Social Sciences
Sampling in Sociology
Sampling in sociology refers to the process of selecting a subset of individuals or groups from a larger population for research purposes. It is a crucial method for gathering data and making generalizations about a population. Different sampling techniques, such as random sampling, stratified sampling, and convenience sampling, are used to ensure the representativeness and reliability of the data collected.
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No longer available |Learn more- (Author)
- 2014(Publication Date)
- Orange Apple(Publisher)
________________________ WORLD TECHNOLOGIES ________________________ Chapter- 1 Introduction to Sampling Sampling is that part of statistical practice concerned with the selection of a subset of individual observations within a population of individuals intended to yield some knowledge about the population of concern, especially for the purposes of making predictions based on statistical inference. Sampling is an important aspect of data collection. Researchers rarely survey the entire population for two reasons (Adèr, Mellenbergh, & Hand, 2008): the cost is too high, and the population is dynamic in that the individuals making up the population may change over time. The three main advantages of sampling are that the cost is lower, data collection is faster, and since the data set is smaller it is possible to ensure homogeneity and to improve the accuracy and quality of the data. Each observation measures one or more properties (such as weight, location, color) of observable bodies distinguished as independent objects or individuals. In survey sampling, survey weights can be applied to the data to adjust for the sample design. Results from probability theory and statistical theory are employed to guide practice. In business and medical research, sampling is widely used for gathering information about a population. Process The sampling process comprises several stages: • Defining the population of concern • Specifying a sampling frame, a set of items or events possible to measure • Specifying a sampling method for selecting items or events from the frame • Determining the sample size • Implementing the sampling plan • Sampling and data collecting Population definition Successful statistical practice is based on focused problem definition. In sampling, this includes defining the population from which our sample is drawn. A population can be
No longer available |Learn more- (Author)
- 2014(Publication Date)
- Orange Apple(Publisher)
________________________ WORLD TECHNOLOGIES ________________________ Chapter- 1 Sampling Sampling is that part of statistical practice concerned with the selection of a subset of individual observations within a population of individuals intended to yield some knowledge about the population of concern, especially for the purposes of making predictions based on statistical inference. Sampling is an important aspect of data collection. Researchers rarely survey the entire population for two reasons (Adèr, Mellenbergh, & Hand, 2008): the cost is too high, and the population is dynamic in that the individuals making up the population may change over time. The three main advantages of sampling are that the cost is lower, data collection is faster, and since the data set is smaller it is possible to ensure homogeneity and to improve the accuracy and quality of the data. Each observation measures one or more properties (such as weight, location, color) of observable bodies distinguished as independent objects or individuals. In survey sampling, survey weights can be applied to the data to adjust for the sample design. Results from probability theory and statistical theory are employed to guide practice. In business and medical research, sampling is widely used for gathering information about a population. Process The sampling process comprises several stages: • Defining the population of concern • Specifying a sampling frame, a set of items or events possible to measure • Specifying a sampling method for selecting items or events from the frame • Determining the sample size • Implementing the sampling plan • Sampling and data collecting Population definition Successful statistical practice is based on focused problem definition. In sampling, this includes defining the population from which our sample is drawn. A population can be
eBook - ePubDissertation Research Methods
A Step-by-Step Guide to Writing Up Your Research in the Social Sciences
- Philip Adu, D. Anthony Miles(Authors)
- 2023(Publication Date)
- Routledge(Publisher)
Figure 16.1 illustrates the major steps in the sampling process.Source: Adopted from Sekaran and Bougie (2013).Figure 16.1The sampling process.Before choosing a specific type of sampling technique, a broad sampling technique needs to be decided on. One of the difficult aspects of research is launching an experience-sampling study. It can provide a challenge to even the most seasoned researcher (Christensen et al., 2003 ). When a researcher decides to begin the research process, they must go through the sampling process first. The first stage in the sampling process is to clearly define the problem. Defining the problem is the basis for all research. This also relates to determining the sampling frame. The sampling frame must be representative of the population of interest. This is vital to the research.The next step is to determine the sample design. Establishing the sample design is very important to the researcher. The next step is to determine the appropriate sample size for the study. We have to remember that the population is commonly related to the number of people living in a particular area of the country. Lastly, the researcher has to execute the sampling process. Again, sampling can be used to make inferences about a population.Types of Sampling
The practice of sampling can be used to make an inference about a population or to make a generalization in relation to an existing theory. This is based on the choice of sampling technique.The practice of evaluating the characteristics of an entire population through a representative sample can be an arduous endeavor. As with many research studies, the best strategy to investigate a problem in an entire population is through the use of sampling. How, it is not always possible to conduct a study on an entire population. So, we use the practice of sampling. Thus, the researcher studies a sample of the population, which is a suitable representation of the entire population. We must think of a sample as a subset of the population; thus it is selected to be representative of the population. Again, one of the advantages of sampling is that it can be less costly and more efficient. Being able to use a sample to generalize the results to a whole population requires the use of one of the statistical sampling methods for evaluation. Taking a subset from a chosen sampling frame or entire population is the basic practice of sampling.
- Frederic B. Mayo(Author)
- 2013(Publication Date)
- Wiley(Publisher)
122 CHAPTER 8 Sampling Issues in Research INTRODUCTION Sampling is one of the most critical aspects of planning research since it is impos- sible to survey a complete population all at the same time. It involves selecting a small number of respondents from a total population in a carefully planned manner so that you can generalize findings from the smaller number of respondents to the larger population. The sample is the smaller group and the population refers to all persons that might be surveyed if you had unlimited resources. Surveying an entire population is referred to as a census . In conducting qualitative, quantitative or mixed method research, there are a number of sampling issues to consider: rationale for sampling, selecting a sample, types of sampling, sampling bias, and sample size. The size of the sample, the scope of the research, the nature of the people (or data) you consult makes a tremendous impact on the quality of your findings. Therefore, it is critical to consider the range of sampling issues in designing any research project. Sampling is a challenge for most researchers; in both qualitative and quantitative research, you must consider how to sample observations as well as select individuals to interview or question, documents to analyze, or participants for focus groups. In all of these situations, making careful and intentional decisions will affect the quality and rigor of your research. Although some think sampling is a simple issue of select- ing individuals to interview or complete questionnaires, thinking and planning care- fully can make a huge difference in the validity and reliability of your research. (For more information on validity and reliability, see Chapter 9, Validity, Reliability, and Credibility in Research.) In fact, when you hear polling results, you often hear about the sampling issues when the report includes an error factor of plus or minus a few points and informa- tion about confidence level.
eBook - PDFResearch Methods For Business
A Skill Building Approach
- Uma Sekaran, Roger Bougie(Authors)
- 2020(Publication Date)
- Wiley(Publisher)
For this purpose, the first 15 people who chose the special item might be interviewed, and their reactions obtained. In such cases, having instant information may be more gainful than obtaining the most representative facts. It should, however, be noted that the results of such convenient samples are not reliable and can never be generalized to the population. Sampling is the process of selecting a sufficient number of the right elements from the popula-tion, so that a study of the sample and an understanding of its properties or characteristics make it possible for us to generalize such properties or characteristics to the population elements. The major steps in sampling include: 1. Define the population. 2. Determine the sample frame. 3. Determine the sampling design. 4. Determine the appropriate sample size. 5. Execute the sampling process. THE SAMPLING PROCESS 226 CHAPTER 14 Sampling DEFINING THE POPULATION Sampling begins with precisely defining the target population. The target population must be defined in terms of elements, geographical boundaries and time. For instance, for a banker inter-ested in saving habits of blue-collar workers in the mining industry in the United States, the target population might be all blue-collar workers in that industry throughout the country. For an advertising agency interested in reading habits of elderly people, the target population might be the German population aged 50 and over. These examples illustrate that the research objective and the scope of the study play a crucial role in defining the target population. DETERMINING THE SAMPLE FRAME The sampling frame is a (physical) representation of all the elements in the population from which the sample is drawn. The payroll of an organization would serve as the sampling frame if its members are to be studied. Ideally, a sampling frame is a complete and correct list of the population elements. However, practical circumstances often make such a list incorrect and incomplete.
eBook - PDFSalsa Dancing into the Social Sciences
Research in an Age of Info-glut
- Kristin Luker(Author)
- 2009(Publication Date)
- Harvard University Press(Publisher)
6 h On Sampling, Operationalization, and Generalization S o now you have a case, or perhaps a wonderful and vexing re-search interest, and by proper application of the disciplines and ex-ercises in the previous chapters you’ve begun to coax it into some-thing resembling a research question. You are starting to have an idea of how you are going to frame your research in terms of the in-tellectual conversations you are interested in (and the subdiscipline you want to be employed in), and you have developed an acquain-tance with what you have defined as the relevant literature. The next step is to get some data. But how do you figure out what data you need? And how do you figure out where to get it? (The question of specifically how, that is, what kind of data-gathering method you will be using, comes in the next two chapters.) In order to do this part of your research project, we have to go back to the practices we’ve borrowed from the canonicals—sampling, opera-tionalization, and generalization—and discuss them in some detail. Sampling I will repeat myself here because this is so important: when canoni-cal social scientists say the word “sample” what they really mean is a “systematic random probability sample,” one drawn from a popula-tion where each and every element has a statistically equal chance of being chosen. Many canonical sociologists, by the way, don’t actually sample, but they do perform secondary analyses on data drawn from random probability samples. An old saying goes that people should never look too closely at how laws and sausages are made, and in that same spirit, random samples when looked at closely are not all that they are cracked up to be. Because national random probability surveys are expensive, many social scientists use the surveys undertaken by the federal gov-ernment or large organizations such as the National Opinion Re-search Center (NORC) who have the means and motivation to do such studies and to repeat them year after year.
eBook - PDF- Roger Wimmer, Joseph Dominick(Authors)
- 2013(Publication Date)
- Cengage Learning EMEA(Publisher)
CHAPTER 4 SAMPLING CHAPTER OUTLINE Population and Sample Research Error Types of Sampling Procedures Sample Size Sampling Error Finite Population Correction Factor Summary Key Terms Using the Internet Questions and Problems for Further Investigation References and Suggested Readings 88 Copyright 2012 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part. Due to electronic rights, some third party content may be suppressed from the eBook and/or eChapter(s). Editorial review has deemed that any suppressed content does not materially affect the overall learning experience. Cengage Learning reserves the right to remove additional content at any time if subsequent rights restrictions require it. When it comes to research, we live in a world of small sample statistics. This chapter des-cribes the basics of the sampling methods used in mass media research. However, because sampling theory has become a dis-tinct discipline in itself, there are some stud-ies, such as national surveys, that require consultation of more technical discussions of sampling. POPULATION AND SAMPLE One goal of scientific research is to describe the nature of a population — a group or class of subjects, variables, concepts, or phenom-ena. In some cases, an entire class or group is investigated, as in a study of prime-time tele-vision programs during the week of Septem-ber 10 – 16. The process of examining every member in a population is called a census . In many situations, however, an entire population cannot be examined due to time and resource constraints. Studying every member of a population is also generally cost prohibitive and may, in fact, confound the research because measurements of large numbers of people often affect measurement quality. The usual procedure in these instances is to take a sample from the population. A sample is a subset of the population that is representative of the entire population.
eBook - PDFEducational Research
A Contextual Approach
- Ken Springer(Author)
- 2015(Publication Date)
- Wiley(Publisher)
I describe three types of nonprobability sampling here: convenience sampling, purposive sampling, and quota sampling. CONVENIENCE SAMPLING Convenience sampling (also called ‘‘haphazard’’ or ‘‘opportunistic’’ sampling) is a procedure in which sampling focuses on whoever is available in a particular place at a particular time. Most research is based to a greater or lesser extent on convenience, since researchers tend to obtain samples from groups they have ready access to. It is sometimes said in jest that social science research consists of the study of undergraduates. This comment refers to the fact that a substantial amount of research in psychology and other social sciences is conducted with undergraduate students, the population closest to and most easily accessed by professors who conduct research. In the same way, a great deal of research in education and other social sciences is conducted wherever the researcher has existing connections, or is otherwise able to obtain permission. Although a convenience sample is clearly at risk for nonrepresentativeness, researchers can circumvent at least some potential problems by being clear about the target population, as well as the characteristics of the sample, so that others can judge whether generalizations from sample to population are warranted. PURPOSIVE SAMPLING Purposive sampling is a procedure in which the researcher samples whoever he or she believes to be representative of a given population. The difference between purposive sampling and probability sampling approaches is that purposive sampling is based on the researcher’s informal ideas about representativeness. For example, if researchers know in advance that 82% of elementary-level teachers in a particular school district are female, the researchers could use a stratified sampling technique to ensure that 82% of their sample is female.
No longer available |Learn more- Frederick J Gravetter; Lori-Ann B. Forzano; Tim Rakow, Frederick Gravetter, Frederick Gravetter, Lori-Ann Forzano, Tim Rakow(Authors)
- 2021(Publication Date)
- Cengage Learning EMEA(Publisher)
LO6 Describe quota sampling, recognize examples of this technique in research reports and explain why it is used. Convenience sampling The most commonly used sampling method in behavioural science research is probably convenience sampling. In convenience sampling, researchers simply use as participants those individuals who are easy to get. People are selected on the basis of their availability and willingness to respond. Examples are conducting research with students from an Introductory Psychology class or studying the children in a local daycare centre. A researcher who teaches at the Uni- versity of Amsterdam and uses university students as participants is likely to use students enrolled at that university. A researcher at the University of Basel is likely to use students enrolled there. Convenience sampling is considered a weak form of sampling because it does not require knowledge of the population and does not use a random process for selection. The researcher exercises very little control over the representativeness of the sample and, therefore, there is a strong possibility that the obtained sample is biased. This is especially problematic when individuals actively come forward to participate as with phone-in radio surveys or mail-in magazine surveys. In these cases, the sample is biased because it contains only those individuals who listen to that station or read that magazine and feel strongly about the issue being investigated. These individuals are probably not representative of the general population, or even of a more restricted population that the researcher might be interested in. Despite this major drawback, convenience sampling is probably used more often than any other kind of sampling. It is an easier, less expensive, more timely technique than the probability sampling tech- niques, which involve identifying every individual in the population and using a laborious random process to select participants.
- Stephen Gorard(Author)
- 2003(Publication Date)
- Continuum(Publisher)
Nineteenth-century psychol-ogy was often based on what researchers found out about themselves (introspection), while later twentieth-century psychology was chiefly based on what psychologists found out about each other. There are some hopeful signs that in the twenty-first century psychology is becoming more concerned with people at large. NON-PROBABILITY SAMPLES An implicit assumption has been made in the chapter so far that our sample will be what is termed a 'probability' sample, where cases will be selected either randomly or systematically. There are two good reasons for this focus. First: this kind of sampling is generally more technical than its alternatives, so requiring more explanation for a new researcher. Second: this kind of sampling is preferable in 72 Quantitative Methods in Social Science almost every way to any of its alternatives in all research situations. Thus, a simple guideline would be that probability samples should be used in all circumstances in which they are possible. A high-quality sample is crucial for safe generalization to take place (for high 'external validity'). Non-probability samples should therefore be reserved only for those projects in which there is no other choice. The most common and over-used form of non-probability sampling is the convenience sample, composed of those cases chosen only because they are easily available. A researcher standing in a railway station or shopping centre or outside a student union and stopping people in an ad hoc manner would thereby create a convenience sample and not a random one. This approach is often justified by the comment that a range of people use such places, so the sample will be mixed in composition. The approach is sometimes strengthened, for example in market 'research', by determining quotas for groups of cases (such as men and women) and then deselecting people (e.g. by not stopping them) once the quota for each group is filled.
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