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Y3 S1 > ERMS Exam 3 > Flashcards

ERMS Exam 3 Flashcards

1
Q

ANOVA

A

Analysis of variance

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2
Q

Variance

A

measure of statistical dispersion, how far from the expected value its values typically are

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3
Q

why do we use
ANOVA

A

used to evaluate mean difference between 2+ groups

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4
Q

Multigroup research

A

contains more than 2 groups

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5
Q

Factor

A

the independent (or quasi-independent) variable that indicates the groups that are being compared

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6
Q

Factorial design

A

study design that has 2+ factors (aka, more than 1 IV)

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7
Q

Levels

A

conditions or values that make up a factor

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8
Q

Alpha

A

tells us how often we should expect to mistakenly reject a null hypothesis

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9
Q

ANOVA Null hypothesis

A

There is no difference, anywhere, between ANY groups.

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10
Q

ANOVA Alternative Hypothesis

A

there is a difference, somewhere, between at least 2 groups.

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11
Q

F-Statistic

A

Divides the variance (differences) we see between our sample means by the variance we would expect if there was no effect ???

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12
Q

f-statisitic formula??

A

Between groups Variance + error / Within groups Variance

ms between/ ms within?

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13
Q

Between-treatment variance

A

the variance between groups, systematic treatment affects, but unsystematic factors.

is the denominator of f-ratio

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14
Q

Within treatments Variance

A

Random, unsystematic factors, Denominator of f-ratio.

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15
Q

Within-groups Variance

A

Variability that naturally occurs within a level/condition, Comes from people having naturally different scores within a group

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16
Q

One-way ANOVA

A

Uses 1 categorical IV with 3+ levels

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17
Q

Factorial ANOVA

A

Uses 2+ categorical IV with 2+ groups

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18
Q

K

A

number of groups

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19
Q

N

A

Number of participants

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20
Q

Post Hoc tests

A

Follow-up tests done to determine exactly which mean differences are significant and which are not.

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21
Q

Planned comparisons:

A

when researcher make plans ahead of time: Plan which pairs of groups\levels they intend to compare

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22
Q

how do Bonferroni Correction tests work

A

Series of T-test for every possible pair of groups, Is going to correct for family wise error inflation by dividing alpha by the number of tests giving you a new alpha.

alpha/# of tests = new alpha

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23
Q

Tukey’s HSD

A

Honestly significant difference, makes adjustment to deal with inflated family wise error.
Determines the minimum difference needed to have statistical significance at that alpha level

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24
Q

Confidence interval

A

A range of scores that extends equally in both directions from an estimate that are considered plausible based on the data

(the scores that are liekly 95%).

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25
Q

what do we see with Interactions?

A

mean differences among levels of combined factors.
How multiple factors affect a DV together.

26
Q

Main effects

A

mean differences (vairance) among levels of one factor
How a factor affects a DV independently
how each independent variable effect the dependent variable.

27
Q

Why is anova one-tailed

A

Because f-ratios are computed from two variances, they are always positive numbers. variability can’t be negative

28
Q

how do you calculate F

A

f = MSbetween/MSWithin

29
Q

what are the differences between one way and factorial anova

A
30
Q

hypothesis testing in factorial H0 and H1

A

H0: no difference (all three must be true to fail to reject the null)
- Part 1: the means of all levels in factor 1 are ALL
equal
- Part 2: the means of all levels in Factor 2 are ALL
equal
- Part 3: The effect of on factor on the DV does not depend on the level of another factor
H1: sig dif (only one must be true to reject the null)
- At least one of the means is different in factor 1
- At least one of the means is different in factor 2
- The effect of one factor on the DV/criteria does not depend on the level of another factor.

31
Q

Synergism interaction

A

the effects of one factor gets stronger based on the other

32
Q

Antagonistic interaction

A

the lines cross. Ideal interaction

33
Q

Why should you use ANOVA instead of several t tests to evaluate mean differences when an experiment consists of three or more treatment conditions?

A

A ANOVA can test more than 2 independent variables at once, instead of W vs N, C vs N, AOVA can do W vs N vs C. it also saves time and rescores, and can uncover non-linear relationships

Each test we run has a alpha, if we run too many tests, we run the risk of returning a type 1 error. This is called family wise error. ANOVA solves this by looking at all mean differences all at once.

34
Q

Within-groups/treatments degrees of freedom formula

A

Df(within) = N- K

35
Q

Between groups/treatments degrees of freedom formual

A

Df(between) = K - 1

36
Q

two factor study

A

a experimental deisgn in which data is collected for all possible combinations of all levels of the two factors of interest

37
Q

alpha is always

A

.05

38
Q

Means squared: what is it and how do we calculate it?

A

measurement of variabilty, ss/df = MS

39
Q

why do we use post- hoc

A

when we want to see the which levels are significant and in order to not get type 1 error it helps with family wise error

40
Q

when do we use a post hoc

A

we use a post hoc when there is a different somewhere between the groups. when the critical f is greater than .05

40
Q

what questions do a factorial anova answer

A

Is there a main effect of just the 1rst factor (IV/predictor)
Is there a main effect of just the 2nd factor (IV/Predictor)
Do the effects of the IVS or predictor on the DV depend on each other

40
Q

Do the effects of the IVS on the DV depend on each other

A
40
Q

t-statistic

A
41
Q

f-statsitic denominator

A

Ms within + error

41
Q

One-way Null hypothesis

A

there is no difference, anywhere, between any of the groups.

42
Q

moderator

A

which factor you think has the larger effect.

43
Q

what does a simple effects tests do

A

Takes one of the factors and splits it up into each of its levels
2. “Freezes” one level of the factor and looks at the “simple effect” of the other factor’s levels on the DV

44
Q

ANOVA scources of vairance

A

Different in scores in between and within groups

45
Q

anova error variance

A

within variance (f-stat denominator)

46
Q

coneceptually describe Error

A

error is the factoring in of variance.

47
Q

why does partitioning cariance change for factorial anovas? what is the change

A

changes becuase added more vairables (factors)

48
Q

anova advantages of t-ests

A

can have a control and compare multiple groups

49
Q

Goal of ANOVA

A

to find the difference in variance between 2+ groups

50
Q

Family wise error rate

A

When we run multiple t-test’s the likely-hood of type 1 error increases and the alpha adds onto each other

51
Q

one-way anova alternative hypothesis

A

there is a difference somewhere between any of the groups.

52
Q

why does anova use variance instead of mean difference

A

inferences about means are made by analyzing variance

53
Q

Why should you use ANOVA instead of several t tests to evaluate mean differences when an experiment consists of three or more treatment conditions?

A

With 3 or more groups, when you run multiple t-test the rick of running as type 1 error increases. Anova looks at variance so we’re able to look at all the means at the same time. The ANOVA performs all of the tests simultaneously with a single, fixed level for α

54
Q

f-statistic numberator

A

ms Between groups

55
Q

why doesnt ANOVA have directional hypotheses

A

ANOVA doesnt have directional hypothesis becuase they’re measuring variance. you can either have no variance or some variance. so either 0-1

56
Q

when do we reject the null

A

P < A

57
Q

pairwise comparisons

A

in a post hoc test when Tests you compare two individual means at a time (t-tests).

Y3 S1 (4 decks)

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