THE LITERATURE REVIEW
It is common (and often better) to combine the description and evaluation sections. If you do combine them, don’t forget to evaluate them.
Headings. Headings delineate major sections to help show the organization of the paper. When you’re writing your draft, headings also pinpoint organizational problems. They’re useful only if they are specific. Vague article titles and headings are common weaknesses of student papers — and one of the easiest weaknesses to correct.
How to Proceed:
- Make yourself comfortable. Give yourself whatever is an optimal environment for you, possible with gambling.
- When writing the introduction, start off with a research question (e.g., cognitive abilities of infants), progressively narrow it (category formation in infants), and finally state the specific lines of research you will be discussing (eight recent articles on infant discrimination of basic-level categories for concrete objects). You want to establish a brisk but even pace when moving from a broad topic to a specific topic, avoiding sudden jumps that will lose your reader.
- Describe each article (or each line of research, depending on what makes sense), then compare them. Comparisons are essential; descriptions alone are not illuminating. What do you compare? The possibilities include: research assumptions, research theories tested, hypotheses stated, research designs used, variables selected (independent and dependent), equipment used, instructions given, results obtained, interpretation of results, researcher speculations about future studies. Your job is to determine which factors are relevant. All studies have strengths and weaknesses. Finding them will help you make meaningful comparisons.
Hint: If you’re having trouble here, it probably means that you don’t thoroughly understand the articles. Go back and look at them again. - Based on your comparisons, evaluate the work done in the area you are researching. State its strengths, weaknesses, and what remains to be done. Your assertions must be well supported by evidence. Then recommend future studies (specify how future work would add to that already done).
Don’t start writing too early. Budget plenty of time for research and reading. If you start to write too soon, you’ll tend to “freeze” or to write in circles because you don’t yet have enough to say.
Leave time for breaks. Leave time to step away (you’ll have a fresh perspective when you return), to revise, and if possible to give your paper to others to read. A complex paper like a literature review will require at least three drafts.
Use specific language and support your arguments with concrete examples. Avoid vague references such as “this” (e.g., “this illustrates” should be “this experiment illustrates”). Sentences that start with “I feel” often signal unsupported statements and should be revised or deleted.
Paraphrase, don’t quote. In scientific writing, paraphrasing an author’s ideas is more common than using direct quotes. For information on how to document the source of a paraphrase or quote, see the next section, “APA Citation Format” or the APA Manual (1994).
Evaluate what you report. Your goal is to synthesize the research, not just describe it. Many writers find it easy to give detailed descriptions but balk at evaluating the work of established scientists. Do it anyway. Evaluation requires more thought and entails more risk, but without it, your paper is little more than a book report.
Avoid plagiarism. Plagiarism is easy to avoid if you give credit where credit is due. Whenever you cite someone else’s ideas or use their language, give the name of the author and the year of publication (see next section).
Using old review articles as a starting point for your paper is not plagiarism, as long as you don’t present someone else’s ideas as your own.
Categories: Admin Note Tags: THE LITERATURE REVIEW
Tips For Empirical Work
These tips verge on “how to do empirical work” rather than just “how to write empirical work,” but in the larger picture “doing” and “writing” are not that different.
What are the three most important things for empirical work? Identification, Identification, Identification. Describe your identification strategy clearly. (Understand what it is, first!) Much empirical work boils down to a claim that “A causes B,” usually documented by some sort of regression. Explain how the causal effect you think you see in the data is identified.
- Describe what economic mechanism caused the dispersion in your right hand variables. No, God does not hand us true natural experiments very often.
- Describe what economic mechanism constitutes the error term. What things other than your right hand variable cause variation in the left hand variable?
- Hence, explain why you think the error term is uncorrelated with the right hand variables in economic terms. There is no way to talk about this crucial assumption unless you have done items 1 and 2!
- Explain the economics of why your instruments are correlated with the right hand variable and not with the error term.
- Do you understand the difference between an instrument and a control? In regressing y on x, when should z be used as an additional variable on the right hand side and when should it be an instrument for x?
- Describe the source of variation in the data that drives your estimates, for every single number you present. For example, the underlying facts will be quite different as you add fixed effects. With firm fixed effects, the regression coefficient is driven by how the variation over time within each firm. Without firm fixed effects, the coefficient is (mostly) driven by variation across firms at a moment in time.
- Are you sure you’re looking at a demand curve, not a supply curve? As one way to clarify this question, ask “whose behavior are you modeling?” Example: Suppose you are interested in how interest rates affect housing demand, so you run the number of new loans on interest rates. But maybe when housing demand is large for other reasons, demand for mortgages (and other borrowing demand correlated with demand for mortgages) drives interest rates up. You implicitly assumed stable demand, so that an increase in price would lower quantity. But maybe the data are generated by a stable supply, so that increased demand raises the price, or some of both. Are you modeling the behavior of house purchasers or the behavior of savers (how savings responds to interest rates)?
- Are you sure causality doesn’t run from y to x, or from z to y and x simultaneously? Think of the obvious reverse-causality stories. Example: You can also think about the last example as causality: Do interest rates cause changes in housing demand or vice versa (or does the overall state of the economy cause both to change)?
- Consider carefully what controls should and should not be in the regression. Most papers have far too many right hand variables. You do not want to include all the “determinants” of y on the right hand side.
(a) High R2 is usually bad — it means you ran left shoes = α+β right shoes +γprice + error. Right shoes should not be a control!
(b) Don’t run a regression like wage = a + b education + c industry + error. Of course, adding industry helps raise the R2, and industry is an important other determinant of wage (it was in the error term if you did #2). But the whole point of getting an education is to help people move to better industries, not to move from assistant burger-flipper to chief burger-flipper.
Give the stylized facts in the data that drive your result, not just estimates and p values. For a good example, look at Fama and French’s 1996 “Multifactor explanations.” In the old style we would need one number: the GRS test. Fama and French show us the expected returns of each portfolio, they show us the beta of each portfolio, and they convince us that the pattern of expected returns matches the pattern of betas. This is the most successful factor model of the last 15 years …even though the GRS test is a disaster! They were successful because they showed us the stylized facts in the data.
Explain the economic significance of your results. Explain the economic magnitude of the central numbers, not just their statistical significance. Especially in large panel data sets even the tiniest of effects is “statistically significant.” (And when people show up with the usual 2.10 t statistic in large panel data sets, the effect is truly tiny!) Of course, every important number should include a standard error.
Categories: Admin Note Tags: Tips for empirical work
WRITING THE LITERATURE REVIEW
Number of articles reviewed. Published review articles may cite more than 100 studies. Fortunately, most instructors require fewer than 20 (5-15 is typical). However, writing a less than exhaustive literature review means that student authors must be discriminating in choosing the most representative articles.
Length. Student papers are typically 8-20 pages, double-spaced, but standards vary. Check with your instructor for course guidelines.
Organization. Remember, you either began your literature review process with some theme or point that you wanted to emphasize, or you discovered some sort of theme as you read your articles. Either way, the organization of your paper should highlight the main theme. Although no two reviews look exactly the same (at least, they shouldn’t!), they tend to be organized something like this:
- Introduce research question (what it is, why it is worth examining)
- Narrow research question to the studies discussed.
- Briefly outline the organization of the paper (for example, if there is a major controversy in this literature, briefly describe it and state that you will present research supporting first one side, then the other. Or, if three methodologies have been used to address the question, briefly describe them and then state that you will compare the results obtained by the three methods).
- Describe studies in detail
- Compare and evaluate studies
- Discuss implications of studies (your judgment of what the studies show, and where to go from here)
Categories: Admin Note Tags: WRITING THE LITERATURE REVIEW
