Flashcards in Quiz 2 Deck (31):

1

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What are the null hypotheses (one-way)?

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-Ex:

-H(sub o): There is no statistically significant difference between the population means of males and females with regard to their GPAs.

-H(sub o): u(sub m) = u(sub f)

-With null hypothesis, you always state that there is no statistically significant difference.

2

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What are the characteristics of Orthogonal Comparisons?

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1. A priori hypotheses

2. ∑a(sub i) = 0 (valid comparison)

∑a(sub i)b(sub i) = 0 (independence)

3. Each comparison is on 1 degree of freedom

4. You get as many comparisons as g-1 degrees of freedom

5. Most powerful test of the three

6. Significance level = alpha

3

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What are the characteristics of Bonferroni?

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1. A priori hypotheses

2. ∑a(sub i) = 0 (valid comparison)

Don’t need independence, so ∑a(sub i)b(sub i) does not have to equal 0

3. Each comparison is on 1 degree of freedom

4. You get as many comparisons as you want, but you lose power with each subsequent comparison

5. Moderate power of the three

6. Significance level = alpha/c (c is the number of comparisons)

4

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What are characteristics of Scheffe?

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1. A posteriori or post hoc hypotheses

2. ∑a(sub i) = 0 (valid comparison)

Don’t need independence, so ∑a(sub i)b(sub i)does not have to equal 0

3. Each comparison is on the among degrees of freedom

4. You can make as many comparisons as you want

5. Lowest power of the three tests

6. Significance level = alpha

5

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What are experimentwise errors?

### -Probability of making at least 1 Type I Error over the entire experiment (set of comparisons)

6

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What are per comparison errors?

### -Per Comparison Error: Probability of making a Type I Error for each individual comparison

7

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What are the problems with multiple t-tests?

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-Not okay for multiple t-tests because these are NOT independent and you’ve got an inflated Type I error rate

-You may find differences that may not even be there

8

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What are the null hypotheses (two-way)?

###
-Ex:

1. H(sub o): There is no statistically significant difference between the population means of the massed practice and distributed practice groups with regard to stats test scores

-H(sub o): u(sub m) = u(sub d)

2. H(sub o): There is no statistically significant difference between the population means of the in-class and online teaching methods with regard to stats test scores.

-H(sub o): u(sub IC) = u(sub ON)

3. H(sub o): There is no teaching method x (by) study habit interaction in the population.

9

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What is a main effect?

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-The overall difference among the levels of the independent variable tested

-Average of the simple effects

10

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How many main effects in a specific design?

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-The number of main effects is the number of independent variables that you have

-You have as many main effects as IVs

11

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What is a simple effect?

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-The effect of an independent variable at a single level of the other independent variable

-Lack of parallelism (do not cross)

-LOOK ON PAGE 11 OF NOTES

12

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How many simple effects in a two-way design?

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-The additive rather than the multiplicative

-Ex: 2x2 = 4

-Ex: 2x4 = 6

13

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What is an interaction?

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-The lack of parallelism in the simple effect

OR

-The effect of an independent variable differs depending upon the levels of the other IV

-If asked to define this, we have to define “simple effect” too

14

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How do you plot an interaction? Graph form and in words.

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1. Choose an IV to place on the x-axis

2. Values of the cell means go on the y-axis

3. Plot a single curve for each level of the other IV

-MAKE HAND-WRITTEN NOTE CARD (Page 11)

15

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What are range tests?

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-All pairwise comparisons between means

1. Tukey A

2. Student Newman-Kewls

3. Tukey B

16

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What is the studentized range distribution?

### -A sampling distribution of a t-test run between the largest and smallest means from a set of k means

17

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How do you perform the Tukey A test?

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1. Order the means from smallest to largest

2. Compute q for each comparison

3. df(sub # of means, df error)

Compare each q with the critical values of q on the number of means and df error

18

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How do you perform the SNK test?

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1. Order the means from smallest to largest

2. Compute q for each comparison

3. df(sub # of ordered means, df error)

Compare each q with the critical values of q based on the number of ordered means (or number of steps) and df error

*More power

19

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How do you perform the Tukey B test?

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1. Order the means from smallest to largest

2. Compute q for each comparison

3. Average the critical values of the Tukey A and SNK for each comparison

20

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How do you perform the Fisher-Hayter test?

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1. Order the means from smallest to largest

2. Compute the q for each comparison

3. df(sub # of means-1, df error)

Use the number of means - 1 and the df error to obtain the critical value of q

21

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What was the rationale behind Duncan’s multiple range test and what are its problems?

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-Rationale:

-Focused on trying to obtain greatest power possible

1. Experimentwise error rate similar to the orthogonal comparisons 1-(1-alpha)^g-1 -- “protection level”

2. Step-down procedure similar to SNK

3. Derives out his own critical value tables

-Problems:

-Comparisons not independent

-Inflated Type I error rate; kind of like doing t-tests, so error rates go through the roof

22

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The relationship between q and t?

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-HAND-WRITTEN NOTE CARD (PAGE 13)

-Gosset and help forgot “2” when computing, which is why we have “t” and “q”

q(sub 2, infinity) = 2.77 (.05); 3.64 (.01) t(sub infinity) = 1.96 (.05); 2.576 (.01)

q=t x(times) square root of 2 t=q/2

23

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Problem with unequal n with a one-way ANOVA?

### -No problems mathematically, but you lose power.

24

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What are the characteristics (coefficients) for orthogonal comparisons with unequal n?

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- ∑n(sub i)a(sub i) = 0 (valid comparison)

- ∑n(sub i)a(sub i)b(sub i) = 0 (independence)

25

## What are the problems with unequal n in a two-way design?

### -Destroy factorial nature of the design, and it lacks robustness

26

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What is a factorial design?

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-Design in which each level of each independent variable occurs equally often with each level of every other independent variable

-Must have equal n

27

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How do you get rid of unequal n?

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1. Randomly discard data

2. Yates Substitution Formula

3. Least Squares Solution

-Mathematically ideal

-SAS

4. Unweighted Means Solution

-Used on SPSS

1. SSeffect → use cell means

2. SSwithin → use new data

*LOOK ON PAGE 2

28

## What is the harmonic mean and why do you use it? Purpose?

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-Average the reciprocals and take the reciprocal of that average

-HAND-WRITTEN NOTE CARD HAS FORMULA

-Used when dealing with unequal n

-purpose:

-weighs smaller samples more and larger samples less

29

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What is complete confounding?

### -A design in which you do not know where the difference lies

30

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What are the null hypotheses (three-way)?

###
Ex:

1. H(sub o): There is no statistically significant difference between the population means of males and females in regard to Machiavellian personality scores.

-H(sub o): u(sub m)=u(sub f)

2. There is no statistically significant difference between the population means of Political Science majors and Psychology majors in regard to Machiavellian personality scores.

-H(sub o): u (sub psy) = u (sub pol)

3. There is no statistically significant difference between the population means of graduates and undergraduates in regard to Machiavellian personality scores.

-H(sub o): u (sub U) = u (sub UG)

4. H(sub o): There is no sex by major interaction in the population.

5. H(sub o): There is no sex by class interaction in the population.

6. H(sub o): There is no major by class interaction in the population.

7. H(sub o): There is no sex by major by class interaction in the population.

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