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Advanced Research Skills > Week 3 > Flashcards

Week 3 Flashcards

1
Q

What are the 3 assumption of ANOVA?

A
  1. homogeneity of variance
  2. normality of scores
  3. independence of observations
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2
Q

How can we test the assumption of normality, non statistically?

A

plot

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3
Q

How can we test the assumption of normality, statistically? What does it mean if this test is significant?

A

Shapiro Wilk.

If significant, this is BAD - significant deviation from normative distribution.

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4
Q

What is one way to see what is going wrong in a violation of the assumption or normality?

A

Quantile-quantile plots using skew

straight line is good - check slides for kurtosis evaluation

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5
Q

What is homogeneity of variance?

A

Equal variation in each of the groups in your study (i.e., equal standard deviation).

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6
Q

How can we test homogeneity of variance?

A

Using Levene’s test

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7
Q

What does it mean if we have a significant homogeneity of variance score, a SIGNIFICANT Levene’s test?

A

If p is significant, is BAD. Your data does not meet the assumption of homogeneity.

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8
Q

When would hefty violations of the homogeneity of variance (using Levene’s test) be okay and why?

A

If you have the same amount of people in each group, this will still give you trustworthy results.

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9
Q

If you do get a homogeneity of variance violation (using Levene’s test) you might want to hunt down where this violation is occuring. How can we do this?

A

Looking at density plots.

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10
Q

What if we are not sure about violations of the assumptions of homogeneity of variance and normality?

A

Then maybe do a non-parametric version of analysis.

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11
Q

If we are not sure about violations of the assumptions of homogeneity of variance and normality, we do a parametric version of the analysis, how do we interpret the results if they ARE similar?

A

If the results are similar to the ANOVA, we can trust the results.

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12
Q

If we are not sure about violations of the assumptions of homogeneity of variance and normality, we do a parametric version of the analysis, how do we interpret the results if they are NOT similar?

A

Then we have to say we are not that confident with the results of the ANOVA.

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13
Q

How do we practically make sure not to violate the assumption of independence of residuals?

A

By allocating people to different groups and making sure they don’t interact too much.

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14
Q

When will the assumption of independence of residuals be violated? (2)

A

If the same participants take part in each of the IV conditions.
Violated if there are relationships between people in groups.

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15
Q

How do we test for the assumption of independence of residuals?

A

We don’t usually, we build it into the design.

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16
Q

What is power in statistics?

A

The power of a statistical test is defined as the probability of correctly rejecting the null hypothesis. Therefore, it’s the probability of finding a difference between means, if it is there.

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17
Q

What is a type 1 error?

A

Accepting a relationship as significant when in fact it is due to something else (bias, sampling error, or chance).

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18
Q

What is a type 2 error?

A

Refers to when a Ho is false, but we have decided to retain it.

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19
Q

When can a type 2 error occur?

A

When we have set the confidence limit too strictly (e.g., a

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20
Q

Why is it difficult to uncover a type 2 error?

A

Because we have to know the true variability of the population for the treatment group.

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21
Q

What is the model of power?

A

Power = 1 - (the chance of making a type 2 error)

22
Q

What is another way to define power?

A

Probability to replicate a genuine significant finding using exactly the same treatments and the same population of subjects.

23
Q

What is a priori power estimate?

A

An estimate of power BEFORE we start the experiment. Helps ensure we have adequate numbers of participants.

24
Q

What is a post-hoc power estimate?

What can the value size be between?

A

Estimates power AFTER the experimental data has been gathered: estimates the likelihood of being able to replicate results. This adds validity.

0 and 1.

25
Q

Which type of power estimate is easier to calculate?

A

post hoc

26
Q

What program can you use to determine post hoc and a priori power estimate?

A

G power

27
Q

What do you need in G power in order to calculate power estimates?

A

means, average variance (MSE) and sample size.

28
Q

Estimation of power usually begins with an estimation of:

A

effect size.

29
Q

What are two ways to figure out a priori power estimate?

A

look at previous studies to look at their effect size .

30
Q

If it is an entirely new study, how do we go about getting a priori power estimate?

A

We might say that we want a large effect for the treatment to be considered useful, or moderate, then figure out the same size we need.

31
Q

The most useful aspect of estimating a priori power is at the ____ stage to make sure that the study will be powerful enough to detect a required degree of difference between the groups.

A

Design.

32
Q

Of the factors affecting power (sample size, variance, magnitude of difference etc), what is considered to be the most readily manipulated?

A

sample size

33
Q

What makes you type 1 error greatly increase?

A

Doing lots of tests.

34
Q

What is the per comparison error rate

A

The probability of making a type 1 error on any given comparison. We set this at alpha. (p

35
Q

What is a family wise error rate?

A

The probability that a ‘family’ of comparisons will contain at least one type 1 error.

36
Q

What are pre programmed contrasts?

A

Pre programmed contrasts in Jamovi allow you to only test the comparisons that you absolutely have to.

37
Q

If each per comparison error rate is 5% (p<0.05), what is the error rate of 4 tests for example?

A

5% X 4% = 20% or 0.20.

38
Q

How do we calculate the Bonferroni adjustment?

A

probability you want to keep the family wise error rate at (5%) divided by the amount of comparisons you are doing

39
Q

Bonferonni is really simple to do, however, it does increase your chance of making what?

A

Type 2 errors

40
Q

Benjamini and Hochberg thought that Bonferroni may’ve been too conservative. Instead of looking at the family wise error rate, they suggested looking at what?

A

The false discovery rate

41
Q

What is the false discovery rate?

A

What proportion of the significant results are false discoveries (type 1 errors)?

42
Q

How do we calculate the false discovery rate?

A

new critical p = (order of comparison make, so if you’ve made 4 you rate them in order of significance (t value) from lowest to higher DIVIDED by number of comparisons made)…… TIMES by the desired false discovery rate, typically 0.05

P=(i/c)a

43
Q

The Tukey HSD test for difference between all pairs of means:

A

Good for equal number of people within each group,
good for between 3-5 levels. Controls for the over type 1 error rate independently of whether an F test is significant, but loses some power in the process.

44
Q

The Ryan Procedure:

A

Retains more power than Tukey, but doesn’t work so well if unequal number of participants in each group.

45
Q

Holm uses a similar approach to ____ but the critical value ___ according to the index of the compaison

A

Bonferroni
changes

This is calculated automatically in Jamovi.

46
Q

What is the Scheffe test for complex comparisons and data snooping (don’t really have a hypothesis)?

A

-Controls the family wise type 1 error rate at a for all possible linear contrasts (so loses a lot of power)
Very conservative and not recommended for simple pair wise comparisons.

47
Q

If small number of priori contrasts, use what for multiple comparisons:

A

Bonferroni adjusted t-tests

48
Q

IF testing several post hoc comparisons, use what for multiple comparisons?

A

Tukey or Holm.

49
Q

If there are unequal sample sizes or there is a violation of the homogeneity of variance assumption (or robust t-tests), which test of multiple comparisons should we use?

A

Games - Howell

50
Q

Only resort to Scheffe if examining:

A

multiple, complex, and post hoc (unplanned) comparisons

51
Q

Is Holm or Tukey slightly better at protecting against type 2 errors?

A

holm, however most people use Tukey.

Advanced Research Skills (12 decks)

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