Experimental Design

Question 1

IV

Answer 1

• 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)

Question 2

DV

Answer 2

• dependent variable

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

Question 3

Manipulating IV

Answer 3

• 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”

Question 4

MIV: Indirect Manipulation (Example)

Answer 4

• 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

Question 5

EV

Answer 5

• 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)

Question 6

CV

Answer 6

• 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

Question 7

Measuring DV

Answer 7

• 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

Question 8

MDV: DV Selection Issues (Example)

Answer 8

• 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)

Question 9

MDV: DV Selection Solutions (Example)

Answer 9

• 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

Question 10

MDV: Relevance-Sensitivity Trade-Off

Answer 10

• 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

Question 11

QED: Variables

Answer 11

• 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

Question 12

QED: Self-Selection Bias (Example)

Answer 12

• 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

Question 13

Quasi-Experimental Design

Answer 13

• 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.

Question 14

QED VS ED

Answer 14

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.

Question 15

QED: ED Interaction

Answer 15

• manipulated and QED variables often blend in studies

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

Question 16

Between-Participants Design

Answer 16

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

Question 17

BPD: Evaluation

Answer 17

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

Question 18

BPD: Random Allocation

Answer 18

• 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

Question 19

BPD-RA: Blocked Random

Answer 19

• 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

Question 20

BPD-RA: Stratified Random

Answer 20

• 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

Question 21

BPD-RA: Matching Procedure

Answer 21

• 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

Question 22

Within-Participants Design

Answer 22

• 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

Question 23

WPD: Evaluation

Answer 23

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

Question 24

WPD: Order Effects

Answer 24

• 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

Question 25

WPD-OE: Counterbalancing

Answer 25

- 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)

Question 26

Experimenter Bias

Answer 26

- 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)

Question 27

Participant Bias

Answer 27

- 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

Question 28

PB: Control

Answer 28

CONTROLLED VIA: - DECEPTION: pps naive to study purpose; reduces DC/evaluation fear - PLACEBO: pps falsely given "treatment"; distinguishes real effects