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Flashcards in 9. Repeated Measures Designs Deck (94):
1

what is the process of research design and data analysis?

review previous research
operationalise IV and DV
choose appropriate design
Determine sample size for adequate power
collect data
analyze data and report findings

2

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

independent groups
matched-pairs
repeated measure

3

what are things to consdering when designing an experiment

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

4

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

then you will have to use an independent groups design

5

what is the definition of a repeated measures design

all participants contribute a score at each level of the IV

6

what is repeated measure design also known as?

dependent groups or within groups design

7

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

with intervention (pre and post therapy)
natural change (changes in cognitive ability in children over time)

8

what are levels of IV not related to time

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

9

what are the advantages of RM designs?

economy of participants
sensitivity is enhanced by separating individual differences from experimental error

10

what are the disadvantages of RM designs?

cant use with all IVs (e.g. ethnicity)
order effects (practice, fatigue, carrover)

11

define precision matched

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

12

what are common issues with RM designs?

maturation
history
attrition/mortality

13

what is maturation

changes naturally occurring with time eg. learining

14

what is history?

uncontrolled event occurres between testing conditions

15

what is attrition or mortality?

participants drop out of study

16

what are common order effects?

practice effect
fatigue effect
carry-over effect

17

what are practice effects

performance at one level improves to the next

18

what are fatigue effects

performance declines on repeated testing

19

what are carry-over effects?

one level of IV affects another level

20

what are remedies of practice and fatigue effects?

can be controlled by counter balancing or randomisatin and prior exposure

21

what is counterbalancing or randomising?

randomisation or the oder to treatments across participants

22

what is prior exposure

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

23

how does one control a carryover effect?

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

24

what does counterbalancing aim to do?

seek to diminish the effect of order effects

25

what is the process of randomisation?

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

26

what is the process of counterbalancing?

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

27

what do we compare in independent groups analyses?

we compare groups to each other

28

what contributes to error in independent groups analyses?

individual differences

29

what does RM analyses allow for control of?

individual differences that can contribute to error

30

why do RM analyses control individual differences?

because we compare each participanross conditions

31

what does RM analyses statistically do to reduce error?

removes variability due to individual differences

32

what does RM analyses allow further partioning of?

SS_total (index of variability)

33

how does SS_total (total variability) partition?

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

34

how does WG variability (SS_within) partition firther?

WG variability (SS_within) partitions to:

Participant variability (SS_participant or subject)

and

Error variability (SS_error or residual OR SS_AxS)

35

what is N?

the number of scores (not participants)

36

what is n?

number of participants

37

what is the equation for df_total?

N-1 or an-1

38

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

a-1

39

what is the equation for df_within

N-a

40

what is the equation for df_participants

n-1

41

what is the equation for df_error)

(n-1)(n-a)

42

how does one calculate subjects variability (SS_subjects or participants)

aΣ(M_s - GM)^2

(participant mean - grand mean)

where a=number of conditions

43

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

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

where n = number of participants

44

how does one calculate total variability (SS_total)?

each score - gran mean

Σ(X_ij = GM)^2

compare every single score obtained to the grand mean

45

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

SS_error = SS_total - (SS_between + SS_subjects)

46

what does 'a'represent here?

number of conditions

47

what is SS_total?

SS_total = SS_between + SS_participants/subjects + SS_error

48

what is the equation of MS?

SS/df

49

what is the equation of F for repeated measures?

F = MS_Between / MS_error

50

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

high f ration means low P value

51

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

f ratio will always be larger in a RM design

52

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

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

53

what are the assumptions of RM analyses?

normality
independence
sphericity

54

what is the assumption of normality in RM designs?

is required as in the IG case

55

what is the assumption of independence>

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

which assumption is specific to RM designs?

sphericity

57

what is sphericity?

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

58

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

serious inflation of type 1 error

59

why is sphericity often breached?

because it is a very restrictive assumption

60

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

the traditional method and multivariate method

61

what is the traditional test of sphericity that SPSS uses?

Maulchley's sphericity test

62

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

the sphericity assumption is breached

63

when is Maulchley's sphericity test significant?

when p is LESS THAN .05

64

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

because it assumes that we have already met the sphericity assumption

65

what is the process of correcting breaches of sphericity?

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

what are epsilon values?

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

67

what is the range for epsilon values?

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

68

what do epsilon values do?

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

69

what are the epsilon value adjusted df used for

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

70

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

results in larger critical value & more conservative test

71

if Maulchly's test of sphericity is significant...

the assumption has been breached

72

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

there is no significant violation of sphericity

73

what do the epsilon values indicate?

how bady the sphericity assumption has been breached

74

which is the most commonly used epsilon figure?

greenhouse geisser correction

75

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

either greenhouse-geisser for both the IV and error

76

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

sphericity assumed for both IV and error

77

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

retain it

78

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

apply epsilon correction

79

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

reject

80

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

accept

81

what is the multivariate approach to RM ANOVA?

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

82

what is a RM t-test based on?

difference scores

83

what happens when we analyse difference scores?

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

84

what is the multivariate approach to RM based on?

analysis of difference scores?

85

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

becuase there will be multiple difference scores

86

what does the multivariate approach do?

it treats each set of differences scores as separate dependant variables

87

what is the multivariate approach using to analyse?

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

88

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

the multivariate approach and traditional approach

89

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

they dont, they are identical

90

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

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

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

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

92

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

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

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

Pillai's trace

94

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

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