Comparison of dependent & independent samples designs

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Dependent samples design can have problem of

rank order effects or carry over effects. Counterbalancing may reduce impact of rank order effects, but not carry over effects.

provided scores are positively correlated between repeated measures, dependent samples design leads to

less error variance than independent samples design (because participant variables are held constant rather than allowed to vary randomly)

Standard error of M1 - M2 when samples are dependent

is smaller than when samples are independent

For independent samples, M1 and M2 values will be uncorrelated

because, on any replication, different participants contribute to M1 and M2

For dependent samples, M1 and M2 values will be correlated

because, on any replication, the same participants contribute to both M1 and M2

For independent samples, correlation between scores in Condition 1 and Condition 2

should be zero (necessarily so)

For dependent samples, correlation between scores in Condition 1 and Condition 2

should be positive.

Carry-over effects

effects of one condition carry over to the next

eg perception expt - four conditions, A B C D
C = flashbulb, then C will mask effects of conditions that follow it.
eg order 1: A C D B - effect of C will carry over to condition D (and maybe B)
order 2: C B A D - effect of C will carry over to condition B (and maybe A , D)

not solved by counter-balancing

Counterbalancing

e.g. two conditions A and B
half participants do A then B; half participants do B then A
e.g. three conditions, A B C
one third do ABC; one third do BCA; one third do CAB
random permutations
each participant does a random order of conditions

Negative rank order effects

fatigue, boredom

Positive rank order effects

practice, learning, reduction in nervousness or anxiety

Problem of rank order effects can be solved by

counter-balancing

Rank order effects

extraneous influences on DV can arise when multiple conditions are presented, where conditions presented earlier may be responded to differently than conditions presented later.

random allocation refers

to control of extraneous variables, increases internal validity

Measuring same participants (repeated measures design) or matched pairs produces a

dependent samples design

Measuring same participants (repeated measures design) or matched pairs produces a dependent samples design Benefits:

Ensures that distribution(s) of scores on EVs related to participants are held constant from one condition to next. This can greatly reduce the standard error for the difference between conditions

random sampling

refers to how participants are sampled from the population; and ensures that the sample is representative of the population; hence results can be generalised
increases external validity

random sampling increases

external validity

random allocation increases

internal validity

random allocation is used to convert possible systematic errors

into random errors

does not equate groups; rather random allocation

distributes EVs impartially between groups

Independent and dependent samples designs provide two different ways of controlling for EVs related to participants

individual difference variables such as intelligence, experience

random allocation of participants to groups

(independent samples design)

measuring the same participants

(or matched pairs) across all conditions (dependent samples design)

Random allocation of participants to levels of IV

provides some control over all potential participant EVs

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