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Stats > Power And Effect Size > Flashcards

Power And Effect Size Flashcards

1
Q

With F-ratios that exceed F-critical we…

A
  • reject the null hypothesis
  • independent variable(s) influence(s) the dependent variable.
  • Statistically significant effect.
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2
Q

•When a finding does not exceed alpha level (p <0.05) we…

A
  • fail to reject the null hypothesis:
  • Ho=all means are equal implies no evidence of an effect of the treatment
  • No evidence of a statistical difference.
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3
Q

“no statistical difference” does not…

A
  • prove the null hypothesis.
  • We simply do not have evidence to reject it.
  • A failure to find a significant effect does not necessarily mean the means are equal.
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4
Q

So it is difficult to have confidence in the null hypothesis:

A

Perhaps an effect exists, but our data is too noisy to demonstrate it.

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5
Q

Sometimes we will incorrectly fail to reject the null hypothesis –

A
  • a type II error.

* There really is an effect but we did not find it

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6
Q

Statistical power is the probability of…

A

detecting a real effect

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7
Q

power is given by:

A

1- 
where  is the probability of making a type II error
•In other words, it is the probability of not making a type II error

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8
Q

Power is your ability to find a …

A

difference when a real difference exists.

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9
Q

The power of a study is determined by three factors:

A
  • Alpha level.
  • Sample size.
  • Effect size=
  • Association between DV and IV
  • Separation of Means relative to error variance.
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10
Q

Power and alpha

By making alpha less strict, we can…

A

•increase power.
(e.g. p < 0.05 instead of 0.01)

However, we increase the chance of a Type I error.

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11
Q

Low N’s have very little…

A

Power

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12
Q

Power saturates with many…

A

Subjects

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13
Q

Power and sample size

One of the most useful aspects of power analysis is the estimation of the

A

sample size required for a particular study
•Too small an effect size and an effect may be missed
•Too large an effect size too expensive a study

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14
Q

Different formulae/tables for calculating sample size are required according to

A

Experimental design

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15
Q

Power and effect size

•As the separation between two means increases the power…

A

Also increases

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16
Q

Power and effect size

As the variability about a mean decreases power …

A

Also increases

17
Q

Measures of effect size for ANOVA

A 
•Measures of association=
Eta-squared (2)
R-squared (R2)
Omega-squared (2)
•Measures of difference=
d
f
18
Q

Eta squared is the proportion of the total variance that…

A

Is attributed to an effect

19
Q

ETA squared equation

A

n2 = SStreatment / SStotal

20
Q

Partial eta-squared is the

A

proportion of the effect + error variance that is attributable to the effect

21
Q

Partial eta-squared equation

A

N2p = SStreatment / SStreatment + SSerror

22
Q

Measures of association

ETA squared and partial ETA squared are both kinds of

A

Measures of association of the sample

23
Q

Measures of association- R squared

In general R2 is the proportion of…

A

variance explained by the model

  • Each anova can be thought of as a regression-like model in which each IV and interaction between Ivs can be thought of as a predictor variable
  • In general R2 is given by
24
Q

R squared equation

A

R2 = SSmodel / SStotal

25
Q

Measures of association

Omega squared is an estimate of the

A

dependent variable population variability accounted for by the independent variable.

26
Q

Measures of difference -d

When there are only two groups d is the…

A

standardised difference between the two groups

27
Q

Measures of difference - f

Cohen’s (1988) f for the one-way between groups analysis of variance can be calculated as follows

A

F= square root of w2 / 1-w2

It is an averaged standardised difference between the 3 or more levels of the IV (even though the above formula doesn’t look like that)

28
Q

Measures of difference

Cohens f
Small
Medium
And later effects

A

Small effect - f=0.10; Medium effect - f=0.25; Large effect - f=0.40

29
Q

What can simple power analysis program available on the web called GPower do?

A

This program can be used to calculate the sample size required for different effect sizes and specific levels of statistical power for a variety of different tests and designs.

30
Q

There are two ways to decide what effect size is being aimed for:

A
  • On the basis of previous research
  • Meta-Analysis: Reviewing the previous literature and calculating the previously observed effect size (in the same and/or similar situations)
  • On the basis of theoretical importance
  • Deciding whether a small, medium or large effect is required.

The former strategy is preferable but the latter strategy may be the only available strategy.

31
Q

Calculating f on the basis of previous research
•This example is based on a study by Foa, Rothbaum, Riggs, and Murdock (1991, Journal of Counseling and Clinical Psychology).

A
  • The subjects were 48 trauma victims who were randomly assigned to one of four groups. The four groups were
  • 1) Stress Inoculation Therapy (SIT) in which subjects were taught a variety of coping skills;
  • 2) Prolonged Exposure (PE) in which subjects went over the traumatic event in their mind repeatedly for seven sessions;
  • 3) Supportive Counseling (SC) which was a standard therapy control group
  • 4) a Waiting List (WL) control.
  • The dependent variable was PTSD Severity
32
Q

What should we report?

A
  • Practically any effect size measure is better than none particularly when there is a non-significant result
  • SPSS provides some measures of effect size (though not f)
  • Meta-analysis (e.g. the estimation of effect sizes over several trials) requires effect size measures
  • Calculating sample sizes for future studies requires effect size information
33
Q

Things to be avoided if possible

A
  • “Canned” effect sizes
  • The degree of measurement accuracy is ignored by using fixed estimates of effect size
  • Retrospective justification
  • Saying that a non-significant result means there is no effect because the power was high
  • Saying that there is a non-significant result because the statistical power was low
34
Q

What are canned effect sizes?

A

The degree of measurement accuracy is ignored by using fixed estimates of effect size

35
Q

What is retrospective judgement?

A

Saying that a non-significant result means there is no effect because the power was high
•Saying that there is a non-significant result because the statistical power was low

Stats (19 decks)

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