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Research Methods in Psych > Ch13 > Flashcards

Ch13 Flashcards

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1
Q

Quasi-Experiments

A

researchers can’t necessarily randomly assign participants to the level of the IV (which also may be a quasi-IV)

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2
Q

types of quasi-experiments

A
  • Independent groups quasi-experiments
  • Repeated measures quasi-experiments
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3
Q

Non-equivalent control group design

A

an ind. group quasi-experiment design- at least one treatment group and one comparison group (separate participants) but they haven’t been randomly assigned

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4
Q

types of Non-equivalent control group design (ind groups)

A
  • Non-equivalent control group posttest-only design
  • Non-equivalent control group pretest/posttest design
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5
Q

Non-equivalent control group posttest-only design

A

(ind groups) not randomly assigned to groups, and tested only after exposure to one level of the IV or the other

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6
Q

Non-equivalent control group pretest/posttest design

A

(ind groups) not randomly assigned to groups, tested both before and after intervention

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7
Q

Repeated-Measures Quasi-experiments (+ what does it rely on)

A
  • analogous to repeated measures designs in true experiments- all participants experience all levels of IV, IV not manipulated
  • Relies on something already occurring, like an already scheduled event, new policy, etc (ex: food break and parole decisions)
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8
Q

types of repeated measure quasi-experiments

A
  • Interrupted time series design
  • Non-equivalent control group interrupted time series design
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9
Q

Interrupted time series design

A
  • repeated measures quasi-experiment study that measures participants repeatedly on a DV before, during, and after an “interruption” caused by an event (ex:food break and parole decisions)
  • Also can be in real experiments, with a manipulated IV
  • In is one- measured ordinally (1st, 2nd, 3rd)
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10
Q

Non-equivalent control group interrupted time series design

A

combines interrupted times series (repeated measures) and non-equivalent control group (independent groups) designs- with no experimental control over either IV

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11
Q

Internal validity threats in quasi-experiments

A
  • selection effects
  • design confounds
  • maturation effects
  • regression to the mean
  • attrition threats
  • testing and instrumentation threats
  • observer bias, demand characteristics, placebo effects
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12
Q

selection effects (how to help)

A

Unaccounted for differences between groups- only relevant to ind groups
- Matched groups can help
- Waitlist design- have half the group get surgery right away, half wait (true experiment)

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13
Q

design confounds ( + how to help)

A

Extraneous variables (vary systematically)
- Look into and rule out

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14
Q

maturation effect (+ help)

A
  • pretest/posttest- an observed change could’ve emerged over time
  • Comparison group
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15
Q

history threat (+ help)

A

External historical event happens to everyone at the same time
- Comparison group
- Selection-history threat if only applies to one group

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16
Q

regression to the mean (+ help)

A

Occurs when extreme finding caused by random factors, unlikely to happen again, gets less extreme over time
- pretest/posttest only, only if the initial score is extreme

  • In experiments, random assignment prevents it
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17
Q

attrition threats (+help)

A

When people drop out systematically- pretest/posttest
- Missing values analysis- shows if drop-outs were systematic

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18
Q

Testing and instrumentation threats (+ help)

A

Occurs when measured more than once- if not measured the exact same way
- Testing threats- participants’ answers change b/c they’ve been tested before

  • Comparison group
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19
Q

observer bias (observer bias, demand characteristics and placebo effect) + help

A

Human subjectivity
- experimenter’s expectations influence interpretation of results- both construct and internal validity threatened
- masked or double blind

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20
Q

Demand Characteristics (Observer bias, Demand Characteristics, and Placebo Effects)

A

human subjectivity

  • participants guess what the study is about change behavior
21
Q

placebo effect (Observer bias, Demand Characteristics, and Placebo Effects)

A

human subjectivity

when participants taking a placebo improve because they think they’re receiving a treatment
- Comparison group
- Hiding what is being measured? Double blind?

22
Q

External validity of Quasi-experiments

A

Real-world settings can enhance external validity due likelihood patterns observed will generalize

23
Q

ethics in quasi- experiments

A

Many of the questions addressed in quasi-experiments would be unethical to study experimentally/ manipulate variables

24
Q

construct validity in quasi-experiments

A

Usually show excellent construct validity for the IV - participants are actually doing the thing being measured
- Ask how successfully the DV was measured- reliable and valid?

25
Q

Statistical validity in quasi-experiments

A

ask how large group differences were (effect size) + if results were statistically significant

26
Q

Quasi-Experiments vs Correlational Studies

A
  • Both can use independent-groups design
  • Neither use random assignment
  • Neither* use manipulated variables
27
Q

Quasi-Experiments vs Correlational Studies- ind groups design

A

Both can use independent-groups design
- Comparing two or more levels of an IV (without random assignment)in order to predict one or more DVs,

28
Q

Quasi-Experiments vs Correlational Studies- Neither use random assignment

A

Neither use random assignment
- IV not manipulated in quasi-experiments- but use a variety of techniques to enhance internal validity
- Can obtain people by targeting key locations, seeking out
comparison groups provided by nature or public policy
- In correlational studies, simply select some people, measure two variables, and test the relationship

29
Q

Quasi-Experiments vs Correlational Studies- Neither* use manipulated variables

A

*Quasi-experiments use techniques like “active selection”
- Can obtain people by targeting key locations, seeking out comparison groups provided by nature or public policy

30
Q

Small-N Designs

A

a study of only a few individuals
- Sometimes only 1 participant- single-N

31
Q

for external validity, what is important?

A

How sample is selected is more important than size- for external validity
- If a large effect size is expected, a small sample can detect it

32
Q

Large-N vs small-N (participants)

A

Participants:
- large-N: grouped- all participants in each group combined, studied together
- small-N: each participant treated as separate experiment
- Repeated-measures design- how they respond to
systematically designed conditions

33
Q

Large N vs small N- data

A

Data:
large-N: data represented as group avgs
small-N: data for each individual

34
Q

balancing priorities in case study research

A

Experimental control, manipulation, and replication

35
Q

how are case studies like quasi-experiments?

A

Just like quasi-experiments take advantages of natural events, small-N studies often take advantage of special medical cases under controlled conditions

36
Q

disadvantages of small-N studies

A
  • Internal validity- difficult to isolate what’s being studied
  • external validity
    -Participants in small-N studies may not represent general
    population
        - One option: triangulate
37
Q

triangulate

A
  • Compare case studies result to research using other methods
  • Variety of evidence supports a parsimonious theory

triangulate- combining results of single-N studies with other groups

38
Q

Behavior-change studies in clinical settings

A
  • Can use Small-N designs in clinical settings to learn whether their interventions work
  • Often used in behavior analysis- a technique in which practitioners use use reinforcement principles to improve a client’s behavior
  • Studies designed to modify the behaviors of certain individuals to produce a desired result
39
Q

behavior analysis

A

a technique in which practitioners use use reinforcement principles to improve a client’s behavior

40
Q

Stable baseline designs

A

Stable baseline designs: a researcher observes a behavior for an extended baseline period before intervention or treatment is introduced

  • If baseline is stable and the target behavior changes after the treatment, its effective
  • Internal validity- stable baseline allows us to rule out maturation, regression to the mean, or a history effect

Then we can use replication to further enhance causal claims

41
Q

Expanded rehearsal

A

Expanded rehearsal: a memory technique used on a real alzheimers patient
(standard baseline design)

42
Q

Multiple-baseline designs + ex

A

researchers stagger interventions across situations, times, or contexts

ex: Contact desensitization plus reinforcement (autistic child gets treat when moving closer to therapy dog)

43
Q

Reversal designs (ABA design):

A

Reversal designs (ABA design): researchers usually observes a baseline of behavior before treatment, next behavior during treatment, then they remove the treatment to see if the behavior reverts back (reversal period)

  • Appropriate mainly in situations where a treatment may not cause lasting change
44
Q

types of Behavior-change studies in clinical settings

A
  • Stable baseline designs
  • Multiple-baseline designs
  • Reversal designs (ABA design)
45
Q

internal validity in small-N designs

A

Can be very high if study is carefully designed- behaviors measured repeatedly

46
Q

external validity in small-N designs

A

Can be problematic depending on goals of study- generalization not always relevant

1)triangulate- combining results of single-N studies with other groups

2) can specify population they want to generalize- might limit to a particular subset

3) might not care about generilizing in this study- might still be useful if only to one person

47
Q

construct validity in small-N designs

A

Can be very high if definitions and observations are precise
- Sometimes need multiple observers and check for interrator reliability in case one observer is biased or behavior is difficult to identify

48
Q

statistical validity in small N

A

Not always relevant to small-N studies- do not typically use traditional statistics
- Still draw conclusions from data and should treat it appropriately
- Often visual summary such a graphs are strong enough

Research Methods in Psych (14 decks)

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