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Determining the Sample Plan

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PowerPoint PresentationThe Sample Plan is the process followed to select units from the population to be used in the sampleSlide 3Slide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18A two-step area cluster sample (sampling several clusters) is preferable to a one-step (selecting only one cluster) sample unless the clusters are homogeneousProbability Sampling Methods Stratified Sampling MethodSlide 21Slide 22Why is Stratified Sampling more accurate when there are skewed populations?Why is Stratified Sampling more accurate when there are skewed populations? Continued..Slide 25Slide 26Slide 27Slide 28Slide 29Slide 30Slide 31Slide 32Slide 33Slide 34Chapter 12Determining the Sample PlanThe Sample Plan is the process followed to select units from the population to be used in the sampleBasic Concepts in Samples and Sampling•Population: the entire group under study as defined by research objectives. Sometimes called the “universe.”Researchers define populations in specific terms such as heads of households, individual person types, families, types of retail outlets, etc. Population geographic location and time of study are also considered.Basic Concepts in Samples and Sampling• Sample: a subset of the population that should represent the entire group• Sample unit: the basic level of investigation…consumers, store managers, shelf-facings, teens, etc. The research objective should define the sample unit• Census: an accounting of the complete populationBasic Concepts in Samples and Sampling…cont.•Sampling error: any error that occurs in a survey because a sample is used (random error)•Sample frame: a master list of the population (total or partial) from which the sample will be drawn•Sample frame error (SFE): the degree to which the sample frame fails to account for all of the defined units in the population (e.g a telephone book listing does not contain unlisted numbers) leading to sampling frame error.Basic Concepts in Samples and Sampling…cont.•Calculating sample frame error (SFE): Subtract the number of items on the sampling list from the total number of items in the population. Take this number and divide it by the total population. Multiply this decimal by 100 to convert to percent (SFE must be expressed in %)If the SFE was 40% this would mean that 40% of the population was not in the sampling frameReasons for Taking a Sample•Practical considerations such as cost and population size•Inability of researcher to analyze large quantities of data potentially generated by a census•Samples can produce sound results if proper rules are followed for the drawBasic Sampling Classifications•Probability samples: ones in which members of the population have a known chance (probability) of being selected •Non-probability samples: instances in which the chances (probability) of selecting members from the population are unknownProbability Sampling MethodsSimple Random Sampling•Simple random sampling: the probability of being selected is “known and equal” for all members of the population•Blind Draw Method (e.g. names “placed in a hat” and then drawn randomly)•Random Numbers Method (all items in the sampling frame given numbers, numbers then drawn using table or computer program)•Advantages: •Known and equal chance of selection•Easy method when there is an electronic databaseProbability Sampling MethodsSimple Random Sampling•Disadvantages: (Overcome with electronic database)•Complete accounting of population needed•Cumbersome to provide unique designations to every population member•Very inefficient when applied to skewed population distribution (over- and under-sampling problems) – this is not “overcome with the use of an electronic database)Probability Sampling MethodsSystematic Sampling (A Cluster Method)•Systematic sampling: way to select a probability-based sample from a directory or list. This method is at times more efficient than simple random sampling. This is a type of cluster sampling method.•Sampling interval (SI) = population list size (N) divided by a pre-determined sample size (n)•How to draw: 1) calculate SI, 2) select a number between 1 and SI randomly, 3) go to this number as the starting point and the item on the list here is the first in the sample, 4) add SI to the position number of this item and the new position will be the second sampled item, 5) continue this process until desired sample size is reached.Probability Sampling MethodsSystematic Sampling•Advantages: •Known and equal chance of any of the SI “clusters” being selected•Efficiency..do not need to designate (assign a number to) every population member, just those early on on the list (unless there is a very large sampling frame).•Less expensive…faster than SRS•Disadvantages:•Small loss in sampling precision•Potential “periodicity” problemsProbability Sampling MethodsCluster Sampling•Cluster sampling: method by which the population is divided into groups (clusters), any of which can be considered a representative sample. These clusters are mini-populations and therefore are heterogeneous. Once clusters are established a random draw is done to select one (or more) clusters to represent the population. Area and systematic sampling (discussed earlier) are two common methods. •Area samplingProbability Sampling MethodsCluster Sampling•Advantages•Economic efficiency … faster and less expensive than SRS•Does not require a list of all members of the universe•Disadvantage:•Cluster specification error…the more homogeneous the cluster chosen, the more imprecise the sample resultsProbability Sampling MethodsCluster Sampling – Area Method•Drawing the area sample:•Divide the geo area into sectors (subareas) and give them names/numbers, determine how many sectors are to be sampled (typically a judgment call), randomly select these subareas. Do either a census or a systematic draw within each area.•To determine the total geo area estimate add the counts in the subareas together and multiply this number by the ratio of the total number of subareas divided by number of subareas.A two-step area cluster sample (sampling several clusters) is preferable to a one-step (selecting only one cluster) sample unless the clusters are homogeneousProbability Sampling MethodsStratified Sampling Method•This method is used when the population distribution of items


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