Psychology
Random Sampling
Random sampling is a method of selecting a sample from a larger population in a way that each member of the population has an equal chance of being chosen. This technique is used to ensure that the sample is representative of the population, allowing for generalization of findings. In psychology, random sampling is crucial for obtaining unbiased and reliable data for research.
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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)
Hancock & Mueller, 2010 ).The practice of probability sampling is based on a sampling technique in which each unit in a population has a specifiable chance of being selected. The rationale behind using probability sampling is to generate a sample that is representative of the population from which it was drawn. With probability and Random Sampling, it does not guarantee that every random sample perfectly represents the population. Instead, what it means is that the sample will be close to the population most of the time, and that one can calculate the probability of a particular sample being accurate (Alvi, 2016 ; Tong, 2006 ; Gelo et al., 2008 ; Singh & Masuku, 2013 ; Sarstedt et al., 2017 ).Quantitative Sampling Designs for Research
Simple Random Sampling
Simple Random Sampling is a method in which any two groups of equal size in the population are equally likely to be selected. Mathematically, simple Random Sampling selects n units out of a population of size N. So that every sample of size n has an equal chance of being drawn in this case, we have a full list of sample units or participants (sample basis), and we randomly select individuals using a table of random numbers. This sampling technique provides an unbiased and better estimate of the parameters if the population is homogeneous (Brown, 1947 ; Sudman, 1976 ; Lomax, 2001 ; Acharya et al., 2013 ; Singh & Masuku, 2013 ; Taherdoost, 2016 ; Fricker, 2020 ; Rai & Thapa, 2015 ).In situations where the population is heterogeneous regarding the measures of interest, simple Random Sampling easily leads to estimates with unacceptably high variance, especially when the sample size is restricted. Interestingly, in survey research, much time and effort are spent in following a method of simple Random Sampling until an individual is actually identified and enrolled in the sample. However, this is called cluster sampling. Given that there is usually a tendency for individuals found within a cluster to share characteristics, however, the use of cluster sampling can be expected to decrease the precision of the sample result (Lasswell, 1949 ; Henderson & Sundaresan, 1982 ; Scheaffer et al., 2006 ; Sarstedt et al., 2017
- 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.
eBook - PDF- R. D. Savage(Author)
- 2013(Publication Date)
- Pergamon(Publisher)
This scarcity, however, is no excuse for ignoring the funda-mental conditions for drawing a random sample, nor does it justify the pro-mulgation of methods for checking representativeness which are decidedly questionable. The writer considers it axiomatic that a large amount of psychological research must, of necessity, depend upon sampling for the simple reason that human variation exists. The importance to be attached to sampling will, of course, vary from field to field, and a few investigators may be fortunate enough in their research interests to be able to ignore the problem. t Reproduced by kind permission of the author and the American Psychological Association Incorporated from Psych. Bull., 1940, 37, 331-365. This paper was prepared with financial aid from the Social Science Research Council and during the tenure of a visiting fellowship at Princeton University, Autumn, 1938. The writer is indebted to S. S. Wilks for aid on certain mathematical points, but Dr. Wilks should not be held responsible for any errors occurring herein. 381 382 PSYCHOMETRIC AND STATISTICAL TECHNIQUES It also seems axiomatic that the validity of a scientific inference must depend very largely upon the precision of the data on which it is based. The requisite degree of precision in either the individual measurements or the statistical constants determined from a composite of individuals will likewise vary from field to field. In general, it is desirable to secure the requisite precision in statistical measures with a minimum expenditure of time and effort. The precision of statistical constants, like that of individual measurement, is contigent upon two broad types of errors: random or chance and con-stant or biased.
- Jung-der Wang(Author)
- 2002(Publication Date)
- World Scientific(Publisher)
Besides, summary data obtained from a census study only have the advantage of smaller variance than sampling, which is usually not cost-effective. Thus, in our daily life, one usually performs sampling to explore the fact in a population to save time and all kinds of resources. Then, how can we conduct sampling in a study? 9.2 The concept of probability sampling Probability sampling is a sampling method, in which the selection of a Chapter 9 Sampling method and practical applications 2 13 subject or unit depends on a predetermined probability, or each unit of the sample space has a predetermined probability to be selected into the sample. Essentially, probability sampling has two characteristics: 1) a collection of sample space {S,,S 2 , ... S n } exists in the source population, in which every sample Sj has a corresponding non-zero probability of n { to be selected; 2) the selection of Sj is random. In simple Random Sampling, each subject or unit of the population has the same probability of being selected. To clarify this concept, let us examine several examples that did not conform to probability sampling. Example 9.1 Aflatoxin contamination of soybeans In a study surveying average level of aflatoxin contamination on soybeans in Taiwan, investigators took samples from stores in 5 major Taiwanese cities. They drove along the highway, entered each city, randomly selected 3 stores selling soybeans, and contamination randomly selected samples from each store. Although they attempted to obtain a random sample, their sampling procedure, in fact, was not dependent on any predetermined probability. By choosing stores more accessible from the highway, they introduced a bias into their sample. If this bias could be determined to be unrelated to the exposure, then the sample could still be considered quasi-random.
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- 6 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 - PDF- Carl McDaniel, Jr., Roger Gates(Authors)
- 2020(Publication Date)
- Wiley(Publisher)
99 CHAPTER 5 Sample Design PeopleImages/Getty Images LEARNING OBJECTIVES 1. Understand the concept of sampling. 2. Learn the steps in developing a sampling plan. 3. Understand the concepts of sampling error and nonsampling error. 4. Understand the differences between probability samples and nonprobability samples. 5. Gain an appreciation of a normal distribution. 6. Learn how to determine sample size. As noted elsewhere in the text, the world that marketing researchers operate in has changed to a tremendous extent over the last 30 years. PCs, tablets, mobile devices in general, the Internet, and social media are major forces behind these changes. They present new chal- lenges and opportunities. One fundamental issue has not changed and that relates to the process of generating a sample that is representative of the population. Many think that really big samples, think big data, are a substitute for representative samples. But the basic rules still apply, the sample must be representative, it must be a miniature of the popula- tion. If we have a proper sample then we can determine the level of sampling error. All of these concepts are discussed in detail in this chapter. 100 CHAPTER 5 Sample Design Concept of Sampling Sampling, as the term is used in marketing research, is the process of obtaining information from a subset (a sample) of a larger group (the universe or population). We then take the results from the sample and project them to the larger group. The motivation for sampling is to be able to make these estimates more quickly and at a much lower cost than would be possible by other means. It has been shown time and again that sampling a small percent- age of a population can produce very accurate estimates about the population. One exam- ple that you are familiar with is polling in connection with political campaigns and elections.
eBook - PDFResearch Methods For Business
A Skill Building Approach
- Uma Sekaran, Roger Bougie(Authors)
- 2016(Publication Date)
- Wiley(Publisher)
Executing the sampling process The following two examples illustrate how, in the final stage of the sampling process, decisions with respect to the target population, the sampling frame, the sample technique, and the sample size have to be implemented. EXAMPLE A satisfaction survey was conducted for a computer retailer in New Zealand. The objective of this survey was to improve internal operations and thus to retain more customers. The survey was transactional in nature; service satisfaction and several related variables were measured following a service encounter (i.e., a visit to the retailer). Hence, customer feedback was obtained while the service experience was still fresh. To obtain a representative sample of customers of the computer retailer (the target population), every tenth person, leaving one out of ten randomly selected stores, in randomly selected cities, in randomly selected regions, was approached during a one‐week period (the sampling technique). Trained interviewers that were sent out with standardized questionnaires approached 732 customers leaving the stores (the sample size). A young researcher was investigating the anteced- ents of salesperson performance. To examine his hypotheses, data were collected from chief sales exe- cutives in the United Kingdom (the target population) 242 research methods for business PROBABILITY SAMPLING When elements in the population have a known, nonzero chance of being chosen as subjects in the sample, we resort to a probability sampling design. Probability sampling can be either unrestricted (simple Random Sampling) or restricted (complex probability sampling) in nature. Unrestricted or simple Random Sampling In the unrestricted probability sampling design, more commonly known as simple Random Sampling, every element in the population has a known and equal chance of being selected as a subject. Let us say there are 1000 elements in the population, and we need a sample of 100.
eBook - PDFResearch Methods For Business
A Skill Building Approach
- Roger Bougie, Uma Sekaran(Authors)
- 2021(Publication Date)
- Wiley(Publisher)
To examine his hypotheses, data were collected from chief sales executives in the United Kingdom (the target population) via mail questionnaires. The sample was initially drawn from a published business register (the sampling frame), but supplemented with respondent recommendations and other additions, in a judgment sampling methodology. Before distributing the question- naires, the young researcher called each selected company to obtain the name of the chief sales executive, who was contacted and asked to participate in the study. The questionnaires were subsequently distributed to chief sales executives of 450 companies (the sample size). To enhance the response rate, pre-addressed and stamped envelopes were provided, anonymity was assured and a summary of the research findings as an incentive to the participants was offered. Several follow-up procedures, such as telephone calls and new mailings, were planned in order to receive as many responses as possible. 228 CHAPTER 14 Sampling When elements in the population have a known, non-zero chance of being chosen as subjects in the sample, we resort to a probability sampling design. Probability sampling is used when the researcher wants to generalize the research findings to the population. Probability sampling can be either unrestricted (simple Random Sampling) or restricted (complex probability sampling) in nature. UNRESTRICTED OR SIMPLE Random Sampling In the unrestricted probability sampling design, more commonly known as simple Random Sampling, every element in the population has a known and equal chance of being selected as a subject. Let us say there are 1000 elements in the population, and we need a sample of 100. Suppose we were to drop pieces of paper in a hat, each bearing the name of one of the elements, and draw 100 of those from the hat with our eyes closed. We know that the first piece drawn will have a 1/1000 chance of being drawn, the next one a 1/999 chance of being drawn and so on.
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.
eBook - PDFResearch Methods For Business
A Skill Building Approach
- Uma Sekaran, Roger Bougie(Authors)
- 2020(Publication Date)
- Wiley(Publisher)
CHAPTER 14 221 Sampling LEARNING OBJECTIVES After completing Chapter 14, you should be able to: 1. Define sampling, sample, population, element, sampling unit and subject. 2. Discuss statistical terms in sampling. 3. Describe and discuss the sam-pling process. 4. Compare and contrast specific prob-ability sampling designs. 5. Compare and contrast specific non-probability sampling designs. 6. Discuss precision and confidence and the trade-off between precision and confidence. 7. Discuss how hypotheses can be tested with sample data. 8. Discuss the factors to be taken into consideration for determining sam-ple size and determine the sample size for any given research project. 9. Discuss sampling in qualita-tive research. 10. Discuss the role of the manager in sampling. Experimental designs and surveys are useful and powerful in finding answers to research ques-tions through data collection and subsequent analyses, but they can do more harm than good if the population is not correctly targeted. That is, if data are not collected from the people, events or objects that can provide the correct answers to solve the problem, the research will be in vain. The process of selecting the right individuals, objects or events as representatives for the entire population is known as sampling , which we will examine in some detail in this chapter (see shaded portion in Figure 14.1). The reasons for using a sample, rather than collecting data from the entire population, are self-evident. In research investigations involving several hundreds and even thousands of elements, it would be practically impossible to collect data from, or test, or examine, every element. Even if it were possible, it would be prohibitive in terms of time, cost and other resources. Study of a sample rather than the entire population is also sometimes likely to pro-duce more reliable results.
eBook - PDFMarketing Research N6 SB
TVET FIRST
- R van der Merwe(Author)
- 2019(Publication Date)
- Macmillan(Publisher)
In the case of a probability sampling method all the elements of the population have a known chance of being selected and at the same time a representative cross section of the population is ensured. […] The non-probability sampling method on the other hand occurs when no or little attempt is made to ensure a representative cross section of the population. From this quote and Example 5.1, we can deduce the following: • Probability: If the process gives every student an equal chance to be selected, it is a Random Sampling method. • Non-probability: If the process does not give every student an equal chance to be selected, it is a non-Random Sampling method. We will look at these methods in more detail in Unit 5.5. But for now, it is important to understand why a sample is better than a census in marketing research. The process of selecting a sample There are different views about the steps to follow when selecting a sample. Factors such as the following will have an effect on the design of the selection process: • How complex the research problem is. • The characteristics of the population. • How accessible the population’s elements are. • Practical elements such as the time available. The steps in the process of selecting a sample There are five main steps in the process of selecting a sample. Did you know? These five steps can be sub-divided into additional steps if required. Eventually, however, the end result of the process is the same. Unit 5.3: Module 5 TVET FIRST 106 Figure 5.3 shows these steps, as Martins et al. (1996:252) define them. The steps in the process of selecting a sample Step 1: Define the population Step 2: Identify the sample frame Step 3: Select the sampling method Step 4: Determine the sample size Step 5: Select the sample elements Figure 5.3: The steps in the process of selecting a sample Researchers need to consider these steps together and separately when they plan the selection of a sample.
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