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Psych 110 > [L5] Repeated Measures Experiments > Flashcards

[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 –

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17
Q

Statistical Tests for Repeated Measures Designs

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

parametric test for
continuous data.

A

The Repeated Measures t-test –

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19
Q

non-parametric test for ordinal
data

A

The Wilcoxon test –

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20
Q

non-parametric test for categorical
data

A

The Sign Test –

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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 -

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22
Q

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

A

Wilcoxon test –

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23
Q

– only deals with data in the form of cat0egories
(nominal data). Easy to understand and calculate.

A

Sign test

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24
Q

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

A

Sign test

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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.

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26
Q

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

A

differences

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27
Q

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

A

nonnormal
distributions

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28
Q

It makes no assumption about the ___ of the
variables.

A

variances

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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
  • Used when data do not satisfy the assumptions of the
    repeated measures t-test.
A

The Wilcoxon Test

32
Q

When is Wilcoxon used

A

The differences are not normally distributed
* The measures are ordinal.
* Non-parametric test

33
Q

– makes inferences about population
parameters.

A

Parametric test

34
Q

use ranks.

A

Non-parametric tests

35
Q

If data were not measured on an ___ scale we convert
them.

A

ordinal; continuous to ranks

36
Q

developed two statistical tests namely
the Wilcoxon-rank sum and the Wilcoxon signed ranks
test.

A

Frank Wilcoxon –

37
Q

Frank Wilcoxon – developed two statistical tests namely
the ___ and the
___

A

Wilcoxon-rank sum; Wilcoxon signed ranks test.

38
Q

Rank-sum – equivalent to ___which is
easier to calculate

A

Mann-Whitney test

39
Q

Thus when we refer to a Wilcoxon test, we refer to the
____ test only.

A

Signed ranks

40
Q
  • Formula used which converts T value into a value of z.
A

Normal Approximation

41
Q

normal approximation Can be used as long as sample size is __

A

above 10

42
Q

Used during times when table can’t be used, for example
when the sample size is ___ than the values given in
the table

A

bigger

43
Q

Instead we could report the ___, and ____, for each group

A

medians, inter-quartile
ranges

44
Q

used to correct for continuity.
The z distribution is continuous – that is, any value at
all is possible.

A

Continuity Correction –

45
Q
  • Used when we have nominal data with two categories,
    and have repeated measures data.
A

Sign Test

46
Q

Easiest statistical test.

A

Sign Test

47
Q

How to test the statistical significance of a t-score (3 ways)

A
  1. Get the p-value of the t-score (probability of getting
    the score as a result of chance if the NULL is true)
  2. Get the t-critical value and compare the t we got.
  3. Calculate the Confidence Intervals
48
Q

For result to be significant, p-value should be low. It
should ____ we use for
significance testing (alpha levels could be 0.05, 0.01,
0.001 etc. Choice depends on a researchers tolerance for
error)

A

be equal to or less than the alpha level

49
Q

The
____ is the t-score which has a p-value equal to
the alpha level we use. It is relative to the sample size as
exemplified by the use of the concept of degrees of
freedom

A

t-critical

50
Q

For our t (the t-score we got) to be significant it should
be ___the t-critical value.

A

equal to or more than

51
Q

The CI tells us the likely ___, or if the population is measured instead of
the sample.

A

`range of the score in the
population

52
Q

In this test, the score we are referring to is the __
_ (summation of difference scores computed from
the two groups/conditions)

A

difference
score

53
Q

We are calculating the CI because we do not usually
have means to measure the population, hence we will
really not know the _____. The most that
we can do is estimate the ___

A

TRUE DIFFERENCE
SCORE.

54
Q

The CI provides us that estimate by giving us the likely
__

_ if the population
is measured

A

range of values of the difference score

55
Q

Why the middle 95% of cases? Because, the scores
within this area, range, or interval are the scores
considered to have
“____ compared to the other scores in the
distribution.

A

higher p-values or probabilities of
occurring

56
Q

Thus, if our result is statistically significant, it should be
contained within the CI. It follows the rule that, if the __ could
be true, it should have a high probability of occurring

A

alternative hypothesis

57
Q

This is opposite of what we assume in the first place that
if the Null Hypothesis is true, our result should have a
_
_

__

A

low p-value or probability of occurring.

58
Q

The Null Hypothesis then should not be contained within
the CI for our result to be ___

A

significant

59
Q

If it happens that the Null hypothesis is contained in the
CI, the result is
__

A

not significant.

60
Q

However, the Wilcoxon T distribution is not truly
continuous, because it can only change in __

A

steps

61
Q

It is hard to see how we could have got a value of 28.413
from our data, because we added __.

A

ranks

62
Q

In continuity correction; we just add __ to the top of the
equation:

A

-0.5

63
Q

if we employ a continuity correction, we
are sure that our __rate is controlled at, or
below 0.05.

A

type 1 error

64
Q

If we don’t, then the type 1 error rate might be ___

A

above
0.05.

65
Q

However, the price of using the continuity correction is
(always) a ___

A

slight loss of power.

66
Q

Sheskin (2003) suggests that we should perhaps analyze
the data ___, once with and once without the
continuity correction.
* If it makes a difference, then we should collect more
data.

A

twice

67
Q

the calculation of z assumes that
there are ___ – that is, no one had the
same score on both occasions. If this is the case, we need another/further correction.

A

no ties in the data

68
Q

We would add the number of ties, called t, into the
___

A

equation

69
Q

The test statistic from the sign test is called __, the smaller of these two values.

A

S,

70
Q

The sign test uses N as the total number of people from
whom there was ___

A

not a tie.

Psych 110 (10 decks)

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