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Statistics > Quiz 2 > Flashcards

Quiz 2 Flashcards

1
Q

What are the null hypotheses (one-way)?

A
  • 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.

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2
Q

What are the characteristics of Orthogonal Comparisons?

A
  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
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3
Q

What are the characteristics of Bonferroni?

A
  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)
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4
Q

What are characteristics of Scheffe?

A
  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
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5
Q

What are experimentwise errors?

A

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

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6
Q

What are per comparison errors?

A

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

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7
Q

What are the problems with multiple t-tests?

A
  • 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
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8
Q

What are the null hypotheses (two-way)?

A
  • 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)
  1. 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)
  2. H(sub o): There is no teaching method x (by) study habit interaction in the population.
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9
Q

What is a main effect?

A
  • The overall difference among the levels of the independent variable tested
  • Average of the simple effects
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10
Q

How many main effects in a specific design?

A
  • The number of main effects is the number of independent variables that you have
  • You have as many main effects as IVs
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11
Q

What is a simple effect?

A
  • 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
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12
Q

How many simple effects in a two-way design?

A
  • The additive rather than the multiplicative
  • Ex: 2x2 = 4
  • Ex: 2x4 = 6
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13
Q

What is an interaction?

A

-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

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14
Q

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

A
  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)
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15
Q

What are range tests?

A

-All pairwise comparisons between means

  1. Tukey A
  2. Student Newman-Kewls
  3. Tukey B
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16
Q

What is the studentized range distribution?

A

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

17
Q

How do you perform the Tukey A test?

A
  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
Q

How do you perform the SNK test?

A
  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
Q

How do you perform the Tukey B test?

A
  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
Q

How do you perform the Fisher-Hayter test?

A
  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
Q

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

A
  • 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
Q

The relationship between q and t?

A
  • 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
Q

Problem with unequal n with a one-way ANOVA?

A

-No problems mathematically, but you lose power.

24
Q

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

A
  • ∑n(sub i)a(sub i) = 0 (valid comparison)

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

25
Q

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

A

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

26
Q

What is a factorial design?

A
  • 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
Q

How do you get rid of unequal n?

A
  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
Q

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

A
  • 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
Q

What is complete confounding?

A

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

30
Q

What are the null hypotheses (three-way)?

A

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.
31
Q

How do you perform the unweighted means solution?

A

1) For SSeffects, use cell means
2) For SSwithin, use the raw data
3) Use harmonic mean to calculate SS within (define and how to use it)

Statistics (3 decks)

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