Professional writing in political science

Here is another useful article about writing in political science - reading highly recommended!

Professional Writing in Political Science: A Highly opinionated Essay

This essay is a compendium of the reactions to student writing over a long career, the kinds of ideas that are notes on critiques of numerous papers, articles, theses, and especially, dissertations. It is a set of principles and guide-lines for how to turn the product of political science research into something readable. Or to put it in the negative, it is guidelines for how not to have your work rejected because it is dragged down by the quality of your writing.

1. Attitude

Writing is hard. At its best it is bringing intelligence—to the limit of what we have—toward clarifying a world that is messy in its natural state. None of us do it as well as it could be done. It is a shame that writing is captured by the humanities and taught by those with humanistic inclinations, because the artsy idea that it is “expressive” gets in the way of understanding that it is the application of highly disciplined intelligence.

Writing is thinking. The idea that one might have good ideas but be unable to express them is, I think, wrong. If you can’t express an idea, you haven’t had it. When your prose is muck, it is delusion to believe that you have a clear understanding of a topic. If you’d had it, you could have written it.

Writing is hard also because what you know about your topic is radically different from what a reader knows and also from what he or she wants to know. To succeed in communication, you must figure out what the reader knows and wants to know. That means developing the discipline of reading your own words, from beginning to end, and simulating the reality in the reader’s mind of knowing only what can be known from your words, and in the order you have written them.

(Bad) writers often assert, “I know what I want to say and if the reader can’t figure it out, that is his or her problem!” Wrong. It is your problem. Readers who can’t follow an argument usually conclude that its author is not very smart. And, having been both author and reader, I think readers are right. So, if you have this attitude, that figuring out what your prose means is the reader’s problem, change it or prepare yourself for professional failure.

2. Structure: The Kosher Principle

Part of the rules of keeping kosher is that certain foods ought never to touch one another, that contact contaminates one or both. I have long thought that this idea is a useful principle for good scientific writing, that an exposition contains logically quite different features which ought never to touch one another because doing so contaminates their logical clarity in the reader’s mind.

I strongly suggest as a strategy for writing that these segments be written in a kosher form, that theory is never discussed in the same section, much less paragraph, as literature or design or analysis. Running them together confuses their logical structure in the minds of readers, which is probably caused by the even more dangerous confusion in the mind of the author.

Sometimes this confusion is from intent. Authors who know that they have no original theory like to write sections called “Theory” which are in fact literature review. Having many words to say about other scholar’s theories covers up the absence of original ideas in the current document.

While I don’t like the mechanical aspect of having sections named, “Problem,” “Literature,” “Theory,” and so forth, I do think that this initial structure ought to dominate the author’s conception of the writing task. These are logical requisites, each of which must be accomplished for success.

I take them up in order.

The first section of a paper is the most crucial because readers form initial judgments of the quality of a paper, proposal, or whatever on-line, and initial impressions are unlikely to change if a dull or confused introduction is later followed by brilliant writing in other sections. When I once served on the NSF panel, reading several hundred proposals a year, I compared notes with other panelists, all of whom agreed that we had made pretty solid “fund,” “no fund” decisions from reading page 1, which rarely were altered by finishing the proposal.

So if you don’t capture interest in the problem section, you’ve probably lost the game before the first inning starts. Your challenge is to present the problem, the big picture context of what your work is and why, briefly in a manner that teases the reader into wanting to know the details that come later. This is very hard to do, but its success is so critical that this should be your most careful prose, word for word getting more attention than anything else you write.

A sure fire way to lose reader interest is to begin by writing about the literature. “The literature” is a boring (but necessary) part of scientific writing. But you don’t want to lead with boredom. And how many times do we need to read an author expressing amazement or—with the dishonesty of a mortician pretending grief—expressing misfortune that his or her particular topic has not received attention in the literature. If your justification for your work is a gap in the literature, what is to follow is certain to be tedious and trivial.

One of Mike MacKuen’s former colleagues (I don’t remember for proper citation) once put it perfectly, “This paper fills a much needed gap in the literature.” That’s what I think whenever I see an author berating a gap, that there is probably an excellent reason why all previous scholars have decided that this is a problem that deserves to be ignored.

And a matter of attitude: In real science we build on what came before. So a review that asserts that all previous work is trash, the work of scholars of below normal intelligence, puts me in the frame of mind of thinking that what is likely to follow is so bad that it can only be justified against a literature that is terrible. If your contribution is good, it ought to improve on understanding that is already strong. Compare “I have a better mousetrap” to “No other mousetraps are any good.”

Or, just follow the kosher principle: The problem section is for introducing the problem—and nothing else.

2.2 Literature

Graduate students are usually pretty good at reviewing the literature, which probably explains why they nearly always overdo it. The point of a lit review is not to prove how much you know—this is not an exam. It is to lay the foundation of what is known so that you can move on to what is new. As such it should be directed to that minimum necessary for the foundation, the focus being ideas and issues, not lists of authors, articles, and books.

Since excessive length is usually an issue in scientific writing, the lit review is an excellent place to achieve economy. When you review too much literature, you not only lengthen your work but probably also undermine the real goal of building the foundation for your own innovation.

And the kosher principle—nearly always violated: Once you are done reviewing the literature, stop. It doesn’t belong anywhere else in the paper. When it appears elsewhere, in the middle of theory, design, or analysis, it usually causes confusion. The problem is that the literature is a crutch graduate student authors turn to when they want to avoid writing about those other, more difficult, issues.

2.3 Theory and Model

A suggestion: Begin the theory section with the words, “I have no theory.” which serve as a useful reminder that social research has no other purpose and the present piece should not be written so long as the statement is true.




I have said that writing is hard. Writing theory is the hardest writing there is—and the most important. It is no wonder that theory sections are usually a mishmash of literature review and design, something, anything, to cover up the embarrassment of no theory.

Theory is usually written in the subjunctive mood, statements of abstract logical relationships. “Given condition x, then pattern y should follow.” The “should” is a logical statement, not an empirical one.

2.3.1 Censoring

Two kinds of self-censoring are common in writing social theory. Authors sometimes censor the theory itself, to tailor it to predict what will be observed in the study and nothing else. Second is operationism, a common disease of political science writing, which defines concepts in terms of the indicators which measure them.

Censoring I: Fitting Theory to the Study
To restrict a theory by limiting it to only that which will be observed in the present study is harmful, robbing the theory of most of its richness. To censor a theory to predict only what will be observed usually will make it so specific as to deprive it of the logic which drives it. Such a theory, if it really remains a theory at all, is so limited that we should care little whether or not it is true. It is a hallmark of bad social science to have theories which predict exactly what will be observed in the study. All who have done social research understand that there is probably dishonesty at work when things work too well.

Any decent theory will have empirical implications vastly beyond what any study can observe. Readers understand that. It isn’t a problem. So one develops a theory in its full richness and then asserts that some small fraction of empirical implications can and should be observed. All that is needed is a brief transition statement at the end which specifies that a small set of empirical implications is observable and will reflect, if only partially, on the truth of the explanation.

Censoring II: Operationism
The building blocks—the nouns as it were—of theory are concepts. Concepts are theoretical and abstract ideas, as general as the words which form them. In a more convenient world they would match one to one with a wonderful set of indicators. In the real world concepts imply a great deal more than can be measured with even the best possible indicator. In an old usage, the “epistemic” correlation is the idea that the match is imperfect and partial, r smaller than 1.0. Part of the research task is to optimize the fit of indicator to concept, going from totally invalid to, at best, very partially valid. Part of inference is to recognize the role that low epistemic correlation plays in findings. Concepts must be presented as ideas. To do the reverse, to assert that they are what the indicators measure, is a failed strategy of science from which no number of studies can ever lead to a theory. Keeping kosher implies simply no reference whatsoever to indicators in a theory section. The issue of whether the indicators fit the concepts needs explicit treatment in the design section.

2.4 Design

The problem to which a section on design is the solution is this: there must be a connection between what theories imply and what can be observed. If a theory is general enough to be worth proposing and worth testing, then it will have sweeping implications across studies of many types and for numerous indicators. The section on design is where the censoring that was inappropriate in developing the theory becomes appropriate. The reader needs to understand how the general theory comes to ground in a specific test.

Hence what is required is delimiting the portion of the theory that is subject to testing in the current work and then detailing how theory leads to empirical implications. This can be seen as two tasks, (1) fitting theory to the design of the study, and (2) fitting theoretical concepts to observable indicators. Both are creative choices made by the author and both are subject, like any assumption, to error.

Bad writing tends to treat design decisions as if they had somehow been dictated by necessity. The honest approach is to admit that they are your own decisions and that each is subject to skepticism from reasonable readers. The author’s task to explain the logic that lead him or her to those choice sin order to bring the reader along. Readers are reasonable on average. They will accept difficult decisions about how to do things when the author lays out his or her logic. But the logic must be there.

It may be easier to say what a failed design section looks like. It is usually a list of variables, then followed by a regression. If the reader asks, as any reader should, what about those regression coefficients impinges on the truth of the theory, the usual answer is nothing. That is, what a regression implies is often only that the conditions necessary for software estimation were met, but the coefficients in the table imply nothing for the theory. The “logic” is something like, “I can do a regression and therefore the theory is true.”

2.4.1 Hypotheses

Hypotheses are the means by which the implications of the theory become translated into empirically observable facts. Good hypotheses will always have the attribute that failure implies that the theory is not true. They are worth testing for that reason only. Empirical expectations, what you know from knowledge of the data, should never be presented as hypotheses. If they are not logically linked to the theory, they are not worth testing.

Bad hypotheses usually test the author’s intuition. It needs to always be remembered that the quality of your intuition is of no interest to anyone but yourself. It just doesn’t matter if what you “think” is likely to be seen in the data ends up being seen unless that expectation is linked to theory.

Should you state formal hypotheses? I don’t have a hard-line position on this issue, but find that formal hypotheses, like unnecessary equations, often convey an attitude of pseudo-science instead of the real thing.

2.5 Analysis

Good analysis is hard writing. It needs to conquer the problem of planting expectations in the reader’s mind before he or she sees data and then working through the data presentation with some care. Authors always overestimate readers’ ability to comprehend results. Readers aren’t dumb; they just haven’t spent the thousands of hours the author spent on every detail of complex analysis. They need to be brought along.

If reviews of the literature are almost always too long, analysis is almost always too short. There are many steps, often skipped. For theoretically relevant variables, we need first to set up expectations for the size (if possible),sign, and significance of estimated coefficients. Then we need commentary on each, measuring what was observed against what was expected. Here untrained authors almost always overemphasize significance and underemphasize size. Significance is often not interesting. In large N studies coefficients tapping relatively trivial effects will usually be significant—measuring the power of much data, not the importance of the phenomenon. For crucial effects we need more. It is often extremely useful to go beyond coefficients and talk about the size of effects in the units of the dependent variable.

In another sense amateur analysts often have the reverse problem, not taking significance seriously. When you can’t reasonably exclude the possibility that the true parameter is zero, it doesn’t make much sense to go on and on about a variable’s sign and size. Fundamentally, non significance tells us that we have no reliable information about sign or size.

The rule for tables (below) is that both text commentary and tables must stand on their own. Thus prose like “Table x shows that the theory is true.” miserably fails. The statement passes all of the hard work of analysis to the reader. And it can’t be evaluated except by discontinuing reading and studying the table.

All coefficients are not equal. Often the key test of the theory will hang on one or two coefficients, with everything else included just to get the specification right. Emphasis should reflect that. Key coefficients deserve much more attention than they usually get and the others less.

Fit vs. Coefficients: Amateur analysts usually emphasize model fit, for-getting that how well the data fit a statistical model has almost no bearing on whether or not a theory is true. It is useful to remember that analysis is testing theory. What matters is what did the theory predict and did it happen or not. A similar issue arises with discussions of the relative explanatory power of variables. This question almost never impinges on the truth or falsehood of theory. It just isn’t relevant and attention given to it can only detract from what analysis ought to do, test theory.

2.5.1 Tables: Presenting Linear Model Findings

Table design is important, and often done badly. It requires you to think about what the reader knows and wants to know from your work and then very carefully lay out the table to tell the story.

A beginning point is this: APSA and the journals we write for have official rules of table styles. You need to know them and it is wise to use them when you create the table (not sometime later). Violating the official table and figure styles is a good way to advertise your amateur standing. If you want readers to think, “This was written by a graduate student,” then by all means proceed with your (or Microsoft’s) favorite table design. If you want to be treated as a serious professional, don’t.

A Rule: Tables should always be composed so that a reader can pick one up and understand its content, without having read the text. That means it must be fully self-contained, depending on nothing that is explained only in the text. The opposite is also true; a reader should be able to skip the table and understand the analysis completely from the text.

Professional type-setting practice in recent years has moved toward simplicity and away from extensive use of highlighting, i.e., all the things that Microsoft likes to do. So minimize or eliminate entirely the use of bold and italic type for various table features. Forget the pretty tables Microsoft Word will design for you; they all violate professional standards of table formatting. Also, never use vertical rules.

Table Editors: Composing a good table is a very demanding task, one that exceeds the capabilities of ordinary word processors. That’s why we have table editors. They make it possible to do the difficult layout tasks that ordinary word-processing tools cannot handle. Don’t know how to use one? You are a professional author. Learn to use the tools of authorship or choose a profession for which you are better suited.

Title: The title should convey something to the reader about the logical role and meaning of the presentation, what is being tested and how. The reader is asking, why am I looking at these numbers? and the title should answer that question. Titles tend to err on the side of being too short, of not saying enough so that readers can figure out what the numbers mean.

Do not name tables for the statistical estimator employed, another sure sign of amateur standing. A title like “Negative Binomial Estimates of ...” tells the reader that you are mighty impressed that you know about the negative binomial and have kind of forgotten what substantive purpose the table serves.

The Stub
The stub is the leftmost column in which you name the indicators for which coefficients will be presented. The usual problem is that the names are too brief to convey what the indicator is. (And remember the rule about being self-contained: if the reader needs to page back to find out what some ambiguous name stands for, you have violated the rule and caused reader impatience.) Abbreviate nothing. And never ever ever use computer variable names to stand for concepts. These are personal code words that convey no meaning to readers.

Since interpreting an unstandardized coefficient requires us to know about the measurement of the indicators, then more information is better than less. So instead of “Income,” which can be measured in several ways, use something more descriptive, for example, “Income (in $thousands)” or “Income, (ANES categories).” For dummy variables it is useful to tell us which category is coded 1. So “Gender: Female” or “Concern for Election Outcome: Medium or High.” Good descriptive stubs are usually impossible to create without a table editor. Otherwise you just can’t force sufficient content into available spaces.

Notes: There will usually be material that the reader needs to know, which is not in the body of a table, and not important enough to go in the title. So you need notes, for example, “Estimates from a negative binomial regression model.” or “* p2.5.2 Figures

Figures are called “figures” (not graphs and not charts) and have captions beneath the picture.

Useful Advice for Excel Users: DO NOT CREATE FIGURES IN COLOR. Professional publication is in black and white only. It is very important to see what your figure is going to look like when you are creating it. If you use color, you will produce a perfectly sensible picture in its original color, which then becomes unreadable, for lack of ability to discern which lines or shapes are which, in black and white. This problem is the source of many, maybe even most, of the worst published figures. It takes a lot more work to recreate a B&W figure than it does to start from scratch with one.

How? Create a master graph, changing all elements to black and white, then save it as your default. From the figure worksheet, use Chart-Chart Type, select User-defined, click Add, then name it something like B&W line graph. Then you select that chart type as the first step when you are creating graphs, so everything is in black and white from the beginning. Then you use line styles to distinguish different lines, a combination of thickness and dashing.

Defining Lines: Graphic software will produce legends (but in color, why bother, all the lines will look the same—see above). Usually they are tacky looking, another sign of amateur standing. I employ text boxes and arrows to put the explanatory content right into the picture instead. It is a fair amount of work, but so is doing an R&R or a new submission because readers thought your product looked amateurish.

2.6 Conclusions?

The first question, not asked as often as it should be, is do you need a conclusion section at all. Most of the time I think the answer should be no.

A question is raised in theory, refined in design, and answered in analysis. To answer it again is to add excess length and to insult the reader’s intelligence by the implicit assumption that he or she wasn’t swift enough to catch the point the first time. Authors who repeat themselves are likely to have angry readers.

A conclusion is appropriate mainly, I think, when it is useful to make general observations that do not follow directly from the analysis. It might a pattern not captured by any single analysis, but seen repeatedly. It might be some emergent conclusion when posterior beliefs differ from priors.

In any case, the conclusion is the second most important piece of an article for reader impact—after the problem statement. So it should never be a summary of things already written.

3. Other Issues

The usual view about perfection in all the details of a manuscript is that it is necessary for final publication only, that drafts can be imperfect in style, illustrations, and so forth. I think it is bad strategy. Although you might suffer mild embarrassment from flaws in print, it is the copy sent out for review on which success or failure as a professional will depend. Journal review is a difficult process for all concerned, the result often deeply dissatisfying. The implication is this: perfection in all its details must precede submission, not follow it. Referee judgment is unavoidably affected by small writing problems having nothing to do with the merit of the research. It is just human nature to assume that sloppiness or lack of professional knowledge anywhere is probably indicative of sloppiness and lack of professional knowledge everywhere.

Never use journal submission as a means to clean up an imperfect manuscript. The process is way too costly for that—to the profession and more importantly to you. That’s what friends are for.

I am reluctant to advise how to write, because different strategies work for different people. But unless it fundamentally violates how you work, a corollary point is that the achievement of perfection should begin with very early drafts. That way the small problems that are present are likely to get caught and fixed in revisions. If you write ugly in early drafts and then hope to achieve perfection all in one final revision, you are likely to fail. Small problems stand out against a clean background. If the draft is messy, you are likely to miss many of them.

Original article: www.unc.edu/~jstimson/Writing.pdf

Writing political science papers (part 4)

Part One
Part Two
Part Three

Writing in Political Theory

Political Theory differs from other subfields in Political Science in that it deals primarily with historical and normative, rather than empirical, analysis. In other words, political theorists are less concerned with the scientific measurement of political phenomena than with understanding how important political ideas develop over time. And they are less concerned with evaluating how things are than in debating how they should be. A return to our democracy example will make these distinctions more clear and give you some clues about how to write well in Political Theory.

Earlier, we talked about how to define democracy empirically so that it can be measured and tested in accordance with scientific principles. Political theorists also define democracy, but they use a different standard of measurement. Their definitions of democracy reflect their interest in political ideals--for example, liberty, equality, and citizenship--rather than scientific measurement. So, when writing about democracy from the perspective of a political theorist, you may be asked to make an argument about the proper way to define citizenship in a democratic society. Should citizens of a democratic society be expected to engage in decision-making and administration of government or should they be satisfied with casting votes every couple of years?

In order to substantiate your position on such questions, you will need to pay special attention to two interrelated components of your writing: (1) the logical consistency of your ideas and (2) and the manner in which you use the arguments of other theorists to support your own. First, you need to make sure that your conclusion and all points leading up to it follow from your original premises. If, for example, you argue that democracy is a system of government through which citizens develop their full capacities as human beings, then your notion of citizenship will somehow need to support this broad definition of democracy. A narrow view of citizenship based exclusively or primarily on voting probably will not do. Whatever you argue, however, you will need to be sure to demonstrate in your analysis that you have considered the arguments of other theorists who have written about these issues. In some cases, their arguments will provide support for your own; in others, they will raise criticisms and concerns that you will need to address if you are going to make a convincing case for your point of view.

Drafting Your Paper

If you have used material from outside sources in your paper, be sure to cite them appropriately in your paper. In political science, writers most often use APA or Turabian (a version of the Chicago Manual of Style) style guides when formatting references. Check with your instructor if he or she has not specified a citation style in the assignment.

Source: The Writing Center, University of North Carolina at Chapel Hill.

Writing political science papers (part 3)

Part One
Part Two

Writing Political Science Research Papers

Your instructors use this type of assignment as a means of assessing your ability to understand a complex problem in the field, to develop a perspective on this problem, and to make a persuasive argument in favor of your perspective. In order for you to successfully meet this challenge, your research paper should include the following components: (1) an introduction; (2) a problem statement; (3) a discussion of methodology; (4) a literature review; (5) a description and evaluation of your research findings; and (6) a summary of your findings. Here's a brief description of each component.

In the introduction of your research paper, you need to give the reader some basic background information on your topic that suggests why the question you are investigating is interesting and important. You will also need to provide the reader with a statement of the research problem you are attempting to address and a basic outline of your paper as a whole. The problem statement presents not only the general research problem you will address but also the hypotheses that you will consider. In the methodology section, you will explain to the reader the research methods you used to investigate your research topic and to test the hypotheses that you have formulated. For example, did you conduct interviews, use statistical analysis, rely upon previous research studies, or some combination of all of these methodological approaches?

Before you can develop each of the above components of your research paper, you will need to conduct a literature review. A literature review involves reading and analyzing what other researchers have written on your topic before going on to do research of your own. There are some very pragmatic reasons for doing this work. First, as insightful as your ideas may be, someone else may have had similar ideas and have already done research to test them. By reading what they have written on your topic, you can ensure that you don't repeat, but rather learn from, work that has already been done. Second, to demonstrate the soundness of your hypotheses and methodology, you will need to indicate how you have borrowed from and/or improved upon the ideas of others.

By referring to what other researchers have found on your topic, you will have established a frame of reference that enables the reader to understand the full significance of your research results. Thus, once you have conducted your literature review, you will be in a position to present your research findings. In presenting these findings, you will need to refer back to your original hypotheses and explain the manner and degree to which your results fit with what you anticipated you would find. If you see strong support for your argument or perhaps some unexpected results that your original hypotheses cannot account for, this section is the place to convey such important information to your reader. At this point, you should also suggest further lines of research that will help refine, clarify inconsistencies with, or provide additional support for your hypotheses. Finally, in the summary section of your paper, you should reiterate the significance of your research and your research findings and speculate upon the path that future research efforts should take.

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Writing political science papers (part 2)

Part One

However, research in political science seldom yields immediately conclusive results. In this case, for example, although in most recent presidential elections our hypothesis holds true, President Franklin Roosevelt was reelected in 1936 despite the fact that the national unemployment rate was 17%. To explain this important exception and to make certain that other factors besides high unemployment rates were not primarily responsible for the defeat of incumbent presidents in other election years, you would need to do further research. So you can see how political scientists use the scientific method to build ever more precise and persuasive theories and how you might begin to think about and analyze the topics that interest you as your write your paper.

Since political scientists construct and assess theories in accordance with the principles of the scientific method, writing in the field conveys the rigor, objectivity, and logical consistency that characterize this method. Thus, in contrast to scholars in such fields as literature, art history or classics, political scientists avoid the use of impressionistic or metaphorical language, or language which appeals primarily to our senses, emotions, or moral beliefs. In other words, rather than persuade you with the elegance of their prose or the moral virtue of their beliefs, political scientists persuade through their command of the facts and their ability to relate those facts to theories that can withstand the test of empirical investigation. In writing of this sort, clarity and concision are at a premium. To achieve such clarity and concision, political scientists precisely define any terms or concepts that are important to the arguments that they make. This precision often requires that they "operationalize" key terms or concepts, which simply means that they define them so that they can be measured or tested through scientific investigation.

Fortunately, you will generally not be expected to devise or operationalize key concepts entirely on your own. In most cases, your professor or the authors of assigned readings will already have defined and/or operationalized concepts that are important to your research. And in the event that someone hasn't already come up with precisely the definition you need, other political scientists will in all likelihood have written enough on the topic that you're investigating to give you some clear guidance on how to proceed. For this reason, it is always a good idea to explore what research has already been done on your topic before you begin to construct your own argument.

To give you an example of the kind of "rigor" and "objectivity" political scientists aim for in their writing, let's examine how someone might operationalize a term. Reading through this example should clarify the level of analysis and precision that you will be expected to employ in your writing. Here's how you might define key concepts in a way that allows us to measure them.

We are all familiar with the term "democracy." If you were asked to define the term, you might make a statement like the following: "Democracy is government by the people." You would, of course, be correct--democracy is government by the people. But, in order to evaluate whether or not a particular government is fully democratic or is more or less democratic when compared with other governments, we would need to have more precise criteria with which to measure or assess democracy. Most political scientists agree that these criteria should include the following rights and freedoms for citizens:

1. Freedom to form and join organizations

2. Freedom of expression

3. Right to vote

4. Eligibility for public office

5. Right of political leaders to compete for support

6. Right of political leaders to compete for votes

7. Alternative sources of information

8. Free and fair elections

9. Institutions for making government policies depend on votes and other expressions of preference

By adopting these nine criteria, we now have a definition that will allow us to measure democracy. Thus, if you want to determine whether or not Brazil is more democratic than Sweden, you can evaluate each country in terms of the degree to which they fulfill the above criteria.

What Counts as Good Writing in Political Science?

While rigor, clarity, and concision will be valued in any piece of writing in political science, knowing the kind of writing task you've been assigned will help you to write a good paper. Two of the most common kinds of writing assignments in political science are the research paper and the theory paper.

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Writing political science papers (part 1)

This pamphlet will help you to recognize and to follow writing practices and standards in political science. The first step toward accomplishing this goal is to develop a basic understanding of political science and the kind of work political scientists do.

Defining Politics and Political Science

At its most basic level, politics is the struggle of "who gets what, when, how." This struggle may be as modest as competing interest groups fighting over control of a municipal budget in Small Town, U.S.A., or as overwhelming as a military stand-off between international superpowers. Political scientists study such struggles, both small and large, in an effort to develop general principles or theories about the way the world of politics works. Think about the title of your course or re-read the course description in your syllabus. You'll find that your course covers a particular sector of the large world of "politics" and brings with it a set of topics, issues, and approaches to information that may be helpful to consider as you approach a writing assignment. The diverse structure of political science reflects the diverse kinds of problems the discipline attempts to analyze and explain. In fact, political science includes at least eight major sub-fields:

• American Politics examines political behavior and institutions in the United States.
• Comparative Politics analyzes and compares political systems within and across different geographic regions.
• International Relations investigates relations among nation states and the activities of international. organizations such as the United Nations, the World Bank, and NATO as well as international actors such as terrorists, Non-Governmental Organizations (NGOs) and Multi-National Corporations (MNCs).
• Political Theory analyzes fundamental political concepts such as power and democracy and fundamental questions such as, "How should the individual and the state relate?".
• Political Methodology deals with the ways that political scientists ask and investigate political science questions.
• Public Policy examines the process by which governments make public decisions.
• Public Administration studies the ways that government polices are implemented.
• Public Law focuses on the role of law and courts in the political process.

What is Scientific about Political Science?

In order to write a good paper, it's helpful to know what constitutes good practice of political science. Although political scientists are prone to debate and disagreement, the majority view the discipline as a genuine science. As a result, political scientists generally strive to emulate the objectivity as well as the conceptual and methodological rigor typically associated with the so-called "hard" sciences (e.g., biology, chemistry, and physics). They see themselves engaged in revealing the relationships underlying political events and conditions. And from these revelations they attempt to construct general principles about the way the world of politics works. Given these aims, it is important for political scientists' writing to be conceptually precise, free from bias, and well-substantiated by empirical evidence. They want to build and refine ever more precise and persuasive theories. Knowing that political scientists value objectivity may help you in making decisions about how to write your paper and what to put in it. Political theory is an important exception to this empirical approach.

Since theory-building serves as the cornerstone of the discipline, it may be useful to see how it works. You too may be wrestling with theories or proposing your own as you write your paper. Consider how political scientists have arrived at the theories you are reading and discussing in your course. Most political scientists adhere to a simple model of scientific inquiry when building theories. The key to building precise and persuasive theories is to develop and test hypotheses. Hypotheses are statements that researchers construct for the purpose of testing whether or not a certain relationship exists between two phenomena. To see how political scientists use hypotheses, and to imagine how you might use a hypothesis to develop a thesis for your paper, consider the following example. Suppose that we want to know if presidential elections are affected by economic conditions. We could formulate this question into the following hypothesis: "When the national unemployment rate is greater than 7 percent at the time of the election, presidential incumbents are not reelected."

In the research model designed to test this hypothesis, the dependent variable, or the phenomenon that is affected by other variables, would be the reelection of incumbent presidents; the independent variable, or the phenomenon that may have some effect on the dependent variable, would be the national unemployment rate. You could test the relationship between the independent and dependent variables by collecting data on unemployment rates and the reelection of incumbent presidents and comparing the two sets of information. If you found that in every instance that the national unemployment rate was greater than 7 percent at the time of a presidential election the incumbent lost, you would have significant support for our hypothesis.

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