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EPA revision > Untitled Deck > Flashcards

Untitled Deck Flashcards

1
Q

What is experimental psychology?

A

The use of scientific methodology to measure individual responses in a controlled situation or experiment to investigate the mind and or behaviour.

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2
Q

What are the steps in the research process?

A
  1. Develop research question based on initial observations and generate theory and hypothesis.
  2. Identify IVs and DVs.
  3. Design study to collect data testing the theory (Between/within subjects).
  4. Analyse data and graph data or fit a model.
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3
Q

What makes a good statistical model?

A

Fitting the data well.

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4
Q

What is a population in research?

A

A group of individuals that we want to generalise findings or a statistical model to.

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5
Q

What is a sample?

A

A subset of the population which is studied to infer info about the larger population.

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6
Q

What are the steps to calculate standard deviation?

A
  1. Calculate the sum of deviations.
  2. Square each deviation.
  3. Divide by df.
  4. Square root these.
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7
Q

What does high SD mean?

A

High deviation…platykurtic.

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8
Q

What does low SD mean?

A

Low deviation…leptokurtic.

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9
Q

What is ratio data?

A

Absolute 0…no values below it.

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10
Q

What is interval data?

A

Continuous with no meaningful 0.

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11
Q

What is ordinal data?

A

Ranked data 1,2,3,4,5,6,7 etc.

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12
Q

What is nominal data?

A

Categorical.

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13
Q

What is mesokurtic?

A

Normal distribution - symmetrical, same median mean and mode.

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14
Q

What is negative skew?

A

Lower mean, higher mode.

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15
Q

What is positive skew?

A

Higher mean, lower mode.

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16
Q

What is platykurtic?

A

Thinner tails…lack outliers.

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17
Q

What is leptokurtic?

A

Thicker tails…too many outliers.

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18
Q

What is z-skew?

A

Skewness/std error skewness.

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19
Q

What is z-kurtosis?

A

Kurtosis/std error kurtosis.

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20
Q

What range do z-skew and z-kurtosis values have to fall in to be normally distributed?

A

Z-scores have to be within the +/-1.96 range.

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21
Q

What are the types of statistical tests?

A

Independent samples t-test and then paired samples t-test.

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22
Q

How to solve normality issues?

A
  1. Check for outliers and remove or manipulate them.
  2. Transform the data with a mathematical function.
  3. Use a non-parametric test.
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23
Q

Why do non-parametric tests not mind if data is not normally distributed?

A

Because they rank the data, they don’t make assumptions about distributions and are not affected by outliers.

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24
Q

What is variance?

A

An estimate of average variability (spread) of a set of data.

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25
Q

What are degrees of freedom?

A

Number of people who can choose - 1.

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26
Q

What are examples of normally distributed data?

A

Babies birthweight.

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27
Q

What do standard deviations mean for a normal distribution?

A

+/-1 sd from the mean has 68% of data.
+/- 1.96 sd from mean has 95% of data.
+/-3 sd from mean has 99.7% of data.

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28
Q

What is a z-score?

A

(your score - mean score) / standard deviation.

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29
Q

Standard error

A
  • Standard deviation of sample means.
  • Tells you how widely spread sample means are around the population mean.
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30
Q

confidence intervals

A

use sample mean and standard error to estimate out the range of mean values we have 95% confidence that the true population mean lies

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31
Q

Large confidence interval

A

sample mean is further away from the true mean of the population

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32
Q

Small confidence interval

A

sample mean is very close to the true mean of the population

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33
Q

what do error bars on plots represent

A
  • 95% confidence intervals
  • or standard error of the mean
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34
Q

test statistic

A

variance explained by the model/variance not explained by the model

effect/error

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35
Q

how to calculate a t-test

A

difference in means/standard error

=

explained variance/unexplained variance

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36
Q

purpose of significance testing

A

to determine which hypothesis to reject/accept…null vs alternative

37
Q

range p values can fall in

A

0-1

38
Q

alpha level used in psych

A

0.05

39
Q

type 1 error

A

we believe there is an effect when there isn’t
the probability is the alpha level 0.05

40
Q

type 2 error

A

we believe there is no effect in the population when there is

the probability is the beta level usually 0.2

41
Q

effect size

A

standardized measure of the size of an effect

allows people to objectively evaluate the size of an observed effect

42
Q

measures of effect size

A
  • for t-tests: Cohens d
  • for Mann- whitney: r
    -correlation: r
  • chi square: odds ratio
43
Q

descriptive Stats

A

means, SD, etc

44
Q

inferential Stats

A

t-test, anova, etc

45
Q

Samples from same population

A

IV has no effect on DV, difference is due to chance fluctuations due to sampling

46
Q

Samples from different populations

A

differences in dv are due to changes in iv

47
Q

delete this card

A
48
Q

Why cant we run lots of t-tests?

A

at p<0.05 there is a 5% chance of making a type 1 error…if we ran 20 tests then one of them would be significant just through chance

49
Q

Assumptions of an ANOVA

A
  • homogeneity of variance (the variances are the same/similar) or sphericity
  • data on an interval scale
  • sampling distribution of means is normal
50
Q

What measures homogeneity of variance in a between groups ANOVA

A

levenes

significant-assumption violated
non-significant-assumption met

51
Q

What measures homogeneity of variance in a repeated measures ANOVA

A

Mauchly’s test of sphericity

significant-assumption violated
non-significant-assumption me

52
Q

ANOVA

A

allows us to compare multiple conditions in a single, powerful test

explained variance/unexplained variance

53
Q

explained variance in a between group anova

A

variance between the groups

54
Q

unexplained variance in between-group anova

A

variance within each group…also known as error

55
Q

how many sources of variance are there in a repeated measure vs between participants design?

A

repeated measure- 3
between participants- 2

56
Q

sphericity

A

equality of variance of the differences between all combinations of related groups
(or levels)

57
Q

How to know which correction to make on Mauchly’s sphericity test

A

if greenhouse Geisser is less than 0.75 we make a GG correction…if not you make a huynd-Feldt

58
Q

planned comparisons

A

comparisons that are hypothesised before any data is collected

59
Q

unplanned comparisons/post-hoc tests

A

pairwise comparisons that test every possibility

60
Q

Bonferroni correction calculation

A

0.05/number of comparisons

61
Q

which measure of error to use in ANOVA eta squared or partial eta squared

A

repeated measures anova partial eta squared

between groups anova eta squared

Remember this by saying…repeated measures is fewer people and so partial eta squared is used

62
Q

effect sizes

A

small effect= 0.01
medium effect =0.06
large effect =0.14

63
Q

non-parametric equivalent for the one-way repeated measures ANOVA

A

Friedmans test

64
Q

non-parametric equivalent for the one-way between groups ANOVA

A

kruskal-Wallis

65
Q

ANOVA dfs

A

Between subjects:
df1= k (number of levels to the iv) -1
df2= n (total number of observations) - k

Within subjects/repeated measure:
df1= k (number of levels to the iv) -1
df2= (no of observations - 1) - (no of ppts -1) - (k - 1)

66
Q

Kendals W

A

Friedman’s test effect size calculation

chi squared/
sample size x (no of conditions-1)

0.1 small
0.3 medium
0.5 large

67
Q

What test is used for friedman post hoc testing

A

wilcoxon test with a bonferoni correction

68
Q

what test is used for kruskal wallis post hoc testing

A

mann-whitney test

69
Q

main effect

A

the effect of one IV on the DV

70
Q

Difference between a one way and a two way anova

A

one way has one iv
two way has two ivs

71
Q

mixed anova

A

one iv is within one iv is between

72
Q

when to use mauchlys test

A

for within IV- only when 3 or more levels

73
Q

how to transform a positive skew

A

square root the values

74
Q

which rows can you compare in post hoc tests

A

1 + 6 or 2 + 5

75
Q

What is statistical power?

A
  • the probability that a study will detect an effect when there is one
  • correctly reject the null hypothesis when it is false. i.e. probability of avoiding type 2 error (false negative)
76
Q

how can statistical power be expressed

A

1- beta

77
Q

low powered studies produce

A
  • unreliable findings
  • more false negatives
78
Q

what contributed to the replication crisis

A

low powered studies

79
Q

Winners curse

A

the statistical unreliability of positive results from small published studies…as statistical power increases, bias falls

80
Q

how many published psych studies have been found to be reliable?

A

one third

81
Q

replication crisis

A

when studies were repeated they didn’t yield the same findings

82
Q

limitations of null hypothesis significance testing

A
  • overreliance on p values
  • p values aren’t standardised as they also reflect the sample size
  • a small study may be non-significant solely because of the sample size
  • encourages all or nothing thinking
83
Q

power analysis/ priori power analysis

A

checks what sample size is needed to detect an effect of this magnitude at this probability level

84
Q

why are underpowered studies problematic?

A

have a lower chance of correctly rejecting the null hypothesis. waste of research resources…ppts burdened…impact of results may not be ethical as it is based on unreliable research

85
Q

how to calculate cohens d

A

group a mean- group b mean
/ pooled standard deviation

86
Q

what impacts statistical power?

A
  • effect size
  • sample size
  • acceptable error rates
87
Q

open science

A

advocates for transparency, sharing, and inclusivity within scientific research

88
Q

examples of open science

A
  • open source
  • open methodology
  • open peer review
  • open access
  • open educational resources
  • open data

EPA revision (5 decks)

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