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PSYCH 225: Research Methods > Final > Flashcards

Final Flashcards

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1
Q

three components of ethics

A
  1. Human participants
  2. Non-human subjects
  3. Academic integrity
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2
Q

nuremberg code

A

Ten core ethical principles that drive research laws in many countries

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3
Q

belmont report

A

Three main principles for ethical decision making

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4
Q

belmont report

A

Three main principles for ethical decision making:
1. Respect for persons
2. Beneficence
3. Justice

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5
Q

Why are the ethical principles broad rather than specific?

A

Meant to represent core values that serve as the basis for developing specific rules that can be applied & refined at different times & for different situations

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6
Q

what shapes how ethical guidelines evolve?

A

-Changes in ethical codes by groups/nations/orgs
-Changes in federal laws
-Public input in response to proposed changes
-Public demand for changes

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7
Q

who are interest holders?

A

Who is potentially affected by the decision

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8
Q

beneficence

A

-“Doing good”
-Researchers must strive to protect participants from harm (Study designs should minimize risk; Procedures should assess risk & harm)
-Researchers must consider how the community will be helped or harmed (Consider the costs of NOT doing the research as well)

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9
Q

justice

A

-Research should involve a fair distribution of harms & benefits across different types of people
-Fair balance between people that participate & people that benefit
-Are the participants representative of those who stand to benefit?
-Ensure that participant recruitment is inclusive

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10
Q

respect for persons

A

People should be free to decide whether to participate

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11
Q

informed consent

A

Potential participants have the right to learn about the research & its potential costs/benefits before deciding whether to participate

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12
Q

special protections for vulnerable pops:

A

-Children
-People with intellectual & developmental disabilities
-Prisoners

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13
Q

special protections for children

A

Children are given the opportunity to leave studies at any time (provide assent)

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14
Q

special protections for prisoners

A

-Power dynamics involving race (institutional systematic racism)
-Coercion & restriction of freedom
-Drug dependence/addiction & mental illness are disproportionately high (Some studies could exacerbate these)

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15
Q

research with animal subjects (the three Rs)

A

-Replacement
-Refinement
-Reduction

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16
Q

replacement (animals)

A

Find alternatives to using animals when possible

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17
Q

refinement (animals)

A

How could research procedures & aspects of animal care be altered to reduce animal distress?

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18
Q

reduction

A

Use the fewest number of animals as possible

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19
Q

types of deception

A

-Omissions (Withholding details of a study from participants)
-Commission (Lying to participants)

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20
Q

when can deception be used?

A

When it is justified & there is no alternative

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21
Q

when must debriefing be done?

A

-When deception is used
-When scientists feel a responsibility to explain their study to people (even without deception)

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22
Q

what must debriefing include?

A

-Must include description of & rationale for deception
-Must “correct” any false information given

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23
Q

institutional review boards (IRBs)

A

decide if a research practice is unethical

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24
Q

IRBs are composed of:

A

-At least five members of varying backgrounds
-At least one scientist
-At least one non-scientist
-At least one community representative

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25
Q

how are IRB members selected?

A

-Often volunteer
-Community members might see notices (soliciting)

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26
Q

types of IRB review

A

-Exempt
-Expedited
-Full review

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27
Q

IRB exempt review

A

Very little risk

Includes research conducted in education settings for educational purposes, archival studies, or where there are no risks to people

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28
Q

IRB expedited review

A

Minimal risk

No potentially risky manipulations or invasive procedures

Little to no emotional impact

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29
Q

IRB full review

A

Manipulations, special populations, invasive procedures, high risk studies, deception

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30
Q

three requirements of causation:

A
  1. Covariance (Are the variables systematically related?)
  2. Temporal precedence (Does a change in one variable always come before a change in the other variable?)
  3. Internal validity (Are alternative explanations sufficiently ruled out?)
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31
Q

maturation

A

Experimental group changes over time, but only because of natural development or spontaneous improvement/decline

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32
Q

history (internal validity threat)

A

Experimental group changes over time because of an external factor or event that affects most members of the group

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33
Q

how to rule out maturation & history threats?

A

Using the right comparison groups

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34
Q

types of comparison groups

A

-Control
-Placebo
-Treatment
-Wait list control

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35
Q

control group

A

Level of an IV representing a neutral condition

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36
Q

placebo group

A

Control group that is exposed to a fake or inactive treatment

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37
Q

wait list control group

A

A control group that receives the same treatment/intervention as the treatment group, but not until after the treatment group

-Allows for isolation of the IV & comparison of groups
-Allows for individuals in control groups to obtain treatment

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38
Q

control variables

A

A variable that an experimenter holds constant on purpose

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39
Q

confound

A

An alternative explanation

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40
Q

design confound

A

A second variable that happens to vary systematically along with the intended IV

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41
Q

two main types of experimental designs

A

-Between-subjects design
-Within-subjects design

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42
Q

between-subjects design

A

Different groups of participants are placed into different levels of the IV (only experience one level/condition of the IV)

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43
Q

posttest only design (between)

A

Random assignment to a group => Treatment/IV applied => DV measured

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44
Q

pretest/posttest design (between)

A

Random assignment to a group => DV measured => Treatment/IV applied => DV measured again

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45
Q

when may a pretest/posttest design be used?

A

If you want to see how large the improvement/decline is (can track change over time)

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46
Q

selection effects

A

The kinds of participants in one level of the IV are systematically different from those in the other

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47
Q

way to avoid selection effects:

A

Matched groups

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48
Q

matched groups

A

Participants in different conditions are matched on an extraneous variable

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49
Q

extraneous variable

A

A variable that is not the IV, but has the potential to affect the DV

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50
Q

within-subjects design

A

Each participant is presented with all levels of the IV

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51
Q

concurrent measures design (within)

A

Participants exposed to all levels of the IV & DV measured, all around the same time

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52
Q

repeated measures design (within)

A

Participants are measured on a DV more than once, after exposure to each level of the IV

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53
Q

order effects

A

Being exposed to one condition change how participants react to another condition (“carryover effects”)

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54
Q

testing effects

A

Type of order effect in which an experimental group changes over time due to repeated testing affecting participants

Fatigue or practice

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55
Q

fatigue effects

A

Type of testing effect in which participants get tired/bored over time

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56
Q

practice effects

A

Type of testing effect in which participants get better at the task over time

57
Q

how to avoid order effects:

A

counterbalancing

58
Q

counterbalancing

A

Levels of the IV are presented to participants in different sequences

59
Q

full counterbalancing

A

All possible condition orders are tested

60
Q

partial counterbalancing

A

Only some possible condition orders are tested

If many IVs/levels, a limited sample size, the nature of the experiment doesn’t allow certain orders to occur

61
Q

demand characteristics

A

Participants guess what the study’s purpose is & change their behavior in the expected direction (most likely in within-subjects designs)

62
Q

within-subjects pros

A

-Participants in groups are equivalent & serve as their own controls
-Require fewer participants than between-groups design

63
Q

within-subjects cons

A

-Potential for order effects
-Greater potential for demand characteristics
-Might not be practical or possible

64
Q

regression to the mean

A

When a group avg. is unusually extreme at pre-test, it is likely to be less extreme when measured again

65
Q

how to identify a regression to the mean issue:

A

Treatment group is more extreme, but ends up at same score as comparison group

66
Q

attrition threat

A

A certain type of participant systematically drops out of the study before it ends

67
Q

how to avoid attrition threats:

A

If a participant drops out, all of their scores should be removed

Some statistical techniques can help prepare for this if the sample size is small

68
Q

instrumentation

A

An experimental group changes over time, but only because the measurement instrument has changed

69
Q

observer bias

A

Researchers’ expectations influence their interpretation of results

70
Q

placebo effect

A

Participants in an experimental group improve, but only because they believe in the efficacy of the therapy/drug they receive

71
Q

how to solve a placebo effect:

A

double-blind placebo control study => Neither the experimenter nor the participants know which group they have been assigned to

72
Q

what could a null effect mean?

A

true negative OR false negative

73
Q

positive results bias

A

Authors are more likely to submit and/or editors are more likely to accept positive results (rather than negative or inconclusive)

74
Q

low between-groups variation

A

-Weak manipulation
-Insensitive measure
-Ceiling effect
-Floor effect
-Design confound acting in reverse

75
Q

weak manipulation

A

Manipulation not strong enough to show differences between groups (may cause a ceiling effect)

76
Q

insensitive measure

A

DV isn’t sensitive enough to detect differences (may cause a floor effect)

77
Q

ceiling effect

A

Participants in an experimental group score almost the same on a DV, with all scores on the high end of the dist. (usually caused by a weak manipulation)

78
Q

floor effect

A

Participants in an experimental group score almost the same on a DV, with all scores on the low end of the dist. (usually caused by an insensitive measure)

79
Q

design confound acting in reverse (example)

A

Maybe Ps who had 7 beers all took a cup of coffee in the waiting room, which could be why they did equally well on the memory test

80
Q

empirical approaches to testing construct validity

A

-Pilot studies
-Manipulation checks

81
Q

pilot study

A

A simple study conducted before or after conducting the study to test the effectiveness of a manipulation

82
Q

questions that pilot studies consider:

A

-Do participants understand the instructions?
-Do participants become bored or frustrated?
-Can participants guess the research question or hypothesis?
-Are there demand characteristics?
-How long does the procedure take?
-Are computer programs or other automated -procedures working properly?
-Is data being recorded correctly?

83
Q

manipulation check

A

An extra DV researchers include in order to determine how well a manipulation worked

-Can show if there isn’t enough variability btwn levels
-Can show an ineffective IV manipulation
-Usually done at the END of a procedure

84
Q

high within-groups variation

A

Aka noise, error variance, unsystematic variance

More overlap between groups => Smaller effect sizes => Less likely the groups are significantly different

85
Q

measurement error

A

The degree to which the recorded DV for a participant differs from the true value of the DV

86
Q

how to minimize measurement errors:

A

-Use reliable measures
-Use measures with high construct validity
-Increase sample size

87
Q

examples of high within-groups variation:

A

-Measurement error
-Individual differences
-Situation noise

88
Q

how to minimize individual differences:

A

-Within-groups design => Controls for irrelevant individual differences
-Increase sample size

89
Q

situation noise

A

Unrelated events or distractions in the external environment that create unsystematic variability within groups

90
Q

how to minimize situation noise:

A

Carefully control environmental factors that could influence DV

91
Q

minimizing within-groups variability increases:

A

a study’s STATISTICAL power

92
Q

external validity of causal claims

A

-Internal validity often prioritized over external validity
-Increasing the variability of participants may increase external validity, but decrease statistical power
-The downside of controlling for every extraneous variable, decreasing all situational noise, etc. => Less generalizability

93
Q

factorial designs

A

have 2 or more IVs

94
Q

why use 2 IVs?

A

-Can show differences between conditions
-Can show whether the effect of one IV depends on another IV
-Test for a “difference in differences”
-Tests for main effects of IVs & interactions between IVs
-Testing for moderating variables

95
Q

main effect

A

The overall effect of one IV on a DV, averaging over the levels of the other IV

96
Q

interaction effect

A

An effect in which the difference in levels of one IV changes depending on the level of the other IV

97
Q

moderator

A

A variable that, depending on its level, changes the relationship between two other variables

98
Q

why use ANOVA?

A

-Tests for main effects & interactions
-Conducting multiple t-tests increases the chances of Type I error

99
Q

effects associated with a 2x2 factorial design:

A
  1. Main effect for IV 1
  2. Main effect for IV 2
  3. Interaction effect between IV 1 & IV 2
100
Q

describing factorial designs (_____ x _____)

A

-Each # refers to the number of levels of an IV
-Amount of #s is the # of IVs
-Product of the #s is the total number of conditions

101
Q

between-subjects factorial

A

-Both IVs are manipulated between-subjects
-Each P/S only experiences one condition

102
Q

repeated-measures factorial (within)

A

-Both IVs are manipulated within-subjects
-Each P/S experiences every condition

103
Q

potential issues with a repeated-measures/within-subjects factorial:

A

-Order effects
-Demand characteristics

=> Counterbalancing can solve

104
Q

mixed factorial

A

One IV is manipulated between-subjects, and the other IV is manipulated within-subjects

105
Q

advantages & disadvantages of mixed factorials:

A

Are we concerned about some of the issues (related to between-subjects or within-subjects manipulations) for one IV, but not the other?

106
Q

marginal means

A

The averages for each level of an IV, averaging over the levels of the other IV

107
Q

test used to understand interaction effects:

A

post-hoc tests (test pairwise comparisons)

108
Q

statistical tests

A

ANOVA results tell us if the main effects & interactions are significant

“Post-hoc pairwise comparisons” are needed to understand the nature of the interaction

109
Q

cross-over interaction

A

One IV has an opposite effect at one level of the second IV than at the other level of the second IV

Appearance:
-“X” on a scatterplot
-Mirrored on a bar graph

110
Q

spreading interaction

A

There is an effect of one IV at one level of the other IV; Weak or no effect of the IV at the other level of the other IV

Appearance:
-“<” on scatterplot’
-Difference vs. no/small difference on a bar graph

111
Q

looking for interactions in bar graphs

A

-Look for a difference in differences between bars
-Imagine drawing a line to connect the tops of bars in the same condition

112
Q

looking for interactions in line graphs

A

-If lines are not parallel, there may be a significant interaction (But would need to formally test using statistics)
-Lines do not have to cross for an interaction to be significant

113
Q

hypothetico-deductive method

A

-Do we care about the results of any single study?
-Can a single study really tell us what is going on in the real world?
-How do we interpret results of conflicting studies?

114
Q

single study

A

-Statistical significance?
-Finding likely not due to chance
-High probability to repeat

115
Q

types of replication

A

-Direct
-Conceptual
-Plus extension

116
Q

direct replication

A

Original study is repeated as closely as possible; Uses the same operationalization of the conceptual variables (to reproduce the original study & determine whether the effect is repeated)

117
Q

conceptual replication

A

Relationship between conceptual variables in the original study is tested using different procedures for operationalizing those variables

118
Q

replication plus extension

A

Relationship between variables in original study is tested & additional variables (or conditions) are added to test additional questions

119
Q

why might a replication attempt fail?

A

-Problems with the original study
—Found by chance
—Small sample size
—Questionable research practices in original study
-Problem with the replication
—Only one replication attempt per study (found result by chance)
-Differences between original study & “replication”
—Differences in context
—Different operationalization of conceptual variable shows a different pattern

120
Q

types of research misconduct:

A

-Data fabrication
-Data falsification
-Plagiarism
-P-Hacking
-Hark-ing

121
Q

data fabrication

A

Researchers invent data that fit their hypotheses

122
Q

data falsification

A

Researchers selectively delete observations or influence participants to act in a particular way

123
Q

plagiarism

A

Representing the ideas or words of others as one’s own

124
Q

p-hacking

A

A family of questionable data analysis techniques which can lead to non-replicable results

Ex) In order to obtain a p value of just under 0.05, researchers add participants after the results are initially analyzed, look for outliers to exclude, or try new analyses

125
Q

HARK-ing

A

Hypothesizing after the results are known

126
Q

open science

A

The practice of sharing data & materials freely so others can collaborate, use, & verify results

127
Q

open materials

A

Providing all of a study’s measures & manipulations

128
Q

open data

A

Providing the full dataset

129
Q

preregistration

A

The practice of posting a study’s method, hypotheses, or statistical analyses publicly, in advance of data collection

Peer-review proposals & commit to publishing results regardless of outcomes

130
Q

importance of meta-analyses

A

-Average all effect sizes to calculate an overall effect size
-Can categorize studies into groups to detect patterns in the literature
-Can’t solve the replication crisis (Publication bias against - results => Meta-analysis may not show the whole picture)

131
Q

open science collaboration (OSC)

A

-Selected 100 studies from three major psych journals
-Recruited researchers to conduct direct replications
-Used several metrics to judge where replication was successful

=> 39% of studies were replicated (“replicable”)

132
Q

Many Labs Project (MLP)

A

Conducted up to 36 replications of each study

=> 85% of studies were replicated

133
Q

generalizability (external validity)

A

Replication with extension & conceptual replication are critical because they address generalizability

To assess => Ask how participants were obtained

Experiments don’t automatically have low external validity

134
Q

theory-testing mode

A

Testing claims to investigate support for a theory

-Is there an association or causal relationship between variables?
-Internal validity > External validity

135
Q

generalization mode

A

Investigating whether claims generalize to other populations/settings

(Survey research to support frequency claims is always done in generalization mode)

136
Q

misconceptions about generalizability

A

-Studies with larger sample sizes automatically have greater generalizability
-Experiments always have poor generalizability
-If a sample includes certain types of individuals, findings generalize to that population of individuals (Still matters how the sample was collected)

137
Q

experimental realism

A

The extent to which a laboratory experiment is designed so that participants experience authentic emotions, motivations, & behaviors

138
Q

ecological validity (mundane realism)

A

The extent to which the tasks & manipulations of a study are similar to real-world contexts

PSYCH 225: Research Methods (3 decks)

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