Flashcards in Test 2: Repeated Measures ANOVA Deck (33)

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1

##
Independent t test

### Independent Variable is a between-subject factor (different groups)

2

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Dependent/Paired t test

### Independent Variable is a within-subjects factor; same participants measured twice (pre-test/post-test), matched or correlated samples

3

## When we have more than two groups, what do we use?

### ANOVAs

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##
Simple one-way ANOVA

### Independent Variable is a between-subjects factor

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## One-way repeated-measures ANOVA

### Independent Variable is a within-subjects factor; three or more tests are given (pre-, mid- and post-test)

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## Repeated-Measures

### one of the most frequently used statistical tests in the health sciences; measures the significance of mean differences measured on the same subjects over repeated trials (time points); produces an F value

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## For a simple one-way ANOVA, what does the test assume?

###
that the mean values are taken from independent groups that have no relationship; in this design, we partition the total variance (of our DV) into two sources: Between-group variance (treatment effects), Error

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## Error variance

### Comprised of intraindividual variability (variability within a person’s scores), interindividual variability (variability between people in different groups), and unexplained sources (error)

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## When only one group of subjects is measured more than once,

### the data sets are dependent.

10

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What is the total variability for a single group of subjects measured more than once expected to be?

### less than if the scores came from different groups of people ( if the scores were independent) because interindividual variability has been eliminated by using a single group at multiple time points.

11

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What does the less variability tend to do the mean square error term?

###
This tends to reduce the mean square error term in the denominator of F in a manner similar to the correction made to the standard error of the difference in the dependent t test.

12

## One-way Repeated-Measures ANOVA partitions the total variance into 3 sources:

###
Variance due to treatment (or level of IV), Variance due to participants (intraindividual variability), Error (unexplained variability); Variability between subjects (interindividual variability) is no longer a factor

13

## What does Variance due to participants (intraindividual variability) allow us to do?

### Allows to estimate how much variance is due to different abilities of different participants because each participant is measured for each level

14

## The assumptions of the simple one-way ANOVA (between-subjects designs) also apply to the repeated-measures ANOVA except for...

### the independence of samples assumption and that the repeated-measures ANOVA must also meet an additional assumption of sphericity.

15

## Sphericity

### refers to the condition where the variances of the differences btw all possible pairs of within-subject conditions (i.e. levels of the IV) are equal. The violation of sphericity occurs when this is not the case.

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## Example of Sphericity

### Consider a study in which subjects are measured at 3 time points: time1, time2, and time3. From this, we can calculate the diff scores btw each time period: time1 – time2, time2 – time3, and time3 – time1.

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## Sphericity requires that the variance of the difference scores are...

### equal.

18

## What happens when the assumption of sphericity is violated?

### The Type I error rate will inflate; if alpha is set at 0.05, the true risk of committing Type I error will be higher than 0.05. (The assumption is not applicable in situations where only two repeated measures are used because only one set of differences can be calculated. )

19

## What are the methods used to correct for violations of the assumption of sphericity?

### The Greenhouse-Geisser adjustment and Huynh-Feldt adjustment. (Both corrections modify the degrees of freedom)

20

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What does the application of the Greenhouse-Geisser adjustment assume?

### Maximum violation of the assumption of sphericity; when the violation is minimal, this adjustment to the dof may be too severe, possibly resulting in a Type II error

21

## Type II error

### Accepting/retaining the null hypothesis when it is actually false

22

## What does the Huynh-Feldt adjustment attempt to correct?

###
The amount of violation that has occurred only; in this adjustment the dof for error are multiplied by a value (epsilon, ε) that ranges from zero (maximum violation) to 1.0 (no violation); violation is considered insignificant if ε ≥ .75

23

## Although F may still be significant, these adjustments reduce the...

### confidence we can place in our conclusion that the differences among the means are statistically significant

24

## If the obtained p value from the overall test is close to the rejection level of α = .05 (suppose we get a p =.04), and the adjustment increases it to p = .06 what must we do?

### we must accept the null hypothesis

25

## Epsilon values are more conservative for what method?

### Greenhouse-Geisser method provides better protection against making Type I errors but increasing the risk of making Type II errors

26

##
A strategy for determining the significance of F is discussed in the Vincent text:

### Evaluate F with the G-G adjustment first: If sig, reject the null (If not sig, evaluate F with no adjustment); If F with no adjustment is not sig (most liberal condition), accept the null hypothesis; If F with the G-G adjustment is not sig, but the F with no adjustment is sig, use the H-F adjustment (a moderate condition) to make your final determination

27

## As an alternative to the methods listed above, you could get around a severe violation using a...

### multiple/multivariate analysis of variance (MANOVA) with the repeated measures designated as multiple dependent variables; with this, assumption of sphericity is not required (less powerful and provides better protection against Type I errors, but less against Type II errors. )

28

## The results of the F test only tell us what? (Post Hoc Tests)

### That there is at least one difference among the means; it does not tell us where these differences lie.

29

## Familywise Alpha (Post Hoc Tests)

### When we perform multiple statistical tests at a given alpha level (e.g. 0.05), the cumulative risk of committing at least one type I error across the family of tests will be greater than the original alpha of 0.05

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