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Research Methods B Flashcards

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1
Q

What do parametric tests assume (3)

A

Normal distribution
Homogeneity of variance (if comparing 2 or more groups)
Interval or ratio data

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2
Q

What do non-parametric tests assume

A

Nothing

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3
Q

Advantage and disadvantage of parametric tests compared to non-parametric

A

Adv: More powerful - can detect differences better
Disadv: Less flexible

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4
Q

How can a parametric test still be used if parametric assumptions are not met

A

Transform the data to normalise data distributions

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5
Q

Kolmogorov-Smirnov test (K-S)

A

Tests the probability the data belongs to a normal distribution

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6
Q

Hypothesis and null hypothesis of Kolmogorov-Smirnov test

A

H1: Variable is not normally distributed
H0: Variable is normally distributed

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7
Q

Conclusion if p < 0.05 in Kolmogorov-Smirnov test

A

Significant, reject H0, therefore not normally distributed

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8
Q

Conclusion if p > 0.05 in Kolmogorov-Smirnov test

A

Not significant, accept H0, therefore is normally distributed

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9
Q

What should be reported if SPSS gives p = 0.000 and why

A

0.001, SPSS cannot round past 3 decimal places and p can never be 0

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10
Q

Levenes test

A

Tests the homogeneity of variance, determines data sets are from the same population or not

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11
Q

Hypothesis and null hypothesis of the Levenes test

A

H1: Variance is not equal
H0: Variance is equal (homogenic)

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12
Q

Conclusion if p < 0.05 in the Levenes test

A

Significant, reject H0, variance is not equal

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13
Q

Conclusion if p > 0.05 in the Levenes test

A

Not significant, accept H0, variance is equal

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14
Q

How to test whether to use a parametric test

A

Conduct a Kolmogorov-Smirnov test and Levenes test, if p > 0.05 in both then assumptions have been met and a parametric test can be performed

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15
Q

What kind of data is needed for all parametric tests

A

Interval or Ratio

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16
Q

Chi-squared ‘goodness of fit’’ test (GF)

A

Compares a samples proportions to that of the population

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17
Q

How a Chi-squared test works

A

Frequencies of participants are compared to generated expected frequencies based on the null hypothesis (that nothing is going on). If significantly different from observed, results are significant

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18
Q

Different null hypothesis’ for Chi-squared goodness of fit (2) and examples

A

1) No difference between the categories
Eg - the number of men and women are equal
2) No difference between the categories’ frequency distribution
Eg - number of men and women in computing reflects the proportions at the university
(which one is chosen changes the expected frequencies)

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19
Q

Expected frequencies (for Ch-squared goodness of fit) = …

A

proportion of population x sample size

Proportion depends on which null hypothesis chosen

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20
Q

Chi-squared (X^2) = …

A

sum of ( (frequency observed - frequency expected)^2 / frequency expected )

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21
Q

Chi-squared ‘test for independence’ (TI)

A

Tells whether two groups are associated or independent of influence

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22
Q

Chi-squared ‘test for independence’ (TI) example

A

Does gender influence smoking frequency

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23
Q

Chi-squared ‘goodness of fit’’ test (GF) example

A

Are there more men in the computing department than there would be by chance

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24
Q

How is expected frequencies calculated for Chi-squared test for independence

A

What the results would be completely down to chance…

= column total x row total / n

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25
Q

Conclusion if Chi-squared value is lower than critical value found in the table

A

Results are not significant, accept H0 that nothing is going on

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26
Q

How many variables can a Chi-squared test compare at a time

A

2

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27
Q

T-tests

A

Compare the means of two groups, looking for differences between them (H1), telling us if the difference in means falls n the most extreme 5%

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28
Q

Types of t–test (3)

A

Independent, dependent, one sample

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29
Q

Other names for independent t-tests (2)

A

Unrelated, between groups

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30
Q

Other names for dependent t-tests (4)

A

Related, paired sample, within groups, repeated measures

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31
Q

Requirements to conduct a t-test (2)

A

Data meets parametric assumptions

and is interval or ratio data

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32
Q

Independent t-test example H1

A

Students who do extra reading for their module get higher grades (IV = extra reading, DV = Higher grades)

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33
Q

Conclusion if p < 0.05 for an independent t-test

A

There is an extreme, real difference in groups means, they are a part of different populations

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34
Q

Sample distribution of the difference between means

A

Theory that, if the null hypothesis is correct, plotting all the difference means of randomly selected participants from each group would form a normal distribution curve

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35
Q

What does the t statistic from t-tests show

A

How many standard deviations the difference between the means is from 0

36
Q

One sample t-test

A

Compares the sample mean with that of a known populations’

37
Q

One sample t-test example

A

Are 1st year psychology students more intelligent than the population in general (mean IQ = 100)

38
Q

How to report t-tests

A

t (df) = t-value, p = p-value

39
Q

What does a larger t-value mean

A

the larger the difference of the means

40
Q

One sample Wilcoxon signed rank test

A

Tests if the median of the measurement is equal to a specific value (population median), determining whether the sample is part of the general population

41
Q

Requirements for using the One sample Wilcoxon signed rank test (3)

A

Non-parametric, comparing independent groups (sample and population), interval or ratio data

42
Q

Hypothesis and null hypothesis for the One sample Wilcoxon signed rank test

A

H1: Median significantly different from population median (can be one-directional)
H0: Median similar to population median

43
Q

Conclusion if p < 0.05 in the one sample Wilcoxon signed rank test

A

Significant, so the median is significantly different from the populations

44
Q

Example of independent t-test

A

Is there a difference between male and female numerical ability scores

45
Q

Requirements for an independent t-test (2)

A

Parametric assumptions met, interval or ratio data

46
Q

Conclusion if p < 0.05 for an independent t-test

A

Significant difference between the groups, a part of different populations

47
Q

For an independent t-test, when should the row ‘equal variance not assumed’ be used?

A

Questionable but can be in occasional circumstances when a Levenes test is significant so equal variance cannot be assumed

48
Q

Requirements for Mann-Whitney test (2)

A

Interval, ratio data (sometimes ordinal)

Non-parametric so no assumptions

49
Q

Mann-Whitney test (U)

A

Equivalent to independent t-test, it tests whether the distributions of both groups are equal or significantly different

50
Q

What does the U value for Mann Whitney tests mean

A

the closer to 0, the more the groups medians are equal (H0 being true)

51
Q

Conclusion if p < 0.05 for Mann Whitney test

A

Significant difference between the groups medians, reflecting a real difference in populations

52
Q

When to use the ‘exact sign.’ U and p values in the table given for a Mann Whitney test

A

If the sample is less than 20 (small)

53
Q

What to report when reporting Mann Whitney test results (4)

A

U-value
p-value
Z score
Medians

54
Q

Effect size (R) = …

A

Z / square root N

55
Q

Difference between N and n

A 
N = number of all observations
n = number of observations in the sample
56
Q

Requirement for a dependent t-test (3)

A

Related groups
Parametric assumptions met
interval or ratio data

57
Q

What do dependent t-tests typically measure

A

A sample doing the same thing twice (before and after doing something else)
Can also be matched pairs of similar units

58
Q

Example of dependent t-test

A

Amount of pull-ups done before and after participants train in a bootcamp

59
Q

Conclusion if p < 0.05 in a dependent t-test

A

Significant, H0 rejected, a significant difference between samples before and after

60
Q

Degrees of freedom = …

A

(N.1 - 1) + (N.2 - 1)

61
Q

What to do with results if we have a one-tailed hypothesis

A

divide the p-value by 2 and then see if its significant

62
Q

When are effect sizes used a lot for comparisons

A

In medicine and forensics to compare with (eg) results of other drugs

63
Q

Effect size is…

A

a way to quantify between two groups, it emphasises the size of difference without confounding with the sample
The difference between two sample means

64
Q

How effect size can be measured (2) and when should each of these be used

A

Pearson’s r - for non parametric tests

Cohen’s d - for parametric tests

65
Q

Small medium and large for Pearson’s r

A

Small: > 0.1
Medium: > 0.3
Large: > 0.5

66
Q

How to calculate Cohen’s d

A

Difference between means divided by the ‘pooled’ standard deviation for the means

67
Q

Small, medium and large for Cohen’s d

A

Small: 0.2
Medium: 0.5
Large: 0.8

(it can be over 1)

68
Q

What would a small Cohen’s d likely mean

A

A larger sample is needed

69
Q

Wilcoxon signed rank (matched pairs) test

A

Non-parametric equivalent to repeated measures t-test, it tests the difference of medians for repeated samples

70
Q

Requirements for Wilcoxon signed rank (matched pairs) test (3)

A

Non parametric
Ordinal data or above
Repeated samples

71
Q

Conclusion if p < 0.05 for Wilcoxon signed rank (matched pairs) test

A

Significant difference between the samples medians, accepts H1. They are from different populations

72
Q

What to report for a Wilcoxon signed rank (matched pairs) test (3)

A

Z value
P-value
Medians

73
Q

What does N equal for repeated measures design

A

Still means both samples, so is participants x 2

74
Q

Df =… for chi squared tests

A

(Number of Rows - 1) x (number of columns - 1)

75
Q

Limitation of a p value

A

Only gives statistical significance, not real life significance

76
Q

Other name for One sample Wilcoxon signed rank test

A

Wilcoxon rank sum test

77
Q

z score = …

A

x - mean / SD

78
Q

What does a z score mean

A

The number of standard deviations that the score is away from the mean

79
Q

How to report Chi-squared

A

X^2(df) = Value, p = p-value

80
Q

How to report a Wilcoxon signed rank / rank sum test

A

T = T-value, p = p-value… (state medians before)

Slides say only p-value essential for APA format

81
Q

How to report a Mann-Whitney U test

A

U = U-value, Z = Z-value, p = p-value… state medians before

82
Q

How to report a Wilcoxon signed rank (matched pairs) test?

A

Z = Z-value, p = p-value… state medians before

83
Q

Different null hypothesis’ for Chi-squared test of independence

A

No relationship between categories

Proportions are the same for the categories

84
Q

Hypothesis for Mann-Whitney test

A

Probability of observation from one population exceeding the other is more than 0.5

85
Q

Null hypothesis for Mann-Whitney test

A

Equal distribution of both groups, probability of observation from one group exceeding the other equals the probability of the reverse

Psychology (9 decks)

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