Gary Goertz and James Mahoney
- Published in print:
- 2012
- Published Online:
- October 2017
- ISBN:
- 9780691149707
- eISBN:
- 9781400845446
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691149707.003.0015
- Subject:
- Sociology, Social Research and Statistics
This chapter considers the typical modes of generalization used in the qualitative and quantitative research traditions. Generalization can be descriptive or causal. A descriptive generalization ...
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This chapter considers the typical modes of generalization used in the qualitative and quantitative research traditions. Generalization can be descriptive or causal. A descriptive generalization often involves one variable that “describes” some state of affairs within a population of cases. By contrast, a causal generalizations always involves at least two variables, A and B. Causal generalizations ideally specify the form and strength of the relationship between A and B within a population of cases. The two research cultures have trouble seeing and analyzing each other's typical kind of generalization. The chapter first examines generalizations in qualitative research before discussing the use of 2 x 2 tables to present set-theoretic generalizations. It then explains a well-known problem in statistical analysis involving the so-called “perfect predictors” and concludes with an assessment of the statistical significance of control variables.Less
This chapter considers the typical modes of generalization used in the qualitative and quantitative research traditions. Generalization can be descriptive or causal. A descriptive generalization often involves one variable that “describes” some state of affairs within a population of cases. By contrast, a causal generalizations always involves at least two variables, A and B. Causal generalizations ideally specify the form and strength of the relationship between A and B within a population of cases. The two research cultures have trouble seeing and analyzing each other's typical kind of generalization. The chapter first examines generalizations in qualitative research before discussing the use of 2 x 2 tables to present set-theoretic generalizations. It then explains a well-known problem in statistical analysis involving the so-called “perfect predictors” and concludes with an assessment of the statistical significance of control variables.
Richard M. Goodwin
- Published in print:
- 1990
- Published Online:
- November 2003
- ISBN:
- 9780198283355
- eISBN:
- 9780191596315
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198283350.003.0007
- Subject:
- Economics and Finance, Macro- and Monetary Economics
Seeks to fuse the insights of Schumpeter and Keynes with the argument that market conditions force unrelated innovatory investment decisions to march in step. Aggregate demand matters, therefore, and ...
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Seeks to fuse the insights of Schumpeter and Keynes with the argument that market conditions force unrelated innovatory investment decisions to march in step. Aggregate demand matters, therefore, and the Kahn–Keynes multiplication of expansive and contractive demand provides the missing link. A model is developed in which a control variable stabilizes the system globally while allowing erratic motion locally. The model is extended so that for a 50‐year logistic with plausible parameters, higher output after each wave is guaranteed without assuming full employment. The model is extended to account for the influence of demand on investment.Less
Seeks to fuse the insights of Schumpeter and Keynes with the argument that market conditions force unrelated innovatory investment decisions to march in step. Aggregate demand matters, therefore, and the Kahn–Keynes multiplication of expansive and contractive demand provides the missing link. A model is developed in which a control variable stabilizes the system globally while allowing erratic motion locally. The model is extended so that for a 50‐year logistic with plausible parameters, higher output after each wave is guaranteed without assuming full employment. The model is extended to account for the influence of demand on investment.
Emery Roe and Paul R. Schulman
- Published in print:
- 2016
- Published Online:
- January 2017
- ISBN:
- 9780804793933
- eISBN:
- 9780804798624
- Item type:
- chapter
- Publisher:
- Stanford University Press
- DOI:
- 10.11126/stanford/9780804793933.003.0008
- Subject:
- Business and Management, Organization Studies
This chapter presents a detailed case study of control variables shared across critical infrastructures, the framework’s core concept, and what this implies for reliability and risk management at the ...
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This chapter presents a detailed case study of control variables shared across critical infrastructures, the framework’s core concept, and what this implies for reliability and risk management at the ICIS level. The input-output interconnectivity between water flows at the Banks pumps of the State Water Project near Tracy and electricity flows from the transmission grid to power those pumps is examined. Using a unique multiyear dataset and statistical analysis, the chapter shows how changes in electricity flows to the Banks pumps, an extremely important element in the State Water Project, affect changes in water flows through those pumps and what the implications are for resilience in each infrastructure’s operations.Less
This chapter presents a detailed case study of control variables shared across critical infrastructures, the framework’s core concept, and what this implies for reliability and risk management at the ICIS level. The input-output interconnectivity between water flows at the Banks pumps of the State Water Project near Tracy and electricity flows from the transmission grid to power those pumps is examined. Using a unique multiyear dataset and statistical analysis, the chapter shows how changes in electricity flows to the Banks pumps, an extremely important element in the State Water Project, affect changes in water flows through those pumps and what the implications are for resilience in each infrastructure’s operations.
- Published in print:
- 2005
- Published Online:
- March 2013
- ISBN:
- 9780226092805
- eISBN:
- 9780226092492
- Item type:
- chapter
- Publisher:
- University of Chicago Press
- DOI:
- 10.7208/chicago/9780226092492.003.0003
- Subject:
- Political Science, American Politics
This chapter examines domestic policy appeals. The chapter begins by presenting summary statistics for all nationally televised discretionary speeches from the presidential administrations of ...
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This chapter examines domestic policy appeals. The chapter begins by presenting summary statistics for all nationally televised discretionary speeches from the presidential administrations of Eisenhower through Clinton. These summary data establish for a broad range of policy areas basic patterns regarding the appeals, such as the popularity of the publicized initiatives. The chapter proceeds to develop analysis that compares policymaking when presidents do and do not go public, and that accounts for the possibility presidents go public strategically. In particular, it analyzes annual budgetary negotiations for a recurring set of policy issues during presidencies from the Eisenhower one through to the Clinton one. The policy issues range from the environment to economic development to drug control. For each issue and year, data are collected on whether the president made a public appeal as well as on a variety of control variables, including whether the issue was a presidential priority, media coverage of the issue, and, where available, citizens' policy preferences. Using these data the chapter estimates how political factors affect the likelihood of domestic policy appeals and the legislative influence obtained from them.Less
This chapter examines domestic policy appeals. The chapter begins by presenting summary statistics for all nationally televised discretionary speeches from the presidential administrations of Eisenhower through Clinton. These summary data establish for a broad range of policy areas basic patterns regarding the appeals, such as the popularity of the publicized initiatives. The chapter proceeds to develop analysis that compares policymaking when presidents do and do not go public, and that accounts for the possibility presidents go public strategically. In particular, it analyzes annual budgetary negotiations for a recurring set of policy issues during presidencies from the Eisenhower one through to the Clinton one. The policy issues range from the environment to economic development to drug control. For each issue and year, data are collected on whether the president made a public appeal as well as on a variety of control variables, including whether the issue was a presidential priority, media coverage of the issue, and, where available, citizens' policy preferences. Using these data the chapter estimates how political factors affect the likelihood of domestic policy appeals and the legislative influence obtained from them.
Emery Roe and Paul R. Schulman
- Published in print:
- 2016
- Published Online:
- January 2017
- ISBN:
- 9780804793933
- eISBN:
- 9780804798624
- Item type:
- chapter
- Publisher:
- Stanford University Press
- DOI:
- 10.11126/stanford/9780804793933.003.0004
- Subject:
- Business and Management, Organization Studies
This chapter, and the next two, sets out the framework of the book, focusing on reliability management of ICISs. It begins by demonstrating how the dominant conceptualizations of interconnectivity ...
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This chapter, and the next two, sets out the framework of the book, focusing on reliability management of ICISs. It begins by demonstrating how the dominant conceptualizations of interconnectivity fall short in their almost-exclusive attention on design and technology solutions to interinfrastructure failure and lack of attention to the management dimension necessary for real-time reliability. The chapter lays out the building blocks of a framework for reliable (and safe) operations at the ICIS level: the design-management continuum for reliability management, simple models and definitions of systems of one or more infrastructures, the pivotal concept of control variables shared by infrastructures, types of system resilience and their definition within an ICIS, four basic types of interconnectivity configurations and their shift points, the specific dimensions of interconnectivity, and management of latent interconnectivity. Examples are drawn throughout from the case study.Less
This chapter, and the next two, sets out the framework of the book, focusing on reliability management of ICISs. It begins by demonstrating how the dominant conceptualizations of interconnectivity fall short in their almost-exclusive attention on design and technology solutions to interinfrastructure failure and lack of attention to the management dimension necessary for real-time reliability. The chapter lays out the building blocks of a framework for reliable (and safe) operations at the ICIS level: the design-management continuum for reliability management, simple models and definitions of systems of one or more infrastructures, the pivotal concept of control variables shared by infrastructures, types of system resilience and their definition within an ICIS, four basic types of interconnectivity configurations and their shift points, the specific dimensions of interconnectivity, and management of latent interconnectivity. Examples are drawn throughout from the case study.
Peter Miksza and Kenneth Elpus
- Published in print:
- 2018
- Published Online:
- March 2018
- ISBN:
- 9780199391905
- eISBN:
- 9780199391943
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780199391905.003.0010
- Subject:
- Music, Theory, Analysis, Composition, Performing Practice/Studies
This chapter presents the logic and technique of analyzing data using simple linear regression and multiple linear regression. Regression is a remarkably versatile statistical procedure that can be ...
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This chapter presents the logic and technique of analyzing data using simple linear regression and multiple linear regression. Regression is a remarkably versatile statistical procedure that can be used not only to understand whether or not variables are related to each other (as in correlation) but also for providing estimates of the direction of the relationship and of the degree to which the variables are related. Beginning with a simple bivariate case analyzing a single predictor on a single outcome, the flexibility and ability for regression to analyze increasingly complex data, including binary outcomes, is discussed. Particular attention is paid to the ability of regression to be used to estimate the effect of a predictor on an outcome while statistically “controlling” for the values of other observed variables.Less
This chapter presents the logic and technique of analyzing data using simple linear regression and multiple linear regression. Regression is a remarkably versatile statistical procedure that can be used not only to understand whether or not variables are related to each other (as in correlation) but also for providing estimates of the direction of the relationship and of the degree to which the variables are related. Beginning with a simple bivariate case analyzing a single predictor on a single outcome, the flexibility and ability for regression to analyze increasingly complex data, including binary outcomes, is discussed. Particular attention is paid to the ability of regression to be used to estimate the effect of a predictor on an outcome while statistically “controlling” for the values of other observed variables.
Ronald K. Pearson
- Published in print:
- 1999
- Published Online:
- November 2020
- ISBN:
- 9780195121988
- eISBN:
- 9780197561294
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780195121988.003.0003
- Subject:
- Computer Science, Mathematical Theory of Computation
This book deals with the relationship between the qualitative behavior and the mathematical structure of nonlinear, discrete-time dynamic models. The motivation for ...
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This book deals with the relationship between the qualitative behavior and the mathematical structure of nonlinear, discrete-time dynamic models. The motivation for this treatment is the need for such models in computerized, model-based control of complex systems like industrial manufacturing processes or internal combustion engines. Historically, linear models have provided a solid foundation for control system design, but as control requirements become more stringent and operating ranges become wider, linear models eventually become inadequate. In such cases, nonlinear models are required, and the development of these models raises a number of important new issues. One of these issues is that of model structure selection, which manifests itself in different ways, depending on the approach taken to model development (this point is examined in some detail in Sec. 1.1). This choice is critically important since it implicitly defines the range of qualitative behavior the final model can exhibit, for better or worse. The primary objective of this book is to provide insights that will be helpful in making this model structure choice wisely. One fundamental difficulty in making this choice is the notion of nonlinearity itself: the class of “nonlinear models” is defined precisely by the crucial quality they lack. Further, since much of our intuition comes from the study of linear dynamic models (heavily exploiting this crucial quality), it is not clear how to proceed in attempting to understand nonlinear dynamic phenomena. Because these phenomena are often counterintuitive, one possible approach is to follow the lead taken in mathematics books like Counterexamples in Topology (Steen and Seebach, 1978). These books present detailed discussions of counterintuitive examples, focusing on the existence and role of certain critical working assumptions that are required for the “expected results” to hold, but that are not satisfied in the example under consideration. As a specific illustration, the Central Limit Theorem in probability theory states, roughly, that “sums of N independent random variables tend toward Gaussian limits as TV grows large.” The book Counterexamples in Probability (Stoyanov, 1987) has an entire chapter (67 pages) entitled “Limit Theorems” devoted to achieving a more precise understanding of the Central Limit Theorem and closely related theorems, and to clarifying what these theorems do and do not say.
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This book deals with the relationship between the qualitative behavior and the mathematical structure of nonlinear, discrete-time dynamic models. The motivation for this treatment is the need for such models in computerized, model-based control of complex systems like industrial manufacturing processes or internal combustion engines. Historically, linear models have provided a solid foundation for control system design, but as control requirements become more stringent and operating ranges become wider, linear models eventually become inadequate. In such cases, nonlinear models are required, and the development of these models raises a number of important new issues. One of these issues is that of model structure selection, which manifests itself in different ways, depending on the approach taken to model development (this point is examined in some detail in Sec. 1.1). This choice is critically important since it implicitly defines the range of qualitative behavior the final model can exhibit, for better or worse. The primary objective of this book is to provide insights that will be helpful in making this model structure choice wisely. One fundamental difficulty in making this choice is the notion of nonlinearity itself: the class of “nonlinear models” is defined precisely by the crucial quality they lack. Further, since much of our intuition comes from the study of linear dynamic models (heavily exploiting this crucial quality), it is not clear how to proceed in attempting to understand nonlinear dynamic phenomena. Because these phenomena are often counterintuitive, one possible approach is to follow the lead taken in mathematics books like Counterexamples in Topology (Steen and Seebach, 1978). These books present detailed discussions of counterintuitive examples, focusing on the existence and role of certain critical working assumptions that are required for the “expected results” to hold, but that are not satisfied in the example under consideration. As a specific illustration, the Central Limit Theorem in probability theory states, roughly, that “sums of N independent random variables tend toward Gaussian limits as TV grows large.” The book Counterexamples in Probability (Stoyanov, 1987) has an entire chapter (67 pages) entitled “Limit Theorems” devoted to achieving a more precise understanding of the Central Limit Theorem and closely related theorems, and to clarifying what these theorems do and do not say.
