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SBM II December > Sampling CI > Flashcards

Sampling CI Flashcards

1
Q

What is Sampling?

A

The process to determine who we are going to study/examine

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2
Q

What is the purpose of Sampling?

A

To find out information without talking to everyone.

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3
Q

What are two types of sampling?

A

Nonprobability

Probability

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4
Q

What is Probability sampling?

A

Systemic technique that is used to select respondents - goal is to create a sample as representative of the population as possible.

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5
Q

When is Probability sampling used most frequently?

A

Quantitative research

–> Leaving the selection up to chance

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6
Q

What is Non-probability sampling?

A

Based on researcher subjective judgment rather than random selection

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7
Q

What are traits of Non-probability Sampling?

A
  • Less generalizability; problem with representativeness
  • Lower confidence in findings
  • Useful when probability sampling can’t be used
  • Four common methods
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8
Q

What are the four common methods of Non-probability sampling?

A
  • Purposive
  • Convenience
  • Snowball
  • Quota
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9
Q

What are four traits of Probability Sampling?

A
  1. Used to Generalize population at large
  2. Works toward representativeness
  3. Used in all large-scale surveys/observational studies
  4. Avoids Sampling Bias
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10
Q

What is Sampling Bias?

A

Selecting atypical folks.

-Numerous ways to introduce bias into your sample.

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11
Q

What is a “Representative” sample?

A
  • Your sample is like the population
  • Random selection!
  • All members have an equal chance of being selected…
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12
Q

Probability samples are. . .

A

. . .never perfect.

-More representative than non-probability.

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13
Q

What is an Element?

A

Individual members of the population to which the study would be generalized

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14
Q

What is the Population?

A

The entire set of elements. Doesn’t have to just be individuals - other entities - to which the study findings will be generalized.

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15
Q

What is the Sampling Frame?

A

LIST of all the elements in a population. Want to study students - registrar’s list of students - the students selected to be interviewed would be the elements
-Important for representativeness but not easy to acquire

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16
Q

What are examples of Sampling Frames?

A
  • Telephone directories
  • Tax records
  • Registrar’s list
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17
Q

What is the key question with Probability Sampling?

A

Who can I generalize these findings to?

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18
Q

What is Parameter?

A

Summary of a given variable in a population

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19
Q

What is a Statistic?

A

Summary of a given variable in a sample

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20
Q

What is the Sampling Distribution?

A

All the possible random samples that could be selected

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21
Q

What are random samples?

A

Samples that represent a population

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22
Q

What are four commonly discussed sample types?

A
  1. Simple Random
  2. Systematic
  3. Stratified
  4. Multistage Cluster
    - -PPS sampling (a form of cluster sampling)
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23
Q

What is Simple Random Sampling?

A
  • Base of sampling
  • -Need a list (sampling from)
  • -Assign a number
  • -Select by a random number
  • –> Random number list
  • Seldom used in this deliberate way; some use of computer generated random numbers
24
Q

What is Systematic Sampling?

A
  • Determine number needed
  • Divide population by sample number desired (we call this our sampling interval, denoted here by ‘k’)
  • List and number our elements
  • Randomly select start point
  • Select every k-th elements within groups
  • Caution: avoid periodicity!
25
Q

What is Stratified Sampling?

A
  • Possible modification of previous techniques
  • Random sample from subpopulation
  • Better representativeness
  • Decreases some sampling error
  • -> Homogenous subsets
  • Allows oversampling
26
Q

What is Cluster Sampling?

A
  • More complex methodologically (not conceptually, I hope)
  • Cluster = Groups of elements
  • Multi-stage
  • –> Basic stages/steps: listing and sampling
  • Helps with cost and dispersed populations
  • Increases sampling error potential
  • -> Two samples: double the error opportunity
27
Q

What two techniques are used to make experiments Comparable (between control & experimental groups)?

A
  • Randomization

- Matching

28
Q

What is Randomization?

A

Recruited folks (who may have been selected using nonprobability sampling techniques) are randomly placed into control and exp. groups

29
Q

What is Matching?

A

Assign people to group based on characteristics so groups match

30
Q

As the sample size goes up, the shape of the sampling distribution takes on an important shape. . .

A

. . .the normal curve!

31
Q

What is Sampling Error?

A
  • Variation in values of your sample mean compared to the population mean
  • Because of sampling error, we probably won’t always have completely accurate estimates
  • Deviation between sample results and population
32
Q

How can you reduce Sampling Error?

A
  • Increase sample size

- Increase homogeneity

33
Q

What are six characteristics of the Normal Curve from the Central Limit Theorem?

A
  1. Theoretical distribution of scores
  2. Perfectly symmetrical
  3. Bell-shaped
  4. Unimodal
  5. Tails extend infinitely in both directions
  6. Mean, median, and mode are equal
34
Q

Assumption of normality of a given empirical distribution . . .

A

. . .makes it possible to describe this “real-world” distribution based on what we know about the (theoretical) normal curve.

35
Q

What do we use the normal curve assumption for?

A

To generalize sample findings to a population.

36
Q

How many cases/how much area falls within 1 standard deviation of the mean?

A

0.68 of the area, 0.34 on each side of the mean.

37
Q

How many cases/how much area falls within 2 standard deviation of the mean?

A

0.95 of the area or 95% of cases

38
Q

How many cases/how much area falls within 3 standard deviation of the mean?

A

0.997 of the area or 99% of cases

39
Q

What is the Sampling Distribution used as?

A

An Estimate!

40
Q

If an infinite number of samples were conducted and some outcome was plotted. . .

A

The resulting distribution (for mans and proportions) would be “normal”

41
Q

Over the long run, any particular largish random sample estimate (outcome) has a 95% chance of being within. . .

A

. . .1.96 standard error units of the population parameter it represents.

42
Q

What is the Z-distribution?

A

Just a special case of the normal distribution.

43
Q

What is the mean and S.D. of the Z-distribution?

A 
Mean = 0
S.D. = 1
44
Q

What does the Z-distribution allow us to do?

A

Use a corresponding z-table to look up critical values

45
Q

What are the common z-scores for each confidence level (90%, 95%, 99%)?

A
  1. 65 = 90% CL
  2. 96 = 95% CL
  3. 58 = 99% CL
46
Q

What is the Confidence Level?

A

(Significance Level)

  • Probability our sample statistics fall within a given confidence interval
  • We set this ahead of time and denote as alpha. Most frequently, it’s alpha = 0.05 (95%).
47
Q

What is the Confidence Interval?

A
  • Range within ‘true’ parameters should lie, range of values around the estimate (point estimate)
  • Upper and lower limit for the confidence level
48
Q

What confidence interval do many of the biomedical books use?

A

CI = mean +/- 1.96 (standard errors), but this assumes a 95% confidence level (that’s where they are getting the z-score of +/- 1.96).

49
Q

What does random selection allow us to do?

A

Connect our sample findings to ‘probability theory’ concepts so we can estimate how accurate our findings are.

50
Q

I am x% confident that the population parameter falls between a-b. What is the confidence interval? What is the confidence level?

A

x% = confidence LEVEL (alpha)

Values between a - b = confidence INTERVAL

51
Q

The large the confidence level. . .

A

. . .the narrower our confidence interval (CI).

52
Q

How do you calculate Standard Error?

A

SE = SD/sq. rt. [N]

53
Q

How do you calculate confidence interval?

A

Mean score +/- Z-score (which is usually 1.96) X SE

54
Q

What is the SE for each Confidence level?

A

90% - 1.65
95% - 1.96
99% - 2.58

55
Q

Wider the interval…

A

…weaker the evidence.

56
Q

Narrower the interval…

A

…stronger our case.

57
Q

What does the width of a confidence interval (CI) depend on (three things)?

A
  1. alpha/confidence level: The confidence level can be raised (e.g., to 99%) or lowered (e.g., to 90%)
  2. N: We have more confidence in larger sample sizes so as N increases, the interval decreases
  3. Variation: more variation = more error
    - -> For proportions, % agree closer to 50%
    - -> For means, higher standard deviations

SBM II December (11 decks)

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