Reading Summaries.docx

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Department
Political Science
Course
POLI 311
Professor
Christopher Chhim
Semester
Summer

Description
King, Keohane, and Verba: The major purpose of the book is to show that the differences between quantitative and qualitative traditions are only stylistic and are methodologically and substantively unimportant. All good research can be understood – indeed, is best understood – to derive from the same underlying logic of inference. Both quantitative and qualitative research can be systematic and scientific. This paper is particularly related to scientific method and the research process. Scientific research has the following four characteristics: 1) The goal is inference (explanation): scientific research is designed to make descriptive or explanatory inferences on the basis of empirical information about the world. 2) The procedures are public (intersubjectivity): scientific research uses explicit, codified, and public methods to generate and analyze data whose reliability can therefore be assessed. 3) The conclusions are uncertain (determinism): by definition, inference is an imperfect process; its goal is to use quantitative and qualitative data to learn about the world that produced them. 4) The content is the method (empiricism): scientific research adheres to a set of rules of inference on which its validity depends. All research projects in social sciences should satisfy two criteria: 1) Aresearch 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 affect many people’s lives, or for understanding and predicting events that might be harmful or beneficial. 2) Aresearch 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. Four guidelines for improving data quality: 1) Record and report the process by which the data are generated. 2) Collect data on as many of its observable implications as possible; collect as much data in as many diverse contexts as possible. 3) Maximized the validity of our measurements; validity refers to measuring what we think we are measuring. 4) Ensure that data-collection methods are reliable; reliability means that applying the same procedure in the same way will always produce the same measure. Collier and Levitsky: This article argues that scholars respond to the challenge of conceptualizing democracy by pursuing two potentially contradictory goals: researchers attempt to increase analytical differentiation in order to capture the diverse forms of democracy; scholars are concerned with conceptual validity (avoiding the problem of conceptual stretching). This article is particularly connected to concepts. Satori’s ‘ladder of generality’ is based on a pattern of inverse variation between the number of defining attributes and numbers of cases; concepts with fewer defining attributes commonly apply to more cases and are therefore higher on the ladder of generality, whereas concepts with more defining attributes apply to fewer cases and hence are lower on the ladder. Conceptual differentiation can be increased by moving down the ladder to concepts that have more defining attributes and fit a narrower range of cases. However, moving down the latter can produce conceptual stretching because they presume the cases under discussion are definitely democracies. Diminished subtypes (useful for avoiding conceptual stretching) have two crucial components: 1) In contrast to the classical subtypes, diminished subtypes are not full instances of the root definition of democracy. 2) The distinctive feature of diminished subtypes is that they generally identify specific attributes of democracy that are missing; at the same time, they identify other attributes of democracy that are present. Ex. ‘illiberal democracy.’ Another method to avoid conceptual stretching and achieve conceptual differentiation is précising the definition by adding defining attributes (For example, Mainwaring and Perez- Linan’s democracy definition). Lastly, another strategy is shifting the overarching concept; the classical subtype. Munck and Verkuilen: Argue that there are three main challenges to the study of comparative democracy and data collection: conceptualization, measurement, and aggregation. This article addresses the distinctively methodological task of constructing a comprehensive and integrated framework for the analysis of data. This article is particularly connected to measurement and conceptualization. 1) Conceptualization: - Identification of attributes: avoid maximalist definitions (the inclusion of theoretically irrelevant attributes) or minimalist definitions (the exclusion of theoretically relevant attributes) - Vertical organization of attributes by level of abstraction: isolate the ‘leaves’of the concept tree and avoid the problems of redundancy and conflation 2) Measurement: - Selection of indicators: for validity use multiple indicators and establish the cross-system equivalence of these indicators; use indicators that minimize measurement error and can be crosschecked through multiple sources. - Selection of measurement level: maximize homogeneity within measuremen
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