomings, Solutions, and Standards
- An Overview of Sampling Techniques in Statistics
By Saul McLeod , updated
Suppose you have to run a survey about the coffee drinking habits of high school students of USA. The population of the students is about 4 million. You can not even imagine running the survey by asking each and every student to get the relevant data because of requirement of huge amount of time, money and other resources. The cost of the survey in this case would be too monumental to justify the effort. To solve these types of problem, sampling can be used.
Advantages and Disadvantages of Sampling
Despite their disadvantaged generalizability relative to probability samples, non-probability convenience samples are the standard within developmental science, and likely will remain so because probability samples are cost-prohibitive and most available probability samples are ill-suited to examine developmental questions. In lieu of focusing on how to eliminate or sharply reduce reliance on convenience samples within developmental science, here we propose how to augment their advantages when it comes to understanding population effects as well as subpopulation differences. Although all convenience samples have less clear generalizability than probability samples, we argue that homogeneous convenience samples have clearer generalizability relative to conventional convenience samples. Therefore, when researchers are limited to convenience samples, they should consider homogeneous convenience samples as a positive alternative to conventional or heterogeneous convenience samples. We discuss future directions as well as potential obstacles to expanding the use of homogeneous convenience samples in developmental science. For this reason, a sizable amount of developmental science research is devoted to understanding developmental processes and trends in specific sociodemographic groups as well as differences across two or more sociodemographic groups.
Sampling in Developmental Science: Situations, Shortcomings, Solutions, and Standards
Sampling is a key feature of every study in developmental science. Although sampling has far-reaching implications, too little attention is paid to sampling. Here, we describe, discuss, and evaluate four prominent sampling strategies in developmental science: population-based probability sampling, convenience sampling, quota sampling, and homogeneous sampling. We use sample composition of gender, ethnicity, and socioeconomic status to illustrate and assess the four sampling strategies. Finally, we tally the use of the four sampling strategies in five prominent developmental science journals and make recommendations about best practices for sample selection and reporting. When we undertake to study some phenomenon, we wish to know something about that phenomenon in a population, but in practice we study the phenomenon in a group of individuals who purportedly represent the target or reference population to whom we wish our results to generalize.
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In statistics, sampling entails the selection of a subset of population from within a chosen statistical population to approximate the characteristics or features of the whole population. Statistical sampling is preferred when studying a population as it is cost effective, allows faster data collection as well as provides the possibility of improving quality and accuracy of data www. Several sampling techniques are used depending on nature of studied population. The different types of sampling techniques include;. This is the most common type of sampling technique whereby all subsets of a population are given equal probability.
An Overview of Sampling Techniques in Statistics
Purposive sampling, also known as judgmental , selective or subjective sampling, is a type of non-probability sampling technique. Non-probability sampling focuses on sampling techniques where the units that are investigated are based on the judgement of the researcher [see our articles: Non-probability sampling to learn more about non-probability sampling, and Sampling: The basics , for an introduction to terms such as units , cases and sampling ]. There are a number of different types of purposive sampling, each with different goals. This article explains a what purposive sampling is, b the eight of the different types of purposive sampling, c how to create a purposive sample, and d the broad advantages and disadvantages of purposive sampling.
T he previous three sections in this book reviewed specific research methods employed in public health. This section uses a wider lens to look at research design components that cut across all types of public health research, beginning in this chapter with a discussion of sampling. Sampling is a set of procedures for selecting study elements from, or about, which data are collected. The sampling procedures used in a study determine the size of its data collection effort, the amount of resources and skills necessary to conduct the study, the characteristics of the elements selected, and the appropriateness of different data collection instruments and data analysis procedures. Sampling also determines whether, and how much,
When to use it. Ensures a high degree of representativeness, and no need to use a table of random numbers. When the population is heterogeneous and contains several different groups, some of which are related to the topic of the study.