rmb Flashcards
1
Q
what is a simple way to simplify a large set of numbers?
A
counting how often each number occurs (frequency)
2
Q
what type of data do we use histograms for?
A
continuous
3
Q
where is the centre of a histogram?
A
1
4
Q
what is the benefit of using more bins in a histogram?
A
shows the distribution with higher resolution (but can get noisy)
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
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
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
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
9
Q
what is a dataset?
A
a collection of data acquired for a specific purpose
may relate to multiple experiments or hypotheses
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
11
Q
name all the types of variables
A
nominal
ordinal
interval
ratio
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
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
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
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
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
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
18
Q
what is a sample?
A
the data we’ve actually collected
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
20
Q
do very large datasets reflect the wider population better or worse than small datasets?
A
better
21
Q
what does a sampling distribution tell us?
A
how variable the mean is for a given data sample from a given population
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
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
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
25
what are confidence intervals?
95% confidence intervals define a range of values which have a 95% chance of containing the population mean
26
how do you calculate confidence intervals?
95% CI = 1.96 * SEM
upper = X̄ + CI
lower = X̄ - CI
27
what does the centre line on a distribution graph mean?
the mean
28
what do the white lines inside the distribution represent?
one standard deviation away from the mean on each side
29
what is the standard normal distribution?
a special case of the normal distribution in which the mean is zero and the standard deviation is one
30
what does Shapiro-Wilk test for?
an objective test for whether data is normally distributed
31
what does Shapiro-Wilk W test for?
is a metric indicating how 'normal' the data is, higher values indicate more normal data
32
what does Shapiro-Wilk P test for?
a probability indicating how significant any difference from normality is
33
what does a horizontal line in the centre of a box whisker graph represent?
the median
34
what do the edges of the box in a box whisker graph represent?
the interquartile range
35
what does the vertical line and the dots that occur outside of it represent?
line - 95% intervals of data
dots - often outliers
36
what does it mean if a sample bias is systematic?
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
37
what is ecological validity?
a measure of how test performance predicts behaviours in real-world settings
38
what does WEIRD stand for?
Western
Educated
Industrial
Rich
Democratic
39
what does a histogram represent?
the sample distribution of our value of interest
40
what is each sample we measure an approximation of?
the underlying population which we can't actually measure
41
how do you calculate the sample mean?
X̄ = Σ xj / N
the sample mean is the sum of all the individual data points divided by the total number of data points
42
how do you calculate the sample standard deviation?
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
43
what is the standard error of the mean?
is the likely variability in our estimate of the population mean from a given sample
44
does the SEM increase, decrease, or stay the same when the sample size grows larger?
decreases, larger samples are more reliable
45
what does a wide standard error tell us compared to a small standard error?
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
46
what are confidence intervals?
a range of values around the sample mean that has 95% chance of containing the population mean
47
what is a statistical hypothesis?
a comparison to a single value
48
what is a null hypothesis?
the sample mean is indistinguishable from the reference value
49
why can we not just look at the difference between the mean and the specified value?
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
50
what is the formula for a t-test?
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
51
what is a one-sample t-test ?
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
52
what is a t-value?
a test statistic - intended to provide a single number that tells us the extent to which the data sample matches the null hypothesis
53
what does a small t value suggest for a one sample t-test?
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
54
what does a large t-value suggest for a one sample t-test?
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
55
when does the t-value grow for a one sample t-test?
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
56
when does the t-value shrink for a one sample t-test?
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
57
what is apophenia?
the tendency to see meaningful connections between unrelated things
58
why do we assume the null to be true until otherwise?
this is proof by contradiction
put the burden of proof on the alternative hypothesis
59
give an example of a one-sample hypothesis and a one-sample null hypothesis
attendance in class is more than 80%
attendance in class is NO different from 80%
60
what does a t-test account for?
uncertainty in our estimate of the mean by using the standard error of the mean
61
what were will, merit, jenkins & kingston interested in for the medusa effect?
whether pictures capture something of the mind that is significant to us, albeit at reduced potency
62
what does a two-sample hypothesis entail?
a test hypothesis that asks whether two groups have different means
63
what does the statistical null hypothesis state for a two sample t-test?
the sample means of the two groups cannot be distinguished
64
what is a between subjects design?
two independent groups of data points
each participant is in a single group and contributes a single data point
65
what is a within subjects design?
two dependent groups of data points
each participant completes two conditions and contributes two data points
sometimes called repeated measures
66
what is an independent samples t-test?
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
67
what is the equation for an independent samples t-test?
t(df) = X̄1 - X̄2 / Sp √2/N
mean of group 1 - mean of group 2 / pooled standard error of the difference
68
how do you find the standard error of the difference?
by using the pooled standard deviation of the two groups
69
what is a pooled standard deviation?
a single deviation to represent the variability in both groups - assuming that both groups have the same variability
70
what does a large positive t-value indicate for an independent samples t-test?
the mean of group 1 is above than the mean of group 2
71
what does a near zero t-value indicate?
the mean of group 1 is indistinguishable from the mean of group 2
72
what does a large negative t-value indicate for an independent samples t-test?
the mean of group 1 is below the mean of group 2
73
what does a levene's test, test for?
homogeneity of variance
74
what does a levene's test assess?
assesses the null hypothesis that different groups of samples are from populations with equal variances
75
what does a significant value indicate for a levene's test?
that the groups are likely to have different variances - suggesting that a pooled estimate of standard deviation is not appropriate
76
when do you use a paired samples t-test?
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
what is the equation for a paired samples t-test?
mean of paired differences - 0 / standard error of the mean paired difference
78
what are the pros and cons of a within subject design?
pros:
removes individual differences
need fewer participants
cons:
practice or order effects
longer participation per individual
79
what are the pros and cons of between subject design?
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
when should we use a t-test?
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
what are some assumptions of a t-test?
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
what is a welch's t-test?
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
what two columns are needed for analysis in jamovi?
a categorical grouping variable
a continuous outcome variable
84
what are test statistics?
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
what makes us more confident in estimating a difference?
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
what is cohen's d?
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
what are t-statistics?
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
what size sample produces more extreme t-values?
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
how do we determine how likely it is to observe another test statistic more extreme than the one we have from our data?
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
what does our null model describe?
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
what does a null model tell us?
what t-values we might reasonably see by chance in our experiment
92
name features of the t-distribution as a null model
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
how do you calculate the degrees of freedom for a one sample t-test?
N - 1
94
how do you calculate the degrees of freedom for an independent sample t-test?
N1 + N2 - 2
95
how do you calculate the degrees of freedom for a paired sample t-test?
N - 1
96
what is a p-value?
the probability of observing a result at least as extreme as the one from the data
97
when do we consider a value significant?
less than 5% chance of observing a result the same size or larger by pure chance
significant result = p < 0.05
98
how should we interpret p-values?
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
what does the effect size measure?
the strength (or magnitude) of the apparent difference, irrespective of how significant or probable that effect may be
100
what are t-tests directly targeted at?
comparisons between one group and a reference value or between two groups
101
what is the null hypothesis for multi hypotheses?
that all groups are the same, in other words the mean of all the groups is the same
102
what does ANOVA (analysis of variance) test for?
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
what is sum square total?
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
what is sum square within?
sum squares of the difference between each data points and the groups individual mean
105
how do you calculate sum square between?
SSbetween = SStotal - SSwithin
106
what is the total variance in the data?
the sum of the difference between data points within groups and the difference between the groups
107
how do you calculate mean square error within?
MSEwithin = SSwithin / N - G
108
how do you calculate mean square error between?
MSEbetween = SSbetween / G - 1
109
How do you calculate ANOVA?
F = MSEbetween / MSEwithin
110
what is a large F?
F is large when the mean square error between groups is a large compared to the variability within groups
111
what does a large F suggests?
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
what is a small F?
F is small when the mean square error between groups is smaller than or similar to the variability within groups
113
what does a small F suggest?
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
what does a significant ANOVA result suggest?
indicates that it would be unlikely to observe this data if there were no true differences between the means of these groups
115
what factors does ANOVA part variance into?
what is the total variability in the dataset?
how much of that variability occurs within each group?
how much occurs between groups?
116
what is the null hypothesis for ANOVA?
that all groups can be described equally well using the same mean
117
what is a factor?
a categorical (nominal) variable containing the labels of a set of groups
118
what are levels?
different groups within a factor
119
what are assumptions of independence for between-subjects ANOVA?
data observations must be unrelated
120
what are assumptions of normal distributions for between-subjects ANOVA?
data must be reasonably normally distributed
121
what are assumptions of equality of variance for between-subjects ANOVA?
groups should each have equal variance
122
what are assumptions of categorical factors for between-subjects ANOVA?
predicting factors must be divided into separate groups
123
what data type is needed for ANOVA?
interval or ratio
124
what does the hypothesis test Shapiro-Wilk test for?
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
what is Shaprio-Wilk W?
a metric indicating how 'normal' the data is, higher values indicate more normal data
126
what is Shaprio-Wilk p?
a probability indicating how significant any difference from normality is
127
do non-parametric tests work with the mean or the median?
median
it is a more robust measure of central tendency that the mean when data are not normally distributed
128
what is a median?
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
when do you use wilcoxon signed-rank test?
one sample
130
what is the wilcoxon signed rank test?
tests whether ranks are symmetrical around zero
131
how do you calculate a wilcoxon signed-rank test?
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
when do we use Wilcoxon Mann Whitney U test?
independent samples
133
how do you calculate wilcoxon mann whitney U test?
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
how can shapiro-wilk be affected by different samples sizes?
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
what are QQ "quantitative quantitative" plots?
helpful visual tests that compare two distributions, Jamovi use them to compare our data to a normal distribution
136
what do QQ plots tell us?
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
when can we run rank-based non-parametric tests?
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
what do we mean by traditional data collection methods?
methods often used within qualitative research,e.g., interviews and focus groups
139
what do we mean by alternative data collection methods?
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
what are interviews in psychology?
aim to find out as much as possible about the participants experiences and meanings
141
what are the types of interviews?
structured
semi-structured
loosely structured
un-structured
142
what are focus groups?
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
why do we use focus groups?
contextualise collective understandings and sense-making
useful in considering peoples' shared understandings
sensitive to points of consensus and disparity
144
describe face to face focus groups
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
describe online focus groups
the format may be different, but content seems relatively stable between FSF and online focus groups
146
what are the types of online focus groups?
asynchronous online focus group
synchronous online focus group
groups in the virtual world
147
what are the advantages and limitations of an asynchronous online focus group?
+ more time to think about responses
- there could be technological issues associated with them
148
what are the advantages and limitations of a synchronous online focus group?
+ technology can provide different types of environments for participants to engage with
- requires a good, and consistent bandwidth, and reliant on individual schedules
149
what are the advantages and limitations of groups in the virtual world?
+ avatars may lead to greater engagement and co-creation activities
- assumes a certain level of skill / ability is needed
150
are surveys a quantitative or qualitative tool?
quantitative
151
how are surveys used in data collection?
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
what are the features of a qualitative survey?
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
describe prompt methods
use video/vignettes/activities/audio to start discussion and debate
good for 'sensitive' topics
discussion becomes participant-led
154
describe photo-voice as participant-led prompts
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
what are some key points of reviews of photo-voice studies?
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
describe story completion as a new method of data collection
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
what things should we consider when developing story stems?
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
what are solicited diaries?
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
describe apps as a diary method
apps found to be easy to use compared to paper diaries in the DBT treatment programme of patients with BPD
160
describe media data
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
describe user generated content
can (sometimes) use pre-existing 'naturalistic' data from online sources in qualitative research
e.g. forums, blogs, social media, etc
162
what is 'data harvesting'?
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
describe user generated content, discussion boards
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
what are some concerns regarding ethics in user generated content?
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
what is thematic analysis?
foundation for other types of qualitative analyses
process of identifying meaningful patterns (themes)
a way of ordering and understanding participants' social world
166
what constitutes a theme?
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
what is ontology?
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
what is epistemology?
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
what is positivism?
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
what is contextualism?
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
what is social constructionism?
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
what are the three ontological approaches?
realism
critical realism
relativism
173
what is realism?
a pre-social reality exists that we can access through research
174
what is critical realism?
a pre-social reality exists but we can only ever partially know it
175
what is relativism?
'reality' is dependent on the ways we came to know it
176
how is poor practice of thematic analysis characterised?
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
what is reflexive thematic analysis?
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
what is conversational analysis?
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
what is grounded theory?
identification of a model/theory generated from the data (no preconceived ideas on what might be found)
180
what is content analysis?
count frequency of pre-defined behaviour
181
what is interpretative phenomenological analysis?
attempting to understand participants experiences from their perspective (through themes which include descriptive, linguistic and conceptual comments)
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what is discourse analysis?
talk as social action - people convey their social position through their language and language itself is an interaction
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what are the qualities of IPA?
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
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what are features of IPA?
- 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
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what are the differences between TA and IPA processes?
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
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when should we use reflexive TA instead of IPA?
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
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what are the qualities of discourse analysis (DA)?
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
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when should we use reflexive TA instead of DA?
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
