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Chapter 8: Sampling - Coggle Diagram
Chapter 8: Sampling
Statistics of Sampling
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Frequency distribution: responses from different respondents to the same item or observation can be graphed.
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Population parameters: standard deviations that could be obtained if we could sample the entire population. population characteristics are always unknown.
Sampling error: sample statistics may differ from population parameters if the sample is not perfectly representative of the population, the difference between the two.
Sampling distribution: frequency distribution of a sample statistic from a set of samples, while the commonly referenced frequency distribution is the distribution of a response from a single sample.
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Confidence interval: the estimated probability that a population parameter lies within a specific interval o sample statistic values.
Probability Sampling: a technique in which every unit in the population has a chance of being selected in the sample, and this chance can be accurately determined.
Simple random sampling: all possible subsets of a population are given an equal probability of being selected.
Systematic sampling: the sampling frame is ordered according o some criteria and elements are selected at regular intervals through that ordered list.
Stratified sampling: the sampling frame is divided into homogeneous and non-overlapping subgroups and a simple random sample is drawn within each subgroup.
Cluster sampling: divide a large population into "clusters" randomly sample a few clusters, and measure all units within that cluster.
Matched-pairs sampling: categorize a sampling frame of firms into "high profitable" firms and "low profitable firms: based on gross margins, earnings per share, or some other measure of profitability.
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Non-Probability Sampling: sampling technique which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined.
Convenience sampling: a technique in which a sample is drawn from that part of the population that is close to hand, readily available or convenient.
Quota sampling: the population is segmented into mutually-exclusive subgroups and then non-random set of observations is chosen from each subgroup to meet a predefined quota.
Proportional quota sampling: proportion of respondents in each subgroup should match that of the population.
Non-proportional quota sampling: less restrictive, you do not have to achieve a proportional representation but perhaps meet a minimum size in each subgroup.
Expert sampling: respondents are chosen in a non-random manner based on their expertise on the phenomenon being studied.
Snowball sampling: start by identifying a few respondents that match the criteria for inclusion in your study, and then ask them to recommend others they know who also meet your selection criteria.
The Sampling Process
Sampling: a statistical process of selecting a subset of a population of interest for purposes of making observations and statistical inferences about that population.
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