In this part of the course we suggest few strategies for the preparation of research proposal. With examples which aimed to answer the request for a seminar, MA and PhD. Much of the material here is of general nature. It is useful to any kind of research.
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Easy guides for writing a paper: Yael Yishai's guide HTML FORMAT, or Word (rtf) format David Levi-Faur's guide HTML FORMAT, or Word (rtf) format Criteria for the evaluation of research papers HTML FORMAT, or Word (rtf) format (passward required) |
| The research Question: "Ideally, all research projects in the social sciences should satisfy two criteria. First, a research project should pose a question that is "important" in the real world. The topic should be consequential for political, social, or economic life, for understanding something that significantly affects many people's lives, or for understanding and predicting events that might be harmful or beneficial. Second, a research project should make a specific contribution to an identifiable scholarly literature by increasing our collective ability to construct verified scientific explanations of some aspect of the world (king et al., 1994, 15). Making an explicit contribution to the literature can be done in many different ways. We list a few of the possibilities here: 1. Choose a hypothesis seen as important by scholars in the literature but for which no one has completed a systematic study. If we find evidence in favor of or opposed to the favored hypothesis, we will be making a contribution. 2. Choose an accepted hypothesis in the literature that we suspect is false (or one we believe has not been adequately confirmed) and investigate whether it is indeed false or whether some other theory is correct. 3. Attempt to resolve or provide further evidence of one side of a controversy in the literature - perhaps demonstrate that the controversy was unfounded from the start. 4. Design research to illuminate or evaluate unquestioned assumptions in the literature. 5. Argue that an important topic has been overlooked in the literature and then proceed to contribute a systematic study to the area. 6. Show that theories or evidence designed for some purpose in one literature could be applied in another literature to solve an existing but apparently unrelated problem (king et al., 1994, 16-17). Our two criteria for choosing research questions are not necessarily in opposition to one another. In the long run, understanding real-world phenomena is enhanced by the generation and evaluation of explanatory hypotheses through the use of the scientific method. But in the short term, there may be a contradiction between practical usefulness and long-term scientific value. (king et al., 1994, 17). |
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Improving Theory: The development of a theory is often presented as the first step of research. It sometimes comes first in practice, but it need not. In fact, we cannot develop a theory without knowledge of prior work on the subject and the collection of some data, since even the research question would be unknown. Nevertheless, despite whatever amount of data has already been collected, there are some general ways to evaluate and improve the usefulness of a theory: First, choose theories that could be wrong. Second, to make sure a theory is falsifiable, choose one that is capable of generating as many observable implications as possible. Third, in designing theories, be as concrete as possible. Vaguely stated theories and hypotheses serve no purpose but to obfuscate. Theories that are stated precisely and make specific predictions can be shown more easily to be wrong and are therefore better. Some researchers recommend following the principle of "parsimony." (king et al., 1994, 19-20). |
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Improving Data Quality: "Data" are systematically collected elements of information about the world. They can be qualitative or quantitative in style. Our first and most important guideline for improving data quality is: record and report the process by which the data are generated. Without this information we cannot determine whether using standard procedures in analyzing the data will produce biased inferences. Only by knowing the process by which the data were generated will we be able to produce valid descriptive or causal inferences. Our second guideline for improving data quality is in order better to evaluate a theory, collect data on as many of its observable implications as possible. Our third guideline is: maximize the validity of our measurements. Validity refers to measuring what we think we are measuring. Our fourth guideline is: ensure that data-collection methods are reliable. Reliability means that applying the same procedure in the same way will always produce the same measure. When a reliable procedure is applied at different times and nothing has happened in the meantime to change the "true" state of the object we are measuring, the same result will be observed.' Reliable measures also produce the same results when applied by different researchers, and this outcome depends, of course, upon there being explicit procedures that can be followed. Our final guideline is: all data and analyses should, insofar as possible, be replicable. Replicability applies not only to data, so that we can see whether our measures are reliable, but to the entire reasoning process used in producing conclusions. On the basis of our research report, a new researcher should be able to duplicate our data and trace the logic by which we reached our conclusions. Replicability is important even if no one actually replicates our study. Only by reporting the study insufficient detail so that it can be replicated is it possible to evaluate the procedures followed and methods used (King et al., 1994, 23-26) |
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Extracts from King, Keohane and Verba, Designing Social Inquiry, Princeton University Press, 1994
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Thinking and Writing like Social Scientist: Reporting Uncertainty All knowledge and all inference in quantitative and in qualitative research is uncertain. Qualitative measurement is error-prone, as is quantitative, but the sources of error may differ. The qualitative interviewer conducting a long, in-depth interview with a respondent whose background he has studied is less likely to mis-measure the subject's real political ideology than is a survey researcher conducting a structured interview with a randomly selected respondent about whom he knows nothing. All good social scientists - whether in the quantitative or qualitative traditions report estimates of the uncertainty of their inferences. The point is not that reliable inferences are impossible in qualitative research, but rather that we should always report a reasonable estimate of the degree of certainty we have in each of our inferences. Skepticism and Rival Hypotheses The uncertainty of causal inferences means that good social scientists do not easily accept them. When told A causes B, someone who "thinks like a social scientist" asks whether that connection is a true causal one. It is easy to ask such questions about the research of others, but it is more important to ask them about our own research. There are many reasons why we might be skeptical of a causal account, plausible though it may sound at first glance. The skeptical social scientist asks about: (1) the accuracy of the data; (2)what else might explain the effects: Are there other variables that might explain the result? Source: King, Keohane and Verba, Designing Social Inquiry, Princeton University Press, 1994, pp. 31-33. |
1. Choose one or more of the levels of comparisons that are offered in the table below:
| Types of Comparisons | Examples | Cross issue | Comparing agenda setting in the the case of child abuse with the case of "guest workers". |
| Cross-sectoral | Comparing the liberalization of telecom and electricity in Israel |
| Cross-national | comparing the liberalization of telecom in Israel and the UK |
| Asyncronic/ longitudinal Comparisons | Comparing the nationalization of electricity in Israel in the 1950s to its Privatization in the 1990s |
If you want to write the instructor an e-mail levi@poli.haifa.ac.il
If you want to address the class write e-mail to method@research.haifa.ac.il
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