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Research Methods > exam 2 > Flashcards

exam 2 Flashcards

1
Q

manipulation

A

changing one variable (independent variable) to determine its affect on another (dependent variable)

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2
Q

measurement

A

collection of data that allows you to determine a quantifiable change/difference in variables of interest

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3
Q

within-subjects experimental design

A

comparing two or more sets of scores within one group of individuals to see how dependent scores have changed across independent variable groups

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4
Q

between-subject experimental design

A

comparing two or more sets of scores across two or more groups of individuals to see how dependent variables scores across the subjects

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5
Q

Why are between-subjects good?

A

it removes the effects of contamination across conditions

  • practice or experience
  • fatigue or boredom (situps)
  • contrast reactions or effects (testing two drugs on same subject)
  • necessity
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6
Q

faults of between-subjects experiments

A
  • need for a large sample group
  • confounding variables (8am vs 2pm groups)
  • variability variance
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7
Q

randomization

A

restricted random assignment (equal groups)

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8
Q

How do we minimize variability issues?

A
  • standardizing procedures and treatments (keep everything constant)
  • controlling for individual differences (keep the groups balanced)
  • larger sample size
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9
Q

central limit theorem

A

as our sample size gets larger, our group averages from the populations are less varied

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10
Q

unequal (differential) attrition

A

effectiveness of highly involved treatment versus control group (being nice or mean to the average students)

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11
Q

ways of diffusion of effects

A
  • shared information (horoscope experiment where the groups talked to each other and then knew what was going on)
  • compensatory rivalry (video game experiment where everyone wanted to be the best: texters stopped texting, callers stopped listening)
  • resentful demoralization (the control group becoming mad they arent being helped)
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12
Q

advantages of within-subject designs

A
  • smaller sample requirements
  • control over differences between groups
  • control over differences between individuals
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13
Q

dangers of within-subject experiments

A
  • overall
    • third-variable interaction effects
  • internal validity issues
    • enviromental factors
    • time-related factors
  • order effects
    • carryover effect
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14
Q

counterbalancing

A

switching the order of condition presentation in order to test and control for order effects
(have some people part of the control group first and then some people part of the experimental group first)

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15
Q

matched subject designs

A

pairing individuals that are considered equivalent and placing them into separate conditions

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16
Q

nonexperimental research stragety

A

comparisons across groups where we make no attempts to avoid threats to internal validity
ex. no manipulation

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17
Q

quasi-experimental research stragety

A

approximation of a true experiment due to its attempts to avoid threats to internal validity
ex. how people react after earthquakes but you can’t control when the earthquake happens

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18
Q

internal validity

A

validity established if the study produces a single, unambiguous explanation for the relationship between variables

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19
Q

assignment/selection bias

A

the process of creating groups that are inherently different to begin with (teens compared to the elderly)

20
Q

differential effect

A

situations where group differences can be explained by different histories or experiences

21
Q

differential research design

A

comparing a variable across pre-existing groups (reaction time between two groups)

22
Q

posttest only design

A

comparing a variable across groups that received a manipulation and those that did not (both groups are the same in the beginning but end up different such as comparing cancer treatments)

23
Q

pretest-posttest design

A

a posttest design where a group is compared before and after the manipulation

24
Q

one-group pretest-posttest design

A

similar to a within-subjects experimental design, but there is no control for internal validity (weightloss commercials)

25
Q

time-series design

A

a number of observations before an event and after an event

26
Q

interrupted time-series event

A

observations that are done only after the event occured

27
Q

single-subject designs

A

time series designs with only one participant

28
Q

cross-sectioal design

A

comparing variables of individuals across ages at the same time

29
Q

cohort effects

A

differences between groups caused by their distinct experiences (naturally different)

30
Q

longitudinal developmental research design

A

measuring variables of individuals over time

31
Q

cross-sectional longitudinal design

A

comparing different age groups over time

32
Q

terminology issues

A

in quasi-experimental studies, we still have independent and dependent variables but they are called quasi-independent variables and quasi-dependent variables

33
Q

causation issues

A

causation can be inferred but not definite

34
Q

factorial design

A

when we are running two experiments for a quantitative DV ex. testing how well a student does depending on both the number of friends and difficulty of the class

35
Q

factors

A

the different independent variables in a factorial design

36
Q

interaction effects

A

when more than one IV effects the DV

37
Q

quantitative interactions

A

the pairing of a second variable’s levels strengthens the impacts of the first variable’s levels ex. alcohol n sleeping pills

38
Q

qualitative changes

A

the pairing of the variables reverses the impact of the individual variables ex. a difficult class makes you do worse but more friends makes it easier

39
Q

phases

A

series of observations made under different conditions

40
Q

baseline phase

A

observations made before a treatment

41
Q

treatment phase

A

observations made during or after a treatment

42
Q

ABAB design

A

researchers generate single-subject designs that look at an effect more than once

43
Q

dismantling design

A

a series of phases that adds or subtracts components from a complex treatment to determine each component’s effectiveness ex. give all the drugs at once and then slowly take away some of the drugs to see which one is working

44
Q

alternating-treatment design

A

a series of trials where the treatments of interest are randomly selected over time

45
Q

single-subject research designs allow for:

A
  • a small sampling
  • a chance to find hints at cause-and-effect
  • a chance to change treatments quickly and cheaply
  • a chance to directly change something in an individual
46
Q

single-subject designs cannot account for..

A
  • generalizability
  • demand characteristics
  • extraneous variables
  • small effects

Research Methods (3 decks)

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