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Research Method for Business chapter 10

This document provides an overview of sampling techniques and procedures. It begins with definitions of key terms like population, sample, and sampling. It then outlines the sampling design process, which includes defining the target population, determining the sampling frame, selecting a sampling technique, determining sample size, and executing sampling. The document categorizes sampling techniques into non-probability and probability methods. It provides examples and descriptions of specific techniques like simple random sampling, stratified sampling, and snowball sampling. Tables summarize and compare the strengths and weaknesses of different sampling approaches.

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Chapter Ten Sampling: Design and Procedures
11-3 Sampling
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11-8 Sampling
11-9 Chapter Outline 1) Overview 2) Sample or Census 3) The Sampling Design Process i. Define the Target Population ii. Determine the Sampling Frame iii. Select a Sampling Technique iv. Determine the Sample Size v. Execute the Sampling Process
11-10 Chapter Outline 4) A Classification of Sampling Techniques i. Nonprobability Sampling Techniques a. Convenience Sampling b. Judgmental Sampling c. Quota Sampling d. Snowball Sampling ii. Probability Sampling Techniques a. Simple Random Sampling b. Systematic Sampling c. Stratified Sampling d. Cluster Sampling e. Other Probability Sampling Techniques
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11-13 Sampling Sampling; • The process of selecting a small number of elements from a larger defined target group of elements the information gathered from the small group will allow judgments to be made about the larger groups
11-14 14 Population Sample Sampling
11-15 15 1 9 2 3 4 5 6 7 8 10 1716 15 13 14 12 11 Sample The Sample
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11-17 Census 1 9 2 3 4 5 6 7 8 10 1716 15 13 14 12 11 List of Units 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1 9 2 3 4 5 6 7 8 10 1716 15 13 14 12 11 Census Sample or Census
11-18 Sample vs. Census Conditions Favoring the Use of Type of Study Sample Census 1. Budget Small Large 2. Time available Short Long 3. Population size Large Small 4. Variance in the characteristic Small Large 8. Attention to individual cases Yes No
11-19 19 • Budget and time constraints (in case of large populations) • High degree of accuracy and reliability (if sample is representative of population) • Sampling may sometimes produce more accurate results than taking a census as in the latter • There are more risks for making interviewer and other errors due to the high volume of persons contacted and the number of census takers • Some of whom may not be well-trained Reasons for Sampling
11-20 Who is the target group for the study? This is called the study population Who in the target group should be surveyed? This is called the sample. How many people should be surveyed? This is called the sample size. How should the people to be surveyed by selected? This is called the sampling method. The Sampling
11-21 Acknowledgments to Uma Sekaran
11-22 22 • A sample is a subset of a larger population of objects individuals, households, businesses, organizations and so forth. • Sampling enables researchers to make estimates of some unknown characteristics of the population in question • A finite group is called population whereas a non-finite (infinite) group is called universe • A census is a investigation of all the individual elements of a Population Population Sample Sampling
11-23 Classification of Sampling Techniques Sampling Techniques Non-probability Sampling Techniques Probability Sampling Techniques Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Systematic Sampling Stratified Sampling Cluster Sampling Other Sampling Techniques Simple Random Sampling
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11-25 The Sampling Design Process Fig. 11.1 Define the Population Determine the Sampling Frame Select Sampling Technique(s) Determine the Sample Size Execute the Sampling Process
11-26 26 Define the Target population Select a Sampling Frame Determine if a probability or non-probability sampling method will be chosen Plan procedure for selecting sampling units Determine sample size Select actual sampling units Conduct fieldwork1 2 3 4 5 6 7 The Sampling Process
11-27 27 Defining the Target Population • The target population is that complete group whose relevant characteristics are to be determined through the sampling and a target population may be, for example, • All faculty members in the PRESTON University Islamabad, • All housewives in Islamabad • All pre-college students in Rawalpindi • All medical doctors in Pakistan • The target group should be clearly defined if possible, for example, do all pre-college students include only primary and secondary students or also students in other specialized educational institutions?
11-28 Define the Target Population The target population;  The target population should be defined in terms of elements, sampling units and time. • An element is the object about which or from which the information is desired, e.g., the respondent. • A sampling unit is an element, or a unit containing the element, that is available for selection at some stage of the sampling process. • Time is the time period under consideration.
11-29 • A list of population elements (people, companies, houses, cities, etc.) from which units to be sampled can be selected. • Examples; • A student telephone directory (for the student population), • The list of companies on the stock exchange, • The directory of medical doctors and specialists, • The yellow pages (for businesses) • Difficult to get an accurate list. • Sample frame error occurs when certain elements of the population are accidentally omitted or not included in list • Information relating to sampling frames can be obtained from commercial organizations The Sampling Frame
11-30 30 List of Units 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1 9 2 3 4 5 6 7 8 10 1716 15 13 14 12 11 Sample The Sampling Frame
11-31 31 • The sampling unit is a single element – or group of elements – subject to selection in a sample. • Examples:  Every student at PRESTON whose first name begins with the letter “F”  All child passengers under 18 years of age who are traveling in a train from destination X to destination Y  All jeweler shops in sectors F-6, F-7 and F-8 in Islamabad The Sampling Units
11-32 Classification of Sampling Techniques Sampling Techniques Non-probability Sampling Techniques Probability Sampling Techniques Convenience Sampling Judgmental Sampling Quota Sampling Snowball Sampling Systematic Sampling Stratified Sampling Cluster Sampling Other Sampling Techniques Simple Random Sampling
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11-34 Sampling Probability sampling; 1. Simple Random Sampling; 2. Systematic Sampling; 3. Stratified Sampling; 4. Cluster Sampling;
11-35 Simple Random Sampling Simple random sampling; • SRS is a method of probability sampling in which every unit has an equal nonzero chance of being selected
11-36 Systematic Random Sampling Systematic random sampling; • A method of probability sampling in which the defined target population is ordered the sample is selected according to position using a skip interval
11-37 Systematic Sampling
11-38 Systematic Sampling  The sample is chosen by selecting a random starting point and then picking every ith element in succession from the sampling frame.  The sampling interval, is determined by dividing the population size N by the sample size n and rounding to the nearest integer.  When the ordering of the elements is related to the characteristic of interest, systematic sampling increases the representativeness of the sample.
11-39 Systematic Sampling  If the ordering of the elements produces a cyclical pattern, systematic sampling may decrease the representativeness of the sample. For example, there are 100,000 elements in the population and a sample of 1,000 is desired. In this case the sampling interval, that is 100. A random number between 1 and 100 is selected. If, for example, this number is 23, the sample consists of elements 23, 123, 223, 323, 423, 523, and so on.
11-40 Systematic Sampling
11-41 Stratified Sampling
11-42 Stratified Random Sampling Stratified random sampling; • A method of probability sampling in which the population is divided into different subgroups and samples are selected from each subgroup.
11-43 Stratified Sampling Stratified Sampling;  A two-step process in which the population is partitioned into subpopulations, or strata  The strata should be mutually exclusive and collectively exhaustive in that every population element should be assigned to one and only one stratum and no population elements should be omitted  Next, elements are selected from each stratum by a random procedure, usually SRS.  A major objective of stratified sampling is to increase precision without increasing cost.
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11-45 Stratified Sampling  The elements within a stratum should be as homogeneous as possible, but the elements in different strata should be as heterogeneous as possible.  In proportionate stratified sampling, the size of the sample drawn from each stratum is proportionate to the relative size of that stratum in the total population.  In disproportionate stratified sampling, the size of the sample from each stratum is proportionate to the relative size of that stratum and to the standard deviation of the distribution of the characteristic of interest among all the elements in that stratum.
11-46 Cluster Sampling  The target population is first divided into mutually exclusive and collectively exhaustive subpopulations, or clusters.  Then a random sample of clusters is selected, based on a probability sampling technique such as SRS.  For each selected cluster, either all the elements are included in the sample (one-stage) or a sample of elements is drawn probabilistically (two-stage).  In probability proportionate to size sampling, the clusters are sampled with probability proportional to size.
11-47 Types of Cluster Sampling Fig. 11.3 Cluster Sampling One-Stage Sampling Multistage Sampling Two-Stage Sampling Simple Cluster Sampling Probability Proportionate to Size Sampling
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11-50 50 Random Sampling Error ;– • The “difference between the sample result and the result of a census conducted using identical procedures” and is the result of chance variation in the selection of sampling units • If samples are selected properly (for e.g. through the technique of randomization), the sample is usually supposed to be a good approximation of the population and thus capable of delivering an accurate result • Usually, the random sampling error arising from statistical fluctuation is small, but sometimes the margin of error can be significant The Sampling Errors (1)
11-51 51 Systematic (Non-Sampling) Errors; • These errors result from factors such as an • Improper research design that causes response error or • Or errors committed in the execution of the research • Errors in recording responses • Non-responses from individuals who were not contacted or who refused to participate • Both Random sampling errors and systematic (non- sampling) errors reduce the representativeness of a sample and consequently the value of the information which is derived by business researchers from it The Sampling Errors (2)
11-52 52 Total Population Sampling Frame Error Random Sampling Error Sampling Frame Planned Sample Non-Response Error Respondents (actual sample) The Sampling Errors
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Yes Choose one of the PROBABILITY sampling designs. If purpose of study mainly is for: No Generalizability . Assessing differential parameters in subgroups of population. Collecting information in a localized area. Gathering more information from a subset of the sample To obtain quick, even if unreliable information To obtain information relevant to and available only with certain groups Choose one of the NONPROBABILITY sampling designs. If purpose of study mainly is: Choose cluster sampling if not enough $. Choose systematic sampling. Choose simple random sampling. All subgroups have equal number of elements? Yes No Choose disproportionate stratified random sampling Choose proportionate stratified random sampling. Choose convenience sampling. Looking for information that only a few “experts” can provide? Need responses of special interest minority groups? Choose judgment sampling. Choose quota sampling. Choose area sampling. Choose double sampling. Is REPRESENTATIVENESS of sample critical for the study? Diagram 11.3 Choice Points in Sampling Design.
11-55 Acknowledgments to Uma Sekaran
11-56 Sampling Non-probability sampling; 1. Convenience sampling; 2. Judgmental sampling; 3. Quota sampling; 4. Snowball sampling;
11-57 Convenience Sampling Convenience sampling; • Attempts to obtain a sample of convenient elements. • Respondents are selected because they happen to be in the right place at the right time • Use of students, and members of social organizations • Mall intercept interviews without qualifying the respondents • Department stores using charge account lists • “people on the street” interviews
11-58 Judgmental Sampling Judgmental sampling;  A form of convenience sampling in which the population elements are selected based on the judgment of the researcher.  Test markets  Purchase engineers selected in industrial marketing research  Expert witnesses used in court
11-59 Quota Sampling Quota sampling;  may be viewed as two-stage restricted judgmental sampling  The first stage consists of developing control categories, or quotas, of population elements.  In the second stage, sample elements are selected based on convenience or judgment. Population Sample composition composition Control Characteristic Percentage Percentage Number Sex Male 48 48 480 Female 52 52 520 100 100 1000
11-60 Snowball Sampling Snowball sampling;  An initial group of respondents is selected, usually at random  After being interviewed, these respondents are asked to identify others who belong to the target population of interest  Subsequent respondents are selected based on the referrals
11-61 Snowball Sampling
11-62 Snowball Sampling
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11-64 2. For smaller samples (N ‹ 100), there is little point in sampling. Survey the entire population. 1. The larger the population size, the smaller the percentage of the population required to get a representative sample Rules of thumb for determining the sample size...
11-65 4. If the population size is around 1500, 20% should be sampled. 3. If the population size is around 500 (give or take 100), 50% should be sampled. 5. Beyond a certain point (N = 5000), the population size is almost irrelevant and a sample size of 400 may be adequate. Rules of thumb for determining the sample size...
11-66 Technique Strengths Weaknesses Nonprobability Sampling Convenience sampling Least expensive, least time-consuming, most convenient Selection bias, sample not representative, not recommended for descriptive or causal research Judgmental sampling Low cost, convenient, not time-consuming Does not allow generalization, subjective Quota sampling Sample can be controlled for certain characteristics Selection bias, no assurance of representativeness Snowball sampling Can estimate rare characteristics Time-consuming Probability sampling Simple random sampling (SRS) Easily understood, results projectable Difficult to construct sampling frame, expensive, lower precision, no assurance of representativeness. Systematic sampling Can increase representativeness, easier to implement than SRS, sampling frame not necessary Can decrease representativeness Stratified sampling Include all important subpopulations, precision Difficult to select relevant stratification variables, not feasible to stratify on many variables, expensive Cluster sampling Easy to implement, cost effective Imprecise, difficult to compute and interpret results Table 11.3 Strengths and Weaknesses of Basic Sampling Techniques