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Research Methods > Experimental Design > Flashcards

Experimental Design Flashcards

1
Q

IV

A
  • independent variable
  • systematically varied/manipulated by researcher
  • 2 comparison levels
  • SITUATIONAL (ie. bystanders in helping beh study)
  • TASK VARIABLES (ie. groups w/differing logical problems to solve)
  • INSTRUCTIONAL (ie. groups instructed to memorise objects via images OR no instructions)
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2
Q

DV

A
  • dependent variable

- outcome/measurement the effects upon which are observed by researcher

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3
Q

Manipulating IV

A
  • non-systematic random allocation (ie. coin toss), ruling out systematic differences (ie. IQ, personality), to either:
  • CONTROL GROUP/CONDITION: no manipulation
  • EXPERIMENTAL GROUP/CONDITION: manipulation
  • direct manipulation often impossible so…
  • INDIRECT MANIPULATION: theoretical variables affected indirectly then checked via “manipulation check”
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4
Q

MIV: Indirect Manipulation (Example)

A
  • IV = attribution for failure (internal/external)
  • purposeful failure exposure (ie. test) followed by reflection of INTERNAL contribution (ie. character)
  • other pps asked for reflection of EXTERNAL contribution (ie. lack of revision/luck)
  • manipulation check (ie. scale of internal/external failure after reflection) to see if outcome is desired (ie. internal reflectors answer on internal scale)
  • DV difference testing now possible
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5
Q

EV

A
  • extraneous variable
  • not of interest but influence DVs and threaten validity of findings via obscuring measurement of interest
  • if uncontrolled, they may systematically influence DVs, leading to a confounding result
  • not all are possible confounds (ie. as long as age is similar in a sample, it’s fine) BUT still negatively impact study:
    IE. A study w/only 60y+ pps can have limited EXTERNAL VALIDITY; findings ungeneralisable beyond age (ie. kids)
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6
Q

CV

A
  • confounding variable (“special EV”)
  • unintended/accidental EVs associated w/IV, providing alternative result interpretation
  • a systematic effect of EV on DV could be mistaken for effect of IV
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7
Q

Measuring DV

A
  • refer to previous research
  • PILOT STUDY to find:
  • CEILING EFFECT; task too easy/overly high scores; disguised pp differences
  • FLOOR EFFECT; task too hard/overly low scores; disguised pp differences
  • SOLUTION; task moderate; found via pilot testing
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8
Q

MDV: DV Selection Issues (Example)

A
  • DV selection can often be complicated by practical constraints
    IE. Researcher looking at impact of alcohol consumption on roach fatalities:
  • IV manipulated via experimental groups consuming various alcohol quantities BUT unethical
  • irresponsible/unethical/illegal for DV manipulation (aka. pps in driving accidents)
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9
Q

MDV: DV Selection Solutions (Example)

A
  • high alcohol group consume legal limit but then DV (accidents) isn’t sufficiently sensitive to detect IV impact and still unethical
  • DVs must be relevant to outcome but sensitive to IV, so…
  • RELEVANCE-SENSITIVITY TRADE OFF looking at reaction times (critical determinant of safe driving)/VR driving simulator removing legal/ethical concerns
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10
Q

MDV: Relevance-Sensitivity Trade-Off

A
  • the more sensitive DV is to IV changes, the less relevant it may be to IRL phenomena
  • DV + IRL link may be strenuous, undermining EXTERNALL VALIDITY as “proxy measures” may not imitate target variable enough
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11
Q

QED: Variables

A
  • include gender/age/cultural group/IQ/personality traits; unmanipulated/self-selected but can be basis of group allocation
  • require additional considerations to avoid possible confounds
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12
Q

QED: Self-Selection Bias (Example)

A
  • think putting yourself forward as a volunteer; automatically you have qualities which may affect the DV in the study
  • WALD (1939); WWII; aircraft came home w/bullet holes; suggested reinforcement of areas must susceptible to damage; Wald said these were the planes RETURNING, so the other areas must be reinforced as that’s where grounded planes were being hit
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13
Q

Quasi-Experimental Design

A
  • some studies compare variables; IV differences but are untouched
  • causal inference unestablished
  • CANNOT claim IV causes DV; only that IV groups differ when interacting w/DV
  • think opposite of experimental designs.
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14
Q

QED VS ED

A

IE. Studying effect of self-esteem on altruistic behaviour.
ED) Manipulate self-esteem (ie. praise); random allocation to high/low esteem conditions, then altruism measured.
OUTCOME = can argue high self-esteem causes altruistic behaviour; opportunity of causality to explain relationship; possible contribution to relevant theory.
QED) Measure self-esteem; group based on scores (ie. high/low) then altruism measured.
OUTCOME = only claim that high self-esteem pps where more likely to behave altruistically than low self-esteem pps; no inferred causality so limited/impossible theory contribution.

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15
Q

QED: ED Interaction

A
  • manipulated and QED variables often blend in studies

- BANDURA’S BOBO DOLL (1973); EV = type of exposure to violence; QEV = gender (self-selected)

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16
Q

Between-Participants Design

A
  • ie. independent/non-repeated measures
  • pps assigned 1 condition
  • comparison between groups assigned randomly
  • used when IV is self-selecting
17
Q

BPD: Evaluation

A 
POSITIVES: 
- each pp fresh/naive to hypothesis
NEGATIVES: 
- more pps required
- unsuspected differences between pps
18
Q

BPD: Random Allocation

A
  • every pp has same chance of being placed into any condition
  • objective to spread important individual differences evenly across conditions
  • EVALUATION: groups may not be equal; doesn’t necessarily target important differences; confounding variables
  • equal groups achieved via:
    BLOCK RANDOM ASSIGNMENT
    STRATIFIED RANDOM ASSIGNMENT
    MATCHING PROCEDUCRE + RANDOM ALLOCATION
19
Q

BPD-RA: Blocked Random

A
  • allocate random block p/pp, then each block to a group; groups are now equal
    EVALUATION:
  • again, doesn’t necessarily target important characteristics
  • confounding variables
20
Q

BPD-RA: Stratified Random

A
  • identifies important characteristics and groups pps accordingly
  • allocate pps to applicable characteristic block and group/condition in turn
  • each block includes all conditions in randomised order
  • guarantees equal spread of pps p/group
  • ensures each condition has pp before condition repetition
  • required anticipation/measurement/accommodation of possible extraneous variables
    EVALUATION:
  • again, doesn’t necessarily target important characteristics
21
Q

BPD-RA: Matching Procedure

A
  • must have reason for variable affecting DV
  • get a score p/pp for matching variable in logical/accurate procedure
  • arrange scores in ascension
  • make 5 pairs, each w/adjacent scores
  • randomly assign 1 pp p/condition for each pair
  • variable is effectively controlled
    EVALUATION:
  • impractical/impossible if sample is large
22
Q

Within-Participants Design

A
  • ie. repeated measures
  • pps take part in 2/+ conditions
  • comparison within 1 group
  • used when conditions have briefs tests but extensive preparations (ie. psychophysiology) or pop is small
23
Q

WPD: Evaluation

A 
POSITIVES:
- small samples
- more data p/pp
- reduced error variance
NEGATIVES:
- small samples
- threats internal validity (ie. maturation of work)
- order effects
24
Q

WPD: Order Effects

A
  • PRACTICE EFFECT: later performance improved via practice
  • FATIGUE EFFECT: later performance reduced via fatigue
  • CARRYOVER EFFECT: one condition sequence differs in results to another; experience of C1 affects C2/vice versa
25
Q

WPD-OE: Counterbalancing

A
  • using 1+ sequences of conditions
  • either:
  • COMPLETE: every possible sequence used at least once (ie. 6 sequences for 3 conditions)
  • PARTIAL: uses subset of total sequences either sampling all possibilities OR randomising condition order p/pp (ie. Latin square)
26
Q

Experimenter Bias

A
  • errors in procedure due to researcher’s beliefs/behaviour/desires for results
    CONTROLLED VIA:
  • automate procedure
  • double-blind procedure (researcher/pps don’t know which condition is being tested)
27
Q

Participant Bias

A
  • errors in procedure due to participants’ unintentional/intentional influence
    INCLUDES:
  • Hawthorne effect; pps change behaviour as they know they’re being studied
  • demand characteristics (“please-u”/”screw-u” effects); “good” pp/”bad” pp gues hypothesis and try to help/destroy it respectively
  • evaluation apprehension
  • acquiescence effect
28
Q

PB: Control

A

CONTROLLED VIA:

  • DECEPTION: pps naive to study purpose; reduces DC/evaluation fear
  • PLACEBO: pps falsely given “treatment”; distinguishes real effects

Research Methods (10 decks)

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