Week 12 Flashcards

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1
Q

What are the two types of t-tests

A
  1. Paired (dependent) t-test

2. Independent t-test

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2
Q

Define paired dependent t–test

A
  • Examines differences in one group across two time-points or levels
    • No assumption of variance
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3
Q

What does a paired dependent t-test determine

A
  • Test to determine the significance of the difference between two related sets of cores
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4
Q

what does a unpaired t-test determine

A

Test to determine the significance of the difference between two independent sample means

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5
Q

Define unpaired t-test

A

Examines differences between two groups

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6
Q

Define degrees of freedom

A
  • Number of individual scores that are free to vary without changing the means
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7
Q

Define homogeneity of variance

A
  • The spread of scores around each mean is approximately equal
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8
Q

Define one-way ANOVA

A
  • A parametric inferential statistical technique for determining if there are differences in the means between three or more different groups/ conditions on a particular dependent variable
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9
Q

How does a one-way ANOVA work

A

Examines differences for three or more groups

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10
Q

What is used to determine where the difference are

A

Post hoc

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11
Q

A significant what will tell you there is a difference between groups

A

F-ratio

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12
Q

Define Repeated Measures Anova

A
  • Analysis of variance in which the independent variable (factor) has been measured on the same participants in all conditions
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13
Q

What do we get from a repeated measures anova

A
  • A main effect for time for repeated measures ANOVA

- If the main effect is significant, follow-up tests (Bonferroni) to see where the change/ differences occurred

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14
Q

What does a repeated measures anova examine

A
  • Examines differences over three or more time-points
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15
Q

Define mixed Model anova (mxed factorial design)

A
  • Several factors some of which are repeated measures (on the same people) and others are between-subjects (om different people)
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16
Q

What do we get from a mixed model anova

A
  • A main effect for group
    • A main effect for time
    • Potentially an interaction effect
17
Q

What does a mixed model anova examine

A
  • Examines differences between groups and over time
18
Q

What is Benferroni correction and when to use it

A
  • Type 1 error, increase chance of falsely saying there was a difference
    • Need to use Bonferroni correction
    • 0.05/4 = 0.0125
    • So, to be a significant difference we need p<0.0125