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PSY279 > Exam > Flashcards

Exam Flashcards

1
Q

What are measures of variability?

A
  • Statistical procedures to show how spread out the data is around a point
  • The difference between the mean and the data point tells us how far away each data point i from the mean
  • Range, SD & variance
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2
Q

What is standard deviation?

A

Represents how far from the average each data point is

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3
Q

What is variance?

A

A number indicating how spread out the data is

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4
Q

What is the population?

A

The complete collection to be studied

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5
Q

What is a Sample?

A

A section of the population

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6
Q

Why do we need measures of variability?

A

You want the sample to reflect the overall population, so you use measures of variability to see how comparable your sample data is to the estimated population

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7
Q

How do you prepare the data before calculating variance/SD

A
  1. Take each value and square it
  2. Add all of the values together
  3. Add the squared values together
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8
Q

How do you calculate variance?

A
  1. Sample size x Calculated squared value
  2. Square the standard value
  3. subtract one from the other
  4. Divide this by sample size times sample size -1

n(sum of x squared) - (sum of x) squared
_______________________________
n(n-1)

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9
Q

How do you calculate the standard deviation?

A

Find the square root of the variance.

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10
Q

What is the standard error (of the means)?

A

Tells you how good your sample is

The standard deviation of the sample distribution

The average difference between our sample and the target population

Measure of uncertainty in the mean.

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11
Q

What is the sampling distribution?

A

This is used to estimate how much deviation we will find between our sample and the target population.

A distribution of something like the sample mean - not the raw data values

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12
Q

How do you calculate the standard error of the mean?

A

Standard deviation
______________
Square root of n

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13
Q

What does the SD (high/low) for the SE tell us?

A

SD high number = more variable = mean is not as good estimate of he population mean

SD low number = less variable = mean is a much more reliable estimate of the population mean

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14
Q

What does N (high/low) for the SE tell us?

A

Higher N = lower SE

Lower N = higher SE

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15
Q

What does the 95% confidence interval tell us?

A

That we can be 95% confident that the population mean will fall between the upper and lower boundaries

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16
Q

What do error bar graphs represent?

A

The 95% confidence interval

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17
Q

What does overlap of the error bars mean?

A

Less overlap indicates more effect.

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18
Q

Statistical significance is…

A

A difference that is most probably not due to chance.

This is affected by the sample size. A large enough sample will always result in statistical significance.

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19
Q

Effect size is…

A

The magnitude of the relationship or difference found.

Looks at whether the difference is enough to be of practical significance.

This is not affected by the sample size.

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20
Q

How so you calculate the Effect size (Cohen’s d)?

A

Mean of group 1 - Mean of group 2
___________________________
SD of the population

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21
Q

What does the Effect value (high/low) tell us?

Cohen’s convention for interpretation

A

Large Effect = .8

Medium Effect = .5

Small Effect = .2

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22
Q

What does Cohen’s d actually mean?

A

The differences in two groups based on the SD.

(can be thought of as the % of non-overlap between a condition and a control group on a bell curve - the two would sit bang on top of each other.)

Larger effect = less overlap

Smaller effect = more overlap

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23
Q

When would you use Cohen’s d?

A
  • When comparing 2 means

- T-test

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24
Q

When would you use a partial eta squared?

A

When statistical tests are based on variance rather than SD

ANOVA

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25
Q

What is partial eta squared?

A

An indication of the proportion of variance in the dependant variable that is explained by the independent variable.

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26
Q

How do you interpret a partial eta squared value?

A

Ranges from 0 - 1

Small Effect = .01

Medium Effect = .06

Large Effect = .138

  • The % of variance in the DV that is explained by the IV
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27
Q

What is the correlation coefficient?

A

Pearson’s r

The effect for hypothesis that look at relationship.

28
Q

How do you interpret Pearson’s r?

correlation coefficient

A

No relationship = 0

Small = .10 - .29

Medium = .30 - .49

Large = .50 - 1

Positive or negative - still interpreted the same.

r squared = the % of variance in one that is explained by the other.n

29
Q

What is the relationship between effect and error?

A

If you have more effect than error then you’re more likely to see the results in the target population

Effect
_____
Error

30
Q

How do you Interpret the t value?

A

T = > 1 - There is more effect than error so it is significant

T = < 1 - There is more error than effect so it is NOT significant

31
Q

What is the p value?

A

The probability as to how true our results are.

The probability of our null hypothesis being true.

A percentage

Reported to 3 decimal places.

32
Q

What is a Type 1 error?

A

When it looks like there is an effect but THERE IS NOT AN EFFECT

We have selected a sample that was better overall, before out manipulation.

33
Q

What is a Type 2 error?

A

Sampling from the correct group and thinking that there isn’t an effect but THERE IS AN EFFECT

34
Q

How do you interpret the p value?

A

P<0.05 = significant

Bigger than the p value = accept the null hypothesis - no effect

Smaller than the p value = reject the null hypothesis and accept the directional hypothesis - effect.

35
Q

What is a sampling error?

A

When you use the wrong population

36
Q

What is the standard deviation error?

A

A measure of how much error we would expect.

37
Q

What do you aim for with the sampling error and the standard deviation error?

A

The difference between the two to be low.

38
Q

When would you use the Kruskal Wallace H test?

A

Violated assumptions of a 1 way ANOVA

NON parametric

Between subjects

39
Q

What is the Kruskall Wallace H test?

A

The non parametric anova for between subjects design

40
Q

What is the post hock test after a kruskall Wallace significant output

A

Man Whitney U

41
Q

What output do you report for man Whitney U?

A

U and P

42
Q

What is the non parametric anova for within participants design?

A

Friedman

43
Q

What is the post hock test after a significant Friedman output?

A

Wilcoxon

44
Q

What output do you report for wilcoxon

A

Z and P

45
Q

When would you use a Friedman test

A

1 way ANOVA

NON parametric

within subjects design

46
Q

How do you run a Kruskal Wallace test?

A

Analyse - non parametric tests - legacy dialogues - K independent samples - move variables - define range (amount of groups) - options - descriptives (and quartiles if you want medians) - continue - ok.

47
Q

How do you run a Friedman test

A

Analyse - non parametric tests - legacy dialogues - K RELATED samples - move groups across - tick Friedman - click statistics - select quartiles - ok - continue

48
Q

When do you use a one way ANOVA? // What are the 4 assumptions of an ANOVA?

A

1 factor with more than 2 levels

IV - Factor
DV - Levels

Between participants design

normally distributed data

equal sphericity

49
Q

Why do we use the ANOVA?

A

Otherwise, we would need to do 3 (or more) paired t-tests each with a p value of .05 which makes an overall chance of error of 15% (or more) which is far too high.

50
Q

What does the ANOVA find?

A

Whether the effect is bigger than the error.

51
Q

How does the ANOVA find out whether the effect is bigger than the error?

A

F = Between groups variance
____________________
Within groups variance

52
Q

What is the treatment effect?

A

The difference in something before and after manipulation.

53
Q

What is the grand mean and how do we calculate it?

A
  • The figure that is used to compare the individual group mean scores to which gives us the within group variance (measure of error)
  • Add up the three groups means, then divide by the number of groups
54
Q

How do you interpret the F value?

A

An F value greater than 1 means that there is more effect that error.

55
Q

How do you report the ANOVA results?

A

A one way ANOVA showed that there is an effect between the (conditions)
F(between, within) = F value, P value.

56
Q

What is the post hock test for an ANOVA?

A

Independent t test.

57
Q

What is a Bonferonni correction?

A

Correcting the p value to reflect the amount of conditions.

3 conditions = 0.05/3 = new Bonferroni corrected p value of P = 0.17

58
Q

What is a test of homogeneity?

A

A method of finding whether there is equal variance between the groups.

59
Q

What test of homogeneity would you use for an independent t test?

A

Levene’s test

60
Q

What does a significant Levene’s test result mean?

A

There is a difference in equal variance between the two groups so you’re violating one of the assumptions of the ANOVA.

This means you have to revert back to a non parametric ANOVA.

61
Q

What does an insignificant Levene’s test result mean?

A

There is equal variance between the groups and you have met the assumption of the ANOVA

Continue with post hock tests.

62
Q

When would you use a related ANOVA?

A

Within participants design.

63
Q

What changes with a related ANOVA?

A

The between groups variance becomes the between conditions variance.

We don’t have to account for individual difference so we don’t compare the grand mean with condition means.

The f value uses the error and variance.

64
Q

What is the Greenhouse geisser correction?

A

This works out the difference score and the mean variances of the differences for a related ANOVA

65
Q

What does it mean when the mean variances of different scores are significantly different for a related ANOVA

A

We have violated the assumption of sphericity and therefore have to read the greenhouse geisser correction

66
Q

When do we report the greenhouse geisser correction?

A

We always report this in front of the f value for related ANOVA’s

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