Determining How to Select a Sample Edited & Complied By
Sanjeev S. Malage Associate Professor FMS Department , NIFT, Bangalore
Learning Objectives 1. To understand the concept of sampling. 2. To learn the steps in developing a sampling plan. 3. To understand the concepts of sampling error and nonsampling error. 4. To distinguish between probability samples, and nonprobability samples. 5. To understand sampling implications of surveying over the Internet. Sanjeev Sadashiv Malage NIFT, Bangalore
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Definition of sampling
Procedure by which some members of a given population are selected as representatives of the entire population
Sanjeev Sadashiv Malage NIFT, Bangalore
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The Concept of Sampling
To understand the concept of sampling.
Ø Sampling Defined:The process of obtaining information from a subset of a larger group. Ø A market researcher takes the results from the sample to make estimates of the larger group. Ø Sampling a small percentage of a population can result in very accurate estimates.
Sanjeev Sadashiv Malage NIFT, Bangalore
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Why do we use samples ?
Get information from large populations – At minimal cost – At maximum speed – At increased accuracy – Using enhanced tools
Sanjeev Sadashiv Malage NIFT, Bangalore
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What we need to know • Concepts – Representativeness – Sampling methods – Choice of the right design
Sanjeev Sadashiv Malage NIFT, Bangalore
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Sampling and representativeness
Sampling Population
Sample
Target Population
Target Population è Sampling Population è Sample Sanjeev Sadashiv Malage NIFT, Bangalore
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Steps in Developing a Sample Plan
Step 7. Execute Operational Plan
Step 2. Choose Data Collection Method Step1. Define the Population of Interest
Step 6. Develop Operational Plan
Step 5. Determine Sample Size
Step 3. Choose Sampling Frame
(4) Select a Sampling Method
Sanjeev Sadashiv Malage NIFT, Bangalore
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Steps In Developing A Sampling Plan
To learn the steps in developing a sample plan.
Step One: Defining the Population of Interest Specifying the characteristics from whom information is needed. Define the characteristics of those that should be excluded. Step Two: Choose Data Collection Method Impacts for the sampling process. Step Three: Choosing Sampling Frame A list of elements or members from which we select units to be sampled. Sanjeev Sadashiv Malage NIFT, Bangalore
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Basic Concepts in Sampling • Population: the entire group under study as defined by research objectives – Researchers define populations in specific terms such as “heads of households located in areas served by the company who are responsible for making the decision.” 10 Sanjeev Sadashiv Malage, NIFT, Bangalore
Basic Concepts in Sampling • Sample: a subset of the population that should represent the entire group • Sample unit: the basic level of investigation • Census: an accounting of the complete population
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Steps In Developing A Sampling Plan
To learn the steps in developing a sample plan.
Step Four: Select a Sampling Method The selection will depend on: • The objectives of the study • The financial resources available • Time limitations • The nature of the problem
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Steps In Developing A Sampling Plan Step Five: Determine Sample Size • Available budget • Rules of thumb Step Six: Develop of Operational Procedures for Selecting Sample Elements Specify whether a probability or nonprobability sample is being used Step Seven: Execution the Sampling Plan The final step of the operational sampling plan Include adequate checking of specified procedures. 13 Sanjeev Sadashiv Malage, NIFT, Bangalore
Classification of Sampling Methods
Sampling methods
Probability samples
Nonprobability samples
Simple random
Systematic
Cluster
Stratified
Convenience
Judgement
Snowball
Quota 14
Sanjeev Sadashiv Malage, NIFT, Bangalore
Two Basic Sampling Methods • Probability samples: ones in which members of the population have a known chance (probability) of being selected into the sample • Non-probability samples: instances in which the chances (probability) of selecting members from the population into the sample are unknown 15 Sanjeev Sadashiv Malage, NIFT, Bangalore
Probability Sampling: Simple Random Sampling • Simple random sampling: • the probability of being selected into the sample is “known” and equal for all members of the population – E.g., Blind Draw Method – Random Numbers Method 16 Sanjeev Sadashiv Malage, NIFT, Bangalore
Simple random sampling
• Principle –Equal chance of drawing each unit
• Procedure –Number all units –Randomly draw units 17 Sanjeev Sadashiv Malage, NIFT, Bangalore
Probability Sampling: 1.Simple Random Sampling – Advantage: • Known and equal chance of selection
– Disadvantages: • Complete accounting of population needed • Cumbersome to provide unique designations to every population member 18 Sanjeev Sadashiv Malage, NIFT, Bangalore
Simple random sampling Example: evaluate the prevalence of tooth decay among the 1200 children attending a school • • • •
List of children attending the school Children numerated from 1 to 1200 Sample size = 100 children Random sampling of 100 numbers between 1 and 1200 How to randomly select? 19 Sanjeev Sadashiv Malage, NIFT, Bangalore
Simple random sampling
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Probability Sampling Systematic Sampling • Systematic sampling: way to select a random sample from a directory or list that is much more efficient than simple random sampling – Skip interval=population list size/sample size
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Systematic sampling • N = 1200, and n = 60 Þ sampling fraction = 1200/60 = 20 • List persons from 1 to 1200 • Randomly select a number between 1 and 20 (ex : 8) Þ 1st person selected = the 8th on the list Þ 2nd person = 8 + 20 = the 28th etc ..... 22 Sanjeev Sadashiv Malage, NIFT, Bangalore
Systematic sampling
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Systematic sampling
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Probability Sampling Systematic Sampling – Advantages: • Approximate known and equal chance of selection…it is a probability sample plan • Efficiency…do not need to designate every population member • Less expensive…faster than SRS
– Disadvantage: • Small loss in sampling precision 26 Sanjeev Sadashiv Malage, NIFT, Bangalore
Probability Sampling Cluster Sampling • Cluster sampling: method in which the population is divided into groups, any of which can be considered a representative sample – Area sampling
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Cluster sampling • Principle – Random sample of groups (“clusters”) of units – In selected clusters, all units or proportion (sample) of units included
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Cluster Sampling • In cluster sampling the population is divided into subgroups, called “clusters.” • Each cluster should represent the population. • Area sampling is a form of cluster sampling – the geographic area is divided into clusters. 29 Sanjeev Sadashiv Malage, NIFT, Bangalore
Cluster Sampling • One cluster may be selected to represent the entire area with the one-step area sample. • Several clusters may be selected using the two-step area sample.
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A Two-Step Cluster Sample • A two-step cluster sample (sampling several clusters) is preferable to a one-step (selecting only one cluster) sample unless the clusters are homogeneous.
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Example: Cluster sampling Section 1
Section 2
Section 3
Section 5 Section 4 32 Sanjeev Sadashiv Malage, NIFT, Bangalore
cluster sampling To evaluate vaccination coverage: • Without list of persons • Total population of villages • Randomly choose 30 clusters • 30 cluster of 7 children each= 210 children
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Probability Sampling Cluster Sampling – Advantage: • Economic efficiency…faster and less expensive than SRS – Disadvantage: • Cluster specification error…the more homogeneous the clusters, the more precise the sample results 34 Sanjeev Sadashiv Malage, NIFT, Bangalore
Stratified Sampling • When the researcher knows the answers to the research question are likely to vary by subgroups…
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Probability Sampling Stratified Sampling • Stratified sampling: method in which the population is separated into different strata and a sample is taken from each stratum – Proportionate stratified sample – Disproportionate stratified sample
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Stratified sampling • Principle : – Classify population into internally homogeneous subgroups (strata) – Draw sample in each strata – Combine results of all strata
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Stratified Sampling – Research Question: “To what extent do you value your college degree?” Answers are on a five point scale: 1= “Not valued at all” and 5= “Very highly valued” • We would expect the answers to vary depending on classification. Freshers are likely to value less than Alumni. We would expect the mean scores to be higher as classification goes up. 38 Sanjeev Sadashiv Malage, NIFT, Bangalore
Stratified Sampling – Research Question: “To what extent do you value your college degree?” • We would also expect there to be more agreement (less variance) as classification goes up. That is, seniors should pretty much agree that there is value. Freshers will have less agreement.
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Stratified Sampling • Why is stratified sampling more accurate when there are skewed populations? – The less variance in a group, the less sample size it takes to produce a precise answer. – Why? If 99% of the population (low variance) agreed on the choice of Brand A, it would be easy to make a precise estimate that the population preferred Brand A even with a small sample size. 40 Sanjeev Sadashiv Malage, NIFT, Bangalore
Stratified Sampling – But, if 33% chose Brand A, and 23% chose B, and so on (high variance) it would be difficult to make a precise estimate of the population’s preferred brand…it would take a larger sample size…
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Stratified Sampling – Stratified sampling allows the researcher to allocate more sample size to strata with less variance and less sample size to strata with less variance. Thus, for the same sample size, more precision is achieved. – This is normally accomplished by disproportionate sampling. Seniors would be sampled LESS than their proportionate share of the population and freshmen would be sampled more. 42 Sanjeev Sadashiv Malage, NIFT, Bangalore
Probability Sampling Stratified Sampling – Advantage: • More accurate overall sample of skewed population…see next slide for WHY
– Disadvantage: • More complex sampling plan requiring different sample size for each stratum 43 Sanjeev Sadashiv Malage, NIFT, Bangalore
Nonprobability Sampling • With nonprobability sampling methods selection is not based on fairness, equity, or equal chance. – Convenience sampling – Judgment sampling – Referral sampling – Quota sampling 44 Sanjeev Sadashiv Malage, NIFT, Bangalore
Nonprobability Sampling • May not be representative but they are still used very often. Why? – Decision makers want fast, relatively inexpensive answers… nonprobability samples are faster and less costly than probability samples.
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Nonprobability Sampling • May not be representative but they are still used very often. Why? – Decision makers can make a decision based upon what 100 or 200 or 300 people say…they don’t feel they need a probability sample.
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Nonprobability Sampling • Convenience samples: samples drawn at the convenience of the interviewer – Error occurs in the form of members of the population who are infrequent or nonusers of that location
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Nonprobability Sampling • Judgment samples: samples that require a judgment or an “educated guess” as to who should represent the population – Subjectivity enters in here, and certain members will have a smaller chance of selection than others 48 Sanjeev Sadashiv Malage, NIFT, Bangalore
Nonprobability Sampling • Referral samples (snowball samples): samples which require respondents to provide the names of additional respondents – Members of the population who are less known, disliked, or whose opinions conflict with the respondent have a low probability of being selected 49 Sanjeev Sadashiv Malage, NIFT, Bangalore
Nonprobability Sampling • Quota samples: samples that use a specific quota of certain types of individuals to be interviewed – Often used to ensure that convenience samples will have desired proportion of different respondent classes
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Online Sampling Techniques • Random online intercept sampling: relies on a random selection of Web site visitors • Invitation online sampling: is when potential respondents are alerted that they may fill out a questionnaire that is hosted at a specific Web site
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Online Sampling Techniques • Online panel sampling: refers to consumer or other respondent panels that are set up by marketing research companies for the explicit purpose of conducting surveys with representative samples
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Basic Concepts in Sampling • Sampling error: any error in a survey that occurs because a sample is used • A sample frame: a master list of the entire population • Sample frame error: the degree to which the sample frame fails to account for all of the population…a telephone book listing does not contain unlisted numbers 53 Sanjeev Sadashiv Malage, NIFT, Bangalore