Description
I attached the study guide
Qualitative Methods:
Lecture:
Content analysis, intercoder reliability, examples
of content analysis your professor has done,
code sheets as a way to systematize content analysis
Chapter 8: reactivity, primary data, secondary data, participant observation, field study, direct and
indirect observation, overt and covert observation, str
uctured and unstructured observation, ethnography,
ethical issues in observation (threat to subjects in comparison to other methods), Institutional Review
Boards (IRBs), the difference between an erosion measure and an accretion measure, potential problem(
s)
with physical trace measures in studying political phenomena
Chapter 9: document analysis
–
qualitative, quantitative or both, content analysis and its procedures,
sampling frame, recording units (what are they, what if they are too small?), intercoder
reliability, running
record vs. episodic record (what each is, examples, advantages and disadvantages), advantages and
disadvantages of archival (written) records
Survey Research
Lecture:
most important lesson for us as consumers of surveys, s
ampling, po
pulation, sample, the logic
of sampling (why it makes sense with the rules of statistics that a sample is a reasonable estimate of the
population), confidence interval and margin of error, confidence level, types of information that questions
generally ask
for (knowledge, opinions, experiences, feelings), common sources of error in survey
research (timing, phrasing of questions, order of questions, interpretation of responses)
, American
Journalism Review study, Bradley effect, intangible problem in sampling
discussed in lecture,
Chapter 10:
survey research vs. interviewing, survey instrument, the importance of pre
-
testing
questionnaires,
response rates, response quality, possible types of bias (leading questions, interviewer
bias, etc.), ways to prevent bi
as in surveys, sample
-
population congruence, open
-
ended vs. close
-
ended
questions (advantages, disadvantages, reasons to use one over the other), types of surveys (face to face,
telephone, internet, etc.), potential problems with questions (leading, narrow
, ambiguous, double barreled,
etc.),
the impact of interviewer characteristics, probing, question wording and ordering effects
Stats
Intro, Distributions, Descriptive Statistics
Lecture: the normal distribution, standardized (Z) scores, the bell curve, pr
operties of the normal
distribution (shape, symmetry, meaning of standard deviation, empirical rule, ability to use standardized
scores), percentiles (what are they, how are they different from a percentage), t Distribution (what is it,
what do we use it f
or?)
;
descriptive statistics,
frequency distributions, percentages as a VERY easily
understood statisti
c, measures of central tendency and the levels of measurement to which they
correspond,
measures of dispersion
Chapter 11: response set, frequency distribution, relative frequency, descriptive statistics, trimmed mean
and outliers, positive and negative skew, measures of central tendency, mode, median, mean, range,
minimum and maximum, inter
-
quartile range, resist
ant measures, measures of dispersion, standard
deviation, variance, types of charts and graphs
Chapter 12: statistical hypothesis, null hypothesis, absolute value, sampling, Type I vs. Type II error, as
standard deviation increases in size what happens to
the standard error of the mean, level of statistical
significance, factors that affect significance, steps for hypothesis testing, significance tests of a mean
(normal distribution vs. small (t) distribution), degrees of freedom in t, finding the t Value
(alpha
–
see
example in Figure 12
-
4), a z
-
score of 1.96 means what, confidence intervals and levels (what are they,
why do we use them
, the general form of confidence interval
)
Measures of Relationships
Lecture: percentage differences as the simplest way
to show relationships, comparing measures of central
tendency, strength of relationships (logic: the extent to which changes in one variable are accompanied
by changes in another
–
no matter what level of measurement, the basic logic is the same
),
Yule’s
Q
and its properties, ultimately what do we want to do?
We want to reduce error! The idea for all of our
measures is, ultimately, to know how much we can reduce error in our estimates of a dependent
variable by knowing the values of an independent variab
le (or multiple independent variables)
, the
basic equation (in words) of the measure of reduction in error, measures for nominal data (lambda, tau),
measures for ordinal data (gamma, somer’s d), measures of relationship for interval level variables (r, r
-
s
quared), steps: start with a graph (three elements of a graph), the regression line (
what does it tell us
about the variables,
think of it as a prediction), parts of the regression line: slope, direction, strength of
relationship, what the slope
(b)
tell
s us,
what the Y intercept with zero tells us,
what
Pearson’s
r and r
-
squared tell us
, rule of thumb about a “strong” value of r
Chapter 13:
levels of measurement and the statistical procedures that go with them, types of relationships
(association, monot
onic and linear correlation), types of correlation, what does a measure of association
tell us, what do cross
-
tabulations show us, nominal measures of association, ordinal measures of
association (what are concordant pairs, discordant pairs, tied pairs), b
ounded measures such as Pearson’s r
vary between
-
1 and 1,
if the categories of an independent variable are across the top of a table (across the
columns) then what should the percentages down each column add up to (100%), the effect of increased
sample si
ze on Chi
-
squared
Multiple variables
Lecture: two kinds of information in multiple correlation/multiple regression (cumulative and partial),
time series analysis, interpreting the strength of a relationship
–
what do
relationship measures tell us,
when ar
e relationship measures particularly useful,
Chapter 14: analyzing multivariate relationships with
nominal and ordinal level data (what can you do? Don’t worry about technicalities
–
just understand that
you can do this with cross
-
tabulation
, how can you
control for a third variable?
),
multiple linear
regression (used with a
dependent variable
of what level of measurement?),
constants (beta
–
y when all
the independent variables have a value of zero), partial regression coefficients, interaction between
variables, homoscedasticity, multicollinearity and assumptions about the error terms in linear models (see
helpful hints tabl
e on p 530), dummy variables,
spurious relationships, standardized regression
coefficients,
ways in which
standardized and unstandardized
regression results are similar and different
,
logistic regression (when do we use this? It has to do with the type of
dependent variable)
Statistical Significance
Lecture (posted on Canvas): how statistical significance differs from strength of relationship; review of
the normal distribution and standard deviation and standard errors, difference between margin of error
and confidence level; Verba and Nie example, examples of different measures of statistical significance
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Explanation & Answer
Attached.
Alahamdi1
Political Statistics
Question 1
Part 1
Primary data- This is the information that you collect for research purpose. The positive side
of this kind of information is that it is specifically tailored to suit your research needs. The
negative side is that it is very difficult to obtain. A good example is data collected by a
marketing company for its market analysis.
Secondary data- This information collected by someone else other than the user for example
Google scholar on behalf of students and tutors. The main advantage of secondary data is that
it is easily available. The main disadvantage is that you cannot be too sure if the data
collected is quality.
Participant observation- A of data collection that is commonly used in qualitative research,
mostly used in European ethnography. It entails the anthropologist cultivating a relationship
with local informants so that they can be able to learn about a certain culture.
Field study- It’s a general method for collecting data about users, user needs and product
requirements that involves observation and interviewing, it’s mostly done in natural settings.
The main purpose is that it helps students to become personally involved in their study.
Direct observation- this is a method of collecting evaluative information in which the
evaluator watches the subject in his or her usual environment. The main difference between
this and participant observation is that the observer does not become a participant in the
exercise.
Indirect observation – this has to do with observing results of behaviour rather than the
behaviour itself.
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Overt observation – this involves the observer being open in their research and making sure
everyone is aware of their intention.
Covert observation- in this kind of observation identity of the observer is hidden.
Structured observations- It’s also known as systematic observation, it’s a collection method
that researchers collect data without direct involvement with the participants.
Unstructured observations- a technique used in observation as a devise to collect market
research data.
Ethnography- This is the study that is built on study of people and cultures. It is used to
explore cultural phenomena where the researchers observe society from the point of view of
the subject of study.
Part 2
Ethical issues in observation
The first issue that could arise is if the informant has not given prior consent. This can lead to
the researcher being sued by the informant. The second issue is that if the researcher does not
handle the information with care in turn breaching the confidentiality then ethical issues
would arise. Thirdly there is risk of the observer being biased in his presentation so his
integrity is paramount.
Institutional review board
Institutional review board is administrative body established to protect the rights and welfare
of human research subjects recruited to participate in research activities conducted under the
auspices of the institution with which it is affiliated.
Difference between an erosion measure and accretion measure
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Erosion measure is used to measure selectively worn out items for instance the number of
broken items within a span of time while accretion measure is used to measure the build up
material.
Potential problems with physical trace measures in studying political phenomena
Physical trace measures are valuable when it comes to understanding animal behaviour
because they can’t be interviewed, they also offer hints about human activity. The main
problem is that the information can be misleading.
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Question 2
Document analysis- it is a social research method which is used as a tool for obtaining
relevant documentary evidence to support and validate facts stated in a research, especially
during the chapter of literature review. The exercise involves analytic reading and review of
lots of written material. It is a form of qualitative research.
Content analysis- is a qualitative research technique. It is used to quantify patterns in
communication.
Procedure of content analysis
The first step is to copy and read through the transcript and make brief notes when you find
valuable information. Then go through the notes made and list the different types of
information found. The next step is to read through the list and categorize each time in a way
that offers a description of what it is about, then identify whether or not categories can be
linked in anyway and list them in major categories or minor categories then compare and
contrast this categories. If there’s more than one transcript repeat the five steps above for
each, after that you collect all of the categories and examine each in detail and consider if it
fits its relevance, once all data is categorised into minor and major categories review the data
to ensure it is categorized properly, then review categories and ascertain whether some
categories can be merged and some need to be sub categorized. Then finally return to the
original transcripts and ensure that all the information that needs to be categorized has been
categorized.
Sampling frame is the source material from which a sample is drawn.
Recording units – if they are too small the content analysis the semantic validity suffers and
content analysis tends to be shallow.
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Intercoder reliability- this is the extent to which two or more independent coders agree on the
coding of the content of interest with an application of the same coding scheme.
Running record- it is an assessment tool that teachers use to identify patterns in reading
behaviours of students. Examples include baseline data. It helps teacher monitor the progress
in a students learning.
Episodic record- used to capture spectra over a period of time. The advantage of this is that it
can be used to capture several events in a row. A good example is when a wind vane is used
to measure the intensity of wind when it reaches a particular threshold.
Part 2
Advantages and disadvantages of archival records
Archival records have several advantages which include they provide ready and reliable
source of education, they are also easy to preserve s...