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Samples and Surveys (Observational Studies & Experiments ((Types of…
Samples and Surveys
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Sampling Error
Sampling Error Vocabulary
an error that occurs in a statistical estimate. Specifically, it occurs when some members are inadequately represented in the sample. Example: survey error
Under coverage occurs when some groups in the population are left out of the process of choosing the sample.
Convenience Sampling is choosing individuals who are easiest to reach.
Example: asking people on a JetBlue flight what airline they like to fly with
Sample Error is the error that arises in a data collection process as a result of taking a sample from a population rather than using the whole population.
Example: people who call into a radio show poll and have strong opinions about the topic
Voluntary Response Variable consists of people who choose themselves by responding to a general appeal. Voluntary response samples show bias because people with strong opinions (often in the same direction) are most likely to respond.
Non-sampling Error
Non-Sampling Error
deviations of estimates from their true values that are not a function of the sample chosen, including various systematic errors and random errors that are not due to sampling. Wording Bias: where respondents are given questions that influence them in a particular direction. Example; If there was a surveyor who was asking respondents about the opinions on two different ice cream stores. To be classified as wording bias the surveyor would have describes one of the ice cream stores to be the most amazing in order to manipulate the crowd to answer the question the way the surveyor wants them to.
Response Bias: the tendency of a person to answer questions on a survey untruthfully or misleadingly. Example; Respondents may feel pressure to give answers that are socially acceptable.
Non-response: when respondents differ in meaningful ways from respondents. Example; Nonresponse is a problem with mail surveys, where the response rate can be very low.
Bias
Bias
tendency of measurement process to over- estimate or under-estimate the value of a population parameter. Source: Data provider
Direction: What the analyzer wants audience to believe
Cluster Sampling
Cluster Sampling
to take a cluster sample, first divide the population into smaller groups. Ideally, these clusters should mirror the characteristics of the population. Then choose an SRS of the clusters. All individuals in the chosen clusters are included in the sample.
Example: Surveying the academic performance of high school students in the US. Dividing the population of US by the clusters, or cities. Then, choose a cluster based SRS.
Advantage: you can find information from one or more areas
Disadvantage: It's a biased sample
Stratified SRS
Stratified SRS
To select a stratified random sample, first classify the population into groups of similar individuals, called strata. Then, choose a separate SRS in each stratum and combine these SRSs to form the full sample.
Example: Male vs. Female Junior/Senior: Would you want a half day on prom day with a half day of community service to make it up?
Advantage: a stratified SRS is a more precise sample
Disadvantage: A stratified SRS cannot be used in all studies
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