terms and definition Flashcards
(103 cards)
1
Q
inductive method
A
research first, theory follows
2
Q
standard deviation
A
a statistical measure that tells you how much your data is spread out or dispersed from the average (mean) value
Is defined as the average of the squared deviations from the mean. To calculate the variance, you first subtract the mean from each number and then square the results to find the squared differences. You then find the average of those squared differences. The result is the variance
3
Q
participant observation
A
participation is the goal, to put yourself in the shoes of the people you are observing
Classical
Digital
4
Q
positionality
A
access, how interlocutors relate to you/respond to you
5
Q
deductive method
A
Theory first, empirical test follows
6
Q
outliers
A
An outlier is an unusual data point that lies far outside the rna of the majority…
They can skew the data, the distribution of data
Sometimes we remove the outlier from the normal distribution
7
Q
unimodal
A
one peak in the data set
8
Q
bimodal
A
two peaks in the data set
9
Q
mean,mode and median
A
mean: the average
mode: the number that occurs most often in a data set
median: the middle value
10
Q
different stances in ethics
there are two
A
a. universalism (Ethical principles should never be broken)
b. deontological (An act is either right or wrong)
11
Q
Etnography
A
an integration of first-hand empirical investigation with the comparative or theoretical
interpretation of social organization and culture.
12
Q
gatekeepers
A
Gatekeepers shape the conduct of the research
The interest of the gatekeeper in shaping the research in a favourable light
13
Q
Coding
A
see the data, put into categories
the identification of categories is central to the process of analysis
14
Q
Types of Sampling
two, think winter
A
Snowball sampling: One subject gives the researcher the name of another subject, who in turn provides the name of a third, and so on
self- selection
Cold contacting: individuals you have no prior personal or professional relationship with, whom you reach out to usually for networking purposes, often in the hope of establishing a beneficial connection
15
Q
Hypothesis
A
A tentative answer or prediction that the researcher will test. It states what the researcher expects to find
16
Q
Research question
A
- what is the study trying to find out
- the central inquiry a study aims to answer, guiding the research process and defining the scope of the investigation
17
Q
The independent
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the presumed cause or influencing factor (what’s manipulated or categorized)
18
Q
Alternative and nul hypothesis
A
no statistical significant difference or relationship between variables in a population
19
Q
Unsolicited and solicited accounts
A
-how the identity and social location of informants influence the accounts they provide. If accounts are used as a source of information, the informant’s social location can offer access to unique knowledge but also potentially introduce bias
- Ethnographers can access unsolicited accounts, which occur naturally in everyday interactions (such as discussions or gossip), by participating or overhearing
- The chapter also covers solicited accounts, focusing on the role of interviewing. Non-directive questions are discussed as a form of eliciting accounts, where informants might be asked to describe situations, compare cases, or provide narratives. The reactivity built into interviews is acknowledged as potentially both helpful and hindering for data production and interpretation
20
Q
Semi structured interview
A
- combines the structure of a structured interview with the flexibility of an unstructured one.
- It utilizes a pre-determined set of open-ended questions, allowing for exploration and follow-up based on the interviewee’s responses.
- This approach facilitates in-depth understanding of the respondent’s perspectives while maintaining a degree of control over the interview process.
21
Q
Research can be 4 things (CEEG)
A
Contextual
Explanatory
Evaluative
Generative
22
Q
Research needs to be (BEF)
A
Bounded
Ethical
Feasible
23
Q
Multivocality
A
the inclusion of multiple perspectives and voices within the research process and the final report
24
Q
Building rapport
A
creating a trusting, comfortable, and collaborative relationship between the researcher and the participant, fostering a safe space for open and honest communicatio
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Reflexivity
Reflexivity helps ensure the credibility and trustworthiness of qualitative research by acknowledging and addressing potential biases
involves the researcher critically examining their own biases, assumptions, and experiences, and how these influence the research process
using or getting around limits of one’s positionality
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Five Focal Points in Ethics: PEDIC
Privacy
Exploitation
Do no harm
Informed consent or passive deception
Consequences for future research
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The ism of ethics (PARU)
Pluralism - multiple can interact
Absolutism - something is always wrong or right
Relativism - What is right and wrong depends on cultural context
Universalism - there are universal patterns of right and wrong
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How are oral accounts used in anthropological research?
As sources of information
As social objects of analysis
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Triangulation - why do we need it? (credibility)
If different methods agree, findings are more credible
increasing the validity and credibility of your research
Finding different analytical approaches to the research
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Types of interviews (SOSU)
Structured interview
Open ended conversations
Semi structured interview
Unstructured interviews
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Profile
A narrative about the person interviewed, told in the words of the interviewee, and crafted by the researcher.
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Vignette
A shorter narrative that usually covers a more limited aspect of a participant’s experience”
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the iterative process
dentifying instances and cases, relating those to
broader, more generic classes or categories, which in turn drive further examination of the
data and one’s understanding of them.
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Elicitation
| has to do with photographs
where photographs prompt discussion and reflection from participants
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Collaboration
| relates to photographs
where ethnographers and participants work together to produce photographs
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goal of quantitative research
A central theme is the importance of measurement in quantitative research, including the methods for devising measures for concepts and evaluating their reliability and validity. The text also explores the primary goals of quantitative research: accurate measurement, establishing causality, ensuring generalisation of findings, and facilitating replication.
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Sample
a subset of the population, and generally provides the data for research
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Descriptive statistics
describe the sample by summarizing raw data
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The independent variable
is the variable that is manipulated by, or is under the control of the researcher.
The researcher can set this variable at different levels
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The dependent variable
is the one of interest to the researcher, and it is not manipulated
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The central tendency
The former describes the most representative or common or central observation of the sample
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Types of variables by measurement
| 4 types (NOIR)
Nominal, ordinal, interval, ratio
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Operationalization: From concept to measurement
Breaks down concept into something tangible
Is to give definition to an ambiguous concept (give more structure)
Making a plan: how will I know it when I see it
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Indicator
A measurable observation that indicates the concept (often a survey question, behaviour count, etc)
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Internal validity
causality: if the study claims x causes y, is that claim solid?
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External validity
generalizability: can the results be generalized to a wider population or different contexts?
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validity
does the study actually measure what it intended, and do the conclusion hold?
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Representative sample
a sample wherein the units are represented in the same proportion as in the general population
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Symmetric distribution
mean, median, mode are equally valued and located at the center
Eg. distribution of height
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Positively skewed (often called right):
the tale is distributed to the right (rights is positive)
Mean is larger than the median and the mode
And both are greater than the mode
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Negatively skewed (often call left):
the tale is distributed to the left
Mean is less than the median and the mode
And both are less than the mode
Eg. mortality rate
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Measures of central tendency
Mean, median and mode
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Norminal
no order (example: color of your eyes, you cant rank those)
The mode is most interesting, the most frequent occurring category
In terms of dispersion it is only frequency because we have only categories
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Ordinal:
| think order
levels, things that can be ordered
- Has categories but these can be ranked
- “Rate your satisfaction on a scale from 1-5”
- It is called the likert scale (the most used one)
- You can do the mode but also the mean
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Interval and ratio:
The difference is based on the meaning of zero
So in one case zero means the absence of something and in the other zero actually holds weight
You can do all three of mean, median and mode
There are quantitative variables
If they are quantitative you are collecting numeric data already
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The bell shaped curve:
Normal distribution
All three (mean, mode, median) are the same or close to the same
Evenly distributed around the central value
General descriptive
No drastic outliers
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Frequency
How often something happens, the frequency of this occurring
The dispersion of how often something occurs
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Histogram
Purpose: reveal shape of data
Where is the data concentrated? (peak=mode)
Spread: are values tightly clustered or widely dispersed?
Skewness: longer tail on left or right? Multiple peaks? Outliers?
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Continuous data
Continuous data is data that can take any value. Height, weight, temperature and length are all examples of continuous data
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Boxplots - The 5-number summary at a glance
They show median, quartiles and outliers
Bottom is 25% and top is 75 the middle is 50%
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Scatterplots - Relationship between two variables
Reveal the relationship between two variables
Correlation does not equal causation
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Stratified sampling
divide population into subgroups, sample within each (ensure representation of key categories)
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Probability sampling
every member has a known non zero chance of selection
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Margin of error
the radius of the confidence interval; an estimate of how far off the sample result might be from the true population value
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Explanatory Sequential Design
Using qualitative lens to understand quantitative results
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Convergent parallel design
Collect quantitative and qualitative data in parallel, during the same timeframe
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Exploratory Sequential Design
Qualitative first, quantitative second. Begins with qualitative exploration, then builds to a quantitative phase
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Sponsors
As already noted, sponsors and assistants can be important not just in opening up
access to social networks and situations but also in tutoring the researcher in how to dress
and behave in order to ‘fit’ into a setting.
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cultivated innocence
the intentional creation or maintenance of a perception of innocence, often in situations where it may not be genuinely deserved or where there are underlying complexitie
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Impression management and personal front
Dressing a certain way
Behaving a certain way
Social skills
To play the part/role playing to get the data you want
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What kinds of questions can anthropology not answer?
Strict quantitative measures
Metaphysical questions
Prediction questions
Behavioral question
Development questions
normative /evaluative questions
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Inferential statistics
what does the sample say about the population
using sample data to draw conclusions about a larger population
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Minimal sampling size
30
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central limit theorem
allows us to make claims about a population on the basis of just one sample, regarding of the distribution of that population
if you sample from a population, regardless of its distribution, the same sample over and over again, the samples will be distributed normally
75
Confidence intervals
A confidence interval is a range of values, constructed using a sample statistic, that is likely to contain the true population parameter.
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statistical significance
how confident can we be that the findings from a sample can be generalised to the population as a whole?
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benchmark of statistical significance
0.05 (5 % probability chance that something is fallng within these extreme outliers)
measures the probability of the null hypothesis being true compared to the acceptable level of uncertainty regarding the true answer
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Standard error
indicates how different the population mean is likely to be from a sample mean
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Confidence interval
related to standard deviation, but its how wide apart are the numbers from eachother
a range of values we are fairly sure our true value lies in
80
null hypothesis
| court room analogy
a statement that assumes there is no statistically significant difference or relationship between variables in a population
no relationship between the variables
court room analogy: innocence until proven guilty, we believe there is no effect until we can proof that there is an effect
81
hypothesis testing
process to check if data support a hypothessis or if observed patterns could be due to chance
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Alternative hypothesis
the research hypothesis - what you suspect is true. It claims sone effect or difference exist
83
Significance levels and P values
it is the threshold we set before looking at our data, the line we draw in the sand, where we say I will consider something statistically significant if the probability of it happening by chance is 0.05 or less
84
P value
the computed probability that if H0 (null hypothesis) is true, the observed data would occur
smal p value: this data would rarely happen if the null hypothesis was true
it is statistically significant
the data given H0 not the probability H0 is true: getting enough evidence to disprove your nul hypothesis
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P (probability) value less or greater than alpha
In hypothesis testing, if the p-value is less than the significance level (alpha), the null hypothesis is rejected. If the p-value is greater than or equal to alpha, the null hypothesis is not rejected
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Two types of hypothesis
Undirected: there is an association but we do not know in what direction
Directed: there is an association and we do know the direction
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Type 1 Error (False positive)
Rejecting H0 when H0 is actuallt true
we claim there is an effect when there really isnt
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Type 2 error (false negative)
failing to reject H0 when H0 is false
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lower risk of type 1
p level of 0,01
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greater risk of type 1
p level of 0,05
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higher risk of type 2
0,01
92
lower risk of type 2
0,05
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different types of codes:
descriptive
analytical
theoretical
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winnowing (siedman) and funnelling
start broad and start narrowing (winnowing down and funneling your focus)
-the process of reducing a large amount of data to a manageable and focused set of themes or categories.
95
Covert vs overt observation (c for closed, o for open)
covert observation involves researchers observing subjects without their knowledge
while overt observation involves researchers being openly identified as researchers and informing participants about the observation
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data of participant observation
-What researchers observe of others behavior in the field
-Researcher's own participation experience
- accounts of those in the field of their/other's behaviour and experiences
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Jottings
mnemonic word or phrases to fix an observation or to recall what someone has just said
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iterative research process
Garther data
Examine data
Compare data
Write more fieldnotes
Gather even more data
Make plans for more data gathering with refined focus
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Purpose of Data analysis
summarize collected data in a dependable and accurate manner. It is the presentation of the findings of the study in a manner that has an air of undeniability (convince and persuade the reader)
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Progressive focusing
start broad and start narrowing (winnowing down and funneling your focus)
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funneling:
funneling" refers to a questioning approach where you start with broad, open-ended questions and gradually narrow down the topic to more specific and detailed inquiries
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Iterative research process
moving in a circular way (like a spiral):
Garther data
Examine data
Compare data
Write more fieldnotes
Gather even more data
Make plans for more data gathering with refined focus
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memos
identify and write about core processes that characterise talk and interaction in a particular setting, a summary of pattern (emerson 2011, 186)
