Research Methods: Summarize Numerical Data

For this assignment, think about a potential study you might want to conduct in which you would need to collect numerical data. You can use the research topic and problem you developed earlier, but make sure to modify your research questions to fit a quantitative method for this assignment. State the research problem and ensure it is supported by at least 3 to 5 scholarly resources within the past 5 years to ensure relevancy. Also keep in mind that your problem statement should reflect your degree (applied or PhD). Discuss whether you had to alter the problem statement to now reflect a quantitative design. How could you use quantitative methodology to address your research problem? State the purpose of the research effort. Make sure the purpose is aligned with the problem.Draft 2 to 3 research questions that would be appropriate for a quantitative study and addresses your research problem and are aligned with your purpose statement. You might choose to revise your previous qualitative research questions or develop new research questions. Either way, ensure alignment with the problem and purpose.Identify and discuss a design that is associated with quantitative methodology that you would consider to answer your research questions.You will find that some problems lend themselves to using one methodology over another. A goal of this assignment was to have you explore how a quantitative methodology might help you address your research questions. You will have an opportunity later in the course to explore mixed methodologies. Based on your analysis above, do you feel the use of a quantitative methodology and your chosen design is best suited to address your research problem and answer your questions. Why or why not?Be sure to use scholarly sources to support all assertions and research decisions.Length: 3-5 pages, not including title and reference pagesGrading RubricCriteriaContent (150 points)Points1Clearly defined the problem, purpose and two to three research questions for a quantitative study502Problem, purpose, and research questions all align to one another 503Discuss the quantitative design that is most appropriate for the studyOrganization (50 point)4Organized and presented in a clear manner. Included a minimum of five scholarly references, with appropriate APA formatting applied to citations and paraphrasing.50Total200Your paper should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards. Be sure to adhere to Northcentral Universityâ??s Academic Integrity Policy.
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Political Research Quarterly
http://prq.sagepub.com
Improving Causal Inference: Strengths and Limitations of Natural Experiments
Thad Dunning
Political Research Quarterly 2008; 61; 282 originally published online Oct 3, 2007;
DOI: 10.1177/1065912907306470
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http://prq.sagepub.com/cgi/content/abstract/61/2/282
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Improving Causal Inference
Strengths and Limitations of Natural Experiments
Political Research Quarterly
Volume 61 Number 2
June 2008 282-293
© 2008 University of Utah
10.1177/1065912907306470
http://prq.sagepub.com
hosted at
http://online.sagepub.com
Thad Dunning
Yale University, New Haven, Connecticut
Social scientists increasingly exploit natural experiments in their research. This article surveys recent applications in
political science, with the goal of illustrating the inferential advantages provided by this research design. When treatment assignment is less than â??as ifâ? random, studies may be something less than natural experiments, and familiar
threats to valid causal inference in observational settings can arise. The author proposes a continuum of plausibility
for natural experiments, defined by the extent to which treatment assignment is plausibly â??as ifâ? random, and locates
several leading studies along this continuum.
Keywords: natural experiment; â??as ifâ? random; exogenous variation; continuum of plausibility; matching
If I had any desire to lead a life of indolent ease,
I would wish to be an identical twin, separated
at birth from my brother and raised in a different social class. We could hire ourselves out to
a host of social scientists and practically name
our fee. For we would be exceedingly rare representatives of the only really adequate natural
experiment for separating genetic from environmental effects in humansâ??genetically identical
individuals raised in disparate environments.
â??Stephen Jay Gould (1996, 264)
1. Introduction
Social scientists are increasingly exploiting natural
experiments in their research. A recent search on â??natural experimentâ? using â??Google Scholarâ? (scholar
.google.com) turned up more than 1 million hits; the
results appearing on the first dozen pages suggest that
economics and epidemiology are the leading fields to
use the term, but political science is also well represented. An impressive volume of unpublished, forthcoming, and recently published studies in political
science suggests the growing influence of the natural
experimental approach. Table 1 provides a nonexhaustive list of several recent studies.
As the name suggests, natural experiments take their
inspiration from the experimental approach. A randomized controlled experiment (Freedman, Pisani, and
Purves 1997, 4-8) has three hallmarks. First, the
282
response of experimental subjects to a â??treatmentâ? (or a
series of treatments) is compared to the response of other
subjects to a â??controlâ? regime, often defined as the
absence of a treatment. Second, the assignment of
subjects to treatment and control groups is done at random. Third, the application or manipulation of the treatment is under the control of the experimental researcher.
Each of these traits plays a critical role in the experimental model of causal inference. For example, in a
medical trial of a new drug, the fact that subjects in the
treatment group take the drug, while those in the control
group do not, allows for a comparison of health outcomes across the two groups. Random assignment
ensures that any difference in average outcomes between
the two groups is not due to confounders, or factors other
than the treatment that vary across the two groups and
that may explain differences in health outcomes. Finally,
experimental manipulation of the treatment establishes
evidence for a causal relationship between the treatment
and the health outcomes.1
Unlike true experiments, the data used in natural
experiments come from naturally occurring phenomenaâ??
actually, in the social sciences, from phenomena that
are often the product of social and political forces.
Because the manipulation of treatment variables is not
Authorâ??s Note: I am grateful to Jake Bowers, Henry Brady, Bear
Braumoeller, David Collier, David Freedman, Alan Gerber, Don
Green, Susan Hyde, Ken Scheve, Jason Seawright, and three
reviewers for their comments and suggestions. An earlier version
of this article was presented at the annual meetings of the American
Political Science Association, August 31â??September 3, 2005.
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Dunning / Improving Causal Inference 283
Table 1
Recent Natural Experiments in Political Science
Study
Substantive Focus
Source of Alleged Natural Experiment
Ansolabehere, Snyder,
and Stewart (2000)
Brady and McNulty (2004)
The personal vote and incumbency
advantage
Voter turnout
Electoral redistricting
Cox, Rosenbluth, and Thies (2000)
Incentives of Japanese politicians
to joint factions
Doherty, Green, and Gerber (2005)
Effect of affluence on political attitudes
Glazer and Robbins (1985)
Congressional responsiveness to constituencies
Grofman, Brunell, and Koetzle (1998) Midterm losses in the House and Senate
Grofman, Griffin, and Berry (1995)
Hyde (2006)
Krasno and Green (2005)
Miguel (2004)
Miguel, Satyanath, and
Sergenti (2004)
Posner (2004)
Stasavage (2003)
Congressional responsiveness to constituencies
The effects of international election monitoring
on electoral fraud
Effect of televised presidential campaign
ads on voter turnout
Nation building and public goods provision
Economic growth and civil conflict
Political salience of cultural cleavages
Bureaucratic delegation, transparency,
and accountability
Precinct consolidation in California
gubernatorial recall election
Cross-sectional and temporal variation
in institutional rules in two houses
of Japanese parliament
Random assignment of level of lottery
winnings to lottery winners
Electoral redistricting
Party control of White House in previous
elections
House members who move to the Senate
â??As ifâ? random assignment of election
monitors to polling stations in Armenia
Geographic spillover of campaign ads in states
with competitive elections to some but not
all areas of neighboring states
Political border between Kenya and Tanzania
Shocks to economic performance caused by
weather
Political border between Zambia and Malawi
Variation in central banking institutions
Note: This nonexhaustive list includes published and unpublished studies in political science that either lay explicit claim to having
exploited a â??natural experimentâ? or that in my view adopt core elements of the approach. The published studies are largely those that
turned up in searches of JSTOR and other electronic sources, while unpublished and forthcoming studies were either previously known
to me or were pointed out to me by other scholars.
generally under the control of the analyst, natural experiments are, in fact, observational studies. However,
unlike other nonexperimental approaches, a researcher
exploiting a natural experiment can make a credible
claim that the assignment of the nonexperimental
subjects to treatment and control conditions is â??as ifâ? random. Outcomes are compared across treatment and control groups, and both a priori reasoning and empirical
evidence are used to validate the assertion of randomization. Thus, random or â??as ifâ? random of assignment to
treatment and control conditions constitutes the defining
feature of a natural experiment.
Natural experiments can sometimes provide social
scientists with an important means of improving the
validity of their empirical inferences. As the examples
discussed below will illustrate, natural experiments can
be useful to political scientists investigating a wide
range of topics; and although their use is becoming
more common, many more natural experiments than
we now realize may be available to researchers. In addition, natural experiments often take place at the intersection of quantitative and qualitative methods (Brady
and Collier 2004). While the analysis of natural experiments is sometimes facilitated by the use of statistical
and quantitative techniques, the detailed case-based
knowledge often associated with qualitative research
is crucial both to recognizing the existence of a natural experiment and to gathering the kinds of evidence that make the assertion of â??as ifâ? random
assignment compelling. For these reasons, a detailed
examination of the logic of natural experiments and a
discussion of concrete applications should be of
interest to a variety of scholars. The goal of this article is therefore to survey the use of natural experiments, particularly in political science, with an eye
both to describing their powerful inferential logic and
also to delineating the sorts of issues over which natural experiments may offer less leverage. After introducing and discussing several examples below, I
make several general points about this increasingly
common research design.
2. Natural Experiments: The Role of
â??As Ifâ? Randomization
A first example comes from a domain far from the
concerns of contemporary political science, but it
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284 Political Research Quarterly
nicely illuminates core features of a successful natural
experiment. Nineteenth-century London suffered a
number of devastating cholera outbreaks. John Snow, an
anesthesiologist who first became interested in the
causes of cholera transmission around 1848 (Richardson
1887/1936, xxxiv), conducted justifiably famous studies
of the disease (Freedman 1991, 1999, 2005). At the time
of Snowâ??s research, a variety of theories existed to
explain choleraâ??s transmission, including the theory of
bad air (miasma). Snowâ??s experience as a clinician and
his studies of the pathology of cholera deaths during
previous epidemics, however, suggested that cholera
might instead be an infectious disease carried through
the water.
Although various â??causal process observationsâ?
(Collier, Brady, and Seawright 2004) supplied crucial
support for the plausibility of Snowâ??s hypothesis, his
strongest piece of evidence came from a natural
experiment which he exploited during the epidemic
of 1853 to 1854. Large areas of London were served
by two water companies, the Lambeth company and
the Southwark and Vauxhall company. In 1852, the
Lambeth company moved its intake pipe further
upstream on the Thames, thereby â??obtaining a supply
of water quite free from the sewage of London,�
while the Southwark and Vauxhall company left its
intake pipe in place (Snow 1855, 68).
This move of the Lambeth water pipe provided Snow
with his natural experiment. He obtained records on
cholera deaths throughout London and also gathered
information on the company that had provided water to
the house of each deceased as well as the total number of
houses served by each company in each district of the
city. Snow then compiled a simple cross-tab showing the
cholera death rate in households during the epidemic of
1853 to 1854, by source of water supply. Among houses
served by Southwark and Vauxhall, the death rate from
cholera was 315 per 10,000; among those served by
Lambeth, it was a mere 37 (Snow 1855, Table IX, 86;
see Freedman 2005).2 This dramatic difference between
the two groups of houses suggested a large treatment
effectâ??and compelling evidence for the impact of
water supply source on deaths from cholera.
Why did the move of the Lambeth water pipe constitute the basis of a credible natural experiment? In a
natural experiment, assignment to treatment and control conditionsâ??here, the water supply sourceâ??must
be â??as ifâ? random. This implies that the water supply
source is independent of observable and unobservable factors that might influence cholera death rates,
and people do not move in response to treatment. At
least as a necessary if not sufficient condition, the treatment and control groups are balanced with respect to
other (measurable) variables that might explain
cholera deaths.
Snow presented various sorts of evidence to establish this â??pretreatment equivalenceâ? between the
groups. His own words may be most eloquent:
The mixing of the (water) supply is of the most
intimate kind. The pipes of each Company go
down all the streets, and into nearly all the courts
and alleys. A few houses are supplied by one
Company and a few by the other, according to the
decision of the owner or occupier at that time
when the Water Companies were in active competition. In many cases a single house has a supply different from that on either side. Each
company supplies both rich and poor, both large
houses and small; there is no difference either in
the condition or occupation of the persons receiving the water of the different Companies. . . . It is
obvious that no experiment could have been
devised which would more thoroughly test the
effect of water supply on the progress of cholera
than this. (Snow 1855, 74-75)
Particularly important for Snow was the fact that
residents did not appear to â??self-selectâ? into their
source of water supply in ways that might be associated with the propensity to contract cholera. Absentee
landlords often took the decision regarding which of
the competing water companies would be chosen for
a particular address; moreover, the decision of the
Lambeth company to move its intake pipe upstream
on the Thames was taken before the cholera outbreak
of 1853 to 1854, and existing scientific knowledge
did not clearly link water source to cholera risk. As
Snow put it, the move of the Lambeth companyâ??s
water pipe meant that more than three hundred thousand people of all ages and social strata were
divided into two groups without their choice,
and, in most cases, without their knowledge
[italics added]; one group being supplied with
water containing the sewage of London, and,
amongst it, whatever might have come from the
cholera patients, the other group having water
quite free from such impurity. (Snow 1855, 75)
Snowâ??s investigation of cholera transmission provides several useful lessons about the elements of a
convincing natural experiment (Freedman 1991,
1999). Snow went to great lengths to gather evidence
and to use a priori reasoning to argue that only the
water supply distinguished houses in the treatment
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Dunning / Improving Causal Inference 285
group from those in the control group and, thus, the
impressive difference in death rates from cholera was
due to the effect of the water supply. Of course, to the
extent that the â??as ifâ? random assignment fails, Snowâ??s
study would be less useful as a way of making valid
inferences about the sources of cholera transmission;
yet the strength of the evidence (and subsequent medical research) bear out Snowâ??s conclusions.
It is also worth noting that while the natural experiment may have been the coup de grace in a painstaking
investigation into the causes of cholera transmission,
Snowâ??s use of this natural experiment was complemented and indeed motivated by the other evidence that
he had compiled. This body of evidence grew from
Snowâ??s detailed knowledge of the progress of previous
cholera outbreaks in England, his ability to cull information from a variety of sources, and especially his
willingness to do on-the-ground â??process tracingâ? and
close-range exploration of seemingly disconfirming
cases.3 This kind of close-range research also gave him
the information he needed to discover and exploit his
natural experiment, while his sense of good research
design led him to recognize the inferential power of the
natural-experimental approach. Snow used quantitative
techniques such as two-by-two tables and cross-tabs
that today may seem old-fashioned, but as Freedman
(1999, 5) put it, â??It is the design of the study and the
magnitude of the effect that compel conviction, not the
elaboration of technique.�
Social-Scientific Examples
Snowâ??s study of cholera provides an early example
of a natural experiment and underscores core elements of a successful application of this research
design. Other phenomena can also provide the basis
for credible natural experiments, howeverâ??and may
provide insight into substantive questions of greater
concern to social scientists.
In one important class of natural experiments,
researchers can take advantage of an actual randomizing device with a known probability distribution
that assigns subjects to the treatment and control conditions. The most frequent example may be natural
experiments that exploit prize lotteries. In a recent
paper, for example, Doherty, Green, and Gerber
(2006) were interested in assessing the relationship
between income and political attitudes. They surveyed 342 people who had won a lottery in an
Eastern state between 1983 and 2000 and asked a
variety of questions about estate taxes, government
redistribution, and social and economic policies more
generally. Comparing the political attitudes of lottery
winners to those of the general public (especially, those
who do not play the lottery) is clearly a nonexperimental comparison, since people self-select as lottery players, and those who choose to play lotteries may be quite
different from those who do not, in ways that may matter for political attitudes. However, levels of lottery winnings are randomly assigned.4 Thus, abstracting from
sample nonresponse and other issues that might threaten
the internal validity of their inferences, Doherty, Green,
and Gerber could obtain a clean estimate of the relationship between levels of lottery winnings and political
attitudes.5
The example may demonstrate the power of natural experiments to rule out alternative interpretations
of the findingsâ??in the case of Doherty, Green, and
Gerberâ??s (2006) study, the finding that lottery winnings affect attitudes toward the estate tax and perhaps some more narrow redistributive issues but not
broader political and social attitudes. This is because
unmeasured factors that might affect political attitudes should be statistically independent of the level
of lottery winnings: just as in a true experiment, randomization takes care of the confounders.6 It is useful to note that in this class of natural experiment,
unlike Snowâ??s, researchers do not need to depend on
a priori reasoning or empirical evidence to defend the
assumption of â??as ifâ? random assignment of subjects
to treatment and control conditions: they simply
exploit the true randomization afforded by the lottery.
To readers in some fields, the idea of taking advantage of a true randomizing device to study the social
world may seem far-fetched. How often will interesting substantive problems yield themselves to the kind
of actual randomization that Doherty, Green, and
Gerber (2006) could exploit? In fact, a number of
studies in economics and political science have been
able to make interesting use of various kinds of random mechanisms with known probability distributions. Researchers have exploited prize lotteries to
study the effects of income on health (Lindahl 2002),
happiness (Brickman, Janoff-Bulman, and Coates
1978; Gardner and Oswald 2001), and consumer
behavior (Imbens, Rubin, and Sa …
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