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Applied Statistics I > Chapter 3 > Flashcards

Chapter 3 Flashcards

1
Q

Good data collection

A

A realistic and sound plan is needed to develop a study that is a good representation of the population

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2
Q

Three steps in designing a study

A
  1. Identify population of interest 2. Compile list of subjects 3. Decide sampling design
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3
Q

Note on identifying population of interest

A

Have to have the population match the study’s question

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4
Q

Sampling Units

A

Subjects of interest

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5
Q

Sampling Frame

A

List of subjects to sample from

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6
Q

Sample Design

A

Method of drawing samples from the sample frame

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7
Q

What makes a good Sampling Design?

A

Resulting sample is a good representative of the population and reflects characteristics of the population

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8
Q

Three types of Sample Designs

A
  1. Simple Random 2. Cluster 3. Stratified
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9
Q

Simple Random Sampling

A

Sample guided by equal chance, given a population of n subjects, each possible sample of that size (n) has the same chance of being selected

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10
Q

Fraternity example

A

Two of five officers set to go on trip, picked randomly from a hat. Possible samples of officers are C(5,2) = 10. Chance of selecting any one of samples is 1/10 and each officer appears 4/10 samples, each has a 4/10 = 2/5 chance of selection

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11
Q

Random Number Tables (Generators)

A

Number assigned to subjects in frame; random numbers of same length as above generated; subjects with numbers generated selected and process stopped when sample size is reached

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12
Q

Cluster Sampling

A

Population divided into large number of clusters and simple random sample of pre-specified number of clusters selected

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13
Q

Cluster Random Sample

A

All samples in the clusters chosen during the cluster sampling

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14
Q

Stratified Sampling

A

Population divided into separate groups (strata) and a simple random sample is selected from each

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15
Q

What you need for Stratified Sampling

A

Access to sampling frame and strata into which each subject belongs

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16
Q

Margin of Error

A

Potential error range in estimations

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17
Q

Sampling Fraction

A

Ratio of sample size n to population size N (n/N)

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18
Q

What if n/N is less than or equal to 0.05?

A

Margin of error is given by:

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19
Q

What if n/N is greater than or equal to 0.05?

A

We use finite population correction:

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20
Q

True proportion

A

Between the observed proportion ± margin of error

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21
Q

Bias

A

Responses from sample tend to favor parts of population and aren’t representative of the whole

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22
Q

Three types of Biases

A
  1. Sampling 2. Non-response 3. Response
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23
Q

Sampling Bias

A

Results from flaw in sampling method, especially if sample is non-random

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24
Q

Under-Coverage

A

Sampling frame that lacks representation from parts of the population

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25
Q

Non-Response Bias

A

Results when some sampled subjects cannot or refuse to participate; even those willing to may only do so since they have strong views on the subject

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26
Q

Response Bias

A

Results from actual responses, may be due to the way the question is asked or if people believe their responses are socially acceptable

27
Q

Convenience Sample

A

Easily obtainable samples such as stopping people on the street

28
Q

Problem with Convenience Sampling

A

May result in serious biases

29
Q

Volunteer Sample

A

Subjects volunteer to be in survey

30
Q

Problem with Volunteer Sampling

A

Inherent biases; one segment may be more likely to volunteer due to their strong opinions

31
Q

Simple Random Sample vs. Non-Random or Convenience Sample

A

Former less likely to be affected by the biases

32
Q

Statistical Association

A

Change in one variable is companied with the change of the other

33
Q

Response Variable

A

Dependent variable, outcome that depends on the independent variable

34
Q

Explanatory Variable

A

Independent variable or covariate, which may explain or be related to the outcome

35
Q

Statistical Association and causal relationships

A

Does not necessarily provide this between the response and explanatory variable

36
Q

Lurking Variables

A

Also called confounders; associated with both the response and explanatory variable giving misleading impressions about their relationship

37
Q

Lurking Variable in the cell phone use - eye cancer - computer use example

A

Computer use

38
Q

Experimental Study

A

Researchers assign subjects to experimental conditions (treatments) based on explanatory variables and then get outcomes on response variable

39
Q

Treatments

A

Experimental condition groups based on levels of one explanatory variable or a combination

40
Q

Observational Study

A

Researches do NOT assign subjects but simply observe response and explanatory variables possessed by subjects

41
Q

Advantage of Experiments w/ causal relationships

A

Experiments can establish causal relationships because they control over lurking variables by allocating subjects to different treatments

42
Q

Disadvantage of Experiments

A

Not easy and often unrealistic to do; subjecting humans to potentially unethical treatments may cause concern

43
Q

Advantages of Observational Studies

A

Preferred especially medical field where results can be gathered without treatments or when researchers aren’t interested in assessing causality

44
Q

Experimental Units

A

Subjects in sample

45
Q

Factors

A

Categorical explanatory variables in experiment

46
Q

Levels

A

Categories of factors

47
Q

Placebo

A

Secondary no-go treatment against which effectiveness of primary treatment is tested

48
Q

Placebo Effect

A

Better responses if people are given a placebo rather than nothing

49
Q

Control Group

A

Group who receives placebo or new treatment against old

50
Q

Randomization

A

Randomly assigning experimental units into treatment groups

51
Q

Three Goals of Randomization

A
  1. Balance treatment groups 2. Eliminate effect of lurking variables 3. Reduce bias
52
Q

Double-Blinded

A

Neither subjects nor data collectors know about treatment assignment

53
Q

Nine Components of Experiment

A
  1. Response Variable 2. Explanatory Variable (Factor/Covariate) 3. Levels 4. Confounders (Lurking Variables) 5. Experimental Units 6. Levels 7. Treatments 8. Control Group 9. Randomization
54
Q

Cross-Sectional Study

A

Sample survey takes a snapshot or cross-section of population at a given point in time

55
Q

Retrospective Study

A

Observational study in which researcher looks for outcome first and then looks at covariate/explanatory variable later

56
Q

Cases

A

Group who has a particular disease or trait to be observed

57
Q

Controls

A

Group without the disease or trait

58
Q

Case-Control Study

A

Retrospective study involving cases and controls

59
Q

Estimating population percentages from Case-Control Study

A

Can’t do it since the cases and controls are often randomly decided

60
Q

Prospective Study

A

Group of subjects (cohorts) followed over time and the outcome is noted

61
Q

Systematic Sampling

A

1/m of a group of n are sampled (say 1/5 of every incoming person into a concert)

62
Q

Z-Score Reminder

A
63
Q

What to do in cases of Bias

A

New sample design/plan to remove bias

64
Q

Determining if a causal relationship is legitimate

A

Groups need to be randomized and experiments are preferred to observations since they can control for confounders

Applied Statistics I (7 decks)

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