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Evaluative research > Evaluative research exam 1 > Flashcards

Evaluative research exam 1 Flashcards

1
Q

what are the features of Random Assignment

A

People assigned to conditions based on chance.
Each person has a nonzero probability of being assigned to any given condition.
Random sampling is a different thing

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2
Q

What does Random assignment do for your study

A

Evenly distributes participant characteristics across conditions.

Rules out selection threat.

Makes it unlikely that other threats to internal validity are confounded with condition.

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3
Q

Basic random assignment design

A

subjects randomly assigned to treatment (Could be more than one) or control condition(or other treatment condition).
Post-test measure of outcome

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4
Q

Potential problem with random assignment

A

Attrition-people dropping out
Feasibility in terms of ethics and time or availibility of people that meet the criteria
it is difficult to randomize correctly
participants receive assigned treatment and no other

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5
Q

Why is attrition a problem in a random assignment design?

A

The people who drop out could be different in important ways (for particular conditions or the study as a whole) and their leaving could threaten validity.
Pretest helps assess the extent of the problem

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6
Q

Pretest-posttest design

A

Same as basic random assignment with a pretest condition, which could be used as a covariate or a repeated measures ANOVA can be performed.

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7
Q

Factorial design

A

Comnine two or more independent variables (factors) that each have at least two levels.
Major advantage: Test interactions along with main effects

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8
Q

Longitudinal design

A

Multiple pretest and posttest measurements of coutcome variable.
Show changes over time

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9
Q

Problems with longitudinal design

A

attriction, and can be unethical to withhold treatment for long periods of time

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10
Q

Crossover design

A

Two groups (randomly assigned), two treatment levels, put each group through both treatment conditions at different times: R O Xa O Xb O and R O Xb O Xa O

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11
Q

list the different Random assignment designs

A

Basic, Pretest-posttest, Factorial, Longitudinal, crossover

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12
Q

When would you use random assignment in feild research

A

when demand is greater than supply

when a treatment can’t be delivered to everyone at once
when temporal isolation is possible
when people are spatially seperated or don’t communicate much
when change is needed but it’s unclear which solution will work best
When there is ambiguous need
when some people have no preference among alternatives
when you can create your own organization
when you have control over experimental units
when loteries are expected

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13
Q

When should you not use random design

A

Short on time
research question is not about causation
impossible or unethical to manipulate the IV
More conceptual or empirical work must be done to determine whether an experiment is a good use of resources

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14
Q

What are some techniques of randomization

A

Simple random assignment
Restricted random assignment to force equal cell sizes
restricted random assignment to force unequal cell sizes
Haphazard assignment

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15
Q

Regression discontinuity design

A

subjects assigned to condition on the basis of a cutoff score on assignment variable
assignment variable mus be continuous and taken before the treatment
post-test measure after treatment

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16
Q

Interrupted time series

A

Building on the pre-test post test design by increasing number of both
the interuption is the treatment
time series because pretest and posttest measurements are taken at intervals
100 is a generally acceptable number of data points

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17
Q

interupted time series designs are vulnerable to what threats of validity?

A

History (biggest)-to alleviate make measurements intervals smaller.
Instrumentation
attrition

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18
Q

ITS additions to increase validity

A 
Nonequivalent control group
Nonequivalent DV
Introducing and then removing treatment
Multiple replications
Switching replications
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19
Q

ITS potential difficulties

A

Gradual interventions
Delayed causation
Short time series
limitations of archival data

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20
Q

Quasi-experiments

A

attempt to test causal hypothesis

called “quasi” because they lack random assignment

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21
Q

How do you determine causation in quasi-experiments?

A

Cause precedes effect
cause co-varies with effect
Alternative explanations are implausible

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22
Q

Validity

A

The approximate truth of an inference
A matter of degree
A property of inferences, not designs or methods

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23
Q

Four types of validity

A

Statistical conclusion
Internal
Construct
External

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24
Q

Statistical Conclusion Validity

A

Validity of inferences about the covariance between treatment and outcome

I.E. How large and how reliable is the co-variation between the presumed cause and effect?

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25
Q

Methods of assessing statistical conclusion validity

A

NHST: P values, Effect size and confidence intervals

26
Q

P values

A

tell us the probability that the results observed in an experiment could have been obtained by chance from a population in which the null hypothesis is true

27
Q

Threats to statistical conclusion validity

A 
Low statistical power
Violated assumptions of statistical tests
Fishing and error rates
Unreliability of measures
restriction of range
Unreliable treatment implementation
extraneous variance in setting
Heterogeneity of respondents
inaccurate effect size estimation
28
Q

Power

A

The probability that a statistical test will reject the null hypothesis when it is actually false and should be rejected
Power is usually set at .80

29
Q

Fishing and error rate

A

Increasing the number of statisitical tests increases probability of a type 1 error

30
Q

Restriction of range

A

Independent variable-Levels too few or too similar

Dependent variable-categorical instead of continuous:floor or ceiling effects

31
Q

Internal validity

A

Validity of the inference that the treatment as implemented had a causal effect on the outcome as measured

Is the co variation causal or would the same co variation have been obtained without the treatment?

32
Q

Local molar causal validity

A

Local- causal conclusions are limited to the particular treatments, outcomes, settings, and persons studied.
Molar-treatments are a complex package with many components (INUS conditions)
Causal-did the treatment as implemented cause changes in the outcome as measured?

33
Q

Threats to internal validity

A 
Ambiguous temporal precedence
Selection
history
maturation
regression to the mean
attrition
Testing
instrumentation
34
Q

Selection threat

A

Participant characteristics are confounded with treatment condition

35
Q

History

A

Occurs when an event outside of the study could have produced the outcome

36
Q

Maturation

A

when natural changes in participants could have produced the outcome

37
Q

Regression to the mean

A

Extreme scores at one point (or measure) tend to be followed by scores closer to the mean

when people are selected for a study because of a high school, a lower score after treatment could be regression instead of treatment

38
Q

Testing

A

Taking a test once can lead to participant changes that are responsible for an observed effect

39
Q

Instrumentation

A

Changes over time in measuring instruments might be responsible for an observed effect

40
Q

Construct Validity

A

Validity of inferences about higher order abstract constructs based on specific manipulations and operationalizations

Which general constructs are involved in the persons, settings, treatments, and observations used in the experiment?

41
Q

Ways to increase construct validity

A

Pilot testing
manipulation checks for IV
explication of constructs
using existing, workable manipulations and measures

42
Q

Inadequate explication of constructs

A

Failure to adequately explicate a construct may lead to incorrect inferences about the relationship between operation and construct

43
Q

Construct confounding

A

Operations usually involve more than one construct and failure to describe all the constructs may result in incomplete construct inferences

44
Q

mono-operation bias

A

Any one operationalization of a construct both under-represents the construct of interest and measures irrelevant constructs, complication inference

45
Q

Mono-method bias

A

When all operationalizations use the same method (e.g. self report) that method is part of the construct actually studied

46
Q

Confounding constructs with levels of constructs

A

Inferences about the construct that best represent study operations may fail to describe the limited levels of the construct that were actually studied

47
Q

Treatment sensitive factorial structure

A

The structure of a measure may change as a result of treatment change that may be hidden if the same scoring is always used

48
Q

Reactive self report changes

A

Self reports can be affected by participant motivation to be in a treatment condition motivation that can change after assignment is made

49
Q

Reactivity to the experimental situation

A

Participant responses reflect not just treatments and measures but also participants’ perceptions of the experimental situation and those perceptions are part of the treatment construct actually tested

50
Q

Experimenter expectancies

A

The experimenter can influence participant responses by conveying expectations about desirable responses and those expectations are part of the treatment construct as actually tested

51
Q

Novelty and disruption effects

A

Participants may respond unusually well to a novel innovation or unusually poorly to one that disrupts their routine a response that must then be included as part of the treatment construct description

52
Q

Compensatory equalization

A

When treatment provides desirable goods or services administrators, staff , or constituents may provide compensatory goods or services to those not receiving treatment and this action must them be included as part of the treatment construct

53
Q

Compensatory rivalry

A

Participants not receiving treatment may be motivated to do as well as those receiving treatment and this must be included in the treatment construct description

54
Q

Resentful demoralization

A

Participants not receiving the treatment may become so resentful or demoralized that they respond more negatively that otherwise

55
Q

Treatment diffusion

A

Participants may receive services from a condition to which they were not assigned

56
Q

External validity

A

Validity of inferences about generalizing the cause-effect relationship to other person. settings, treatments, and measurements

How generalizable is the locally embedded causal relationship over variation in persons treatment, observations, and settings?

57
Q

Four types of replication

A

Exact replication
conceptual replication
constructive replication
participant replication

58
Q

Conceptual replication

A

Investigates the relationship between the same conceptual variables studied in previous research using different operational definitions

59
Q

Constructive replication

A

Tests the same hypothesis of original experiment with added condition(s) to asses specific variables that might change the previously observed relationship

60
Q

Participant replication

A

Conduct the original experiment using a new type of participants

Evaluative research (1 decks)

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