Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Stats 92F > Stats > Flashcards

Stats Flashcards

1
Q

Define: Control Group

A

A group that is similar to the group receiving the intervention.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Types of Experiment?

A

Completely Randomised Block Design

Randomised Block Design

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Define: Completely Randomised Block Design

A

Treatments are allocated entirely by chance.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Define: Randomised Block Design

A

Group paprticipants according to some characteristic and then randomise within each block to balance out unknown factors.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Define: Single Blinding

A

Either the participant knows or the researcher knows who has received which treatment.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Define: Double Blinding

A

Neither the researcher nor the participant knows who has received which treatment. In this situation participants are randomly allocated to treatment groups for the researchers by a thrid party.
Only after measurements are treatment groups revealed.
Double Blinding > Single Blinding

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Define: Placebo

A

This is essentially no treatment, but a participant will not know that they have not received any treatment.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Define: Placebo effect

A

This is an observed actual response to receiving a placebo.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Define: Response Variables

A

Variables of interest in your experiment. The values of these variables will be measured during the experiment.
I.e. Response to a pill or diet might be measured.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Define: Explanatory Variables

A

The variable that attempts to explain or cause the changes observed in the response vairable.
I.e. This might be a new drug or diet. All other variables must be unchanged.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Define: Treatment

A

The intervention that the experiment is attempting to observe.
I.e. Drug dosage, food supplement, fertiliser.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Define: Randomisation

A

Ensures that all participants are equally likely to receive treatments.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Define: Confounding or Lurking Variable

A

A variable that is not under scrutiny but affects the results of the experiment.

I.e. “People who drink more alcohol hace a shorter life expectancy”, confounding variable could be smoking.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Define: Replications

A

The number of times an experiment is repeated.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

What makes a good Experiment?

A
  1. Random Allocation to treatment groups
  2. Use of a Control Group
  3. Use of a Placebo
  4. Use of Blinding
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Define: Random Allocation

A

Used in experiments to ensure that all experimental unit have the same probability of receiving each treatment option.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Define: Hawthorne Effect

A

The Hawthorne effect is a type of reactivity in which individuals modify an aspect of their behavior in response to their awareness of being observed.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Types of Observational Studies

A

Cross-sectional

Longitudinal

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Define: Cross-sectional

A

This type of study provides a snapshot of a group and is largely descriptive in nature.

20
Q

Define: Longitudinal

A

A study which observes a group of people or other units over a long period of time. usually at regular intervals. A longitudinal study can be considered as a sequence of cross-sectional studies.

21
Q

Potentioal Problems with Obervational Studies

A
  1. Confounding Variables
  2. Extending results inappropriately
  3. Using the past as a source of data.
22
Q

Define: Confounding Variables

A

Confounding variables and implications for causation- in an observational study it is impossible to serparate out all potential confounding factors as random allocation of treaments has not been used.

Thus a Causal link cannot usually be claimed from an observational study.

23
Q

Define: Extending results inappropriately

A

Many observational studies use convenience samples which cannot be regarded as representative of a wider population. results of a study can only be extended to a larger population if the sample is representative of that larger population.

24
Q

Define: Using the past as a source of data.

A

Any study that uses the past as a source of data is known as a retrospective study. Such studies rely on people’s ability to recall information about past events accurately - which many people find hard to do! One option to overcome this is to use medical record, if appropriate, as a data source. An additional problem is that overtime the influence of confounding variable may change.

25
Q

Types of Sampling techniques

A
  1. Simple Random
  2. Statified Random
  3. Cluster
  4. Convenience
  5. Quota
  6. Vox Pop
26
Q

Define: Simple Random

A

Random Sample from a whole population.

27
Q

Define: Advantages and Disadvantages of Simple Random

A

A: Highly representative if all subjects participate. (the Ideal)
D: Not Possible without complete list of population members; potentially uneconomical to achieve; can be disruptive to isolate members for a group; time-scale may be too long, data/sample could change.

28
Q

Define: Statified Random

A

Random sample from identifiable groups (strata, subgroups, etc.

29
Q

Define: Advantages and Disadvantages of Statified Random

A

A: Can ensure that specific groups are represented, even proportionally, in the smaple(s) (e.g., by gender) by selecting individuals from strata list.
D: More complex, requires greater effort than simple random; strata must be carefully defined.

30
Q

Define: Cluster

A

Random samples of successive clusters of subjects (e.g., by institution) until small groups are chosen as units.

31
Q

Define: Advantages and Disadvantages of Cluster

A

A: Possible to select randomly when no single list of population members exists, but local lists do; data collected on groups may avoid introduction of confounding by isolating members.

D: Clusters in a level must be equivalent and some natural ones are not for essential characteristics(e.g., geographic numbers equal, but unemployment rates differ).

32
Q

Define: Convenience

A

Hand-pick subjects on the basis of specific characteristics.

33
Q

Define: Advantages and Disadvantages of Convenience

A

A: Ensures balance of group sizes when multiple groups are to be selected.

D: Samples are not easily defensible as being representative of populations due to potential subjectivity of researcher

34
Q

Define: Quota

A

Select individuals as they come to fill a quota by characteristics proportional to populations

35
Q

Define: Advantages and Disadvantages of Quota

A

A: Ensures selection of adequate numbers of subjects with appropriate characteristics.

D: Not possible to prove that the sample is representative of designated population.

36
Q

Define: Vox Pop

A

Just survey people on the street or by phone

37
Q

Define: Advantages and Disadvantages of Vox Pop

A

A: An inexpensive way of ensuring sufficient numbers of a study
D: Can be highly unrepresentative.

38
Q

Define: Sampling Errors

A

A sampling error is an error that arises during data collection as a result of the process of taking a sample from a population rather than using the whole population.

39
Q

Types of Sampling Errors

A
  1. Selection Bias
  2. Self Selection Bias
    ( It is possible to determine the size of your sampling errors.)
40
Q

Define: Non-Sampling Errors

A

Non-Sampling errors arrise during the data collection process as a result of factors other than those arising from sampling.

41
Q

Types of Non-Sampling Errors

A
  1. Non-response bias
  2. Queation effects
  3. Behavioural considerations
  4. Interviewer Effects
  5. Transfer of Findings
  6. Survey Format effect
  7. Interviewer or Interviewee fatigue.
42
Q

Define: Margin of Error

A

Estimate ± margin of error = Confidence Interval

43
Q

Types of Margin of Error

A
  1. Margin of error (1st Group) 1 ÷ √(n)
  2. Margin of error (Between groups ((1 ÷ √(n) + 1 ÷ √(n)) /2)

Between one group is 2x
Between independant groups is 1.5x

44
Q

What makes up an Experiment?

A
  1. Explanatory variable specified
  2. Response variable specified
  3. Participants randomly allocated to treatments
  4. Treatment is the only factor that varies
  5. Causal Claim can be made.
  6. Changes in response variable recorded.
  7. Good Experiments include random allocation to treatments, control groups, placebos and use blinding
45
Q

What makes up an Oberservational Study?

A
  1. Explanitory variable specified
  2. Response variable specified
  3. Participants randomly allocated to treatments, may be unethical
  4. Causal Claim cannot be made.
  5. Changes in response variable recorded.
  6. Good Observational studies acknowledge and account for all potential problems.
46
Q

What are the differences between an Experiment and Observational Study?

A

The most important distinction between the two types of study comes from the inferences they support. An observational study might reveal that on average women live longer than men but cannot reveal why. Often the relationship found by an observational study precedes an experiment.

Stats 92F (5 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions