DECK 7: UNIT 3 part A (experiments and studies) Flashcards

1
Q

What are the two types of observational studies?

A

Retrospective, and Prospective

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2
Q

What is retrospective study?

A

A retrospective study is a study that looks backwards in time (or at the present moment).

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3
Q

What’s the difference between a prospective and a retrospective study?

A

A retrospective study takes a group and looks back at its history while a prospective study watches a group for a period of time and records the data along the way into the future.

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4
Q

What is prospective study?

A

Prospective study is when you study the experimental unit’s present and future.

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5
Q

Is it always better to do a census or to sample?

A

depends on the availablility of the data. If the you want to look at SAT vs GPA, you may easily be able to get all of the school’s data and do that study (a census). If you have to go out and get the info, you may want to take a sample to save time and energy.

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6
Q

What is a simple random sample?

A

put all of the names in a hat. every group is possible. pull the numbers

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7
Q

What are the “good” sampling methods?

A

SRS (simple random sample), stratified, clustered, systematic, multistage

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8
Q

What are the “bad” sampling methods.

A

convenience samples, voluntary samples

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9
Q

What is random sampling?

A

When we use chance to select a sample, like rolling dice, a random number generator, or a random number table in our selection process. We use randomization in all of the “GOOD” sampling methods.

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10
Q

What is systematic sampling?

A

collecting data from every nth subject.

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11
Q

What is stratified sampling?

A

When you break the population into groups with similar attributes and randomly select from each strata.

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12
Q

What is cluster sampling?

A

Cluster- grab clusters of the population. each cluster should be representative ( like the population) use a few clusters.

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13
Q

What is a multistage sample?

A

A sample that combines several sampling methods

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14
Q

A 4 year high school of 2000 students, sampling 40 high students: Describe a stratified sample

A

Stratify by year. Randomly choose 10 FR, 10 SO, 10 JU and 10 SENIORS

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15
Q

A 4 year high school of 2000 students, sampling 40 high students : Describe a systematic sample

A

Get an alphabetical list of all of the students, 2000/40=50. Randomly choose one of the first 50 students and then every 50th student after that.

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16
Q

A 4 year high school of 2000 students, sampling 40 high students: Describe a cluster sample

A

Imagine that all of art classes have 10 students and they are mixed with fr, so, jr and srs… You would randomly choose 4 classes and survey everyone in each of the 4 classes.

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17
Q

A 4 year high school of 2000 students, sampling 40 high students: Describe a convenience sample

A

Ask the first 40 students coming to the locker rooms after school. This is problematic because athletes may not have the same preferences as non athletes.

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18
Q

A 4 year high school of 2000 students, sampling 40 high students: Explain how stratifying has “impossible groups”

A

You couldn’t get all freshmen in your sample

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19
Q

A 4 year high school of 2000 students, sampling 40 high students: Explain how systematic has “impossible groups”

A

You couldn’t get the first 40 people alphabetically (because you are taking every nth)

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20
Q

A 4 year high school of 2000 students, sampling 40 high students: Explain how clustering has “impossible groups”

A

You couldn’t get 2 people from each classroom, because you would be randomly choosing classrooms and asking everyone in those classes.

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21
Q

A 4 year high school of 2000 students, sampling 40 high students: Since ALL GROUPS (samples) are possible and equally likely, show some groups that you could get randomly from and SRS that would not be representative of the entire school.

A

all female, all freshmen, all seniors, all athletes.. these could happen in an SRS (but they are not likely to)

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22
Q

What are the differences between the subjects in strata and the subjects in clusters?

A

the “strata” are homogeneous, or have similar traits within, but each strata are different from the other strata. The clusters may be heterogeneous, or have mixed traits within, but each cluster is similar to the other clusters.

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23
Q

Suppose you want to see the relationship between gender and candy preference in squirrels. How may you do a stratified vs cluster sample

A

STRATIFIED: You can split the list of all of the squirrels in your neihborhood by gender and randomly select 20 males from th list of all of the males, and then 20 females (strata) from all of the females. CLUSTER: you can randomly choose to 5 different trees and survey all of the squirrels in those trees, assuming that there are 4 squirrels living in each tree (clusters, the trees have both M and F).

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24
Q

What is the standard sampling method? (the gold standard)

A

A Simple Random Sample (SRS) is our standard. Every possible group of n individuals has an equal chance of being our sample. That’s what makes it simple. Put the names in a hat.

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25
Q

When sampling, what kind of sample are we striving to get?

A

A representative sample, we want our sample to have similar charactaristics as the population

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26
Q

What is a weakness of a SRS?

A

Suppose you want a sample of 50 high school students, with an SRS, although unlikely you could get “all freshmen” which wouldn’t be representative.

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27
Q

In which sampling methods do all subjects have the same probability of being chosen?

A

SRS, cluster, systematic and stratified all give subjects equal likelihood of being chosen.

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28
Q

In which sampling methods do all GROUPS have the same probability of being chosen?

A

Only in SRS do all GROUPS have the same probability of being chosen, all of the other methods have IMPOSSIBLE GROUPS.

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29
Q

Systematic, how do you find the N for every nth subject, and then how do you proceed?

A

TOTAL POP/SAMPLE SIZE= your n (round down). Then use RAND INT to Randomly choose first. RANDINT(1, n). And then take every nTH.

30
Q

What is the problem with convenience sampling?

A

The sample may not be representative as it is not randomized to include every type of person. Friends and family are convenient but they likely share similar opinions and thus the sample is not representative of a population.

31
Q

What is wrong with using volunteers in a survey?

A

(Volunteers are often upset or emotionally attached) Those who volunteer may not be like the rest of the population. An example may be, if you’re trying to find our how often people volunteer for things. So you ask for volunteers to take the survey. A question may be “when was the last time you volunteered for something?” Well. they all just volunteered for the survey!

32
Q

What’s the difference between stratified and cluster sampling?

A

Stratified- you divide the population up into groups with similar traits, called strata (homogeneous groups) and randomly choose a few from each strata.

33
Q

give an Example of a MULTISTAGE sample

A

Suppose you want to poll urban, suburban and rural citizens, you can divide a map into those strata, and then randomly choose neighborhoods or streets in each and ask everyone on those streets. Here you stratified by community type and then clustered by street.

34
Q

In which sampling methods do the subjects have equal chances of being selected?

A

SRS, Stratified, Clustered, Systematic, and multistage. In all of these, the subjects have an equal chance (but groups have different chances)

35
Q

What is a quality of SRS that is not a quality of Systematic, Stratified or Clustering?

A

In an SRS, all groups are possible, and ALL POSSIBLE GROUPS have the same chance of being picked (like all senior male students.).The other methods have lots of impossible groups. SRS has no impossible groups. Example: -Stratified- an impossible group would be all girls (you’re taking some boys and girls)-Clustered- an impossible group would be all girls (each cluster has boys and girls)-systematic- an impossible group would be first 10 people that are right next to each other (you are taking every nth person, so you will skip)

36
Q

What is a sampling frame?

A

It is the frame from which you get your sample. For instance, if you call people the frame would be “people with phones”

37
Q

How is a sampling frame different from the population?

A

Suppose you are wondering how elderly people on the cape feel about a new medicare law. If you go to nursing homes and randomly sample residents, then the frame is “elderly people at those nursing homes.” Your population is still elderly people on cape cod.

38
Q

How are voluntary and convenience samples similar…

A

With voluntary, people choose them selves, with covenience, the people are just chosen by researcher, neither uses randomness and both are prone to BIAS.

39
Q

Why do you have to Stratify?

A

You don’t have to.. But you might want to if you feel that a simple random sample might not be representative of the population . You want your sample to be like the population. a representative sample (it represents the population well).

40
Q

What is a representative sample?

A

A sample that looks like the population. It has similar characteristics.

41
Q

What is BIAS in sampling?

A

A systematic FLAW in your method.

42
Q

Will larger samples reduce BIAS?

A

No, bias is a systematic flaw, even large samples will still have bias. If you ask more people outside of McDonalds, you still only get answers from people who eat at McDonalds (large samples can reduce error, however)

43
Q

what is the best way to reduce bias?

A

randomness and good sampling methods.

44
Q

What is sampling error?

A

How far your statistic is from the parameter (how far your calculation from your sample was from the population parameter)

45
Q

Can you eliminate sampling error?

A

Only if you take a census. Larger samples have less error.

46
Q

How can you decrease sampling error?

A

Get a larger sample

47
Q

When your sampling frame is different from the population, then you risk ____

A

undercoverage

48
Q

Is sampling error a mistake?

A

IT IS NOT A MISTAKE!!! Because the data in samples are generally different, the statistics calculated from one sample to another vary and are generally not equal to the parameter. This variablilty of STATISTICS is called sampling error.

49
Q

What is wording bias

A

A type of response bias, When the wording of the question impacts response to it. (type of response bias)

50
Q

When we say “statistics vary” or the “variablility of statistics” are we talking about data from an individual?

A

NO… we are stating that summaries of samples (statistics) will vary from sample to sample. Statistics from one sample will differ from statistics from another sample and they will also differ from the parameters. The distance your statistic is from the parameter is called the ERROR.

51
Q

Example of wording bias

A

Do you support food assistance and nutrition programs for children living in poverty? VS. Do you approve of supporting lazy people on welfare?

52
Q

Example of undercoverage

A

You only ask people who go to Home Depot about their views on school lunches.

53
Q

Example of nonresponse bias

A

In a survey, a person does not answer a few questions (or a person is on your list and you can’t get a hold of them)

54
Q

Example of response bias

A

A teenager goes to the doctor’s office with their parents. The doctor asks the teen if they vape. The teen may say “no” because their parent’s are there, even though they do vape.

55
Q

How can the WORDING of the question lead to response bias

A

Words or phrases that impact your feelings tend to influence responses. Look for “devastating, horrific, wonderful… etc.” Sometimes there is a background story like “Many americans lose jobs to illegal aliens every year, do you feel this is fair”

56
Q

What is the difference between response bias and nonresponse bias?

A

Response is when the person’s response is influenced by the question or questioning method (like if a parent asks if you use drugs, as opposed to a friend… there is only one answer to this, but one might respond differently to them), non response is is when the people who don’t respond might have different opinions/views than the people who did.

57
Q

What is response bias? How do you avoid it?

A

Response bias is any influence that may sway the respondent e.g wording of the question, interviewer’s behavior/background. Therefore, in a survey, ask questions that allow respondents to answer comfortably and honestly. Keep the wording “indifferent” or neutral in some way in order to unduly favor one response over another.

58
Q

What is undercoverage?

A

Undercoverage is when a group of the population is not represented in the sample. When the sampling frame isn’t representative.

59
Q

What is difference between non response bias and undercoverage?

A

You may ask someone to take a survey, they may say no. They may feel differently than the people who decide to take the survey. In this case, that is non-response bias. Undercoverage happens when you didn’t even ask some people to take the survey. The people you didn’t even ask might feel different.

60
Q

To make a survey to tell of a restaurant is good, would you ask the people coming out of the restaurant?

A

People at the restaurant are probably there because they already like it. If you asked the question “Is this your first time dining here?” and if they say “yes” you survey them, that would be a better method. But then again.. the people wouldn’t go into an Italian restaurant if they didn’t like that type of food.

61
Q

How is BIAS different from SAMLING error

A

Bias is a systematic flaw in your sampling method. Sampling error is always present even with the best methodology.

62
Q

An unbiased sampling method will eliminate error

A

No, error is always there. Error is not a mistake.

63
Q

How are statistics and parameters different?

A

A statistics is a numerical description of a sample, and a parameter is a description of a population. The difference between these is called ERROR (sampling error).

64
Q

Name types of bias

A

undercoverage, non response, response, voluntary

65
Q

How is undercoverage different from non response

A

undercoverage you don’t even ask people, non-response you ask, but they don’t answer.

66
Q

Things that cause nonresponse bias ?

A

(remember non response is that the people you ask, or try to ask don’t respond) Lazy researcher, shy survey takers, who is the questioner, environment,

67
Q

Suppose you sample 150 people randomly from a city to make an inference about the city, and then you sample 150 people randomly from around the country to make an inference about the entire country, which will you be more confident in????

A

It will tell you just as much about both. Same reliability (if sample is representative). Sample size determines confidence. To get more confidence you need a larger sample (not a smaller population)

68
Q

How can you use random numbers to sample?

A

Number the subjects 00-99 (if less than 100) or 000-999 (if less than 1000) or 0000 to 9999 etc.. then use a random number table taking one, two, three or four numbers at a time. Throw out repeats.

69
Q

How is an observational study different from an experiment?

A

In an experiment you assign treatments and manipulate the environment. In studies you just gather information.

70
Q

When choosing clusters, what do you have to check for?

A

You want to make sure the clusters are similar to each other before you randomly choose them.