Brainscape's Knowledge GenomeTM

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


See full index

PSYC 314: Experimental Research Methods > Exam 2 > Flashcards

Exam 2 Flashcards

1
Q

for a study to be an experiment, it has to have…

A
  • at least one manipulated variable
  • at least one measured variable
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

control variable

A

a variable that an experimenter holds constant on purpose (besides the independent variables)
- not really variables because they do not vary

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

why experiments support causal claims through the 3 criteria

A
  • establish covariance: changes in the independent variables are related to the changes in the dependent variable
  • establish temporal precedence: The causal variable should come before the outcome variable
  • establish internal validity: There are no other likely explanations for the relationship observed
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

placebo (control) groups

A

a group that is exposed to an inert treatment; comparison group may not need to be a control group (i.e., no treatment)

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

confounds

A

an unmeasured variable that influences both the supposed cause and the supposed effect

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

design confounds

A

an accidental second variable varies systematically along with the intended independent variable

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

selection confounds (selection effects)

A

when the kinds of participants in one level of the independent variable are systematically different from those in the other
- avoid selection effects with random assignment
- avoiding selection effects with matched groups

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

types of experimental design

A
  • independent-groups designs (between-sujects)
  • within-subjects designs
  • pottest-only designs
  • pretest/posttest designs
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

independent-groups designs (between-subjects)

A

Separate groups of participants are placed into different levels of the independent variable
- Ex. an experiment exploring how different amounts of sleep affect people’s reaction times → level 1: 3 hours of sleep, level 2: 8 hours of sleep

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

within-subjects designs

A
  • Each person is presented with all levels of the independent variable
  • One set of participants are tested more than once, and their scores are compared
  • Ex. an experiment exploring how different amounts of sleep affect people’s reaction times → all groups do day 1 with 3 hours of sleep and day 2 with 8 hours of sleep
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

posttest-only designs

A

Participants are randomly assigned to independent variable groups and are tested on the dependent variable once

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

pretest/posttest designs

A

Participants are randomly assigned to at least two difference groups and are tested on the key dependent variable twice— once before and once after exposure to the independent variable

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

problems using pretest/posttest design

A

threat to internal validity
- Taking the pretest affects how participants do the posttest → testing threat
- Participants may get tired from a long study with a pretest and posttest

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

repeated-measures design

A

participants are measured on a dependent variable more than once, after exposure to each level of the independent variable

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

concurrent-measures design

A

participants are exposed to all the levels of an independent variable at roughly the same time, and a single attitudinal or behavioral preference is the dependent variable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

advantages of within-groups design

A
  • Participants in your groups are equivalent because they are the same participants and serve as their own controls
  • Gives researchers more power to notice difference between conditions because there is less extraneous error in the measurement
  • Requires fewer participants
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

disadvantages of within-groups design

A
  • potential for order effects
  • carryover effects
  • might not be practical or possible
  • experiencing all levels of the IV changes the way participants act –> demand characteristics
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

demand characteristics

A

subtle cues or aspects of an experiment that might unintentionally signal to participants what the study is about, leading them to change their behavior to fit that perceived expectation

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

order effects

A

When the sequence in which stimuli are presented to participants influences their responses; order of conditions can affect the results

when being exposed to one condition affects how participants respond to other conditions

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

carryover effects

A
  • practice effect –> participants perform better during later treatment conditions because they’ve had time to practice and improve
  • Fatigue effect → participants perform worse during later treatment conditions because they’re tired or fatigued
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

avoiding order effects

A
  • full counterbalancing
  • partial counterbalancing
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

solution to order effects

A

counterbalancing

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

interrogating causal claims with construct validity

A

How well were the variables measured and manipulated?
- dependent variables: check face validity, interrater reliability, and convergent vailidty
- independent variables: How well were they manipulated?
- manipulation check

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

interrogating causal claims with external validity

A

To whom or what can the causal claim generalize?
- generalizing to other people
- generalizing to other situations

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

interrogating causal claims with statistical validity

A

How well do the data support the causal claim?
- accuracy of the conclusions drawn from a study’s statistical analysis
- Is the difference statistically significant?: P-value < .05 usually considered statistically significant
- How large is the effect?: correlation coefficient (r), Cohen’s d
- confidence interval

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

interrogating causal claims with internal validity

A

Are there alternative explanations for the outcome?
- Were there any design confounds?
- If an independent-groups design was used, did they control for selection effects using random assignment or matching?
- If a within-groups design was used, did they control for order effects by counterbalancing?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

manipulation check

A

extra dependent variable that researchers can include to convince them that their experimental manipulation worked

ex. A study comparing the effect of a serious lecture vs. a funny lecture on the memory of lecture information; Manipulation check - how funny was the lecture?

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

correlation coefficient (r)

A

indicates the strength of a linear association between two variables (association claim)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

Cohen’s d

A

standardized effect size for measuring the difference between two group means

(group A mean - group B mean) / pooled standard deviation

  • small (d = 0.2)
  • medium (d = 0.5)
  • large (d = 0.8)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
30
Q

confidence interval

A

a range of values, bounded above and below the statistics’s mean, that likely would contain an unknown population parameter

  • when a study has a small sample and more variability, CI will be relatively wide (less precise)
  • when a study has a larger sample and less variability, CI will be narrower (more precise)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
31
Q

six potential internal validity threats in one-group, pretest/posttest designs

A
  • maturation
  • history
  • regression
  • attrition
  • testing
  • instrumentation
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
32
Q

maturation threat to internal validity

A

A change in behavior that emerges more or less spontaneously over time
- Spontaneous remission is a specific type of maturation
- E.g., people adapt to changed environments; children get better at walking, talking, reading, etc.; plants grow taller

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
33
Q

preventing maturation threats

A

Include an appropriate comparison group

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
34
Q

history threats to internal validity

A
  • Something specific has happened between the pretest and posttest (not just time has passed)
  • “Historical” or external factor that systematically affects most members of the treatment group at the same time as the treatment itself
  • E.g., the rowdy boys started a swimming course and the exercise tired most of them out
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
35
Q

preventing history threats

A

include a comparison group

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
36
Q

regression threats to internal validity

A
  • regression to the mean: the tendency of results that are extreme by chance on first measurement to move closer to the average when measured a second time
  • Occurs only when a group is measured twice, and
  • Only when the group has an extreme score at pretest
  • E.g., the 40 depressed women might have scores exceptionally high on the depression pretest due to random effects, such as recent illness, family or relationship problems
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
37
Q

preventing regression threats

A

include a comparison group

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
38
Q

attrition threats to internal validity

A
  • A reduction in participant numbers that occurs when people drop out before the end of the study
  • Problem for internal validity when attrition is systematic— only a certain kind of participant drops out
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
39
Q

preventing attrition threats

A

Remove the dropped-out participants’ scores from the pretest average too

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
40
Q

testing threats to internal validity

A

A change in the participants as a result of taking a test (dependent measure) more than once

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
41
Q

preventing testing threats

A
  • No pretest
  • Two different forms— one for pretest and one for posttest
  • Include a comparison group
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
42
Q

instrumentation threats to internal validity

A
  • Occur when a measure instrument changes over time
    OR
  • When a researcher uses different forms for the pretest and posttest, but the two forms are not sufficiently equivalent
  • E.g., people judging the rowdy campers’ behavior became more tolerant of loud voices and rough-and-tumble play
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
43
Q

preventing instrumentation threats

A
  • Use a posttest-only design
  • Ensure that the pretest and posttest measures are equivalent
  • Counterbalance the versions of the test
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
44
Q

three potential internal validity threats in any study

A
  • observer bias
  • demand characteristics
  • placebo effects
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
45
Q

observer bias

A

when researchers’ expectation influence their interpretation of the results

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
46
Q

placebo effects

A

eople’s behaviors or symptoms respond not just to the treatment, but also to their belief in what the treatment can do to alter their situation

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
47
Q

controlling for observer bias and demand characteristics

A
  • double-blind study
  • masked design (or blind design)
48
Q

double-blind study

A

Neither the participants nor the researchers who evaluate them
know who is in the treatment group and who is in the comparison group

49
Q

masked design (or blind design)

A

the investigator does not know the identity of the treatment assignment

50
Q

preventing placebo effects

A

Use a double-blind placebo control study:
- One group receives real treatment, and another group receives the placebo treatment
- Neither the participants nor the investigators know who is in the experimental group or in the placebo group

51
Q

interrogating null effects

A

1) the independent variable has no effect on the dependent variable
2) other reasons: a) not enough difference between groups, b) there is too much variability within groups
3) insensitive measures

52
Q

not enough between-groups difference in interrogating null effects

A
  • Weak manipulations
  • Insensitive measures
  • Ceiling and floor effects
  • Reverse design confounds
53
Q

ceiling and floor effects

A

Both control and experimental groups scored very high or very low

54
Q

reverse design confounds

A

Design confounds acting in reverse

55
Q

weak manipulations

A

ask: How did the researchers operationalize the independent variable?

E.g., Does money make people happy?
- A researcher gives one group of participants $1 and another group $2
- The manipulation is not strong → try $1 vs. $50

56
Q

insensitive measures

A
  • Dependence measure was not sensitive enough
  • Solution: use a detailed, quantitative increments measures
57
Q

solution to ceiling and floor effects and dependent variables

A

use a manipulation check

58
Q

within-groups variability in interrogating null effects

A

Too much unsystematic variability within each group → noise (error variance or unsystematic variance)

59
Q

measurement error in interrogating null effects

A
  • Error in the measurement
  • All dependent variables involve a certain amount of measurement error
  • Solution 1: use reliable, precise tools; Have excellent reliability (internal, interrater, and test-retest)
  • Solution 2: measure more instances
60
Q

solution to individual differences in interrogating null effects

A
  • Solution 1: change the design; Use a within-groups design instead of independent-groups design
  • Solution 2: add more participants
61
Q

solution for situation noise in interrogating null effects

A

carefully control the surroundings of an experiment

62
Q

power

A

the likelihood that a study will return an accurate result when the independent variable really has an effect

63
Q

what increases power

A
  • Within-groups design
  • A strong manipulation
  • A larger number of participants
  • Less situation noise
64
Q

experiments with two independent variables can show interactions

A

To test whether the effect of the original independent variable depends on the level of another independent variable
- Interaction = a difference in differences = the effect of one independent variable depends on the level of the other independent variable

65
Q

factorial design

A

when there are two or more independent variables (or factors)
- can test limits
- can test theories

66
Q

factorial design and testing limits

A
  • a form of external validity (testing the generalizability)
  • interactions show moderators
67
Q

moderator

A

a variable that changes the relationship between two other variables

68
Q

interpreting factorial results

A
  • main effects
  • interactions
  • If you have two independent variables, there will be two main effects and one possible interaction between them
  • With three independent variables, there will be three main effects and several possible interactions (including two-way interactions and a three-way interaction)
69
Q

factorial variations

A
  • Independent-groups factorial designs
  • Within-groups factorial designs
  • Mixed factorial designs
  • Increasing the number of levels of an independent variable
  • Increasing the number of independent variables
70
Q

independent-groups factorial design

A

2 x 2 factorial design = 4 conditions
- different people in each group

71
Q

within-groups factorial design

A

2 x 2 factorial design = 4 conditions
- One group of participants participate in all four combinations
- Has more statistical power than an independent-groups factorial design

72
Q

mixed factorial design

A

One independent variable is manipulated as independent-groups (between-groups) and the other is manipulated as within-groups

E.g., cell phone use (on the phone vs. not on the phone) x age (old vs. young)
- Old participants take part in both “on the phone” and “not on the phone” conditions
- Same for the young participants

73
Q

increasing the number of levels of an independent variable for factorial variation

A
  • Simplest factorial design 2 x 2: Two independent variables with two levels in each independent variable
  • Can add one or more levels in one or both independent variables: E..g, two independent variables with three levels in one of the independent variables
74
Q

increasing the number of independent variables for factorial variation

A

E.g., 2 x 2 x 2 factorial, or a three-way design
- Three main effects
- Three possible two-way interactions
- One three-way interaction

75
Q

quasi-experiments

A
  • Do not have full experimental control
  • First select an independent variable and a dependent variable
  • Random assignment might not be possible
76
Q

nonequivalent control group pretest/posttest design

A

ex. study investigating the psychological effects of cosmetic surgery
- group of people who got the surgery vs. group of people who registered at the same clinic but did not receive any procedure

77
Q

nonequivalent control group posttest-only design

A

ex. opt-in vs. opt-out default options of organ donation across countries
- no control over which countries had which defaults
- no random assignments of people

78
Q

interrupted time-series design

A

ex. investigating popular shows and suicide
- the variable (of the suicide rates in the US) was measured repeatedly— before, during, and after the “interruption” caused by some event (the introduction of the show 13 Reasons Why)

79
Q

nonequivalent control group interrupted time-series design

A

ex. investigating the effect of legislation on opioid abuse
- Florida passed laws that medical clinics could not dispense opioids, North Carolina did not
- nonequivalent control group → not randomly assigned to having the pill mill laws or not
- Interrupted time-series → researchers did not have experimental control over the year the laws were passed

80
Q

threats to internal validity in quasi-experiments

A
  • selection effects
  • design confounds
  • maturation threats
  • history threats
  • repression to the mean
  • attrition threats
  • testing threats
  • instrumentation threats
  • observer bias, demand characteristics, and placebo effects
81
Q

selection effects in quasi-experiments

A
  • Relevant only for independent-groups designs, not for repeated-measures designs
  • Applies when the kinds of participants at one level of the independent variable are systematically different from those at the other level

Example 1: nudging people toward organ donation
- No selection effect

Example 2: the psychological effects of cosmetic surgery
- Maybe selection effect, but not likely

82
Q

design confounds in quasi-experiments

A
  • Some outside variable accidentally and systematically varies with the levels of the targeted independent variable

Example 1: nudging people toward organ donation
- Maybe some other government policy co-occur with the presumed consent
- Not likely as all seven countries with presumed consent policies should also have the same or similar policy

83
Q

maturation threats in quasi-experiments

A

In an experiment or quasi-experimental design with a pretest and posttest, the observed change could have emerged more or less spontaneously over time

Example 2: the psychological effects of cosmetic surgery
- The comparison group did not improve over time —> no maturation threat

Example 4: investigating the effect of legislation on opioid abuse
- The comparison group (North Carolina) overdose deaths did not decline —> no maturation threat

84
Q

history threats in quasi-experiments

A

Occurs when an external, historical event happens for everyone in a study at the same time as the treatment

Example 3: popular shows and suicide
- Suicide rates might have increased because of a suicide of a celebrity

Example 4: investigating the effect of legislation on opioid abuse
- The results might have been caused by some change in employment or living conditions

85
Q

regression to the mean in quasi-experiments

A

Only for pretest/posttest designs

Example 2: the psychological effects of cosmetic surgery

86
Q

attrition threats in quasi-experiments

A
  • In a pretest/posttest design, attrition occurs when people drop out of a study over time
  • Becomes a threat to internal validity when systematic kinds of people drop out of a study
87
Q

testing threats in quasi-experiments

A

A kind of order effect in which participants change as a result of having been tested before

Repeated testing:
- Might cause people to improve, regardless of the treatment they received
- Might also cause performance to decline because of fatigue or boredom

Example 2: the psychological effects of cosmetic surgery
The surgery group improved over time while the comparison group declined → no testing threat

88
Q

instrumentation threats in quasi-experiments

A
  • A measurement could change over repeated uses, and this can be a threat to internal validity
  • Having a comparison group helps detect instrumentation threats if there are any
89
Q

why choose a quasi-experiment

A
  • Real-world opportunities
  • External validity: Quasi-experiments capitalize on real-world situations, even as they give up some control over internal validity
  • Ethics: A researcher might choose a quasi-experimental design when the questions they have would be unethical to study in a true experiment
  • Construct validity: Quasi-experiments show excellent construct validity for the quasi-independent variable
90
Q

quasi-experiments and correlational studies

A

In quasi-experiments, the researchers tend to select their samples more intentionally than correlational studies

91
Q

quasi-independent variables compared with participant variables

A

Quasi-independent variables focus less on individual differences and more on potential interventions such as laws, media exposure, or education

92
Q

participant variable

A

a categorical variable, e.g., age, gender, ethnicity

93
Q

small-n designs

A

Obtain a lot of information from just a few cases instead of a little information from a larger sample

94
Q

disadvantages of small-n studies

A

Issues with internal validity

Issues with external validity
- Participants in small-n studies may not represent the general population very well
- Solution: compare a case study results to research using other methods

95
Q

behavior-change studies in applied settings: three small-n designs

A
  • stable-baseline design
  • multiple-baseline design
  • reversal design
96
Q

stable-baseline design

A

a researcher observes behavior for an extended baseline period before beginning a treatment of other intervention

97
Q

reversal design

A

Behavior high during baseline sessions and lower during treatment sessions

98
Q

quality of science

A

replication, transparency, applicability to real-world context

99
Q

replicable (or reproducible)

A
  • part of interrogating statistical validity: size of the estimate (effect size), precision of estimate (95% CI)
  • gives a study credibility
100
Q

types of replication

A
  • direct replication
  • conceptual replication
  • replication-plus-extension
101
Q

direct replication

A

repeat an original study as closely as possible

102
Q

concerns with direct replication

A
  • threats to internal validity or flaws in construct validity in the original study would be repeated
  • when successful, it confirms what we already learned
  • does not test the theory in a new context
103
Q

conceptual replication

A

same research question as the original study, but use different procedures

104
Q

replication-plus-extension

A

replicate the original experiment and add variables to test additional questions

105
Q

Why might a study not be replicable?

A
  • issues with the replication study itself (differences in sample, materials, or geography)
  • issues with the original studies
106
Q

meta-analysis

A

mathematically averaging the results of all the studies (both published and unpublished) that have tested the same variables

107
Q

strengths of meta-analysis

A
  • can sort the studies into categories –> can identify new patterns in the literature –> could lead to new questions to investigate
  • solves the File Drawer Problem by including both published and unpublished data
108
Q

File Drawer Problem

A

studies that were never published could have important insights and add to the effect size

109
Q

questionable research practices

A
  • underreporting null findings
  • HARKing
  • p-HACKing
110
Q

underreporting null findings

A

reporting only strong effects, not the weak ones

111
Q

HARKing

A

Hypothesizing After the Results are Known
- misleads readers about the strength of the evidence

112
Q

p-HACKing

A

running different types of statistics to find p < 0.5

113
Q

transparent research practices

A
  • open data
  • open materials
  • preregistration
114
Q

open data

A

others can analyze the data

115
Q

open materials

A

others can replicate the study

116
Q

preregistration

A

scientists publish their study’s method, hypotheses, or statistical analyses before collecting data

117
Q

ecological validity

A

extent to which a study’s task and manipulations are similar to the kinds of situations participants might encounter in their everyday lives
- one aspect of external validity

PSYC 314: Experimental Research Methods (2 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2025 Bold Learning Solutions. Terms and Conditions