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1 Sampling Fundamentals

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2 Basic Concepts Population: the entire group under study (or of interest) Exercise: Define population for a study seeking to assess SUU student attitudes towards a) program quality and delivery, b) program content, and c) social environment. Sample: subset of the population Used to represent the population Sample unit (elements): basic unit investigated (choose sampling units/elements when sampling) Individuals, households, etc. Census: data collected from EVERYONE in population

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3 Basic Concepts(continued) AGAIN: total error = sampling error + nonsampling error Sampling error: error due to taking a sample (+/-zs) Nonsampling error: everything else (measurement, data analysis, etc.) Sample frame: list from which the sample is selected Sample frame error: Popn members not in frame, and members in frame not in popn of interest

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4 Reasons for Sampling Cost Too much information to handle Sampling can be more accurate Nonsampling errors can overwhelm reduction in sampling errors –Sampling work behaviors example –Census Bureau Time problem

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Developing a sampling plana 1. Define the population of interest. 2. Choose a data-collection method (mail, telephone, Internet, intercept, etc.). 3. Identify a sampling frame. 4. Select sampling method 5. Determine sample size. 6. Develop operational procedures for selecting sampling elements/units. 7. Execute the operational sampling plan. 5

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6 PROBABILITY SAMPLING METHODS Each member of population has a known probability of being selected Simple Random Sampling: Each member has an equal probability of being selected Blind Draw Method Table of Random Numbers Useful for small samples, when Random Digit Dialing (or +1) is appropriate, and computerized lists

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7 PROBABILITY METHODS (Contd) Stratified Sampling: Population is segmented (stratified), and then samples are chosen from each strata using some other method Can be more efficient (smaller sampling error) –Homogeneous within, heterogeneous without Useful when interested in different strata (e.g., small numbers, etc) Disproportionate versus proportionate

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8 PROBABILITY METHODS (Contd) Cluster Sampling: Population is divided into groups, or clusters, and then clusters are randomly chosen. Homogenous without, heterogeneous within Every unit in cluster examined, OR A Random (or systematic) sample is taken from chosen cluster (2-stage or 2-step approach) Careful with the probabilities!

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9 PROBABILITY METHODS (Contd) Systematic Sampling: Randomly choosing a starting point and then choosing every n th member. Example: Need 52 data points (daily sales) for a year –Skip interval = 365/52=7.01 –Randomly choose 1 day out of first 7, then choose every 7 th one after that. Variation: Choose every n th visitor

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10 NONPROBABILITY SAMPLING METHODS Probability of selection not known, and hence representativeness cannot be assessed Technically, confidence intervals, H 0 tests, etc. not appropriate Convenience Samples: Shopping mall intercepts, classes asked to fill out questionnaires, etc. Judgment Samples: Someone puts together what is believed to be a relatively representative sample Ex.: Test markets

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11 Nonprobability Sampling (Contd) Referral (or Snowball) Samples Quota Samples EXAMPLE: Choose sampling units so their representation equals their frequency in the popn (e.g., 52% females, 48% males)

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12 Identifying the Target Population Determining the Sampling Frame Selecting a Sampling Frame Probability Sampling Non-Probability Sampling Determining the Relevant Sample Size Execute Sampling Data Collection From Respondents Information for Decision-Making Reconciling the Population, Sampling Frame Differences Handling the Non- Response Problem The Sampling Process

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13 Nonresponse Bias Reason for nonresponse: Refusal Lack of ability to respond Not at home Inaccessible Handling nonresponse Improve research design Call-backs Estimate effects –Sample nonrespondents; trends

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