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27 Cards in this Set
- Front
- Back
Define population
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the complete set of all conceivable observations of a variable
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Define sample
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a subset of a population is a sample
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Why is information almost always collected from a sample rather than the population?
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1. Economic - less expensive
2. Timeliness - takes less time 3. Size and accessibility - difficult to collect from population 4. Observation and destruction - destruction of element being observed. Cannot destroy population. |
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Describe the two theories associated with sampling
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1. How sample is chosen
2. How inferences are made about population from the sample. |
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List several applications of Sampling
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1. Opinion polls
2. Quality control 3. Checking invoices (auditing) |
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What must exist for correct conclusions to be drawn about a population?
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Samples must be representative.
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Define sampling bias
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Samples are consistently not representative of the population.
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What is the fundamental method of ensuring that samples are representative of the population?
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Using simple random sampling
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List and describe variations of simple random sampling.
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1. Multi-stage sampling - permits collection in just a few sections of area, cutting down on travelling and interviewing expenses.
2. Stratified sampling - using knowledge about population to determine how to sample. |
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Define Judgement Sampling
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all methods that are not random and use personal judgement.
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Primarily process for random sampling
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using random generated numbers that are assigned to the population.
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Describe Multi-Stage Sampling
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1. Population is split into groups, each group split into sub-groups, etc.
2. Samples taken at each stage Benefit is that it is easier to get listing of populatio in this manner. |
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Describe Cluster Sampling
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Same as Multi-Stage Sampling except at the final grouping all individuals are included in sample.
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Describe Ordinary Cluster Sampling
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Describe Stratified Sampling
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1. Use prior knowledge of population to make sample more representative
2. Population is split into subpopulations (strata) of known proportion 3. Sample is taken of each strata in same proportion. |
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How does weighting affect sampling
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If strata exists but proportions are not known before sampling, then weighting providing the adjustment after sampling. The strata's result are weighted according to this formula (% of pop/% of sample)
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Describe probability sampling
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A sampling method which allows for elements to have differing chances of being selected as a sample.
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Describe variable sampling
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A sampling method where some special subpopulation is over-sampled. Due to the importance of gathering information from this subpopulation.
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Describe Area Sampling
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Artificial breaking down of the population to make sampling easier.
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List the main Judgement Sampling methods.
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1. Systematic Sampling - taken at regular intervals from Population
2. Convenience Sampling - easiest way possible, when no other way exists. Medical. Must understand - not random. 3. Quota Sampling - to overcome interviewer bias. List given to interviewer about how many in each sub-group (random from there on). |
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What is the problem of inference in sampling?
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Since no sample can be definitely known to be representative of the population, there is a need to estimate the errors that may arise. Can only be done when Random sampling used.
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How does stratified sampling affect accuracy of estimates?
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Increases level of estimates
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Define the sampling frame
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the complete list from which the sample is selected (not population - due to inaccurate records, etc.) A big difference between population and sampling frame may mean results are unreliable.
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Describe non-response and its impact
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If cannot complete the interview for, then that is non-response. This may lead to bias in the sample.
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List types of sampling bias
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1. Sampling frame error
2. Non-response 3. Inaccurate measurement 4. Interviewer bias 5. Interviewee bias 6. Instrument bias |
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What are the approaches to determining Sample size?
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1. Ask what level of accuracy is needed? Can determine size through mathematics.
2. Collect largest sample that budget allows. |
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Methods of sampling for small population
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1. Replacement - return element to population after selection (may be selected again)
2. Sampling without replacement - uses a different theory for calculating accuracy. More complicated. |