Sample Designs and Sampling Procedures

Sample Designs and Sampling Procedures

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  Sample Designs andSampling Procedures
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  Sampling Terminology  Sample:A subset, or some part, of a larger population.  Population or universe: A complete group of entities sharing some common set of characteristics.  Population element: An individual member of a specific population.  Census / Survey: An investigation of all the individual elements making up a population.
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  Define the targetpopulation Select a sampling frame Conduct fieldwork Determine if a probability or nonprobability sampling method will be chosen Plan procedure for selecting sampling units Determine sample size Select actual sampling units Stages in the Selection of a Sample
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  Two Major Categoriesof Sampling…  Probability sampling: A sampling technique in which every member of the population has known, nonzero probability of selection. [An event that has zero probability may be possible or impossible. However, if an event has a nonzero probability, it must be possible; that is, it cannot be impossible.]  Two types of probability sampling o Simple Random Sampling o Complex Random Sampling: Includes i)Systematic sample [Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point and a fixed periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.],
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  Two Major Categoriesof Sampling… ii) Stratified sample [Stratified sampling, the researcher divides the population into separate groups, called strata. Then, a probability sample (often a simple random sample ) is drawn from each group. Stratified sampling has several advantages over simple random sampling. For example, using stratified sampling, it may be possible to reduce the sample size required to achieve a given precision. Or it may be possible to increase the precision with the same sample size.] iii) Cluster sample [cluster sampling, the researcher divides the population into separate groups, called clusters. Then, a simple random sample of clustersis selected from the population. The researcher conducts his analysis on data from the sampled clusters. For example, given equal sample sizes, cluster sampling usually provides less precision than either simple random sampling or stratified sampling. On the other hand, if travel costs between clusters are high, cluster sampling may be more cost-effective than the other methods.],
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  Two Major Categoriesof Sampling… iv) Multistage area sample [Multistage sampling can be a complex form of cluster sampling because it is a type of sampling which involves dividing the population into groups (or clusters). Then, one or more clusters are chosen at random and everyone within the chosen cluster is sampled. Example, The Australian Bureau of Statistics divides cities into “collection districts”, then blocks, then households. Each stage uses random sampling, creating a need to list specific households only after the final stage of sampling.]  Non probability sampling : A sampling technique in which units of the sample are selected on the basis of personal judgment or convenience. i) Convenience [Examples, One of the most common examples of convenience sampling is using student volunteers as subjects for the research. Another example is using subjects that are selected from a clinic, a class or an institution that is easily accessible to the researcher.],
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  Two Major Categoriesof Sampling ii) Judgment [Judgment sample is a type of nonrandom sample that is selected based on the opinion of an expert. Results obtained from a judgment sample are subject to some degree of bias, due to the frame and population not being identical.], iii) Quota [A sampling method of gathering representative data from a group. As opposed to random sampling, quota sampling requires that representative individuals are chosen out of a specific subgroup. For example, a researcher might ask for asample of 100 females, or 100 individuals between the ages of 20-30.] , iv) Snowball [snowball sampling (or chain sampling, chain- referral sampling, referral sampling) is a nonprobability sampling technique where existing study subjects recruit future subjects from among their acquaintances.]
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  Simple Random Sampling Thesimple random sample is considered a special case in which each population element has a known and equal chance of selection. Advantage Easy to implement with automatic dialing (random-digit dialing) and with computerized voice response systems Disadvantage  Requires a listing of population elements.  Takes more time to implement.  Uses larger sample sizes.  Produces larger errors.
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  Systematic Sampling…  Inthis approach, every kth ele-ment in the population is sampled, beginning with a random start of an element in the range of 1 to k. To draw a systematic sample, do the following:  Identify, list, and number the elements in the population.  Identify the skip interval (k).  Identify the random start.  Draw a sample by choosing every kth entry.
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  Advantage  Simple todesign.  Easier to use than the simple random. Easy to determine sampling distribution of mean or proportion. Disadvantage  Periodicity within the population may skew the sample and results.  If the population list has a mono-tonic trend, a biased estimate will result based on the start point. Systematic Sampling
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  Stratified Sampling Most populationscan be segregated into several mutually exclusive subpopulations, or strata. The process by which the sample is constrained to include elements from each of the segments is called stratified random sampling. Advantage  Researcher controls sample size in strata.  Increased statistical efficiency. Provides data to represent and analyze subgroups.  Enables use of different methods in strata Disadvantage  Increased error will result if subgroups are selected at different rates.  Especially expensive if strata on the population have to be created.
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  Cluster Sampling The populationcan also be divided into groups of elements with some groups randomly selected for study. This is cluster sampling. Cluster sampling differs from stratified sampling in several ways Advantage  Provides an unbiased estimate of population parameters if properly done.  Economically more efficient than simple random.  Lowest cost per sample, especially with geographic clusters.  Easy to do without a population list. Disadvantage  Often lower statistical efficiency (more error) due to subgroups being homogeneous rather than heterogeneous.
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  Why Non probability Sampling? 1.Satisfactorilymeet the sampling objectives. 2. Cost and time 3. Provide acceptable results 4. Feasible alternative
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  Non-probability Sampling  Convenience:The sampling procedure used to obtain those units or people most conveniently available.  Judgment: A technique in which an experienced individual selects the sample based upon some appropriate characteristic of the sample member.  Quota: A procedure that ensures that various subgroups in the population are represented pertinent characteristics to the exact extent that the investigator desire  Snowball: A sampling procedure in which initial respondents are selected by probability methods and additional respondents are obtained from information provided by the initial respondents.
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  Errors… Sampling Error:  RandomSampling Error  Non Sampling Error/Systematic Error Random Sampling Error:  The difference between the sample results and the result of a census conducted using identical procedures
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  Systematic Errors: Occurs when Imperfect aspect of the research design that involves;  Response error  Mistake in the execution of the research  Error comes from such sources as sample bias  Mistake in recording responses  Sampling frame error (when certain sample elements are excluded or when the entire population is not accurately represented in the sampling frame)  Non response error (The statistical difference between a survey that includes only those who responded and a survey that also includes who failed to respond). Errors
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  Determine the SampleSize )1( , 576.29996.195,645.190 )1( 0 0 2 2         Nn Nn n knownSizepopulationWhen SizePopulationN confidenceoflevelerrore proportionp zvaluetabulatedz Where e ppz n