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Research Methods > Final Exam 2 > Flashcards

Final Exam 2 Flashcards

1
Q

notation system for factorials

A

of IV1 X # of IV2, multiply to find total number of conditions

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2
Q

main effect

A

overall effect of Independent Variable, can have as many main effects as independent variables

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3
Q

interaction

A

effect of one factor depends on the level of another factor. If one level of IV is higher in both conditions, no interaction occurred. Also can plot onto graph, parallel lines=no interaction

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4
Q

mixed factor design

A

at least one between subjects factor, at least one within subjects factor

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5
Q

PXE design

A

factorials with subject and manipulated variables, P=person, E=environmental

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6
Q

Pearson’s R

A

ranges from -1 to +1, 0=no correlation

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7
Q

correlations with scatterplots

A

Weak correlations are more spread out

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8
Q

Pearson’s R squared

A

helps determine variability

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9
Q

criterion variable

A

in regression analysis, variable being predicted

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10
Q

predictor variable

A

in regression analysis, variable doing predicting

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11
Q

how to solve problem of directionality

A

can use cross-lagged correlation, longitudinal design

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12
Q

third variable problem

A

what are correlations with other variables? Make chart

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13
Q

problems with correlational research

A

hard to establish cause

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14
Q

reasons to use correlational research

A
  • make predictions
  • when variables cannot be manipulated (subject)
  • test/retest reliability and criterion validity of psychological tests
  • assessing relationships between variables in personality and abnormal psych
  • twin studies
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15
Q

bivariate analysis

A

correlational research, two variables

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16
Q

multivariate analysis

A

correlational research, more than two variables, only one criterion variable

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17
Q

factor analysis

A

examines all possible correlations among each of several scores, identifies clusters of intercorrelated scores

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18
Q

quasi-experimental research

A

research with non equivalent groups: examples=

  • nonequivalent group factorial design
  • P X E factorial design
  • correlational design
  • control group pre-test and post-test design
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19
Q

Quasi-experimental non-equivalent control group design

A

Experimental: 01 treatment 02
non-experimental: 01 no treatment 02
Example of control: Arizona in nightmare study, did not experience earthquakes
baseball coach study: control group=baseball coach from different league who was not trained

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20
Q

Possible confounds of quasi-experimental design

A
  • history
  • subject selection
  • knowledge of participating
  • ceiling/floor effects-too difficult/easy
  • regression to mean-extreme scores at pretest move toward mean at posttest, looks like there was no effect in treatment
21
Q

Best outcome in quasi-experimental pretest posttest design

A

experimental below control in prettest, end=experimental above control. This outcome rules out regression and ceiling/floor effects

22
Q

Time-series design

A

measure at different moments, say 10 moments. Pretest at O1 and posttest at O8. Measure past posttest. Treatment happens at O5. Helps to evaluate longer term trends

23
Q

Problems with time-series design

A

attrition, history (must rule out these confounds)

24
Q

Time-series switching replication

A

Give treatment at different time to rule out effect of history

25
Q

second dependent variable

A

measure a different variable, which should not be influenced by program. If both DVs change, there could be a different trend or extraneous variable influencing both variables.

26
Q

Stages of program evaluation

A
  1. planning
    - research
    - informants/focus groups
  2. formative evaluation
    - -evaluating program while in process
    - is program being implemented as planned?
  3. summative evaluation
    - program effectiveness
27
Q

Program evaluation, failure to reject null hypothesis

A

can be useful in this case because new programs have to prove themselves. Might find program is not cost effective.

28
Q

Naturalistic vs. participant observation

A

In participant observation, experimenter is involved in group being observed
-cult study (Festinger)

29
Q

Problems with participant observation

A
  • ethical issues

- reactivity of group, changing their behavior

30
Q

Challenges with observation research

A

-absence of control, cannot prove hypothesis (can falsify)
-observer bias
-how to counteract: checklist, interobserver reliability,
time and event sampling

31
Q

survey: types

A
  • probability sampling

- representative sampling: sample reflects attributes of target population

32
Q

sampling procedures

A
  • random
  • stratified: same strata as actual population
  • cluster sampling: group of people all have some feature in common like living on the same floor or taking core classes
33
Q

interviews: benefits and costs

A

benefits: comprehensive, can follow up
costs: cost, logistics, interview bias, representative samples

34
Q

phone surveys

A

benefits: cost, efficiency
drawbacks: brief, low response rate

35
Q

electronic survey

A

benefits: cost, efficiency
drawbacks: sampling issues

36
Q

written surveys

A

plus: ease of scoring (vs. interviews)
drawbacks: cost, non-response rate, social desirability bias

37
Q

creating an effective surveys

A
  • open-ended questions when first starting
  • balance favorable and unfavorable statements (avoids response acquiescence)
  • “most important problem” questions
  • use scales (Likert scale)
  • moderate use of I don’t know alternative
  • place demographic info at end of survey
  • avoid ambiguity with a pilot study
  • avoid biased and leading questions
  • don’t ask for two things in one question
  • “do you support or oppose”
38
Q

Small N-design definition

A
  • data reported one participant at a time

- small group or one individual studied

39
Q

Why use small-N design

A
  • practical reason: rare attribute, rare species

- issues with statistical summaries: grouping data can be misleading, poor individual-subject validity

40
Q

Operant conditioning, steps of experimental analysis of behavior

A 
Behaviors result from learning history
Steps
1.Define behavior
2.Understand conditions leading to behavior 
3.Understand reinforcing consequences
41
Q

Applied behavior analysis process

A

1) Baseline A
2) Treatment
3) B

42
Q

Withdrawal designs

A

ABA or ABAB is better

43
Q

Benefits of case study

A
  • level of detail

- can serve falsification

44
Q

Weaknesses of case study

A
  • limited control
  • external validity-generalization
  • faulty memory
45
Q

Alternating treatment design

A

alternate treatments to see which is more effective example: AOC vs. no AOC in autistic girl

46
Q

weaknesses of small N design

A
  • external validity
  • no statistical analysis
  • interactive effects hard to test
  • overreliance on rate or response
47
Q

changing criterion design

A

Shaping behavior, criterion is changed until goal is reached, example: diet studies

48
Q

Multiple baseline design

A

One treatment introduced at three different times, each baseline should improve after treatment is introduced, not before. Example: posting scores for each behavior with football players. This solves problem of ABAB design

49
Q

Types of multiple baseline studies

A
  • treatment introduced in different settings
  • treatment introduced with different subjects, all have different baselines but same general behavior
  • multiple behaviors, one treatment (like football player study)

Research Methods (3 decks)

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