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Research Psychology > Exam 3 > Flashcards

Exam 3 Flashcards

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1
Q

Quasi-Experiments

A

Independent variable is manipulated despite no random assignment, groups aren’t equivalent
- Practical/ethical reasons
- Must carefully monitor threats to internal validity
- Could yield meaningful results even w/ no causation established

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2
Q

Pre-Experimental Designs

A

Data collected in a way that offers multiple explanations for data

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3
Q

Types of Pre-Experimental Designs

A
  • One Shot Study
  • Pretest/Posttest (one group)
  • Nonequivalent control group
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4
Q

One-Shot Study

A

One group of participants is tested
- Disadvantage: How do we know if there was any change?
- Example: count drug-related deaths after a rehab program is implemented

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5
Q

Nonequivalent Control Group

A

An experimental and nonequivalent control group are tested once
- Disadvantage: may already be significant differences between groups
- Example: count drug-related deaths in this and another city after implementing rehab program

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6
Q

Pretest/Posttest

A

One group of participants is tested at pretest and posttest
- Disadvantage: Something else could have happened between the two testings
- Example: compare drug-related deaths before and after a rehab program is implemented

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7
Q

Types of Quasi-Experiments

A
  • Pretest-Posttest Nonequivalent Control Group
  • Time-Series Design
  • Multiple Time-Series Design
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8
Q

Pretest-Posttest Nonequivalent Control Group

A

Two comparable but not randomly assigned groups of participants are tested before and after treatment
- Allows you to measure relative change between two groups

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9
Q

Time-Series Designs

A

One group is tested several times before and several times after treatment
- If data show a consistent trend, the likelihood that results are due to a confounding variable is reduced

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10
Q

Multiple Time-Series Design

A

Two nonequivalent groups are tested several times before and several times after treatment
- Can observe change in experimental group before and after manipulation
- Can measure relative change between both groups when independent variable manipulation is introduced
- Essentially a combo of time-series design and nonequivalent control groups design

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11
Q

Threats to Internal Validity of Quasi-Experimental Designs

A
  • When multiple measures are made
  • Statistical Regression
  • Subject Attrition (Mortality)
  • Selection Bias
  • Interactions of selection with other threats to internal validity
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12
Q

When Multiple Measures are Made (Threats to Internal Validity in Quasi-Experiments)

A

Can lead to history, maturation, testing, and instrumentation effects
- Can be controlled by assessing/accounting for issues

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13
Q

Statistical Regression (Threats to Internal Validity in Quasi-Experiments)

A

Extreme scores are likely to move toward the mean upon retesting
- Can be avoided by not choosing participants on the basis of extreme scores

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14
Q

Subject Attrition/Subject Mortality (Threats to Internal Validity in Quasi-Experiments)

A

Loss of participants’ data either due to participants withdrawing from study OR because a decision was made to drop their data due to criteria
- Threat when dropout rate is very high or when dropout rate varies between groups
- Bigger problem with nonequivalent groups

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15
Q

Selection Bias (Threats to Internal Validity in Quasi-Experiments)

A

Differences between the comparison groups within a study
- Sometimes no better alternative
- Example: if someone had sleep apnea it would be hard to access enough patients to assign some to a no-treatment group, so recruit family/friends

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16
Q

Interactions of Selection with Other Threats to Internal Validity (Threats to Internal Validity in Quasi-Experiments)

A

Occurs when extraneous variables effect one group, but not the other
- Selection can also interact with extraneous variables like maturation, instrumentation, and regression towards the mean

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17
Q

Factorial Designs

A

The effect of a dependent variable of two or more independent variables is assessed simultaneously
- Can save on time and participants

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18
Q

Factor (Factorial Design)

A

Another name for independent or subject variables in the design
- Equal to number of main effects
- Marginal means

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19
Q

Levels of Factors (Factorial Design)

A

Different levels of each factor (would be conditions in non-factorial design)

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20
Q

Unique Condition (Factorial Design)

A

Unique combination of factors
- equal to levels of all factors multiplied

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21
Q

Types of Factorial Designs

A
  • Within-Groups Design
  • Between-Groups Design
  • Mixed Design
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22
Q

Higher Order Design

A

3 or more factors

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23
Q

Within-Groups Design (Factorial Design)

A

All participants experience all levels/combos of all factors
Advantages: saves time, lower error variance, less participants
Disadvantages: carryover effects, can’t test subject variable

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24
Q

Between-Groups Design (Factorial Design)

A

Each combination of factors is experienced by a different group of participants
Advantages: no carryover effects (scores shouldn’t change from multiple testing)
Disadvantages: more participants/time, higher error variance

25
Q

Mixed Design (Factorial Design)

A

Some factors are within-groups and some are between-groups
- Chosen for practical reasons

26
Q

How to Analyze Factorial Designs

A

Use some sort of ANOVA (Analysis of Variance)
- Dependent variable: y-axis
- Independent variable: one on x-axis, others are separate lines
Generally
- Factor w/ most levels OR continuous factor goes on x-axis
- Done as line graphs even if both factors are categorical

27
Q

How to Ensure a Well-Designed Factorial Design

A

Avoid possible confounds
- Pilot Study: mini version of the experiment with a few participants

28
Q

Correlation Coefficients

A

Requires at least 2 scores from each participant to calculate
- Absolute difference from zero = strength
- Sign = direction

29
Q

Regression

A

Using correlation coefficients to predict scores

30
Q

Multiple Correlation

A

Measures relationships between multiple measures and one particular measure
- 0.00-1.00
- only looks at strength, not direction

31
Q

Multiple Regression

A

Use multiple correlation coefficient to predict a particular measure
- Provides info about degree to which each initial measure contributes to prediction of measure of interest

32
Q

Observational Research

A

Investigations involving no manipulation of an independent variable
- Use operational definition
- Requires forethought and planning

33
Q

Naturalistic Observation

A

Unobtrusively observe behavior/do nothing to interfere with natural behaviors
- Participants may not realize they are being observed
- Often broad focus, observe/record many different types of behavior but may have specific focus
Issues:
- Does researcher’s presence affect behavior?/How would the researcher know if it did?

34
Q

Participant Observation

A

Becoming an active participant in the situation being studied
- Passive observation yields limited/biased info and a lack of context

35
Q

Disguised Participant Observation

A

Other participants are unaware researcher is observing them

36
Q

Undisguised Participant Observation

A

Other participants know researcher is observing their behavior
- Often used in anthropology

37
Q

Field Experiments

A

A controlled study that occurs in a natural setting with random assignment
- Independent variable manipulated
- Dependent variable measured
- Determines causation
- Potential issues w/ informed consent
- Greater external validity than lab experiments, but some issues with internal valdiity

38
Q

Habituation

A

Spend enough time among participants that they resume normal behavior

39
Q

Desensitization

A

Gradually move closer to participants until one can be near/along them

40
Q

Laboratory Experiments

A

Experiments done in a controlled lab setting with controlled variables

41
Q

Advantages of Observational Research

A

Offers a better understanding of natural enviroment interactions
- Starting point for research on a new topic (helps avoid jumping into costly lab study)
- Can be used after lab research to view phenomena in a natural setting

42
Q

Disadvantages of Observational Research

A

Most are correlational, therefore NO CAUSATION
- Confounds such as: Hawthorne Effect, Reactivity, Observer Bias, etc.

43
Q

Reactivity

A

The actual change in a participants behavior when they know they’re being studied

Minimizing Reactivity
- Desensitize
- Habituate
- No guarantee measurement isn’t reactive, only unobtrusive measures can guarantee no reactivity

44
Q

Reactive Measures

A

Measured behavior that changes because a participant is aware they are being studied

45
Q

Interobserver Reliability

A

The degree to which a measurement procedure yields consistent results when used by different observers

46
Q

Hawthorne Effect

A

Effect of observer on participant behaviors
- Reference to study at Hawthorne Plant by Western Electric Company

47
Q

Video/Audio Recording (Observational Research)

A

Advantages: Less Conspicuous, Permanent Record
Disadvantages: Less Flexible/Complete, still susceptible to Observer Bias

48
Q

Video Recording (Observational Research)

A

Advantages: May be less intrusive, Gives visual information
Disadvantages: Only records what is in front of it. May miss larger context

49
Q

Audio Recording (Observational Research)

A

Advantages: Easiest to conceal/keep from influencing behavior
Disadvantages: No video

50
Q

Observer/Experimenter Bias

A

Occurs when researcher has an expectation about the study results and unconsciously perceives and records differently

51
Q

Demand Characteristics

A

Participants change their behavior based on what they think the study is looking for

52
Q

How to Calculate Agreement Between Observers

A

(Number of Agreements/Number of Opportunities to Agree) * 100

53
Q

Static vs. Action Checklist

A

Record things that will not change during the observation VS. record presence/absence of specific behaviors/characteristics over time

54
Q

Sampling Techniques

A

How you choose a sample:
- Pick the group your observe
- Pick when you observe them
- Pick where you observe them

Can be used to generalize observations/conclusions to a larger population, or can be used to focus on a specific group

55
Q

Biased Sample

A

A sample that is unrepresentative of the population

56
Q

Behavior Sampling

A

A researcher observes subsets of participant behavior at different times and/or in different situations

57
Q

Time Sampling

A

Record behavior at particular time intervals
- Systematic
- Random

58
Q

Event Sampling

A

Random/Systematic sampling of events that include the behavior of interest
- When behaviors don’t necessarily occur on a continuous basis
- Systematic
- Random

59
Q

Situation Sampling

A

Observations made in different circumstances/settings
- Increases external validity
- Doesn’t rely on a single sample to be representative

Research Psychology (4 decks)

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