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