...| 2108 | Nonprobability sampling in management research ESS has made a survey created to measure attitudes cross-nationally in Europe, using probability sampling. Measuring an attitude across countries is a tough job, but to successfully apply the methods of probability sampling too, seems close to impossible. This essay will look at the sample-problems that this survey faces, and how a non-probability sample can be successfully integrated. Before starting to analyse the survey, I would like to briefly explain what a sample is, and the main differences between the two sampling techniques. First of all the objective of most surveys or research projects is to obtain information about the parameters of a population. To do this a sample is collected representing a subgroup of the population selected for participation in the project. The sample characteristics are used to “make inferences about the population parameters”. (Malhotra, 2010: 370) Meaning that you by selecting a small representation of the population can tell something about the whole population. Non-probability sampling can be defined briefly as “Sampling techniques that do not use chance selection procedures, but rather rely on personal judgement of the researcher” (Malhotra and Birks, 2000, 358) An example of this would be a person who choices people on the street to take part in a survey by using his personal judgement. There are different types of non-probability sampling, the most common are: Convenience...
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...Selection of Research Participants Choose a sampling method. Probability Sampling or Non-probability Sampling. Probability is the preferred sampling method for researchers. Non-Probability sampling is easier to use but is a less accurate portrayal of the population. Probability sampling is comprised of sampling techniques that specifically target the odds that a participant will be selected from a certain population. This representative sampling is more easily reproduced and testable. Non-Probability sampling is different in the sense you cannot specify the chances of choosing a particular individual. Not everyone has an equal opportunity of being selected for the study. This lack of representative sampling of the population brings the validity...
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...census, less costly to administer than a census and it is possible to obtain statistical results of a sufficiently high precision based on samples. There are two types of sampling techniques, probability and non-probability sampling. Probability Sampling A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. Humans have long practiced various forms of random selection, such as picking a name out of a hat, or choosing the short straw. These days, we tend to use computers as the mechanism for generating random numbers as the basis for random selection. To produce our results, we combine the responses from the sample in a way which takes account of the selection probabilities. Our aim is that, if the sampling were to be repeated many times, the expected value of the results from the repeated samples would be the same as the result we would get if we surveyed the whole population. Because we know the probability of getting each sample we select, we can also calculate a sampling error for the results. The sampling error tells us the amount of variation in the results due to the sampling alone. It gives a measure of the quality of the sample design, and of the survey results. A simple random sample is a sample in which each member of the population...
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...What is “Statistics”? Data collection Chapter 1 of text “a way to get information from data” A framework for dealing with variability A way to make decisions under uncertainty Statistical inference: the problem of determining the behaviour of a large population by studying a small sample from that population Why is statistics important in business? Financial management (capital budgeting) Marketing management (pricing) Marketing research (consumer behaviour) Operations management (inventory) Accounting (forecasting sales) Human resources management (performance appraisal) Information systems Economics (summarising, predicting) See http://www.youtube.com/watch?v=D4FQsYTbLoI What is a population? What is a sample? Population: a collection of the whole of something – e.g. all female students of ANU; all people who live in Tuggeranong; all people who play the flute. Sample: a set of individuals drawn from a population e.g. the female students in STAT1008 are a sample of all female students at ANU. If we have a population…. We can get parameters – true values for things like the centre and spread of the population We know the answers – what proportion are this tall? We look at the population and get the answer. If we have a sample… We can get statistics – these are values that estimate the parameters e.g. sample centre and sample spread used to estimate population centre and population spread We have to use inference to do this estimation – what proportion are this...
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...2011 Chapter 7 - Sampling and Sampling Distributions Practice Exam - Solution Instructors: Dr. Samir Safi Mr. Ibrahim Abed SECTION I: MULTIPLE-CHOICE 1. Sampling distributions describe the distribution of a) parameters. b) statistics. c) both parameters and statistics. d) neither parameters nor statistics. 2. The Central Limit Theorem is important in statistics because a) for a large n, it says the population is approximately normal. b) for any population, it says the sampling distribution of the sample mean is approximately normal, regardless of the sample size. c) for a large n, it says the sampling distribution of the sample mean is approximately normal, regardless of the shape of the population. d) for any sized sample, it says the sampling distribution of the sample mean is approximately normal. 3. Which of the following statements about the sampling distribution of the sample mean is incorrect? a) The sampling distribution of the sample mean is approximately normal whenever the sample size is sufficiently large ( n ≥ 30 ). b) The sampling distribution of the sample mean is generated by repeatedly taking samples of size n and computing the sample means. c) The mean of the sampling distribution of the sample mean is equal to µ . d) The standard deviation of the sampling distribution of the sample mean is equal to σ . 4. Which of the following is true about the sampling distribution of the sample mean? a) The mean of the sampling distribution is...
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...Variables Sampling 689 I have edited a portion of Module G from your textbook so that it more closely follows my lecture. I need to acknowledge that this is not my original work and much of it is taken word for word from the 2nd edition of Auditing & Assurance Services by Louwers, Ramsay, Sinason and Strawser. Tad Miller Classical Variables Sampling LEARNING OBJECTIVE Understand the basic process underlying classical variables sampling in an audit examination. When performing substantive procedures, one approach is classical variables sampling. Classical variables sampling methods use normal distribution theory and the Central Limit Theorem to provide a range estimate of the account balance. The auditor uses the sample estimates to determine whether the account balance is fairly stated. The Central Limit Theorem indicates larger sample sizes provide a sampling distribution that more closely reflects a normal distribution. Therefore, larger sample sizes will yield a lower level of sampling risk. In this section, we briefly illustrate mean-per-unit classical variables sampling. We illustrate the manual calculations necessary to determine sample size and evaluate sample results. However, if clients maintain records in electronic format, auditors typically use computer software to perform these tasks. Classical Variables Sampling: Planning In the planning stages of classical variables sampling, the auditor determines the objective of sampling, defines the attribute...
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...BSHS/435 Sampling A sample is drawn from a population, which refers to all possible cases of what the researcher is interested in studying. A sample consists of one or two elements selected from a population. To select a good sample we have to clearly define the population from which we will be drawing a sample. Failure to do so can result in inaccurate conclusions. In probability sampling each element has an equal chance of inclusion. The simplest technique for probability samples is Simple Random Sampling. Simple random sampling treats the target population as a unitary whole. We might start off with a sampling frame which has a list of the whole population—or as much information as we can obtain. Next we would number the elements in the sampling frame sequentially and select elements from the list randomly. We would not know what the outcome would be, but each has an equal shot at being chosen. We can also program the computer to choose a random sample. Researchers may also use non probability sampling. In this type of sampling the researcher does not know the probability of each population’s elements inclusion in the sample (Dejong, 2011). Data Collection There are many different ways to collect data for research. Measures differ from one another in terms called levels of measurement. Nominal measures classify observations into mutually exclusive and exhaustive categories. They represent nominal variables such as sex, ethnicity, religion...
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...Non-Probability Sampling Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected. In any form of research, true random sampling is always difficult to achieve. Most researchers are bounded by time, money and workforce and because of these limitations, it is almost impossible to randomly sample the entire population and it is often necessary to employ another sampling technique, the non-probability sampling technique. In contrast with probability sampling, non-probability sample is not a product of a randomized selection processes. Subjects in a non-probability sample are usually selected on the basis of their accessibility or by the purposive personal judgment of the researcher. The downside of the non-probablity sampling method is that an unknown proportion of the entire population was not sampled. This entails that the sample may or may not represent the entire population accurately. Therefore, the results of the research cannot be used in generalizations pertaining to the entire population. Types of Non-Probability Sampling Convenience Sampling Convenience sampling is probably the most common of all sampling techniques. With convenience sampling, the samples are selected because they are accessible to the researcher. Subjects are chosen simply because they are easy to recruit. This technique is considered easiest, cheapest and least time...
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...Facilitators/Tutors and Tutorials Summary . . . . INTRODUCTION Data collection methodology is a two credit unit first semester course available to first semester course available to students of Bachelor of Education (B.Ed) Library and Information science. 4 Research involves data collection, any discipline of the social sciences, education and even the sciences needs a sound knowledge of research; how to conduct research, ethics of research and generally to write a report or design a study. The use and importance of research cannot be overemphasized. All students undergoing any form of degree programme is required to write a project, thesis or dissertation. This course offers a complete guide to such write ups including statistical techniques in sampling measurements and ethics of research. What you will learn in this course The course consist of units and a course guide which informs you briefly...
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...e-journal resource for the public opinion and survey research community Special Issue on Non-probability Samples This issue covers only one topic – nonprobability sampling. Andy Peytchev selected the articles and edited the issue. Some articles have formulas and the content of many articles is too complex for display using the software we use to publish SP, so we are experimenting with PDFs. The articles span a broad spectrum, including the evaluation of bias in a nonprobability sample, the review of assumptions in a nonprobability sampling method that provide the potential for bias, the conditions under which a nonprobability sampling design can lead to valid conclusions in comparative research, case studies on the use of nonprobability methods and samples to facilitate a probability-based study, and a proposed method to combine probability and nonprobability samples under certain conditions. Gerty Lensvelt-Mulders and colleagues use a probability-based web survey with telephone follow-up and propensity score matching in order to evaluate bias in a nonprobability web panel survey. This design and analytic approach allow them to attempt to separate bias due to self-selection from bias due to undercoverage in the panel survey. Although not nearly as much in the survey literature, Respondent Driven Sampling has received considerable attention as a nonprobability sampling method that claims to produce representative estimates. In her article, Sunghee Lee dissects...
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...Chapter 1: The Role of BR I. The Nature of Research A. BR defined B. Applied & basic BR C. The scientific method II. Managerial Value of BR A. Identifying problems or opportunities B. Diagnosing & assessing problems or opportunities C. Selecting & implementing a course of action D. Evaluating the course of action III. When is BR Needed? A. Time constraints B. Availability of data C. Nature of the decision D. Benefits vs costs IV. BR In The 21st Century A. Communication technologies B. Global BR Chapter 3: Theory Building I. Introduction A. What is a theory? B. What are the goals of theory? II. Research Concepts, Constructs, Proposition, Variables & Hypotheses A. Research concepts & constructs B. Research proposition & hypotheses III. Understanding Theory A. Verifying theory B. Theory building Chapter 5: The Human Side of BR: Organizational & Ethical Issue I. Introduction II. Ethical issue in BR A. Ethical qs are philosophical qs B. General rights & obligation of concerned parties C. Rights & obligation of the research participant * The obligation to be truthful * Participants’ right to privacy * Active & Passive research * Deception in research designs & the right to be informed * Experiment designs * Descriptive research * Protection from harm D. Rights & obligation of the...
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...Due to the time constraint, Wimmer and Dominick (2011) also explained that in many cases, researchers collecting preliminary information operate under time constraints. Since probability sampling is often time consuming which usually the need to conduct experimental procedure, hence a non-probability sampling may meet the need temporarily. In addition, it is because of the amount of error in this type of sampling is acceptable, therefore non-probability is usually adequate. 3.2.3 Sampling Techniques Non-probability sampling also has several different types which are available sample or also known as a convenience sample, purposive sample, quota sampling and snow-ball sampling. This research needs the respondents to answer the set of questionnaire with honesty and aware, therefore the type of non-probability sampling that we used is the purposive sampling. Purposive sampling is a technique that mostly used in qualitative research for the identification and selection of information for the most effective use of limited resources (Patton, 2002). In this research, the sample must have a specific characteristic and requirements such as the respondents must be readers of The Star...
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...Sampling Sampling Third Edition STEVEN K. THOMPSON Simon Fraser University A JOHN WILEY & SONS, INC., PUBLICATION Copyright © 2012 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or...
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...2011 Chapter 7 - Sampling and Sampling Distributions Practice Exam - Solution Instructors: Dr. Samir Safi Mr. Ibrahim Abed SECTION I: MULTIPLE-CHOICE 1. Sampling distributions describe the distribution of a) parameters. b) statistics. c) both parameters and statistics. d) neither parameters nor statistics. 2. The Central Limit Theorem is important in statistics because a) for a large n, it says the population is approximately normal. b) for any population, it says the sampling distribution of the sample mean is approximately normal, regardless of the sample size. c) for a large n, it says the sampling distribution of the sample mean is approximately normal, regardless of the shape of the population. d) for any sized sample, it says the sampling distribution of the sample mean is approximately normal. 3. Which of the following statements about the sampling distribution of the sample mean is incorrect? a) The sampling distribution of the sample mean is approximately normal whenever the sample size is sufficiently large ( n ≥ 30 ). b) The sampling distribution of the sample mean is generated by repeatedly taking samples of size n and computing the sample means. c) The mean of the sampling distribution of the sample mean is equal to µ . d) The standard deviation of the sampling distribution of the sample mean is equal to σ . 4. Which of the following is true about the sampling distribution of the sample mean? a) The mean of the sampling distribution is...
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...THE ELEMENTARY OF A PROPOSAL 1. Introduction The introduction is the part of the paper that provides readers with the background information for the research reported in the paper. Its purpose is to establish a framework for the research, so that readers can understand how it is related to other research. In an introduction, the writer should create reader interest in the topic, lay the board foundation for the problem that leads to the study, place the study within the larger context of the scholarly literature, and reach out to a specific audience. 2. Statement of the Problem State the problem in terms intelligible to someone who is generally sophisticated but who is relatively uninformed in the area of your investigation. A problem statement should be presented within a context, and that context should be provided and briefly explained, including a discussion of the conceptual or theoretical framework in which it is embedded. Clearly and succinctly identify and explain the theoretical framework that guides your study. This is of major importance in nearly all proposals and requires careful attention. 3. Purpose of the Study The purpose statement should provide a specific and accurate synopsis of the overall purpose of the study. If the purpose is not clear to the writer, it cannot be clear to the reader. Briefly define and delimit the specific area of the research. Foreshadow the hypotheses to be tested or the questions to be raised, as...
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