Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

PYB210 > 9. Repeated Measures Designs > Flashcards

9. Repeated Measures Designs Flashcards

1
Q

what is the process of research design and data analysis?

A 
review previous research
operationalise IV and DV
choose appropriate design
Determine sample size for adequate power
collect data
analyze data and report findings
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

what are the options for an experimental design when choosing what design would be the most appropriate?

A

independent groups
matched-pairs
repeated measure

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

what are things to consdering when designing an experiment

A

nature of the IV
Effect size
expense of project or availability of participants

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

when considering the control of order effects, what happens when we cant control them?

A

then you will have to use an independent groups design

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

what is the definition of a repeated measures design

A

all participants contribute a score at each level of the IV

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

what is repeated measure design also known as?

A

dependent groups or within groups design

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

wht are the two categories of levels of IV related to time?

A 
with intervention (pre and post therapy)
natural change (changes in cognitive ability in children over time)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

what are levels of IV not related to time

A

IV is exposure to categorical elements (e.g. light intensity)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

what are the advantages of RM designs?

A

economy of participants

sensitivity is enhanced by separating individual differences from experimental error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

what are the disadvantages of RM designs?

A 
cant use with all IVs (e.g. ethnicity)
order effects (practice, fatigue, carrover)
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

define precision matched

A

where each participant is directly matched with others in the other levels of the IV

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

what are common issues with RM designs?

A

maturation
history
attrition/mortality

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

what is maturation

A

changes naturally occurring with time eg. learining

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

what is history?

A

uncontrolled event occurres between testing conditions

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

what is attrition or mortality?

A

participants drop out of study

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

what are common order effects?

A

practice effect
fatigue effect
carry-over effect

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

what are practice effects

A

performance at one level improves to the next

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

what are fatigue effects

A

performance declines on repeated testing

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

what are carry-over effects?

A

one level of IV affects another level

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

what are remedies of practice and fatigue effects?

A

can be controlled by counter balancing or randomisatin and prior exposure

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

what is counterbalancing or randomising?

A

randomisation or the oder to treatments across participants

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

what is prior exposure

A

prior exposire to measurement before exposure to experimental condition may reduce practice effects

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

how does one control a carryover effect?

A

can rarely be controlled but you can someone help prevent this by a long delay between testing each level of the IV. but you should use a BG design if you suspect that they will operate with the IV you are using

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

what does counterbalancing aim to do?

A

seek to diminish the effect of order effects

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

what is the process of randomisation?

A

each participant gets exposed to each level of IV random;y

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

what is the process of counterbalancing?

A

each conditions appears in a given order an equal number of times

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

what do we compare in independent groups analyses?

A

we compare groups to each other

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

what contributes to error in independent groups analyses?

A

individual differences

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

what does RM analyses allow for control of?

A

individual differences that can contribute to error

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
30
Q

why do RM analyses control individual differences?

A

because we compare each participanross conditions

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
31
Q

what does RM analyses statistically do to reduce error?

A

removes variability due to individual differences

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
32
Q

what does RM analyses allow further partioning of?

A

SS_total (index of variability)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
33
Q

how does SS_total (total variability) partition?

A

total variability partitions into BG variability (SS_between OR SS_A) and WG variability (SS_within)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
34
Q

how does WG variability (SS_within) partition firther?

A

WG variability (SS_within) partitions to:

Participant variability (SS_participant or subject)

and

Error variability (SS_error or residual OR SS_AxS)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
35
Q

what is N?

A

the number of scores (not participants)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
36
Q

what is n?

A

number of participants

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
37
Q

what is the equation for df_total?

A

N-1 or an-1

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
38
Q

what is the equation for df_between (or df_treatment or df_A)?

A

a-1

39
Q

what is the equation for df_within

A

N-a

40
Q

what is the equation for df_participants

A

n-1

41
Q

what is the equation for df_error)

A

(n-1)(n-a)

42
Q

how does one calculate subjects variability (SS_subjects or participants)

A

aΣ(M_s - GM)^2

(participant mean - grand mean)

where a=number of conditions

43
Q

how does one calculate treatment (or between) variability (SS_between)

A

condition mean - grand mean
n(M_j - GM)^2

where n = number of participants

44
Q

how does one calculate total variability (SS_total)?

A

each score - gran mean

Σ(X_ij = GM)^2

compare every single score obtained to the grand mean

45
Q

how does one calculate error variability (AKA SS_residual or SS_AxS)?

A

SS_error = SS_total - (SS_between + SS_subjects)

46
Q

what does ‘a’represent here?

A

number of conditions

47
Q

what is SS_total?

A

SS_total = SS_between + SS_participants/subjects + SS_error

48
Q

what is the equation of MS?

A

SS/df

49
Q

what is the equation of F for repeated measures?

A

F = MS_Between / MS_error

50
Q

what does a high F ratio mean with regard to the p value?

A

high f ration means low P value

51
Q

when will a F ratio be larger with regard to repeated measures design and individual groups design?

A

f ratio will always be larger in a RM design

52
Q

why will the error term of a RM design be smaller?

A

because we are removing all the variability due to individual differences from the error term

53
Q

what are the assumptions of RM analyses?

A

normality
independence
sphericity

54
Q

what is the assumption of normality in RM designs?

A

is required as in the IG case

55
Q

what is the assumption of independence>

A

it is not a problem because although the scores are not independent in a RM design due to the fact that the same participants participate in each condition, these participant effects have been eliminated

56
Q

which assumption is specific to RM designs?

A

sphericity

57
Q

what is sphericity?

A

refers to homogeneity across conditions and participants, so homogeneity of the variance and co-variance matrix

58
Q

for within-subjects factors with more than 2 levels, what can conditions of the sphericity assumption cause?

A

serious inflation of type 1 error

59
Q

why is sphericity often breached?

A

because it is a very restrictive assumption

60
Q

what are the two ways to deal with the restrictiveness of the sphericity assumption?

A

the traditional method and multivariate method

61
Q

what is the traditional test of sphericity that SPSS uses?

A

Maulchley’s sphericity test

62
Q

what does it mean when Maulchley’s sphericity test is significant?

A

the sphericity assumption is breached

63
Q

when is Maulchley’s sphericity test significant?

A

when p is LESS THAN .05

64
Q

why cant we use the normal F distribution any more with sphericity?

A

because it assumes that we have already met the sphericity assumption

65
Q

what is the process of correcting breaches of sphericity?

A

adjust df in line with magnitude of the breach of sphericity to account for type 1 error

if sphericity breached, use these adjusted df to test F ratio

66
Q

what are epsilon values?

A

different formulas for adjusting our df to compensate for breaches of sphericity

67
Q

what is the range for epsilon values?

A

1 to 0
where 1 is perfect
and
0 is extreme violation of sphericity

68
Q

what do epsilon values do?

A

adjust df (reduce them) based on the severity of the violation

69
Q

what are the epsilon value adjusted df used for

A

used to find the critical value of F to which the F_observed is compared

70
Q

what happens when you use the epsilon values adjusted df to find the F_critical and compare it to the F_observed>

A

results in larger critical value & more conservative test

71
Q

if Maulchly’s test of sphericity is significant…

A

the assumption has been breached

72
Q

what does it mean if the significant value is above .05?

A

there is no significant violation of sphericity

73
Q

what do the epsilon values indicate?

A

how bady the sphericity assumption has been breached

74
Q

which is the most commonly used epsilon figure?

A

greenhouse geisser correction

75
Q

on an SPSS output, which rows do we have to look at?

A

either greenhouse-geisser for both the IV and error

76
Q

what row on SPSS output would you look at if you think there is no breach in sphericity?

A

sphericity assumed for both IV and error

77
Q

if sphericity is violated but F isnt significant, what do you do to the H0?

A

retain it

78
Q

if sphericity is violated by F is significant what do you do to the H0?

A

apply epsilon correction

79
Q

if the f is significant for the epsilon corrected df what do you do to the H0?

A

reject

80
Q

if the f is not significant for the epsilon corrected df what do you do to the H0?

A

accept

81
Q

what is the multivariate approach to RM ANOVA?

A

extends the difference scores analysis we used in RM t-tests to within-subjects factors with 3 or more levels

82
Q

what is a RM t-test based on?

A

difference scores

83
Q

what happens when we analyse difference scores?

A

we remove or “partial out” the consistency in scores for each person from one level of the IV to another

84
Q

what is the multivariate approach to RM based on?

A

analysis of difference scores?

85
Q

why does the analysis become more complicated when there are more than two conditions>

A

becuase there will be multiple difference scores

86
Q

what does the multivariate approach do?

A

it treats each set of differences scores as separate dependant variables

87
Q

what is the multivariate approach using to analyse?

A

separate error terms for each pair of conditions rather than a pooled error term, which means we dont have to worry about sphericity

88
Q

what are identical when there are only 2 levels of the IV?

A

the multivariate approach and traditional approach

89
Q

how does power and effect size theoretically differ with RM ANOVA and between groups ANOVA?

A

they dont, they are identical

90
Q

what is the difference between the error term in RM ANOVA and BG ANOVA>

A

the error term is smaller for RM ANOVA

Because MS_residual.error is smaller than MS_within as variance is due to individual differences is partitioned out

91
Q

why is power greater in RM design than in BG design even though they have the same effect size?

A

because in RM design, MS_residual is used which paritions out the issue of individual difference this higher power

92
Q

how do we do post hoc and planned comparisons on RM designs?

A

we can do these using the dependent samples t-test procedure and use a bonferoni adjustment to maintain a good type 1 error rate

93
Q

when looking at the multivariate approach output, what value is usually used?

A

Pillai’s trace

94
Q

how would you interpret the Phillais trace value in a multivariate output?

A

like you would a normal F test - regardless if sphericity is breached or not

PYB210 (11 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions