Summary of Gary King, Robert O. Keohane & Sidney Verba's Designing Social Inquiry , livre ebook

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62 pages

English

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Please note: This is a companion version & not the original book.
Sample Book Insights:
#1 This book is about research in the social sciences. It is not a guide to specific research tasks such as the design of surveys, conduct of field work, or analysis of statistical data. Rather, it focuses on the essential logic underlying all social scientific research.
#2 The styles of quantitative and qualitative research are very different. Quantitative research uses numbers and statistical methods, and it tends to be based on numerical measurements of specific aspects of phenomena. Qualitative research, in contrast, covers a wide range of approaches, and it relies on intensive interviews or depth analysis of historical materials.
#3 The difference between quantitative and qualitative research is only stylistic. All good research can be understood to derive from the same underlying logic of inference. Both quantitative and qualitative research can be systematic and scientific.
#4 The rules of scientific inference are the same for all types of research, even nonstatistical research. They are often more clearly stated in the style of quantitative research, since the abstract, and even unrealistic, nature of statistical models makes the rules of inference stand out.

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Publié par	Everest Media LLC
Date de parution	01 avril 2022
Nombre de lectures	2
EAN13	9781669373407
Langue	English
Poids de l'ouvrage	1 Mo

Informations légales : prix de location à la page 0,0150€. Cette information est donnée uniquement à titre indicatif conformément à la législation en vigueur.

Extrait

Insights on Gary King and Robert O. Keohane & Sidney Verba's Designing Social Inquiry
Contents Insights from Chapter 1 Insights from Chapter 2 Insights from Chapter 3 Insights from Chapter 4 Insights from Chapter 5 Insights from Chapter 6 Insights from Chapter 7 Insights from Chapter 8 Insights from Chapter 9 Insights from Chapter 10 Insights from Chapter 11 Insights from Chapter 12 Insights from Chapter 13 Insights from Chapter 14 Insights from Chapter 15 Insights from Chapter 16 Insights from Chapter 17 Insights from Chapter 18 Insights from Chapter 19 Insights from Chapter 20 Insights from Chapter 21 Insights from Chapter 22 Insights from Chapter 23 Insights from Chapter 24 Insights from Chapter 25 Insights from Chapter 26 Insights from Chapter 27 Insights from Chapter 28 Insights from Chapter 29 Insights from Chapter 30 Insights from Chapter 31 Insights from Chapter 32 Insights from Chapter 33 Insights from Chapter 34 Insights from Chapter 35
Insights from Chapter 1

#1

This book is about research in the social sciences. It is not a guide to specific research tasks such as the design of surveys, conduct of field work, or analysis of statistical data. Rather, it focuses on the essential logic underlying all social scientific research.

#2

The styles of quantitative and qualitative research are very different. Quantitative research uses numbers and statistical methods, and it tends to be based on numerical measurements of specific aspects of phenomena. Qualitative research, in contrast, covers a wide range of approaches, and it relies on intensive interviews or depth analysis of historical materials.

#3

The difference between quantitative and qualitative research is only stylistic. All good research can be understood to derive from the same underlying logic of inference. Both quantitative and qualitative research can be systematic and scientific.

#4

The rules of scientific inference are the same for all types of research, even non-statistical research. They are often more clearly stated in the style of quantitative research, since the abstract, and even unrealistic, nature of statistical models makes the rules of inference stand out.

#5

The rules of inference that we discuss are not relevant to all issues that are of significance to social scientists. Many of the most important questions concerning political life are philosophical rather than empirical, and thus cannot be answered using the rules of scientific inference.

#6

The four characteristics of good research are that it is designed to make inferences about the world, it uses public methods to generate and analyze data, and it is not private. Much social research in the qualitative style follows fewer precise rules of research procedure or of inference.

#7

Scientific research is a social enterprise. It is impossible to reach perfectly certain conclusions from uncertain data, but the goal of inference is to use quantitative or qualitative data to learn about the world that produced them.

#8

The world is not naturally divided into simple and complex sets of events. The perceived complexity of a situation depends on how well we can simplify reality, and our capacity to simplify depends on whether we can specify outcomes and explanatory variables in a coherent way.

#9

The application of counterfactual analysis in a scientific manner is illustrated in a rare event from geology and evolutionary biology, the sudden extinction of the dinosaurs 65 million years ago.

#10

The point is that even seemingly unique events, such as dinosaur extinction, can be studied scientifically if we pay attention to improving theory, data, and our use of the data.
Insights from Chapter 2

#1

Social science research is a creative process of insight and discovery that takes place within a well-established structure of scientific inquiry. The first-rate social scientist does not regard a research design as a blueprint for a mechanical process of data-gathering and evaluation.

#2

There are four components to every research design: the research question, the theory, the data, and the use of the data. These components are not usually developed separately and scholars do not attend to them in any preordained order.

#3

There are no rules that tell us which research project to conduct, nor if we should decide to conduct field work. The specific topic that a social scientist studies may have a personal and idiosyncratic origin, but the methods of science and rules of inference should help scholars choose more powerful research designs.

#4

The first criterion for choosing a topic is that it be important in the real world. The second criterion is that a research project 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.

#5

The second criterion for choosing a research question is to make a contribution to knowledge. This means explicitly locating a research design within the framework of the existing social scientific literature. The work should be important to others, thus improving the success of the community of scholars taken as a whole.

#6

The two criteria for choosing research questions are not necessarily in opposition to one another. Understanding real-world phenomena is enhanced by the generation and evaluation of explanatory hypotheses through the scientific method, but this does not always take priority over practical usefulness.

#7

If we begin with a significant real-world problem rather than with an established literature, we must develop a workable plan for studying it. A proposed topic that cannot be refined into a specific research project should be modified or abandoned.

#8

The development of a theory is the first step of research. It is a reasoned and precise speculation about the answer to a research question, including a statement about why the proposed answer is correct.

#9

After we have gathered our data, we can modify our theory and gather new data, which will generate new observable implications of the new theory. However, we must be extremely cautious and self-restrained when doing so.

#10

The rule we should follow when altering our theory after observing the data is to not just add a restrictive condition and then proceed as if our theory, with that qualification, has been shown to be correct. We should not make our theory more restrictive without collecting new data to test the new version of the theory.

#11

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