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# Sampling Fundamentals

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Sampling Fundamentals

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

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: Pop’n members not in frame, and members in frame not in pop’n of interest

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

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.

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

PROBABILITY METHODS (Cont’d)
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

PROBABILITY METHODS (Cont’d)
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!

PROBABILITY METHODS (Cont’d)
Systematic Sampling: Randomly choosing a starting point and then choosing every nth 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 7th one after that. Variation: Choose every nth visitor

NONPROBABILITY SAMPLING METHODS
Probability of selection not known, and hence representativeness cannot be assessed Technically, confidence intervals, H0 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

Nonprobability Sampling (Cont’d)
Referral (or Snowball) Samples Quota Samples EXAMPLE: Choose sampling units so their representation equals their frequency in the pop’n (e.g., 52% females, 48% males)

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

Nonresponse Bias Reason for nonresponse: Handling 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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