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Advanced research methods > Midterm 1 > Flashcards

Midterm 1 Flashcards

1
Q

Research variables

A

Independent: typically manipulated
Dependent: always measured
Control variable

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2
Q

Experiment (3 steps)

A

Hypothesis
Experimental manipulation (independent variable)
Interesting outcome

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3
Q

Categorical

A

Levels are categories

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4
Q

Ordinal

A

Quantitative variable

Numerals represent a rank order, but distance is not equal

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5
Q

Interval

A

Quantitative variable

Numerals have an equal distance, but no real zero

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6
Q

Ratio

A

Quantitative variable

Numerals have an equal distance but there is a real zero

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7
Q

Reliability

A

Consistency of results over time
Less spread: more reliable
How well we’ve measured something in a technical sense irrespective of what it is we’re measuring

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8
Q

Internal reliability

A

Internally consistent

Correlate with itself

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9
Q

Test-retest

A

Same results over time

Correlate with itself on 2 occasions

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10
Q

Inter-rater

A

Two or more observers agree (scores correlate)

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11
Q

Validity (definition)

A

We have properly captured the construct of interest

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12
Q

Link between validity and reliability

A

For a measure to be valid, it has to be reliable

For a measure to be reliable, it doesn’t have to be valid

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13
Q

Assessing validity (4 questions to ask yourself)

A
  1. Is it possible you’re measuring something else
  2. Are you measuring some part innacurently
  3. Are you measuring all important aspects
  4. Are you measuring it well enough to suit your intended argument
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14
Q

Internal validity

A

Is there a better alternative explanantion?

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15
Q

Construct validity

A

Capture the right thing

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16
Q

Statistical validity

A

How big is the effect

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17
Q

External validity

A

Representative sample

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18
Q

Face validity

A

Does it seem to be valid

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19
Q

Content validity

A

Capture all the aspects

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20
Q

Criterion validity

A

Relate to a concrete outcome

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21
Q

Convergent validity

A

Relate to other things it should

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22
Q

Discriminant validity

A

Not relate to other things it shouldn’t

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23
Q

Good design

A

Captured construct well

Good number of items

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24
Q

Good answers

A

Order effects
Response sets
Faking

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25
Q

Order effect

A

Order of items in a questionnaire matter, so the order of questionnaires within a study, establishing a context for the participant

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26
Q

Acquiescence - Response set

A

Tendency to answer positively to all questions

Threatens to construct validity

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27
Q

Fence-sitting - Response set

A

Selecting neutral answers

Weakens construct validity

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28
Q

Social desirability - Faking

A

Trying to look better in someone’s eyes

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29
Q

Malingering - Faking

A

People try to look bad
May try to say the survey is anonymous, but participants may not take it seriously
Include some reverse questions

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30
Q

Self-reports (4 reasons why they are not good)

A

Untrustworthy and useless because

  1. The order of questions can affect the participants
  2. The order of questionnaires can affect the participants
  3. People don’t answer thoughtfully
  4. People try to emphasize that they are good or bad
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31
Q

Can we believe participants

A

Yes if we ask good questions

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32
Q

Correlations

  • what is it
  • the famous quote
A

The simplest form of association claim (bivariate correlation, zero-order correlation, linear regression)
A strong association doesn’t imply causation
If there’s is no correlation there’s no causation

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33
Q

Features of correlations (4)

A

Only 2 variables
Both variables are measured
Min of -1 and max of +1
0 indicates no correlation

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34
Q

Understanding correlations (value of person’s r)

A 
Use Pearson's r
0,10= small
0,30= medium
0,50= strong
If it's smaller or bigger than expected, this might be a sign that there's a problem with our theory
35
Q

If one variable is an IV and the other is DV, can correlation demonstrate causation?

A

Yes

This is true even if your IV was only measured, not both measured and manipulated

36
Q

Questioning validity (what’s the assumption)

A

Assume that our sample is one homogeneous group, but they can vary relatively normally on both measures

37
Q

Not homogeneous sample

A

May have some important problems
The sample has two or more subgroups that are different from each other
It can happen without any sign and detecting when it’s a problem can be difficult

38
Q

Internal validity of the correlation (sample size)

A

Outliers can influence internal validity, especially with small samples
Better to have large samples so we can increase our confidence

39
Q

Rules of causation (3)

A

Covariance: cause (IV) must be related to the effect (DV)
Temporal precedence: cause before the effect
Internal validity: no other plausible explanation

40
Q

Problems establishing covariance (think about the graph)

A

Correlations detect straight line association even when there is no association

41
Q

Threats to experimental validity (3)

A

Design confounds, selection effects, order effects

42
Q

Design confounds (variable)

A

Does another variable vary systematically with the IV (need for control variable)

43
Q

Selection effects

A

Are different kinds of participants in the groups (independent-group design)
Error while selecting the participants to participate in a study

44
Q

Order effects

A

Are later responses systematically affected by earlier ones (within-groups design)

45
Q

Managing selection effects (2)

A

Random assignment

Matching

46
Q

Preventing order effects

A

For within-group design, we can encounter fatigue, practice effects, or carryover effects
Best solution is counterbalancing

47
Q

Multiple groups

A

Posttest only

Pretest and posttest

48
Q

Single group

A

Concurrent measures

Repeated measures

49
Q

Independent-groups

A

Requires more people (control group)

No contamination across IV levels

50
Q

Within-groups

A

Requires fewer people
Participants are their own controls (power)
Order and demand effect
May not be possible

51
Q

Confidence intervals

A

Confidence around the mean, correlation
If it includes 0, it’s not statistically valid
Large sample for statistical validity

52
Q

When IV doesn’t have an effect

A

When manipulation is not effective at producing a change in the outcome, but it seems like it is
Manipulation seemed somewhat effective or not at all when it should have been

53
Q

Type I error

A

False positive

Reject the hypothesis when it’s true

54
Q

Type II error

A

False negative

Fail to reject the hypothesis when it’s false

55
Q

Threats to internal validity

A

Can’t always eliminate them all, need to recognize their impact
Threats can overlap each other

56
Q

Testing effects

A

Special case of order effect
Testing process itself changes the outcome
Can happen in within-subject design

57
Q

History effects

A

Unrelated event occurs during the study affecting the whole study and group
Can happen any time, impossible not to happen in longitudinal

58
Q

Maturation effects

A

Natural or spontaneous change occurring

Especially with kids

59
Q

Comparisons groups

A

Best way to identify the presence of threats to internal validity
Eliminate if plausible alternatives occurred

60
Q

Attrition

A

Outliers dropped out of the study, normalizing one group
Can’t include the scores of people out dropped out of the study
Not applicable because we get rid of someone who didn’t complete the whole study

61
Q

Regression to the mean

A

Extreme scores at T1 are normal at T2

Can happen if the mean shifts overtime

62
Q

Instrument effects

A

Decay of equipment accuracy or coding method

Can happen over a long period of time

63
Q

Attrition vs regression to the mean

A

Pre rest looks about the same
1 person vs entire group
Group looks equivalent based on means but the number of dots doesn’t (unusual score doesn’t mean unusual mean)
Unusual score has a big effect on the mean, need to take out the unusual score of BOTH groups
Need to justify every time we take out a score

64
Q

Observer bias

A

Ratings (or data) become adjusted to fit the hypothesis, researchers manipulate the data
Double-blind condition
Depends if we have observers

65
Q

Demand effects

A

Participants attempt to produce the hypothesized outcome (participants change their answers to help the researchers)
Need to ask them at the end of the study their idea of the hypothesis
Between subject-design instead of within
Can happen if participants have more time to figure out the purpose of the study

66
Q

Placebo effects

A

Participants can will themselves to improve, change
Control condition can tell us how big the placebo was
The downward slope doesn’t guarantee a placebo effect
Depends if we are manipulating something or not

67
Q

Probabilities

A

Not all threats are equally likely
Regression to the mean, testing, and demand effects are VERY likely
History, attrition, and instrumentation effects are pretty unlikely
Maturation and observer bias can be likely in the right contexts
Placebo is very likely in the right context

68
Q

Recognizing threats (2 reasons why they help us)

A

Understanding the threats is very important
Help us to avoid accepting bad arguments as valid ones
Help us recognize ways to improve for follow-up studies

69
Q

Third variable

A

Plausible alternative explanations can come in the form of alternative causal variables
Try to hold them constant as control variables

70
Q

Multiple regression

  • what does it help us with
  • link with internal validity
  • when is it used
  • whats the letter
A 
Helps us answer questions about an association while controlling some other association
If we take into account a potential confound, we improve internal validity 
If we determine our preferred causal variable as a stronger influence than another cause, we improve internal validity 
It is used when we want to predict the value of a variable based on the value of two or more other variables
Standardized beta (same principle as r)
R= combination of all factors
71
Q

Recognizing the use of MR (3 expressions)

A

controlling for the effect of Z
taking into account the effect of Z
correcting for Z

72
Q

Third variable problems

  • role of MR
  • allow us to do what
  • how is the argument?
A

MR lets us control for other unwanted influences on the association of our IV and DV (Take the bivariate association and rule out any other influences we can think to measure )
Allows us to make more complex arguments
Argument is always causal when we use MR (always a predictor and outcome variable)

73
Q

Other uses of MR

A

Not only good for ruling out third variable
Mediation
Moderation

74
Q

Mediation

A

Occurs when some other variable helps us better understand the nature of the causal association between 2 variables
Third variable is present but it’s not a problem
Mediators are involved in the causal association

75
Q

Mediation vs third variable problem

A

Mediators are a part of the causal chain that connects an IV to a DV
A third variable is the only cause that makes an IV appear to be linked to a DV

76
Q

Moderation

A

Occurs when some other variable controls whether your association does or doesn’t appear
Moderators dictate what kind of association can exist between the IV and DV

77
Q

Correlation and temporal precedence

A

Correlations are good at establishing covariance (MR will improve internal validity)
Correlations are not good at establishing temporal precedence

78
Q

Longitudinal designs

  • problem it addresses
  • what does it do
  • when is it useful
A

Help address the problem of temporal precedence with correlations
Follow participants over a long period of time
More work than cross-sectional design
Useful when experimental design is not possible
If 2 variables use correlations across variables

79
Q

Cross-sectional correlations

A

Only calculate correlation at one-time point

Used to examine if changes in one or more variable are related to changes in another variable(s)

80
Q

Autocorrelations

A

Self-correlations, correlation with same variable

Correlation with itself

81
Q

Cross-lagged correlation

A

Across time and different variables

Study in which two variables are measured once and then again at a later time

82
Q

Parsimony

A

The degree of simplicity in a theory
Occam’s Razor states that all else being equal, the solution that makes the fewest assumptions is usually the correct one

83
Q

Pattern

A

Demonstrating that a series of different findings (preferably different studies) converge on the same solution

84
Q

Pattern and parsimony

A

If we combine the two we really have something
If multiple studies show support for the same simple conclusion, it’s much more likely that your proposed causal association is correct

Advanced research methods (2 decks)

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