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Concept Map (Non-Sampling Error (Wording Bias: Being bias about the…
Concept Map
Non-Sampling Error
Wording Bias: Being bias about the questions in a survey as to influence the answers for the survey taker.
Response Bias: Responding to a survey in a way in which the answer doesn't come from your opinion. You respond to a survey with the intention to choose a specific answer even if you don't actually think that way.
Non-Response: When someone doesn't respond to a question or there is no response to a test or survey
A non-sampling error is an error that results during data collection, causing the data to differ from the true values.
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Only including a certain group of questions in a survey to force the survey taker to state something they don't want to
Only responding to a certain question if you are bias to that question or a certain survey if there are answers that you favor. You can also have non-response, which would be response bias
Not responding to a survey at all or having a failure to reply or respond to something. non-response is the absence of reply.
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Sampling Error
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Voluntary Response Sampling: This consists of people who choose themselves by responding to a general appeal. Voluntary response samples show bias because people with strong opinions are most likely to respond. Write-in and Call-in opinion polls are almost sure to lead to bias because only about 15% of the population participates in these polls. These people tend to be the same people who call in on radio talk shows, which is not a representative of the whole population.
Under coverage: This occurs when some groups in the population are left out of the process of choosing the sample. For example, when a survey goes out to households, their are many people in the population that are missed. Among these people are homeless people, prison inmates and students living in dormitories.
Example: Going to a library and surveying the students about homework habits. The data received would not represent the entire student body because students who hang around the library would most likely be more studious.
Bias
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One source of bias is phrasing the questions in a survey in favor of your opinion, which will cause the survey's answers to be skewed a certain direction. For example, if you were to ask a question about a politician in a survey and preface the questions by all of the good things they have done, it is more likely for the participants of the survey to answer in favor of the politician.
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Another source of bias comes in the form of who is asking the question. For example, if someone that worked for the school asked for your opinion of the principal, it would not be likely for you to share your real opinion of the person.
Good Sampling Techniques
Simple Random Sample
A simple random sample of size n consists of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected. An SRS not only gives each individual an equal chance to be chosen but also gives every possible sample an equal chance to be chosen.
Example: A researcher wants to survey academic performance in a country. He divides the population into clusters and selects a number of clusters depending on his research.
Cluster Sampling
Cluster samples are often used for practical reasons, such as school surveys. Clusters are often chosen for ease or convenience, so they may have as much variability as the population itself. Clusters don't offer the statistical advantage that SRS does.
The sampler can allocate their limited resources to the specific samples to get the most out of the interview or survey
To take a cluster sample, first divide the population into the smaller groups. Ideally, these clusters should mirror inn the characteristics of the population. Then chose an SRS of the clusters. A;; individuals in the chosen clusters are included in the samples.
Stratified SRS
Used with populations that can be easily broken into different subgroups that are based on certain criteria
It can provide a more accurate representation of the population based on what's used to divide it into different subsets. However, you must individually track and verify the data for each stratum for inclusion, which can take a lot more time compared with random sampling.
Lurking Variable
A variable that is not among the explanatory or response variables in a study but may influence the response variable. Confounding is when the effects of two variables on the response variable cannot be separated from each other. An example of this happening is when observing students not doing their homework. Do the students not do their homework because it is too difficult, or because they do not want to.
Observation Studies and experiments differ from each other because observation studies observe individuals and measure variables of interest but do not attempt to influence the responses. Experiments deliberately impose some treatment on individuals to measure their responses. An example of an observational study could be surveying students and asking if they get coffee in the morning. You could look in the group of students for any students who were late to their first class. For an experiment, you could count how many students do get coffee in the morning and ask a randomized selection of the same amount of students to get coffee in the morning and see how many of them are late to their first class.
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