[L5] Repeated Measures Experiments Flashcards

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1
Q

Two Types of Experimental Designs

A

Repeated Measures Design
Independent Groups Design

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2
Q

comparing the scores of
individuals in one condition against their scores in
another condition.

A

Repeated Measures Design

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3
Q

comparing the scores of
one group of people taking one condition against the
scores of a different group of people in the other
condition.

A

Independent Groups Design –

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4
Q

Other Names:

A

Within-subjects studies / designs
Related groups / design
Cross-over studies / design

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5
Q

change within a
group of individuals, rather than between two groups.

A

Within-subjects studies / designs –

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6
Q

people undergoing two
different treatments are closely matched, so that the two
groups are not independent, rather they are related.

A

Related groups / design –

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7
Q

*strictly speaking, a ___design is a type
of related design; however, it is very rare to encounter a
study where both groups are ____.

A

repeated measures; sufficiently closely
matched

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8
Q

term mainly used in
medical research than commonly used in psychology.
People cross-over from one group to the other group.

A

Cross-over studies / design

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9
Q

Common Mistakes:

A

Correlational and Repeated Measures
Designs

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10
Q

__ – when we want to see if people who
were high scorers on one test are also high scorers on the
second test. We are not interested in whether the scores
overall have gone up or down.

A

Correlational

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11
Q

– if people on average, score
higher on one occasion than the other.

A

Repeated Measures

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12
Q

Advantages of Repeated Measures Design

A
  1. There is no need for many participants.
  2. Each person acts as their own (perfectly) matched
    control group.
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13
Q

Disadvantages of Repeated Measures Design

A
  1. Practice effects
  2. Sensitization –
  3. Carry-over effects
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14
Q

participants gets better at a task over
time. (solutions: counterbalancing and practice items)

A

Practice effects –

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15
Q

– participants may perceive that a
dependency exists between two measures, and
deliberately keep their answers similar when we are
looking for change. Alternatively, because the
participants perceive that the researcher is looking for
change, they might change their answers.

A

Sensitization

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16
Q

occurs when something about the
previous condition is “carried over” into the next
condition.

A

Carry-over effects –

17
Q

Statistical Tests for Repeated Measures Designs

A
  1. The Repeated Measures t-test –
  2. The Wilcoxon test
  3. The Sign Test
18
Q

parametric test for
continuous data.

A

The Repeated Measures t-test –

19
Q

non-parametric test for ordinal
data

A

The Wilcoxon test –

20
Q

non-parametric test for categorical
data

A

The Sign Test –

21
Q

the most powerful, and most likely to spot
significant differences in data. It can not be used
however with all repeated measures data. Data should
also satisfy some conditions before this test can be used.

A

t-test -

22
Q

deals with all data that can be ordered
(ordinal data).

A

Wilcoxon test –

23
Q

– only deals with data in the form of categories
(nominal data). Easy to understand and calculate.

A

Sign test

24
Q

Down side of _____ – only deals with crude categories, rather
than rich data of ranks or intervals.

A

Sign test

25
Q

The Repeated Measures t-test
To use this test, we need to make 2 assumptions about our
Data:

A

The data are measured on a continuous (interval)
level.
2. The differences between the two scores are normally
distributed.

26
Q

It makes no assumption about the distribution of the
scores. Only the ___ between the scores.

A

differences

27
Q

It is possible to have variables which have highly ___, but which have normally
distributed differences.

A

nonnormal
distributions

28
Q

It makes no assumption about the ___ of the
variables.

A

variances

29
Q

Given a sufficiently large sample, the repeated measures
t-test is ____ of both these
assumptions.

A

robust against violations

30
Q

Above sample sizes of approximately ___, the test
becomes very robust to violations of distributional
assumptions.

A

50

31
Q
A