Slide 2 Graduate Research Methods and Scholarly Writing in the
Social Sciences: Government and History Harvard Summer School: SSCI
S-100b Section 2 (32761) Joe Bond 6/24/2013 Slide 3 Introduction to
the Course, Social Science Approach, ALM Context and Proseminar
Objectives Course Requirements and Grading Facilitation (1 minimum)
& participation in class discussions 7% In-class exercises (8
of 10 required, but NOT graded) 8% Argument writing assignment (1ST
paper) 10% Book review writing assignment (2nd paper) 15%
Literature review writing assignment (3rd paper) 25% Mid-term exam
10% Research design writing assignment (4th paper) 20% Final class
presentation5% Harvard Extension School is not a traditional
graduate program. Explain. Volunteers for next weeks facilitation
Slide 4 Introductions Slide 5 Basics will be brief but the.ppt will
be posted on the course website Qualitative vs. Quantitative vs.
Mixed Methods Largely a moot debate (more and more studies utilize
mixed methods) Your questions should always determine your
methodological approach, not the reverse Why and when to use A and
why and when to use B depends: Is there a relationship between
regime type and violent conflict? What are the odds that the nation
of Fester will fail in the next 5 years? What is the role of
political culture as it relates to negotiations? What would have
happened if Germany refrained from invading Poland in 1939? Your
choice of methods depends on how you operationalize your variables
(e.g. how do you intend to measure political culture, etc.?) Slide
6 Variables Independent variables (IVs) are those variables that
help explain a dependent variable Independent variables must be
antecedent to dependent variables (e.g. relationship between
education and income) Dependent variables (DVs) are the things you
are trying to explain Example: Relationship between SAT scores (IV)
and success in college (DV) Dependent variable should always be
labeled along the y axis of a graph Slide 7 Level of Measurement
Why is it important? Nominal: (measures not ranked: gender,
religion, etc.) Ordinal (measures rank ordered: economic class)
Interval (measures equally ranked: income) Ratio as characterized
in the social sciences (the measure has an absolute zero: mass,
length, time) Think Nominal, Ordinal, Interval, Ratio (NOIR)! Slide
8 Association An Association between two variables: the values of
one variable tend to coincide (vary or covary) with the values of
the another Example 1: the relationship between sex education and
teen pregnancy. Teen pregnancy as the DV, sex education as an IV
(note: in this example we treat the latter as antecedent to the
former) We might hypothesize that increased exposure to sex
education programs help mitigate the incidences of teen pregnancy
(i.e. they vary: as X goes up, Y goes down) Example 2: the
relationship between education (IV) and income (DV) We might
hypothesize the more education one has, the higher ones future
income will be (i.e. they covary: as X goes up, Y goes up) An
anomaly. Wrong career trajectory. Slide 9 Correlation A statistical
term that indicates the strength and direction of a linear
relationship between variables (e.g. the relationship between
education and income) IMPORTANT! Association or correlation DOES
NOT imply causation example 1: drowning (DV) and consumption of ice
cream (IV) they covary (as ice cream consumption goes up, incidents
of drowning increases) example 2: childrens shoe size (IV) and math
performance (DV) they covary (as shoe size gets bigger, math skills
go up) Example 2 also highlights the importance of definitions,
operationalization and transparency example 1: ice cream
consumption is a proxy for temperature example 2: shoe size is a
proxy for age Slide 10 More on Correlation Correlation is a measure
of the direction and degree of strength between two or more
variables A correlation coefficient (r or Pearsons r) is a
numerical index of that relationship The magnitude of the
correlation coefficient indicates the strength of the relationship
between variables (i.e. -1 to +1) +1 means a perfect positive
correlation (co-vary) while -1 shows a perfect negative correlation
(vary) The closer the correlation coefficient is to +1 or -1, the
stronger the relationship But even a strong [negative or positive]
correlation is meaningless if the level of error (significance) is
large (e.g. p < 0.5 vs. p < 0.01) Slide 11 Hypotheses &
Null Hypotheses H 1 : as education increases, likelihood of voting
increases H 0 : education has no effect () on the likelihood of a
person voting Why do we test the null hypothesis? the strongest
proof is the inability to disprove error cannot be eliminated like
it or not, facts change Avoid words like this proves or this is
irrefutable proof; instead, use supports, lends support to, etc.
Slide 12 Types of Analysis Analysis may have exploratory,
descriptive, explanatory, and predictive objectives or some
combination of these aims Evaluation research is a 5 th type that
is not discussed here, albeit it is no less important Slide 13
Exploratory Research Undertaken when very little is known about a
phenomenon Forms the foundation for subsequent descriptive and
explanatory research In the early 1980s we did not have a good
handle on how many Americans were infected with HIV/AIDS or even
what caused of it. This sort of research is often linked with
activism Slide 14 Descriptive Research Serves to identify important
areas of inquiry Often serves as the first step in explanatory
inquiry Addresses whether a phenomenon is a common occurrence or a
rate event Describe the U.S. electorate and electoral behavior:
Jewish Americans tend to vote for democrats Catholics tended to
vote democratic but the abortion issue has created a rift Latinos
tended to vote overwhelmingly democratic but this began to change
in 1999 and swung back again in 2008 Examples: Observational
Research, Historical Research, etc. Slide 15 Explanatory Research
Scientific inquiry usually does not end with description but
proceeds to explanation Descriptive findings are likely to lead to
the investigation of the factors associated with the outcome and to
attempts to understand how these factors contribute to the
occurrence of the outcome Understanding how something works allows
us to better predict the future (applies to both qualitative and
quantitative research) Examples: Lessons Learned, Counterfactual
Thought Experiments, Regression Analysis, etc. Slide 16 Prediction:
optimistic/happy pop hits predict a bull market six months in
advance Typically follows explanatory research but not always!
State Failures, Stock Predictions, etc. Model, below, yields
between 50-55% excess returns with no compounding using events data
Slide 17 mile Durkheims Suicide (1897): An Example of the Research
Process Slide 18 Durkheims Variables Inductive Approach or Theory
Building Dependent Variable(s) (what is he trying to explain):
RATES of SUICIDE in Europe (1800s) Independent Variables (those
things that help explain the Dependent Variable(s)): CLIMATE, AGE,
GENDER, POLITICAL TURMOIL, RELIGION (limited to Christianity),
MARITAL STATUS, DEPENDENTS, ETC. Recall Levels of Measurement
(NOIR) Nominal (cant be ranked) Ordinal (ranked with unequal or
arbitrary intervals) Interval (equal intervals) Ratio (as interval
with true zero) Slide 19 Some of Durkheims Descriptive Findings
Suicide rates are higher for widowed, single and divorced men than
married men Suicide rates are higher for people without children
than with children Suicide more pronounced in colder climates
Suicide rates are higher among Protestants than Catholics Slide 20
Differences between Protestants and Catholics Suicide is [more of]
a sin for Catholics Role of coroners if no suicide note is left, it
comes down to the coroner's interpretation (circa 1897) Differences
in social integration Catholics tend to have higher levels of
social integration think the movie My Big, Fat Greek Wedding. Slide
21 The Notion of Integration: Going Beyond Religion Catholic
countries tend to be more integrated than Protestant countries,
with closer family ties this is why people who are married and/or
have children commit less suicide simply put, they have more to
live for This is even reflected in physical proximity when speaking
with others Social bonds are composed of two factors: social
integration: attachment to other individuals within society social
regulation: attachment to society's norms Suicide rates may
increase when extremities in these factors occur Slide 22 Building
a Theory: Social Integration abnormally high or low levels of
social integration may result in increased suicide rates; low
levels of social integration result in disorganized society
(chaos); high levels of social integration drive some to suicide in
order to avoid becoming burdens on society Slide 23 Durkheims
Suicide Typology Egoistic suicide Ties attaching the individual to
society are weak Few social ties to keep the individual from taking
his or her own life (Why not?) Altruistic suicide Individuals are
extremely attached to society and have no life of their own (self-
emulation) They believe their death can bring about a benefit to
the society Anomic suicide Weak social regulation between the
society's norms and the individual (life becomes too unpredictable
and uncertain) Often brought on by dramatic changes in economic
and/or social circumstances (e.g. wars, recessions and other
turmoil, etc.) Fatalistic suicide Social regulation is completely
instilled in the individual (suicide bombers) No hope of change
against an oppressive society Slide 24 Research Cycle as an
Iterative Process Durkheim used an inductive approach, moving from
steps #2 & #3 to build step #1 (observation theory) Most
quantitative research involves deductive research (i.e. theory
empirical testing) Slide 25 Group Exercise (groups of 2 or 3)
1.Form groups of 3 or 4 2.For each group, 1.define one of the four
concepts, below 2.operationalize the concept (i.e. how would you
measure the concept in your research?) 3.Reconvene in 5 8 minutes
(max) Attractiveness Democracy Leadership Love Slide 26 State
Failures State Failure project (1994) objective: then VP Gore asked
the CIA to predict which states will fail 5 years out analyzed
thousands of [structural] variables found that 3 variables could
predict failures 85% of the time looking out 5 years infant
mortality (a proxy? for what?) level of democratization openness to
trade other salient factors: youth bulge, religious distributions,
etc. We will return to this later on in the semester Slide 27
Africa Prospects: Predicting State Failure with Structural Data
Slide 28 Africa Prospects Purpose: to assess the vulnerability of
countries to conflict escalation based on its profile or set of
structural indicators. Overall Accuracy: is defined as the ratio of
correct classifications (C) to all classifications (A). Accuracy =
C/A * 100%. Recall: is defined as the ratio of correct
classifications (C) to the observed classification (O). Recall =
C/O * 100% and represents the ability of the algorithm to classify
the conflicts as they were observed. Precision: is defined as the
ratio of correct classifications (C) to correct (C) and incorrect
classifications (I). Precision = C/(C+I) *100%. Illuminates the
algorithms false positives; specifically, the higher the ratio the
lower the false positives. Slide 29 28 Near-perfect forecast model
High recall = 99% High precision = 100% High accuracy =99.5% Bad
forecast model #1 (almost every country will be unstable) High
recall= 99% (1% miss rate) Low precision = 5% (95% false positive
rate) Low accuracy =40-50% NET IS CAST TOO BROADLY Bad forecast
model #2 (few countries will be unstable) Low recall = 5% (95% miss
rate) High precision =100% (0% false positives) Low accuracy
=40-50% NET IS CAST TOO NARROWLY Countries forecast to be unstable
at some level of intensity Countries that experience instability
Countries that DO NOT experience instability # of correct
predictions # of predictions made Recall # of correctly predicted
conflicts # of conflicts that occurred # of correctly predicted
conflicts # of conflicts predicted to occur Precision Overall
Accuracy False positives Misses misses Forecasting Performance
Metrics: Definitions and Illustrations Slide 30 29 5-15 Year
Validation of Forecasting Average Performance Scores For Different
Training Sets / Forecast Periods Forecast Period Low precision
scores (high false positive rates) in the out years indicate that
the world was more stable than would have been expected given
macro-structural conditions. However, high recall scores indicate
the net is cast wide enough to correctly forecast conflicts that DO
occur (errors fall on the side of caution). Accuracy Recall
Precision Slide 31 Independent Variables Caloric Intake: Estimate
of the average number of calories consumed per person, per day. GDP
per Capita: Annual gross domestic product per person measured in
constant 1995 U.S. dollars. Male/Female Infant Mortality: Number of
deaths of male and female children under 1 year of age per 1,000
live births. Life Expectancy: Average life expectancy (males and
females combined). Youth Bulge: Ratio of population aged 15-29 to
those aged 30-69. Among others.. Slide 32 31 Slide 33 32
Instability Levels High intensity (if combined probability > 67%
Moderate intensity (if combined probability > 67% None/Low
intensity (if combined probability > 67% 3 Levels of instability
intensity Maximum level/intensity of conflict per country-year;
source: KOSIMO Data Project, Heidelberg Institute of International
Conflict Research (HIIK), 1975-2003.
http://www.hiik.de/de/index_d.htm Represents a high threshold of
instability Dependent Variable: Index of Instability Key
Assumption: country is unstable if (and only if) the government or
its opponent(s)threatens or initiates a conflict to restore
equilibrium or harmony with respect to its internal or external
relations. Slide 34 Steps 1. Compile a time series data set of the
selected target variables intensity 2. Compile a time series data
set of candidate indicators associated with the targets intensity
3. Train an algorithm that explains the historical target
intensities with the candidate indicators 4. Calculate performance
measures of the explanation from a time series of historical test
data 5. Generate vulnerability scores based on projections from the
current value of the indicators 6. Calculate a confidence level for
the forecasts using the likelihood of occurrence at each intensity
7. Iterate from step #1 for experimentation with alternative target
variables 8. Iterate from step #2 for experimentation with
alternative explanatory variables or indicators Slide 35 In-Class
Writing Exercise 1 June 24, 2013 Educating Sergeant
PantzkeEducating Sergeant Pantzke (7:35) Should take no longer than
10-15 minutes On the opposite side of this paper only, take a
position: The U.S. government should [should not] decide which
schools can receive GI bill funding. For example, veterans working
their way through Harvard should be able to use GI bill funds
whereas vets working on a degree at the University of Phoenix
should be prohibited to fund their education through the GI bill.
Include any evaluation criteria that come to mind if you take the
position that some schools but not others should qualify for GI
bill funding.
