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First year Psychology > rmb > Flashcards

rmb Flashcards

1
Q

what is a simple way to simplify a large set of numbers?

A

counting how often each number occurs (frequency)

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2
Q

what type of data do we use histograms for?

A

continuous

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3
Q

where is the centre of a histogram?

A

1

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4
Q

what is the benefit of using more bins in a histogram?

A

shows the distribution with higher resolution (but can get noisy)

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5
Q

how does a change in mean affect distribution shape metrics?

A

a change in mean keeps the shape of the distribution the same but changes the centre of mass such that the highest bars occur where the most likely values are

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6
Q

how does a change in variance affect distribution shape metrics?

A

a change in variance stretches or compresses the data set to reflect the values in the dataset occurring from a wide range of values or a very narrow range of values

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7
Q

how does a change in skewness affect distribution shape metrics?

A

a dataset with a negative skewness will have a long tail in which that tail points towards negative values in the dataset

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8
Q

how does a change in kurtosis affect distribution shape metrics?

A

kurtosis reflects the peak hardness of our datasets

so data high kurtosis will have a sharp peak, and low kurtosis will have very wide tails

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9
Q

what is a dataset?

A

a collection of data acquired for a specific purpose

may relate to multiple experiments or hypotheses

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10
Q

what is a variable?

A

a number that can ‘vary’ (e.g. take a high or a low value) depending on an attribute that we’re trying to measure

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11
Q

name all the types of variables

A

nominal
ordinal
interval
ratio

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12
Q

what is nominal data?

A

no relationship between different possibilities in scale. sometimes called categorical data

the distinct set of possible answers, and there is no particular order in relating those things together

e.g. country of origin

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13
Q

what is ordinal data?

A

a natural order between possibilities but nothing else. can’t interpret the ‘magnitude’ of differences

e.g. likert scales

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14
Q

what is interval data?

A

the possibilities are ordered and have interpretable magnitudes, though ‘zero’ does not have special meaning

e.g. temperature

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15
Q

what is ratio data?

A

like interval data, but now zero is directly interpretable and we can interpret ratios between values

e.g. reaction times

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16
Q

what is continuous data?

A

a variable that can change freely to take any value

for example - temp could be 4C, 10.34C or -0.0000513C

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17
Q

what is discrete data?

A

a numbered variable that takes one of a fixed set of values

for example - number of cars owned

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18
Q

what is a sample?

A

the data we’ve actually collected

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19
Q

what is a population?

A

in most cases a theoretical or hidden quantity which represents the distribution we would have seen if we were able to collect all possible data to completely describe the group of people we’re interested in

the total set of everyone within a group that we want to test

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20
Q

do very large datasets reflect the wider population better or worse than small datasets?

A

better

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21
Q

what does a sampling distribution tell us?

A

how variable the mean is for a given data sample from a given population

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22
Q

how does a larger standard deviation differ than a smaller one on a distribution graph?

A

with a larger standard deviation we notice a very similar mean/centre of the sampling distribution but the breadth of it is much larger

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23
Q

if a larger sample produced a higher standard error of mean, what does this suggest?

A

that each sample in the larger population is more variable so we can be less precise in our estimation of the mean from one sample of the second population compared to the first

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24
Q

if a dataset is normally distributed, how can we calculate the standard error of the mean?

A

SEM = σ / √N
dividing the standard deviation of the data by the square root of the number of samples

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25
Q

what are confidence intervals?

A

95% confidence intervals define a range of values which have a 95% chance of containing the population mean

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26
Q

how do you calculate confidence intervals?

A

95% CI = 1.96 * SEM
upper = X̄ + CI
lower = X̄ - CI

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27
Q

what does the centre line on a distribution graph mean?

A

the mean

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28
Q

what do the white lines inside the distribution represent?

A

one standard deviation away from the mean on each side

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29
Q

what is the standard normal distribution?

A

a special case of the normal distribution in which the mean is zero and the standard deviation is one

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30
Q

what does Shapiro-Wilk test for?

A

an objective test for whether data is normally distributed

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31
Q

what does Shapiro-Wilk W test for?

A

is a metric indicating how ‘normal’ the data is, higher values indicate more normal data

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32
Q

what does Shapiro-Wilk P test for?

A

a probability indicating how significant any difference from normality is

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33
Q

what does a horizontal line in the centre of a box whisker graph represent?

A

the median

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34
Q

what do the edges of the box in a box whisker graph represent?

A

the interquartile range

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35
Q

what does the vertical line and the dots that occur outside of it represent?

A

line - 95% intervals of data
dots - often outliers

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36
Q

what does it mean if a sample bias is systematic?

A

that the bias will continue to be true even if we recruit a larger sample

e.g. perhaps certain people are just more or less likely to respond to a recruitment email

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37
Q

what is ecological validity?

A

a measure of how test performance predicts behaviours in real-world settings

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38
Q

what does WEIRD stand for?

A

Western
Educated
Industrial
Rich
Democratic

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39
Q

what does a histogram represent?

A

the sample distribution of our value of interest

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40
Q

what is each sample we measure an approximation of?

A

the underlying population which we can’t actually measure

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41
Q

how do you calculate the sample mean?

A

X̄ = Σ xj / N

the sample mean is the sum of all the individual data points divided by the total number of data points

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42
Q

how do you calculate the sample standard deviation?

A

σ = √Σ (xj -X̄) ^2 / N

the sample standard deviation is the square root of the sum of the squared difference between the sample mean and each individual data point divided by the total number of data points

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43
Q

what is the standard error of the mean?

A

is the likely variability in our estimate of the population mean from a given sample

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44
Q

does the SEM increase, decrease, or stay the same when the sample size grows larger?

A

decreases, larger samples are more reliable

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45
Q

what does a wide standard error tell us compared to a small standard error?

A

a wide standard error tells us that our mean is very very varied so if we did this ten times we can expect to get very different numbers

however if our standard error is very small, it’s telling us that we’re going to get the same mean every time

a small standard error = number of data points are high

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46
Q

what are confidence intervals?

A

a range of values around the sample mean that has 95% chance of containing the population mean

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47
Q

what is a statistical hypothesis?

A

a comparison to a single value

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48
Q

what is a null hypothesis?

A

the sample mean is indistinguishable from the reference value

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49
Q

why can we not just look at the difference between the mean and the specified value?

A

noise
no measurement is perfect, there is often an associated error with any data point

sampling bias
we cant measure data from everybody
therefore, we are only working with an estimate of the mean of our group - not the true mean

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50
Q

what is the formula for a t-test?

A

t(24) = X̄ - μ / SEM

T = t value
24 = degrees of freedom (one less than the number of data points in out dataset)
X̄ = mean of the observed data
μ = comparison value (what we compare our observed mean to)
SEM = standard error of the mean

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51
Q

what is a one-sample t-test ?

A

the difference between the mean of observed data and a hypothesised comparison value, all divided by the standard error of the mean of the observed data

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52
Q

what is a t-value?

A

a test statistic - intended to provide a single number that tells us the extent to which the data sample matches the null hypothesis

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53
Q

what does a small t value suggest for a one sample t-test?

A

indicates that the SEM is much larger than the difference

this means we aren’t likely to be able to distinguish between the sample mean and the comparison value

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54
Q

what does a large t-value suggest for a one sample t-test?

A

indicates that the difference is larger than the SEM
this means that we are likely to be able to distinguish the sample mean from the comparison

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55
Q

when does the t-value grow for a one sample t-test?

A

when the difference between the observed data mean and comparison value gets bigger

this is as the top of the fraction gets larger whilst the bottom stays the same

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56
Q

when does the t-value shrink for a one sample t-test?

A

as the variance of the observed data gets bigger

this is as the bottom of the fraction gets larger whilst the top stays the same

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57
Q

what is apophenia?

A

the tendency to see meaningful connections between unrelated things

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58
Q

why do we assume the null to be true until otherwise?

A

this is proof by contradiction
put the burden of proof on the alternative hypothesis

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59
Q

give an example of a one-sample hypothesis and a one-sample null hypothesis

A

attendance in class is more than 80%

attendance in class is NO different from 80%

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60
Q

what does a t-test account for?

A

uncertainty in our estimate of the mean by using the standard error of the mean

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61
Q

what were will, merit, jenkins & kingston interested in for the medusa effect?

A

whether pictures capture something of the mind that is significant to us, albeit at reduced potency

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62
Q

what does a two-sample hypothesis entail?

A

a test hypothesis that asks whether two groups have different means

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63
Q

what does the statistical null hypothesis state for a two sample t-test?

A

the sample means of the two groups cannot be distinguished

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64
Q

what is a between subjects design?

A

two independent groups of data points

each participant is in a single group and contributes a single data point

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65
Q

what is a within subjects design?

A

two dependent groups of data points

each participant completes two conditions and contributes two data points

sometimes called repeated measures

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66
Q

what is an independent samples t-test?

A

the difference between the two groups of data, all divided by the standard error of that difference

it is a ratio between the size of the difference and the precision to which it is estimated

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67
Q

what is the equation for an independent samples t-test?

A

t(df) = X̄1 - X̄2 / Sp √2/N

mean of group 1 - mean of group 2 / pooled standard error of the difference

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68
Q

how do you find the standard error of the difference?

A

by using the pooled standard deviation of the two groups

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69
Q

what is a pooled standard deviation?

A

a single deviation to represent the variability in both groups - assuming that both groups have the same variability

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70
Q

what does a large positive t-value indicate for an independent samples t-test?

A

the mean of group 1 is above than the mean of group 2

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71
Q

what does a near zero t-value indicate?

A

the mean of group 1 is indistinguishable from the mean of group 2

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72
Q

what does a large negative t-value indicate for an independent samples t-test?

A

the mean of group 1 is below the mean of group 2

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73
Q

what does a levene’s test, test for?

A

homogeneity of variance

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74
Q

what does a levene’s test assess?

A

assesses the null hypothesis that different groups of samples are from populations with equal variances

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75
Q

what does a significant value indicate for a levene’s test?

A

that the groups are likely to have different variances - suggesting that a pooled estimate of standard deviation is not appropriate

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76
Q

when do you use a paired samples t-test?

A

it must be used when we’re comparing the means of two dependent distributions - that is, when the same participants have contributed to each condition

77
Q

what is the equation for a paired samples t-test?

A

mean of paired differences - 0 / standard error of the mean paired difference

78
Q

what are the pros and cons of a within subject design?

A

pros:
removes individual differences
need fewer participants

cons:
practice or order effects
longer participation per individual

79
Q

what are the pros and cons of between subject design?

A

pros:
shorter participation per individual
lower practice/order effects

cons:
can be affected by individual differences in sampling
need to decide how to allocate participants to groups
needs more participants

80
Q

when should we use a t-test?

A

comparison of two group means, or a single mean to a reference value

data must be interval or ratio type

assumptions must be met

we must be sure that the data we’re looking at have both an interpretable mean and standard deviation to run a t-test

81
Q

what are some assumptions of a t-test?

A

appropriate data type

data are normally distributed (normality)

data observation are independent (independence)

groups have equal variance * (equality of variance)
*welch’s test removes this assumption

T value tell us the confidence in the difference

student’s t-test assumes homogeneity of variance
i.e. the distributions of the two groups have the same standard deviation - is that always fair?

82
Q

what is a welch’s t-test?

A

uses an unpooled measure of standard deviation which is valid when the group have different variance

the unpooled standard deviation valid whether the groups have equal variances or not

83
Q

what two columns are needed for analysis in jamovi?

A

a categorical grouping variable
a continuous outcome variable

84
Q

what are test statistics?

A

they quantify how much of our data resembles what we would expect under the null hypothesis

  1. the size of the difference from the null
  2. how confidently we have estimated that difference from the data in hand

we need both to account for noise and uncertainty in the data

85
Q

what makes us more confident in estimating a difference?

A

the size of our data sample

we can get really large t-statistics from very subtle differences if we have a lot of data

however this doesn’t tell us anything about the probability of obtaining this result by chance

86
Q

what is cohen’s d?

A

scales with the size of the difference between groups

is not strongly affected by sample size (apart from at very small samples)

purely the size of the difference between groups
no information about confidence in the estimate

87
Q

what are t-statistics?

A

a blend of effect magnitude and our confidence in the estimate

we can compute a ‘pure’ measure of how large a difference using an effect size

we can see the t-values that appear by chance by looking at random data when there is no effect present

if our data meet the parametric assumption of normality, we can write down the exact sampling distribution of t-values we would see if the null hypothesis were true

this distribution varies depending on the sample size and number of conditions - the degrees of freedom

88
Q

what size sample produces more extreme t-values?

A

samples with small numbers of observations

the sampling distribution of t-values becomes the standard normal distribution when we have a very large sample

89
Q

how do we determine how likely it is to observe another test statistic more extreme than the one we have from our data?

A

can be computed from the sampling distribution of our test, assuming that the null hypothesis is true

if our data are normally distributed, e can compute the sampling distribution straightforwardly

90
Q

what does our null model describe?

A

the distribution of t-values that we might expect to see, just due to random noise, if there were no true difference in our data

91
Q

what does a null model tell us?

A

what t-values we might reasonably see by chance in our experiment

92
Q

name features of the t-distribution as a null model

A

all conditional on our parametric assumptions being true

the shape depends on the number of observations

we see more extreme values with smaller sample sizes

this is specified by the degrees of freedom of the analysis

93
Q

how do you calculate the degrees of freedom for a one sample t-test?

A

N - 1

94
Q

how do you calculate the degrees of freedom for an independent sample t-test?

A

N1 + N2 - 2

95
Q

how do you calculate the degrees of freedom for a paired sample t-test?

A

N - 1

96
Q

what is a p-value?

A

the probability of observing a result at least as extreme as the one from the data

97
Q

when do we consider a value significant?

A

less than 5% chance of observing a result the same size or larger by pure chance

significant result = p < 0.05

98
Q

how should we interpret p-values?

A

the p-value tells us the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct

99
Q

what does the effect size measure?

A

the strength (or magnitude) of the apparent difference, irrespective of how significant or probable that effect may be

100
Q

what are t-tests directly targeted at?

A

comparisons between one group and a reference value or between two groups

101
Q

what is the null hypothesis for multi hypotheses?

A

that all groups are the same, in other words the mean of all the groups is the same

102
Q

what does ANOVA (analysis of variance) test for?

A

every possible difference in our data which helps us assess our data using fewer comparisons than we would if we simply ran a t-test for every possible combination

103
Q

what is sum square total?

A

when we’re looking at the difference between the mean and each data point, where we’re using the same mean for every group

quantified by the sum of squared differences to the mean

104
Q

what is sum square within?

A

sum squares of the difference between each data points and the groups individual mean

105
Q

how do you calculate sum square between?

A

SSbetween = SStotal - SSwithin

106
Q

what is the total variance in the data?

A

the sum of the difference between data points within groups and the difference between the groups

107
Q

how do you calculate mean square error within?

A

MSEwithin = SSwithin / N - G

108
Q

how do you calculate mean square error between?

A

MSEbetween = SSbetween / G - 1

109
Q

How do you calculate ANOVA?

A

F = MSEbetween / MSEwithin

110
Q

what is a large F?

A

F is large when the mean square error between groups is a large compared to the variability within groups

111
Q

what does a large F suggests?

A

that there is a substantial benefit from modelling the data with the individual group means

Large F great benefit to groups having individual means

112
Q

what is a small F?

A

F is small when the mean square error between groups is smaller than or similar to the variability within groups

113
Q

what does a small F suggest?

A

suggests that there is no benefit from modelling the data with individual group means, we might as well stick to the overall mean

small F little benefit to groups having individual means

114
Q

what does a significant ANOVA result suggest?

A

indicates that it would be unlikely to observe this data if there were no true differences between the means of these groups

115
Q

what factors does ANOVA part variance into?

A

what is the total variability in the dataset?

how much of that variability occurs within each group?

how much occurs between groups?

116
Q

what is the null hypothesis for ANOVA?

A

that all groups can be described equally well using the same mean

117
Q

what is a factor?

A

a categorical (nominal) variable containing the labels of a set of groups

118
Q

what are levels?

A

different groups within a factor

119
Q

what are assumptions of independence for between-subjects ANOVA?

A

data observations must be unrelated

120
Q

what are assumptions of normal distributions for between-subjects ANOVA?

A

data must be reasonably normally distributed

121
Q

what are assumptions of equality of variance for between-subjects ANOVA?

A

groups should each have equal variance

122
Q

what are assumptions of categorical factors for between-subjects ANOVA?

A

predicting factors must be divided into separate groups

123
Q

what data type is needed for ANOVA?

A

interval or ratio

124
Q

what does the hypothesis test Shapiro-Wilk test for?

A

provides an objective test for whether data is normally distributed

null hypothesis assumes the same/normally distributed

a high value suggests a more normally distributed data set

125
Q

what is Shaprio-Wilk W?

A

a metric indicating how ‘normal’ the data is, higher values indicate more normal data

126
Q

what is Shaprio-Wilk p?

A

a probability indicating how significant any difference from normality is

127
Q

do non-parametric tests work with the mean or the median?

A

median

it is a more robust measure of central tendency that the mean when data are not normally distributed

128
Q

what is a median?

A

the middle value in a sorted list of observations

calculating a median from an even number dataset = two middle numbers added together then divided by 2

129
Q

when do you use wilcoxon signed-rank test?

A

one sample

130
Q

what is the wilcoxon signed rank test?

A

tests whether ranks are symmetrical around zero

131
Q

how do you calculate a wilcoxon signed-rank test?

A

abs sort
rank the values from smallest to largest based of their absolute value
We do this by ignoring the sign (+/-)

Next rank the values from 1 onwards giving the smallest value a number one

Then we add the original signs (+/-) back onto the ranks giving us a signed rank

In order to form a test statistic from this transformed data we must first split the data into two groups, both positives and negatives

We then have to calculate the sum of these ranks

Wilcoxon’s W = min(pos_rank,neg_rank) = 15

132
Q

when do we use Wilcoxon Mann Whitney U test?

A

independent samples

133
Q

how do you calculate wilcoxon mann whitney U test?

A

first we sort the data
from the most negative to least negative
and assign each value to group 1 or 1

Next we rank our values from 1 to however many data values there are

After this we can then compute our ranks and line them up with our data

Finally we can calculate the sum of each groups respective ranks and compare them to the expected summed rank

Mann-Whitney U = min(G1 - Exp, G2 - Exp) = 10.0

134
Q

how can shapiro-wilk be affected by different samples sizes?

A

normality tests at large samples are overly conservative in that they can detect tiny departures from normality that we shouldn’t be worried about which may result in a significant result

135
Q

what are QQ “quantitative quantitative” plots?

A

helpful visual tests that compare two distributions, Jamovi use them to compare our data to a normal distribution

136
Q

what do QQ plots tell us?

A

the percentiles of a normal distribution is on the x-axis and percentiles of the data are on the y-axis

if the data points are close to being linearly related (i.e. are close to the diagonal line) then the data distributions are a close match

137
Q

when can we run rank-based non-parametric tests?

A

valid for both ‘normal’ and non-normal datasets

valid for ordinal, interval and ratio data

valid when you want to compare medians rather than means

138
Q

what do we mean by traditional data collection methods?

A

methods often used within qualitative research,e.g., interviews and focus groups

139
Q

what do we mean by alternative data collection methods?

A

methods that either are not regularly used in qualitative research, e.g. qualitative surveys, or newer additions to qualitative data collection, e.g., story completion

140
Q

what are interviews in psychology?

A

aim to find out as much as possible about the participants experiences and meanings

141
Q

what are the types of interviews?

A

structured
semi-structured
loosely structured
un-structured

142
Q

what are focus groups?

A

aim to find out as much as possible about the participants’ understandings and meanings, with more than one participants

individuals come together to discuss a topic
involves sharing of experiences, ideas, views etc.

143
Q

why do we use focus groups?

A

contextualise collective understandings and sense-making

useful in considering peoples’ shared understandings

sensitive to points of consensus and disparity

144
Q

describe face to face focus groups

A

effort from the researcher: you must act as a facilitator for your participants: ensure the topic is followed

focus group schedule used - like interview schedule, list of questions topics and prompts for discussion

145
Q

describe online focus groups

A

the format may be different, but content seems relatively stable between FSF and online focus groups

146
Q

what are the types of online focus groups?

A

asynchronous online focus group
synchronous online focus group
groups in the virtual world

147
Q

what are the advantages and limitations of an asynchronous online focus group?

A

+ more time to think about responses

  • there could be technological issues associated with them
148
Q

what are the advantages and limitations of a synchronous online focus group?

A

+ technology can provide different types of environments for participants to engage with

  • requires a good, and consistent bandwidth, and reliant on individual schedules
149
Q

what are the advantages and limitations of groups in the virtual world?

A

+ avatars may lead to greater engagement and co-creation activities

  • assumes a certain level of skill / ability is needed
150
Q

are surveys a quantitative or qualitative tool?

A

quantitative

151
Q

how are surveys used in data collection?

A

a group of participants, selected from a population

can generate a lot of data about that group-large sample sizes

usually includes
-measures, e.g. personality measures
-demographic questions

usually self report

152
Q

what are the features of a qualitative survey?

A

less reliant on researcher craft skills

participants can have more control, and consideration over their responses

slightly more scope for possible anonymisation in recruitment

can suit broad and specific topics of interest

usually suits realist, critical realist, or essentialist perspectives

however, no interaction with subject, nuances of emotion or environment lost

153
Q

describe prompt methods

A

use video/vignettes/activities/audio to start discussion and debate

good for ‘sensitive’ topics

discussion becomes participant-led

154
Q

describe photo-voice as participant-led prompts

A

photography to explore people’s worlds and make them accessible to others

encourage documentation and reflection

empowerment through personal and shared experiences

to encourage through personal and shared experiences

to encourage critical dialogue

to speak to those in powerful positions, i.e. policy makers

155
Q

what are some key points of reviews of photo-voice studies?

A
  1. inconsistent implementation of the method, e.g. poor training on camera use or poor overview of how stakeholders were integrated throughout the research process
  2. inconsistent evaluation of the outcomes and impact, e.g. lacking evaluation of how empowered participants felt, whether practices were changed
  3. implementation challenges with specific populations, e.g. those who may struggle to use some of the features on the camera
  4. inconsistent reporting and adherence to ethical procedures, e.g. not gaining/reporting ethical approval, or not addressing ethical concerns (e.g. power imbalance)
156
Q

describe story completion as a new method of data collection

A

projective test, completing a story stem

allows for participant creativity

tap into ways of understanding through overcoming awareness of participants own emotions and barriers of admission

explore a range of assumptions of a given phenomenon

useful for exploring socially sensitive, ambiguous, and contentious issues

theoretically flexible

157
Q

what things should we consider when developing story stems?

A

topic - what is it you want to explore, is it appropriate?
length - shorter for straight forward topics
characters - engaging and authentic
detail
ambiguity - can be good
first or third person - usually third
instructions - need to be clear

158
Q

what are solicited diaries?

A

diary writing within predefined guidelines, intended for research purposes

partial access to thoughts/feelings of the participants

more participant control can be useful for sensitive subjects

159
Q

describe apps as a diary method

A

apps found to be easy to use compared to paper diaries in the DBT treatment programme of patients with BPD

160
Q

describe media data

A

e.g. newspapers, magazines, tv, films, and reader comments

ubiquitous and easily accessible

highlights common messages about populations/issues

taps into our meditated lives, practices, and beliefs

pervasive and accessible (but not necessarily easy)

focus on sampling strategy and justification

161
Q

describe user generated content

A

can (sometimes) use pre-existing ‘naturalistic’ data from online sources in qualitative research

e.g. forums, blogs, social media, etc

162
Q

what is ‘data harvesting’?

A

using existing forums, chats, blogs, tweets etc. and analysing that text

use existing external cites to host purposive research

the data is naturalistic, but may not be fit for purpose

163
Q

describe user generated content, discussion boards

A

online forums where people with shared interests/characteristics interact with each other on particular topics

a good way of exploring a phenomena without asking for it

Process
1. Select the forum
2. Identify a time frame
3. Select the threads, and appropriate number of them
4. Download and format, ready for analysis
5. Select which elements of the data you need to focus on to answer your research question

164
Q

what are some concerns regarding ethics in user generated content?

A

is it personal or public data?
usually avoid private discussion forums/those you have to sign up to access

can you make someone recognisable by quoting them?
some researchers will describe the extracts rather than quoting (as it may identify the poster)

should you ask the moderator/administrator/owner permission?
some may have designed subforums for research purposes/gatekeepers to protect the interests of the group

read the terms and conditions

165
Q

what is thematic analysis?

A

foundation for other types of qualitative analyses

process of identifying meaningful patterns (themes)

a way of ordering and understanding participants’ social world

166
Q

what constitutes a theme?

A

recurrent ideas, topics, statements etc. that generate a pattern which may explain or add meaning to a person’s (or group of people’s) experiences

these patterns (themes) are then brought together into a category which is then labelled by the researcher

167
Q

what is ontology?

A

is that part of philosophy which deals with questions about the nature of what exists, and how different aspects of being are related to each other

concerned with the nature of reality

168
Q

what is epistemology?

A

theory of knowledge, it is concerned with the mind’s relation to reality

what is it for this relation to be one of knowledge?
do we know things?
and if we do, how and when do we know things?

concerned with the nature of knowledge and is a positivism which aligns with realism and contextualisation

169
Q

what is positivism?

A

human experience is knowable, universal and objective

research as a form of investigation for the truth

direct correspondence between perception and things

knowledge is inert and impartial

170
Q

what is contextualism?

A

sits in-between positivism and constructionism

akin to critical realism

contextualism of human acts

no single reality

knowledge comes from contexts

provisional

interests in the truth, despite this being inaccessible, knowledge can be truthful

171
Q

what is social constructionism?

A

historically & culturally contextualised account

research as a form of investigation of an account

questions tacit, taken for granted knowledge

knowledge is (re)constructed through language

knowledge is active, and has power

172
Q

what are the three ontological approaches?

A

realism
critical realism
relativism

173
Q

what is realism?

A

a pre-social reality exists that we can access through research

174
Q

what is critical realism?

A

a pre-social reality exists but we can only ever partially know it

175
Q

what is relativism?

A

‘reality’ is dependent on the ways we came to know it

176
Q

how is poor practice of thematic analysis characterised?

A

a mashing of other approaches, i.e. grounded theory techniques

use of coding reliability measures

treating TA as one approach

confusing summaries of data domains or topics with fully realised theme

177
Q

what is reflexive thematic analysis?

A

associated with reflexivity in Qualitative research

researcher as active, and embedded, in the results

reflecting on, and understanding your position as a researcher in relation to the topic of study

thinking about how you think about the object of investigation and understanding the impact you have on how the topic is investigated

methodologically, theoretically, and epistemology and ontological transparent

being embedded in the decision-making of the project, avoiding recipe-like approaches

draws on informed judgement calls, rather than a recipe

178
Q

what is conversational analysis?

A

focus on how interactions are represented via talk and what action the talk represents in naturally occurring conversations (the process of interaction - how it is managed, constructed etc.)

179
Q

what is grounded theory?

A

identification of a model/theory generated from the data (no preconceived ideas on what might be found)

180
Q

what is content analysis?

A

count frequency of pre-defined behaviour

181
Q

what is interpretative phenomenological analysis?

A

attempting to understand participants experiences from their perspective (through themes which include descriptive, linguistic and conceptual comments)

182
Q

what is discourse analysis?

A

talk as social action - people convey their social position through their language and language itself is an interaction

183
Q

what are the qualities of IPA?

A

IPA is a methodology in its own right and adheres to set of philosophical assumptions, TA is flexible to researcher personality

IPA and TA both embrace researcher subjectivity, in IPA this is explained by the double hermeneutic

  • first hermeneutic: participant making sense of their experiences
  • second hermeneutic: researcher making sense of the participants sense-making
184
Q

what are features of IPA?

A
  • in built philosophical assumptions
  • focus on personal experience and meaning-making. data is is looked at both thematically (across the data set) as well as ideographically (case by case basis)
  • IPA assumes that language reflects, to some extent, people’s thoughts, feelings, and beliefs
  • interviews are usually used in IPA projects as they allow exploration of personal accounting and sense-making
  • IPA tends to rely on small, homogenous (similar) samples to allow for in-depth exploration
  • IPA, whilst interested in personal social contexts, is not as focussed on broader social structures that act as a constructive force
185
Q

what are the differences between TA and IPA processes?

A

similarities in coding, but they tend to be more detailed and may draw on metaphor, psychological processes, and language us (i.e. pronouns)

similarities in thematic structure, but they tend to be more formalised, detailed, and individualised in IPA

186
Q

when should we use reflexive TA instead of IPA?

A
  1. when the research question is interested in exploring something other than personal experiences and meaning/sense-making
  2. when data is not personal enough
  3. if the sample is larger or heterogenous (varied)
  4. when there is a focus on themes across the data (no idiographic focus/approach)
  5. focus is on the individual social contexts, rather than broader social structures
187
Q

what are the qualities of discourse analysis (DA)?

A

associated with philosophical assumptions (e.g. social constructionism)

DA tends to be heavily influenced by theory, and as such the process of analysis tends to be more conceptual and theory driven

language is considered to have a social function, that individuals are active in using to serve a social/performative function

DA has multiple iterations ranging from the specific focus on language use, to taking a broader approach where language is considered to represent broader social discourses

188
Q

when should we use reflexive TA instead of DA?

A
  1. if the researcher is new to Qualitative methods
  2. when wanting a less theory-dense approach
  3. when the research question is not solely focussed on discourses, and particularly social constructionist approaches
  4. when there is an interest in something other than the constructive power of language

First year Psychology (4 decks)

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