Rein Taagepera
- Published in print:
- 2008
- Published Online:
- September 2008
- ISBN:
- 9780199534661
- eISBN:
- 9780191715921
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199534661.003.0001
- Subject:
- Political Science, Comparative Politics, Political Economy
Science is not only about the empirical “What is?” but also very much about the conceptual “How should it be on logical grounds?” Statistical approaches are essentially descriptive, while ...
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Science is not only about the empirical “What is?” but also very much about the conceptual “How should it be on logical grounds?” Statistical approaches are essentially descriptive, while quantitatively formulated logical models are essentially predictive in an explanatory way. Social sciences have overemphasized statistical data analysis, often limiting their logical models to prediction of the direction of effect, oblivious of its quantitative extent. A better balance of methods is possible and will make social sciences more relevant to society. This book is about going beyond regression and other statistical approaches, and also about improving their use.Less
Science is not only about the empirical “What is?” but also very much about the conceptual “How should it be on logical grounds?” Statistical approaches are essentially descriptive, while quantitatively formulated logical models are essentially predictive in an explanatory way. Social sciences have overemphasized statistical data analysis, often limiting their logical models to prediction of the direction of effect, oblivious of its quantitative extent. A better balance of methods is possible and will make social sciences more relevant to society. This book is about going beyond regression and other statistical approaches, and also about improving their use.
Dawn Brancati
- Published in print:
- 2008
- Published Online:
- January 2009
- ISBN:
- 9780199549009
- eISBN:
- 9780191720307
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199549009.003.0006
- Subject:
- Political Science, Comparative Politics, International Relations and Politics
Through statistical analysis, this chapter tests the generalizability of its argument regarding the effect of decentralization and regional parties on ethnic conflict and secessionism. The analysis ...
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Through statistical analysis, this chapter tests the generalizability of its argument regarding the effect of decentralization and regional parties on ethnic conflict and secessionism. The analysis draws on an original dataset, known as the constituency‐level elections (CLE) dataset, which provides election results for national (lower and upper house) elections and regional elections from 1945 to 2002 at the constituency‐level of government, to measure systematically the electoral strength of regional parties. To measure ethnic conflict and secessionism, the chapter draws on the Minorities at Risk dataset (1985–2000), which it corrects for selection bias. The analysis supports the chapter's argument that decentralization diminishes the strength of ethnic conflict and secessionism, while regional parties intensify it, and that the ability of decentralization to reduce conflict decreases as the electoral strength of regional parties increases. The chapter controls for a number of factors that may affect regional party strength (e.g. ethnolinguistic heterogeneity, economic development, democracy, and the executive and electoral system). The chapter also uses instrumental variable regression in this chapter to disentangle the causal relationships between decentralization, regional parties, and ethnic conflict and secessionism.Less
Through statistical analysis, this chapter tests the generalizability of its argument regarding the effect of decentralization and regional parties on ethnic conflict and secessionism. The analysis draws on an original dataset, known as the constituency‐level elections (CLE) dataset, which provides election results for national (lower and upper house) elections and regional elections from 1945 to 2002 at the constituency‐level of government, to measure systematically the electoral strength of regional parties. To measure ethnic conflict and secessionism, the chapter draws on the Minorities at Risk dataset (1985–2000), which it corrects for selection bias. The analysis supports the chapter's argument that decentralization diminishes the strength of ethnic conflict and secessionism, while regional parties intensify it, and that the ability of decentralization to reduce conflict decreases as the electoral strength of regional parties increases. The chapter controls for a number of factors that may affect regional party strength (e.g. ethnolinguistic heterogeneity, economic development, democracy, and the executive and electoral system). The chapter also uses instrumental variable regression in this chapter to disentangle the causal relationships between decentralization, regional parties, and ethnic conflict and secessionism.
Dawn Brancati
- Published in print:
- 2008
- Published Online:
- January 2009
- ISBN:
- 9780199549009
- eISBN:
- 9780191720307
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199549009.003.0007
- Subject:
- Political Science, Comparative Politics, International Relations and Politics
This chapter tests the generalizability of the book's argument regarding the effect of decentralization on the electoral strength of regional parties using statistical analysis. The analysis also ...
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This chapter tests the generalizability of the book's argument regarding the effect of decentralization on the electoral strength of regional parties using statistical analysis. The analysis also draws on the CLE dataset. The analysis shows that political decentralization increases the strength of regional parties and that extensive forms of decentralization strengthen regional parties more than limited forms. The analysis also finds that specific features of decentralization strengthen regional parties more than others, such as large regions and nonconcurrent national and regional elections. Fiscal decentralization, in contrast, as well as having a large number of regions, has the opposite effect. The chapter controls for a number of factors typically associated with conflict and secessionism (e.g. ethnolinguistic heterogeneity, democracy, and the executive and electoral system). The chapter also uses instrumental variable regression in this chapter to disentangle the causal relationships between decentralization and regional parties.Less
This chapter tests the generalizability of the book's argument regarding the effect of decentralization on the electoral strength of regional parties using statistical analysis. The analysis also draws on the CLE dataset. The analysis shows that political decentralization increases the strength of regional parties and that extensive forms of decentralization strengthen regional parties more than limited forms. The analysis also finds that specific features of decentralization strengthen regional parties more than others, such as large regions and nonconcurrent national and regional elections. Fiscal decentralization, in contrast, as well as having a large number of regions, has the opposite effect. The chapter controls for a number of factors typically associated with conflict and secessionism (e.g. ethnolinguistic heterogeneity, democracy, and the executive and electoral system). The chapter also uses instrumental variable regression in this chapter to disentangle the causal relationships between decentralization and regional parties.
Sylvain Baillet
- Published in print:
- 2010
- Published Online:
- September 2010
- ISBN:
- 9780195307238
- eISBN:
- 9780199863990
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195307238.003.0005
- Subject:
- Neuroscience, Behavioral Neuroscience, Techniques
This chapter reviews the statistical tools available for the analysis of distributed activation maps defined either on the 2D cortical surface or throughout the 3D brain volume. Statistical analysis ...
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This chapter reviews the statistical tools available for the analysis of distributed activation maps defined either on the 2D cortical surface or throughout the 3D brain volume. Statistical analysis of MEG data bears a great resemblance to the analysis of functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) activation maps, therefore much of the methodology can be borrowed or adapted from the functional neuroimaging literature. In particular, the General Linear Modeling (GLM) approach, where the MEG data are first mapped into brain space, and then fitted to a univariate or multivariate model at each surface or volume element, is described. A desired contrast of the estimated parameters produces a statistical map, which is then thresholded for evidence of an experimental effect. The chapter also describes several approaches that can produce corrected thresholds and control for false positives: Bonferroni, Random Field Theory (RFT), permutation tests, and False Discovery error Rate (FDR).Less
This chapter reviews the statistical tools available for the analysis of distributed activation maps defined either on the 2D cortical surface or throughout the 3D brain volume. Statistical analysis of MEG data bears a great resemblance to the analysis of functional magnetic resonance imaging (fMRI) or positron emission tomography (PET) activation maps, therefore much of the methodology can be borrowed or adapted from the functional neuroimaging literature. In particular, the General Linear Modeling (GLM) approach, where the MEG data are first mapped into brain space, and then fitted to a univariate or multivariate model at each surface or volume element, is described. A desired contrast of the estimated parameters produces a statistical map, which is then thresholded for evidence of an experimental effect. The chapter also describes several approaches that can produce corrected thresholds and control for false positives: Bonferroni, Random Field Theory (RFT), permutation tests, and False Discovery error Rate (FDR).
Patrick S. Sullivan, Matthew T. McKenna, Lance A. Waller, G. David Williamson, and Lisa M. Lee
- Published in print:
- 2010
- Published Online:
- September 2010
- ISBN:
- 9780195372922
- eISBN:
- 9780199866090
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195372922.003.0006
- Subject:
- Public Health and Epidemiology, Public Health
This chapter includes several new sections on inferential analysis of public health surveillance data. It provides a guide using a thoughtful approach to complex statistical analyses for the data ...
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This chapter includes several new sections on inferential analysis of public health surveillance data. It provides a guide using a thoughtful approach to complex statistical analyses for the data rich environment in which surveillance practitioners find themselves. Included in this chapter are sections on trend analyses, survival (or time-to-event) analyses, analyses of associations in cross sectional data, analyses of data from complex survey designs, aberration detection analysis, detection of clustering, and mapping and geo-analyses. New examples from actual public health surveillance systems are used to demonstrate analytic techniques.Less
This chapter includes several new sections on inferential analysis of public health surveillance data. It provides a guide using a thoughtful approach to complex statistical analyses for the data rich environment in which surveillance practitioners find themselves. Included in this chapter are sections on trend analyses, survival (or time-to-event) analyses, analyses of associations in cross sectional data, analyses of data from complex survey designs, aberration detection analysis, detection of clustering, and mapping and geo-analyses. New examples from actual public health surveillance systems are used to demonstrate analytic techniques.
Rein Taagepera
- Published in print:
- 2008
- Published Online:
- September 2008
- ISBN:
- 9780199534661
- eISBN:
- 9780191715921
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199534661.003.0016
- Subject:
- Political Science, Comparative Politics, Political Economy
The results of existing statistical analysis can sometimes be used to estimate the parameters in quantitatively predictive logical models. This is important, because it expands the value of ...
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The results of existing statistical analysis can sometimes be used to estimate the parameters in quantitatively predictive logical models. This is important, because it expands the value of previously published work in social sciences. Inferring logical model parameters in this way, however, may require more involved mathematics than direct testing.Less
The results of existing statistical analysis can sometimes be used to estimate the parameters in quantitatively predictive logical models. This is important, because it expands the value of previously published work in social sciences. Inferring logical model parameters in this way, however, may require more involved mathematics than direct testing.
Guo Shenyang
- Published in print:
- 2009
- Published Online:
- January 2010
- ISBN:
- 9780195337518
- eISBN:
- 9780199864256
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195337518.003.0001
- Subject:
- Social Work, Research and Evaluation
This introductory chapter begins with a description of survival analysis. It then discusses why and when survival analysis is needed, and the significance of conducting survival analysis. An overview ...
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This introductory chapter begins with a description of survival analysis. It then discusses why and when survival analysis is needed, and the significance of conducting survival analysis. An overview of the subsequent chapters is presented.Less
This introductory chapter begins with a description of survival analysis. It then discusses why and when survival analysis is needed, and the significance of conducting survival analysis. An overview of the subsequent chapters is presented.
Warren E. Miller
- Published in print:
- 1998
- Published Online:
- November 2003
- ISBN:
- 9780198294719
- eISBN:
- 9780191599361
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198294719.003.0011
- Subject:
- Political Science, Reference
Provides a dissemination of the differences between ‘old’ and ‘new’ political behaviour research in data analysis, specifically in voting and the study of democracy. The ‘new’ and the ‘old’ differ in ...
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Provides a dissemination of the differences between ‘old’ and ‘new’ political behaviour research in data analysis, specifically in voting and the study of democracy. The ‘new’ and the ‘old’ differ in methodology, as analysis of large data sets has become more sophisticated and methods of statistical analysis have developed. The new and the old also differ in the range of analysts with access to these methodological advances and data sets. Whilst we have learnt much about voters and the electorate, a new definition of citizenship is emerging. Do the old and the new help us understand it?Less
Provides a dissemination of the differences between ‘old’ and ‘new’ political behaviour research in data analysis, specifically in voting and the study of democracy. The ‘new’ and the ‘old’ differ in methodology, as analysis of large data sets has become more sophisticated and methods of statistical analysis have developed. The new and the old also differ in the range of analysts with access to these methodological advances and data sets. Whilst we have learnt much about voters and the electorate, a new definition of citizenship is emerging. Do the old and the new help us understand it?
Outi Monni and Sampsa Hautaniemi
- Published in print:
- 2009
- Published Online:
- September 2009
- ISBN:
- 9780199532872
- eISBN:
- 9780191714467
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199532872.003.0004
- Subject:
- Mathematics, Probability / Statistics, Biostatistics
This chapter discusses methods to measure and integrate microarray-based copy number and gene expression data. It is well-known that gene copy number alterations are a key factor in cancer ...
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This chapter discusses methods to measure and integrate microarray-based copy number and gene expression data. It is well-known that gene copy number alterations are a key factor in cancer development and progression. Especially gene amplification is known to be important mechanism for the cancer cells to increase expression of cellular proto-oncogenes. Thus, systematic identification of genes with elevated copy number and gene expression levels is important in discovery of potential therapeutic target genes in human cancers. Here, the chapter reviews the main methods to measure genome-wide copy number and gene expression levels. The main aim is to describe systematic computational data analysis approaches for integrating high-throughput copy number and gene expression data.Less
This chapter discusses methods to measure and integrate microarray-based copy number and gene expression data. It is well-known that gene copy number alterations are a key factor in cancer development and progression. Especially gene amplification is known to be important mechanism for the cancer cells to increase expression of cellular proto-oncogenes. Thus, systematic identification of genes with elevated copy number and gene expression levels is important in discovery of potential therapeutic target genes in human cancers. Here, the chapter reviews the main methods to measure genome-wide copy number and gene expression levels. The main aim is to describe systematic computational data analysis approaches for integrating high-throughput copy number and gene expression data.
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.
Judith Feder and Larry Levitt
- Published in print:
- 2005
- Published Online:
- September 2009
- ISBN:
- 9780195149289
- eISBN:
- 9780199865130
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195149289.003.0011
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
This chapter examines the role of statistical analysis in health reform, or the making of the nation's policies on health insurance coverage. It begins with an overview of the policy process in ...
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This chapter examines the role of statistical analysis in health reform, or the making of the nation's policies on health insurance coverage. It begins with an overview of the policy process in general, describing three critical stages: identifying the policy problem, analyzing possible solutions, and debating and deciding on political action. Following a brief discussion of the meaning of health reform and the policy and political process affecting it in the past decade, the chapter describes the role of data and statistics in each phase of the process. It concludes with lessons from that experience for collection and analysis of statistics and the scholars and analysts who undertake it.Less
This chapter examines the role of statistical analysis in health reform, or the making of the nation's policies on health insurance coverage. It begins with an overview of the policy process in general, describing three critical stages: identifying the policy problem, analyzing possible solutions, and debating and deciding on political action. Following a brief discussion of the meaning of health reform and the policy and political process affecting it in the past decade, the chapter describes the role of data and statistics in each phase of the process. It concludes with lessons from that experience for collection and analysis of statistics and the scholars and analysts who undertake it.
Benjamin D Koen
- Published in print:
- 2008
- Published Online:
- January 2009
- ISBN:
- 9780195367744
- eISBN:
- 9780199867295
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195367744.003.0006
- Subject:
- Music, Ethnomusicology, World Music
Chapter 6 details a power-laden and affective symbol, metaphor, poetic and musical sign in Pamir culture that is central to concepts of health and healing. This is done through poetic and musical ...
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Chapter 6 details a power-laden and affective symbol, metaphor, poetic and musical sign in Pamir culture that is central to concepts of health and healing. This is done through poetic and musical analysis that shows the pervasiveness and centrality of this local sign, which is manifest in local belief, the natural and built environment, and the musical and poetic structure of maddâh devotional music. A physiological experiment that was conducted in the context of maddâh ritual performance is presented and statistical data analyzed and interpreted showing a significant downward modulation of systolic blood pressure at p-value of .0003. Further, the culture-transcendent aspects are applied in another research project in the U.S. among a culturally diverse group of people (ages 18-85) where participants learn to create health, healing, or transformation through practices of music, sound, vocalization, and meditation. The GAP — Guided Attention Practice is presented as part of this latter research project.Less
Chapter 6 details a power-laden and affective symbol, metaphor, poetic and musical sign in Pamir culture that is central to concepts of health and healing. This is done through poetic and musical analysis that shows the pervasiveness and centrality of this local sign, which is manifest in local belief, the natural and built environment, and the musical and poetic structure of maddâh devotional music. A physiological experiment that was conducted in the context of maddâh ritual performance is presented and statistical data analyzed and interpreted showing a significant downward modulation of systolic blood pressure at p-value of .0003. Further, the culture-transcendent aspects are applied in another research project in the U.S. among a culturally diverse group of people (ages 18-85) where participants learn to create health, healing, or transformation through practices of music, sound, vocalization, and meditation. The GAP — Guided Attention Practice is presented as part of this latter research project.
John A. Baron and Henrik Toft Sørensen
- Published in print:
- 2010
- Published Online:
- May 2010
- ISBN:
- 9780199239481
- eISBN:
- 9780191716973
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199239481.003.024
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
The term ‘clinical epidemiology’ has been interpreted in various ways, but the core of the discipline is generally understood to be the application of epidemiological and biostatistical techniques to ...
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The term ‘clinical epidemiology’ has been interpreted in various ways, but the core of the discipline is generally understood to be the application of epidemiological and biostatistical techniques to clinical problems. In contrast to traditional etiologic epidemiology, which focuses on the determinants of disease on a general population level, clinical epidemiology studies the determinants of the outcomes of disease and illness. This chapter presents an approach for teaching clinical epidemiology covering teaching and learning objectives, teaching contents, and assessing students' achievements.Less
The term ‘clinical epidemiology’ has been interpreted in various ways, but the core of the discipline is generally understood to be the application of epidemiological and biostatistical techniques to clinical problems. In contrast to traditional etiologic epidemiology, which focuses on the determinants of disease on a general population level, clinical epidemiology studies the determinants of the outcomes of disease and illness. This chapter presents an approach for teaching clinical epidemiology covering teaching and learning objectives, teaching contents, and assessing students' achievements.
Ezra Susser, Sharon Schwartz, Alfredo Morabia, and Evelyn Bromet
- Published in print:
- 2006
- Published Online:
- September 2009
- ISBN:
- 9780195101812
- eISBN:
- 9780199864096
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195101812.001.0001
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
Searching for the causes of mental disorders is as exciting as it is complex. The relationship between pathophysiology and its overt manifestations is exceedingly intricate, and often the causes of a ...
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Searching for the causes of mental disorders is as exciting as it is complex. The relationship between pathophysiology and its overt manifestations is exceedingly intricate, and often the causes of a disorder are elusive at best. This book provides a resource for anyone trying to track these causes. Uniting theory and practice and rather than attempting to review the descriptive epidemiology of mental disorders, this book gives a dynamic exposition of the thinking and techniques used to establish it. The book starts out by tracing the brief history of psychiatric epidemiology, then describes the study of risk factors as causes of mental disorders. Subsequent sections discuss approaches to investigation of biologic, genetic, or social causes and the statistical analysis of study results. The book concludes by following some of the problems involved in the search for genetic causes of mental disorders, and more complex casual relationships.Less
Searching for the causes of mental disorders is as exciting as it is complex. The relationship between pathophysiology and its overt manifestations is exceedingly intricate, and often the causes of a disorder are elusive at best. This book provides a resource for anyone trying to track these causes. Uniting theory and practice and rather than attempting to review the descriptive epidemiology of mental disorders, this book gives a dynamic exposition of the thinking and techniques used to establish it. The book starts out by tracing the brief history of psychiatric epidemiology, then describes the study of risk factors as causes of mental disorders. Subsequent sections discuss approaches to investigation of biologic, genetic, or social causes and the statistical analysis of study results. The book concludes by following some of the problems involved in the search for genetic causes of mental disorders, and more complex casual relationships.
Lorene M. Nelson, Caroline M. Tanner, Stephen K. Van Den Eeden, and Valerie M. McGuire
- Published in print:
- 2004
- Published Online:
- September 2009
- ISBN:
- 9780195133790
- eISBN:
- 9780199863730
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195133790.003.03
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
This chapter focuses on measurement of exposures, risk factors, prognostic factors, in addition to statistical analysis methods for assessing the association of those factors with disease outcomes. ...
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This chapter focuses on measurement of exposures, risk factors, prognostic factors, in addition to statistical analysis methods for assessing the association of those factors with disease outcomes. Separate sections of the chapter are devoted to disease severity measures, general health and quality of life measures, and functional status measures, as well as methods of data collection (medical record abstraction, questionnaires, biological measures). The middle section of the chapter addresses issues regarding measurement error, including types of measurement error (random error, systematic error), misclassification (differential, nondifferential), and the effects of these errors on study validity. Methods for assessing and improving data reliability and validity are discussed. The last section of the chapter provides a concise but informative overview of statistical techniques for analyzing epidemiologic data.Less
This chapter focuses on measurement of exposures, risk factors, prognostic factors, in addition to statistical analysis methods for assessing the association of those factors with disease outcomes. Separate sections of the chapter are devoted to disease severity measures, general health and quality of life measures, and functional status measures, as well as methods of data collection (medical record abstraction, questionnaires, biological measures). The middle section of the chapter addresses issues regarding measurement error, including types of measurement error (random error, systematic error), misclassification (differential, nondifferential), and the effects of these errors on study validity. Methods for assessing and improving data reliability and validity are discussed. The last section of the chapter provides a concise but informative overview of statistical techniques for analyzing epidemiologic data.
Richard Hoefer
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780199735198
- eISBN:
- 9780199918560
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199735198.003.0006
- Subject:
- Social Work, Research and Evaluation
This chapter devotes itself to describing and then applying quantitative methods to evaluate policy outcomes. This chapter provides an overview of quantitative methods, particularly from an ...
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This chapter devotes itself to describing and then applying quantitative methods to evaluate policy outcomes. This chapter provides an overview of quantitative methods, particularly from an evaluation perspective. We then use two examples of evaluations of aspects of the welfare reform law to apply our understanding of the strengths and limitations of this style of analysis. We discuss several aspects of quantitative methods, including secondary data analysis, survey research, standardized measures, design issues and inferential statistical analysis.Less
This chapter devotes itself to describing and then applying quantitative methods to evaluate policy outcomes. This chapter provides an overview of quantitative methods, particularly from an evaluation perspective. We then use two examples of evaluations of aspects of the welfare reform law to apply our understanding of the strengths and limitations of this style of analysis. We discuss several aspects of quantitative methods, including secondary data analysis, survey research, standardized measures, design issues and inferential statistical analysis.
Gregory A. Daddis
- Published in print:
- 2011
- Published Online:
- September 2011
- ISBN:
- 9780199746873
- eISBN:
- 9780199897179
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199746873.003.0001
- Subject:
- Political Science, International Relations and Politics
This opening chapter, covering 1955–1965, provides an overview of contemporary counterinsurgency theory and analyzes the army’s counterinsurgency doctrine. It also illustrates the increasing ...
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This opening chapter, covering 1955–1965, provides an overview of contemporary counterinsurgency theory and analyzes the army’s counterinsurgency doctrine. It also illustrates the increasing influence of statistical analysis in the Department of Defense after Robert S. McNamara’s assumption of duties as the Secretary of Defense. The U.S. Army officer entering combat in 1965 seemingly could draw upon a wealth of counterinsurgency information—unless he was looking for how to measure progress and effectiveness. In the absence of doctrinal suggestions on how to develop metrics of progress and effectiveness, MACV, under pressure from McNamara, turned to computers and statistical analysis to help solve their measurement problems.Less
This opening chapter, covering 1955–1965, provides an overview of contemporary counterinsurgency theory and analyzes the army’s counterinsurgency doctrine. It also illustrates the increasing influence of statistical analysis in the Department of Defense after Robert S. McNamara’s assumption of duties as the Secretary of Defense. The U.S. Army officer entering combat in 1965 seemingly could draw upon a wealth of counterinsurgency information—unless he was looking for how to measure progress and effectiveness. In the absence of doctrinal suggestions on how to develop metrics of progress and effectiveness, MACV, under pressure from McNamara, turned to computers and statistical analysis to help solve their measurement problems.
EDWARD L. GLAESER
- Published in print:
- 2008
- Published Online:
- October 2011
- ISBN:
- 9780195328318
- eISBN:
- 9780199851768
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195328318.003.0013
- Subject:
- Economics and Finance, Economic History
The notion of incentives is a concept rarely undermined in the context of economics because this plays no small part in explaining behavior: the occurrences of a certain act or behavior would rise if ...
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The notion of incentives is a concept rarely undermined in the context of economics because this plays no small part in explaining behavior: the occurrences of a certain act or behavior would rise if the returns attributed to this act would rise as well. It is believed that presenting the right incentives would bring out the best results and behaviors from human beings. However, economic models are intended to not account for the individual selflessness of those who come up with said models. Although there exists a small collection of literature that would assert otherwise and which investigates how researcher initiative affects statistical results, the only significant impact is the uncertainty regarding the significance of the results. In this chapter, ten points are introduced regarding issues of researcher incentives and statistical analysis that focus on how researcher initiative should be accepted as a norm.Less
The notion of incentives is a concept rarely undermined in the context of economics because this plays no small part in explaining behavior: the occurrences of a certain act or behavior would rise if the returns attributed to this act would rise as well. It is believed that presenting the right incentives would bring out the best results and behaviors from human beings. However, economic models are intended to not account for the individual selflessness of those who come up with said models. Although there exists a small collection of literature that would assert otherwise and which investigates how researcher initiative affects statistical results, the only significant impact is the uncertainty regarding the significance of the results. In this chapter, ten points are introduced regarding issues of researcher incentives and statistical analysis that focus on how researcher initiative should be accepted as a norm.
Marc J. Lajeunesse
- Published in print:
- 2013
- Published Online:
- October 2017
- ISBN:
- 9780691137285
- eISBN:
- 9781400846184
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691137285.003.0022
- Subject:
- Biology, Ecology
The common justification for meta-analysis is the increased statistical power to detect effects over what is obtained from individual studies. For ecologists and evolutionary biologists, the ...
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The common justification for meta-analysis is the increased statistical power to detect effects over what is obtained from individual studies. For ecologists and evolutionary biologists, the statistical power of meta-analysis is important because effect sizes are usually relatively small in these fields, and experimental sample sizes are often limited for logistic reasons. Consequently, many studies lack sufficient power to detect an experimental effect should it exist. This chapter provides a brief overview of the factors that determine the statistical power of meta-analysis. It presents statistics for calculating the power of pooled effect sizes to evaluate nonzero effects and the power of within- and between-study homogeneity tests. It also surveys ways to improve the statistical power of meta-analysis, and ends with a discussion on the overall utility of power statistics for meta-analysis.Less
The common justification for meta-analysis is the increased statistical power to detect effects over what is obtained from individual studies. For ecologists and evolutionary biologists, the statistical power of meta-analysis is important because effect sizes are usually relatively small in these fields, and experimental sample sizes are often limited for logistic reasons. Consequently, many studies lack sufficient power to detect an experimental effect should it exist. This chapter provides a brief overview of the factors that determine the statistical power of meta-analysis. It presents statistics for calculating the power of pooled effect sizes to evaluate nonzero effects and the power of within- and between-study homogeneity tests. It also surveys ways to improve the statistical power of meta-analysis, and ends with a discussion on the overall utility of power statistics for meta-analysis.
Timothy J. Fahey and Alan K. Knapp
- Published in print:
- 2007
- Published Online:
- September 2007
- ISBN:
- 9780195168662
- eISBN:
- 9780199790128
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195168662.003.0001
- Subject:
- Biology, Ecology
A uniform guiding set of principles and standards is needed to provide conformity of primary production measurements even though specific procedures must be adapted to accommodate the unique features ...
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A uniform guiding set of principles and standards is needed to provide conformity of primary production measurements even though specific procedures must be adapted to accommodate the unique features of individual sites, varying funding, technological advances, and differing objectives of particular projects. This chapter provides an overview of these guiding principles, including working definitions of primary production, considerations about site selection, problems of temporal and spatial variation, field and laboratory procedures, and statistical analysis.Less
A uniform guiding set of principles and standards is needed to provide conformity of primary production measurements even though specific procedures must be adapted to accommodate the unique features of individual sites, varying funding, technological advances, and differing objectives of particular projects. This chapter provides an overview of these guiding principles, including working definitions of primary production, considerations about site selection, problems of temporal and spatial variation, field and laboratory procedures, and statistical analysis.
