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PSY 313: ARM > Midterm I > Flashcards

Midterm I Flashcards

1
Q

What are the 2 categories of variables?

A

Measured and manipulated

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2
Q

What are the 3 basic types of variables?

A
  • Independent
  • Dependent
  • Control
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3
Q

What is an independent variable?

A

Typically manipulated variable to evaluate what is being measured (DV). Can also be measured tho
In multiple-regression analysis, predictor variable is used to explain variance in criterion variable (DV).

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4
Q

What is a dependent variable?

A

The variable that is being measured
In multiple-regression analysis=single outcome
Criterion variable that researchers are most interested in studying or predicting.

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5
Q

What is operationalization?

A

Process of turning a construct of interest into a measured or manipulated variable. Need 2 definitions of each variable.
How to measure conceptual variable.

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6
Q

What is a conceptual definition?

A

Researcher’s definition of variable at theoretical level.

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7
Q

What is the difference between categorical and quantitative variables?

A

Categorical= consists of categories (e.g. sex)

Quantitative/ continuous variables= coded with meaningful numbers (e.g height, IQ, brain activity)

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8
Q

What are the 3 sub-types of quantitative variables/ levels of measurement? (hint: think of scales)

A

Ordinal: Rank order, distance between each result not necessarily equal
Interval: Distance of equal number between results. No true 0 (e.g IQ scale). Linearity
Ratio: Equal interval between results and true 0 (e.g exam questions/ result on test). Scale with most characteristics to it

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9
Q

What is reliability?

A

Refers to how consistent the results of a measure are

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10
Q

What is validity?

A

Refers to whether the variable was measured adequately, measuring what it is supposed to measure.

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11
Q

What are 2 statistical devices researchers can use to test reliability?

A

Scatterplots and correlation coefficient r because reliability= association claim

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12
Q

What are the 3 kinds of reliability?

A

Test-retest reliability: Study participants will get similar score each time scores are measured again
Interrater reliability: Consistent scores are obtained no matter who measured the variable
Internal reliability: Participant gives consistent pattern of answers, no matter how questions are phrased

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13
Q

What type of reliability is being assessed with this question: Does it correlate with itself?

A

Internal reliability

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14
Q

What type of reliability is being assessed with this question: Does it correlate with itself on two occasions?

A

Test-retest reliability

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15
Q

What type of reliability is being assessed with this question: Do the observers’ scores correlate with each other?

A

Interrater reliability

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16
Q

What does the r coefficient identify?

A

The strength and direction of a relationship

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17
Q

What does an r of 0 indicate?

A

That there is no relationship between the 2 variables

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18
Q

Is it true that for a measure to be valid, it must be reliable?

A

True but not opposite

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19
Q

What does good validity should do?

A

Assess and prove you’re not measuring something else

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20
Q

What are the 9 types of validity? Explain each

A

Internal: Are there better alternative explanations?
External: How representative is your sample? (can it be replicated?)
Construct: Does your measure capture the right thing? (includes face, content, criterion, convergent and discriminant)
Face: At face value, does it seem to be valid?
Statistical: How big is your effect?
Content: Did you capture all aspects?
Criterion: Does it relate well to a concrete outcome?
Convergent: Does it relate to other things it should?
Discriminant: Does it relate to other things it shouldn’t?

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21
Q

Why is it important to think about the order effects in questionnaires?

A

Because can be a threat to internal validity in a within-group design. Exposure to one condition changes responding for later conditions.
Mostly affects people’s quality of responding

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22
Q

What are the two biggest problems with response sets?

A

Response sets often used to end more quickly

  • Acquiescence/ yea-saying: Tendency to answer positively to everything. Threatens construct validity, by effectively changing your questionnaire into a measure of the tendency to think carefully.
  • Fence-sitting: Selecting naturally neutral responses. Weakens construct validity in the same way as yea- saying but can be hard to differentiate from someone who really does have a neutral opinion.
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23
Q

What could be solutions to prevent response sets?

A
  • Use forced-choice questions

- Use reverse-worded questions

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24
Q

Name 2 types of faking techniques

A

Social desirability: Trying to look better in the eyes of others
Malingering: Trying to look bad. (this can be helped by mentioning survey is anonymous

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25
Q

Can self-reports be trusted? Why?

A

Yes, people can be trusted, just need good questionnaires. Think about the questions and potential limitations.

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26
Q

What is needed for a questionnaire to be considered useful?

A 
Good Design
o Captured construct well
o Appropriate number of items
Good answers
o Order effects
o Response sets (similar answers= to identify response sets) 
o Faking
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27
Q

What are bivariate correlations/ linear correlations? What are their primary features?

A
  1. Exactly two variables
  2. Both variables are measured
  3. The minimum value is -1
  4. The maximum value is +1
  5. A value of 0 indicates no association between the variables
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28
Q

What is the range for interpreting a

Pearson Correlation Coefficient (r)?

A
  1. As low as ± .10 shows a small or weak association
  2. As low as ± .30 shows a medium or moderate association
  3. As low as ± .50 shows a large or strong association
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29
Q

If the line of best fit does not seem to work with the data, is the correlation still valid?

A

No

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30
Q

What are the 3 causal criteria?

A
  1. Covariance of cause and effect: Results must show a correlation, or association, between the cause variable and effect variable
  2. Temporal precedence: The method must ensure that the cause variable preceded the effect variable; must come first in time
  3. Internal validity: No other plausible alternative explanations for the relationship between the 2 variables.
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31
Q

What are outliers? Can they be removed from a study?

A

Outliers are unusual observations or scores that are different from the rest. Yes, but justification is needed to prevent attrition and regression to the mean. It is often not needed in a big sample.

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32
Q

What are 3 threats to experimental validity? Why?

A
  1. Design confounds: Does another variable vary systematically with the IV? (Need for control variables)
  2. Selection effects: Are different kinds of participants in the groups? (Independent-groups designs)
  3. Order Effects: Are later responses systematically affected by earlier ones? (Within-groups designs)
33
Q

What are potential solutions to manage selection effects?

A
  • Random Assignment: Everyone has an equal chance to be in whatever group
  • Matched groups: Participants who are similar on some measured variables then randomly assigned to experimental conditions.
34
Q

What could be potential solutions to prevent order effects?

A

By counterbalancing which is to present the levels of IV to participants in different sequences

35
Q

Name some of the benefits of within-group designs and independent group

A

Independent: No contamination across IV levels
Within: Requires fewer people, Individuals are their own controls (power!)

36
Q

Name some of the disadvantages of within-group designs and independent group

A

Independent: Requires more people (control group!)
Within: Order/Demand effects (participants recognize what you’re looking for), may not be possible

37
Q

What is an indicator that a correlation is statistically significant?

A

If the confidence interval does not include 0

38
Q

What is considered a bad or good number of people in an experiment for correlations?

A

Good=60

Bad=40

39
Q

What is it called when a researcher creates artificial circumstances so lower of external validity?

A

Isolated experiences

40
Q

What do we mean when we say that the results in an experiment are invalid?

A

That your manipulation or measured IV is not effective at producing a change in the outcome, but it seems like it is so Type I error
• Could be the case that manipulation seemed only somewhat effective or not at all, yet it should have been
• Threat to internal validity of our claim

41
Q

Can you remove all threats to internal validity?

A

No but can recognize their impacts

42
Q

What is the best way to identify threats to validity?

A

Comparison groups are the best way to identify these threats to validity to do something about it or at least recognize it

43
Q

What are testing effects?

A

Special case of order effects, where the testing process itself changes the outcome (such as response fatigue or guessing)

44
Q

What are history effects?

A

Some unrelated event occurs during the study, affecting the entire study or an entire subgroup (not design cofound), can’t be planned, difference between groups

45
Q

What are maturation effects?

A

Natural or spontaneous change occurring, affecting the entire study or entire subgroup (difference internal to the individual that changes results, natural changes within the person. Often in long-term research)

46
Q

What is attrition?

A

Outliers dropped out of the study, normalizing the group (not include scores from people that did not finish the study, attrition problem is solved except when have a lot of outliers because could mean no effect or that participants didn’t like the study or what was being tested)
Both pre-test groups have similar results and have a different post-test result. Look equivalent as means but not as dots. Would look like a good effect but no effect when outliers are no longer in the study.

47
Q

What is regression to the mean?

A

Extreme scores at T1 have normalized at T2 (comparison groups, random shit happens, think there was an effect)
• If significantly different from comparison group/ the mean, might indicate there was an effect.
• Want to make sure starting point= equivalent (otherwise there might be something unusual causing regression to the mean)

48
Q

What are instrument effects?

A

Decay of equipment accuracy (or coding method) (think of machinery such as computer or if person such as coder, important to train them a lot). Would only reduce reliability if random fluctuations

49
Q

What is observer bias?

A

Rating or data become adjusted to fit hypothesis. To match what you want to see (could be intentional or not) Solution: Don’t tell hypothesis to research assistant and participants

50
Q

What are demand effects?

A

(Demand Characteristics) Participants attempt to produce the hypothesized outcome (if figure out hypothesis, want to be good participants)
Solution: Asking participants what they believe the hypothesis of the study was. Diminished by using a between-subject design

51
Q

What is a placebo effect?

A

Participants will themselves to change (improve). Especially when a drug is involved
Solution: Was there a placebo group?, see similar effect, have a 3rd comparison group

52
Q

In which study design can we trust the figure?

A

Within-subject: Need to look at the stats, can’t trust figure
Between-subject: Can trust the figure

53
Q

Which questions should we ask ourselves when trying to detect threats?

A

o Which part of the hypothesis is being addressed?
o What was the outcome?
o Is the outcome consistent with the hypothesis?

54
Q

What can a control group do?

A

Can explain if there’s an alternative explanation
o Rules out potential selection effect
o Control group makes you confident that you can rule out alternatives.

55
Q

What is the difference between attrition and regression to the mean?

A

Regression to the Mean: One group as more extreme scores at the beginning than the other one but at post-test, there is no difference (=regression to the mean)
Attrition: Both pre-test groups have similar results and have a different post-test result. Look equivalent as means but not as dots. Would look like a good effect but no effect when outliers are no longer in the study.

56
Q

Which threats to validity are most likely to occur?

A

Regression to the mean, Testing and Demand effects are very likely (demand effects especially in letter of informed consent when stating hypothesis)

57
Q

Which threats to validity are very unlikely to occur?

A

History, Attrition, and Instrumentation Effects are pretty unlikely

58
Q

Is the placebo effect likely to be a threat to validity?

A

In the scenario where it is involved, yes!

59
Q

What does critiquing other people’s work allow us?

A

Helps us to avoid accepting bad arguments as valid ones

Helps us recognize ways to improve for follow-up studies

60
Q

What is the 3rd variable problem? How do we deal with it?

A

When there are plausible alternative explanations that can come in form of alternative causal variables
Can hold them constant as control variables in an experiment (see differences in IV and DV, and verify by how much it deviates= identify 3rd variable)

61
Q

What could we do if we can’t manipulate a variable we want to control (3rd variable)?

A

o Multiple Regression

o Partial Correlation (can deal with more 3rd variables at once)

62
Q

What is multiple regression good for?

A

Answer questions about an association (or set of associations) while controlling (correcting or taking into account) some other association

63
Q

How can multiple regression improve internal validity?

A

a. Can take into account a potential confound= improve internal validity
b. If determine preferred causal variable has stronger influence than another cause= improve internal validity

64
Q

What does it mean when after doing the multiple regression there is nothing left?

A

Means that there is no association between IV and DV

65
Q

In the results section, what does a capital R mean?

A

Overall, how well can we predict the outcome can be explained by all the variables combined. Betas is a better predictor.

66
Q

What are the 2 other analyses that can be done with multiple regression?

A
  • Mediation

- Moderation

67
Q

Explain mediation

A

Occurs when other variables help us better understand the nature of the (causal) association between 2 variables.

68
Q

Is the 3rd variable a problem in mediation?

A

No! It’s the mediator that helps us better understand the nature of the relation between IV and DV

69
Q

What is partial mediation?

A

Something that is somewhat affecting the outcome (on IV and DV)

70
Q

What is the difference between mediation and moderation?

A

Mediation: Mediation occurs when some other variable is an intermediary in a causal association
Moderation: Moderation occurs when some other variable controls whether your association does or does not appear, involves causal association

71
Q

Are correlations good at establishing temporal precedence?

A

No

72
Q

What can we do to establish temporal precedence?

A

Longitudinal designs help address the problem of temporal precedence with correlations

73
Q

Why are longitudinal designs good at establishing temporal precedence? How does it work?

A

o Follow participants over longer periods of time (which can be hard though)
o Lot more work than cross-sectional study designs (all done at same time)
o If 2 variables, use of correlations (across variables and when know not manipulating something)
o In longitudinal, still have cross-sectional designs because holding it constant every time by being measured at the same time

74
Q

What are autocorrelations?

A

Correlation with itself, correlation with the same variable overtime, self-correlations

75
Q

What are cross-lagged correlations?

A

Correlating 2 variables across time

76
Q

What is parsimony?

A

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

77
Q

What is a pattern?

A

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

78
Q

What happens when you have both a pattern and pasimony?

A

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

PSY 313: ARM (1 decks)

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