John G. Orme and Terri Combs-Orme
- Published in print:
- 2009
- Published Online:
- May 2009
- ISBN:
- 9780195329452
- eISBN:
- 9780199864812
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195329452.001.0001
- Subject:
- Social Work, Research and Evaluation
This book presents detailed discussions of regression models that are appropriate for discrete dependent variables, including dichotomous, polychotomous, ordered, and count variables. The major ...
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This book presents detailed discussions of regression models that are appropriate for discrete dependent variables, including dichotomous, polychotomous, ordered, and count variables. The major challenge in using such analyses lies in the nonlinear relationships between the independent and the dependent variables, which requires the use of link functions, and particularly in interpreting and presenting the findings in ways that are clear and meaningful. Clear and simple language guides the reader briefly through each step of the analysis and presentation of results to enhance understanding of the link function, the key to understanding these nonlinear relationships. Throughout the book provides detailed examples based on the data, and readers may work through these examples by accessing the data and output on the Internet at the companion Web site. In addition, each chapter provides a list of recommended additional readings and Internet content.Less
This book presents detailed discussions of regression models that are appropriate for discrete dependent variables, including dichotomous, polychotomous, ordered, and count variables. The major challenge in using such analyses lies in the nonlinear relationships between the independent and the dependent variables, which requires the use of link functions, and particularly in interpreting and presenting the findings in ways that are clear and meaningful. Clear and simple language guides the reader briefly through each step of the analysis and presentation of results to enhance understanding of the link function, the key to understanding these nonlinear relationships. Throughout the book provides detailed examples based on the data, and readers may work through these examples by accessing the data and output on the Internet at the companion Web site. In addition, each chapter provides a list of recommended additional readings and Internet content.
Patrick Dattalo
- Published in print:
- 2009
- Published Online:
- February 2010
- ISBN:
- 9780195378351
- eISBN:
- 9780199864645
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195378351.001.0001
- Subject:
- Social Work, Research and Evaluation
Random sampling (RS) and random assignment (RA) are considered by many researchers to be the definitive methodological procedures for maximizing external and internal validity. However, there is a ...
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Random sampling (RS) and random assignment (RA) are considered by many researchers to be the definitive methodological procedures for maximizing external and internal validity. However, there is a daunting list of legal, ethical, and practical barriers to implementing RS and RA. While there are no easy ways to overcome these barriers, social workers should seek and utilize strategies that minimize sampling and assignment bias. This book is a single source of a diverse set of tools that will maximize a study's validity when RS and RA are neither possible nor practical. Readers are guided in selecting and implementing an appropriate strategy, including exemplar sampling, sequential sampling, randomization tests, multiple imputation, mean-score logistic regression, partial randomization, constructed comparison groups, propensity scores, and instrumental variables methods. Each approach is presented in such a way as to highlight its underlying assumptions, implementation strategies, and strengths and weaknesses.Less
Random sampling (RS) and random assignment (RA) are considered by many researchers to be the definitive methodological procedures for maximizing external and internal validity. However, there is a daunting list of legal, ethical, and practical barriers to implementing RS and RA. While there are no easy ways to overcome these barriers, social workers should seek and utilize strategies that minimize sampling and assignment bias. This book is a single source of a diverse set of tools that will maximize a study's validity when RS and RA are neither possible nor practical. Readers are guided in selecting and implementing an appropriate strategy, including exemplar sampling, sequential sampling, randomization tests, multiple imputation, mean-score logistic regression, partial randomization, constructed comparison groups, propensity scores, and instrumental variables methods. Each approach is presented in such a way as to highlight its underlying assumptions, implementation strategies, and strengths and weaknesses.
Myoung-jae Lee
- Published in print:
- 2005
- Published Online:
- February 2006
- ISBN:
- 9780199267699
- eISBN:
- 9780191603044
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0199267693.001.0001
- Subject:
- Economics and Finance, Econometrics
This book brings to the fore recent advances in econometrics for treatment effect analysis. It aims to put together various economic treatment effect models in a coherent fashion, determine those ...
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This book brings to the fore recent advances in econometrics for treatment effect analysis. It aims to put together various economic treatment effect models in a coherent fashion, determine those that can be parameters of interest, and show how these can be identified and estimated under weak assumptions. The emphasis throughout the book is on semi- and non-parametric estimation methods, but traditional parametric approaches are also discussed. This book is ideally suited to researchers and graduate students with a basic knowledge of econometrics.Less
This book brings to the fore recent advances in econometrics for treatment effect analysis. It aims to put together various economic treatment effect models in a coherent fashion, determine those that can be parameters of interest, and show how these can be identified and estimated under weak assumptions. The emphasis throughout the book is on semi- and non-parametric estimation methods, but traditional parametric approaches are also discussed. This book is ideally suited to researchers and graduate students with a basic knowledge of econometrics.
David Sanders and Malcolm Brynin
- Published in print:
- 1998
- Published Online:
- November 2003
- ISBN:
- 9780198292371
- eISBN:
- 9780191600159
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198292376.003.0003
- Subject:
- Political Science, Reference
Setting out the basic regression model, and comparing the analytic efficacy of the OLS the model and its extension to logistic regression.
Setting out the basic regression model, and comparing the analytic efficacy of the OLS the model and its extension to logistic regression.
Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray
- Published in print:
- 2014
- Published Online:
- October 2017
- ISBN:
- 9780691151687
- eISBN:
- 9781400848911
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691151687.003.0008
- Subject:
- Physics, Particle Physics / Astrophysics / Cosmology
Regression is a special case of the general model fitting and selection procedures discussed in chapters 4 and 5. It can be defined as the relation between a dependent variable, y, and a set of ...
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Regression is a special case of the general model fitting and selection procedures discussed in chapters 4 and 5. It can be defined as the relation between a dependent variable, y, and a set of independent variables, x, that describes the expectation value of y given x: E [y¦x]. The purpose of obtaining a “best-fit” model ranges from scientific interest in the values of model parameters (e.g., the properties of dark energy, or of a newly discovered planet) to the predictive power of the resulting model (e.g., predicting solar activity). This chapter starts with a general formulation for regression, list various simplified cases, and then discusses methods that can be used to address them, such as regression for linear models, kernel regression, robust regression and nonlinear regression.Less
Regression is a special case of the general model fitting and selection procedures discussed in chapters 4 and 5. It can be defined as the relation between a dependent variable, y, and a set of independent variables, x, that describes the expectation value of y given x: E [y¦x]. The purpose of obtaining a “best-fit” model ranges from scientific interest in the values of model parameters (e.g., the properties of dark energy, or of a newly discovered planet) to the predictive power of the resulting model (e.g., predicting solar activity). This chapter starts with a general formulation for regression, list various simplified cases, and then discusses methods that can be used to address them, such as regression for linear models, kernel regression, robust regression and nonlinear regression.
Michio Hatanaka
- Published in print:
- 1996
- Published Online:
- November 2003
- ISBN:
- 9780198773535
- eISBN:
- 9780191596360
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198773536.003.0005
- Subject:
- Economics and Finance, Econometrics
This chapter describes the augmented Dickey-Fuller method for the case where Δxt is i.i.d. Difference stationarity is tested as the null hypothesis against trend stationarity, assuming that {xt} may ...
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This chapter describes the augmented Dickey-Fuller method for the case where Δxt is i.i.d. Difference stationarity is tested as the null hypothesis against trend stationarity, assuming that {xt} may possibly contain a linear deterministic trend. It also presents a method that does not work.Less
This chapter describes the augmented Dickey-Fuller method for the case where Δxt is i.i.d. Difference stationarity is tested as the null hypothesis against trend stationarity, assuming that {xt} may possibly contain a linear deterministic trend. It also presents a method that does not work.
Dirk U. Pfeiffer, Timothy P. Robinson, Mark Stevenson, Kim B. Stevens, David J. Rogers, and Archie C. A. Clements
- Published in print:
- 2008
- Published Online:
- September 2008
- ISBN:
- 9780198509882
- eISBN:
- 9780191709128
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198509882.003.0007
- Subject:
- Biology, Disease Ecology / Epidemiology
This chapter presents analytical techniques of regression and discrimination as a means of quantifying the effect of a set of explanatory variables on the spatial distribution of a particular ...
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This chapter presents analytical techniques of regression and discrimination as a means of quantifying the effect of a set of explanatory variables on the spatial distribution of a particular outcome. The chapter is divided into four sections. The first outlines the principles of linear, Poisson, and logistic regression in order to provide a background to the material presented later in the chapter. The second section discusses the options available to identify and account for spatial dependency in data when modelling. The third section reviews the common analytical techniques available for dealing with the three major spatial data types (area, point, and continuous data), and the fourth deals with discriminant analysis.Less
This chapter presents analytical techniques of regression and discrimination as a means of quantifying the effect of a set of explanatory variables on the spatial distribution of a particular outcome. The chapter is divided into four sections. The first outlines the principles of linear, Poisson, and logistic regression in order to provide a background to the material presented later in the chapter. The second section discusses the options available to identify and account for spatial dependency in data when modelling. The third section reviews the common analytical techniques available for dealing with the three major spatial data types (area, point, and continuous data), and the fourth deals with discriminant analysis.
Rein Taagepera
- Published in print:
- 2008
- Published Online:
- September 2008
- ISBN:
- 9780199534661
- eISBN:
- 9780191715921
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199534661.001.0001
- Subject:
- Political Science, Comparative Politics, Political Economy
Society needs more from social sciences than they have delivered. One reason for falling short is that social science methods have depended excessively on regression and other statistical approaches, ...
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Society needs more from social sciences than they have delivered. One reason for falling short is that social science methods have depended excessively on regression and other statistical approaches, neglecting logical model building. 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 predictive in an explanatory way. This book contrasts the predominance of statistics in today's social sciences with predominance of quantitatively predictive logical models in physics. It shows how to construct predictive models and gives social science examples. Only secondary school mathematics is often needed, plus willingness to simplify reality outrageously. The book also shows how to use and report basic statistical analysis in more informative ways, including emphasis on symmetric regression.Less
Society needs more from social sciences than they have delivered. One reason for falling short is that social science methods have depended excessively on regression and other statistical approaches, neglecting logical model building. 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 predictive in an explanatory way. This book contrasts the predominance of statistics in today's social sciences with predominance of quantitatively predictive logical models in physics. It shows how to construct predictive models and gives social science examples. Only secondary school mathematics is often needed, plus willingness to simplify reality outrageously. The book also shows how to use and report basic statistical analysis in more informative ways, including emphasis on symmetric regression.
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/9780199534661.003.0008
- Subject:
- Political Science, Comparative Politics, Political Economy
The outcomes of sociopolitical processes often depend on the factor in shortest supply; this makes multiplication of factors superior to their addition. Naïve linear regression may not detect all ...
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The outcomes of sociopolitical processes often depend on the factor in shortest supply; this makes multiplication of factors superior to their addition. Naïve linear regression may not detect all relationships between physical or social factors, because nonlinear relationships predominate. When x and y can conceptually take only positive values, the simplest conceptually acceptable fit is to connect y to x raised to a power (an exponent). This means that linear regression should be carried out on the logarithms of x and y, not on x and y. Various other constraints, such as forbidden areas and anchor points, lead to exponential, simple logistic, and more complex patterns.Less
The outcomes of sociopolitical processes often depend on the factor in shortest supply; this makes multiplication of factors superior to their addition. Naïve linear regression may not detect all relationships between physical or social factors, because nonlinear relationships predominate. When x and y can conceptually take only positive values, the simplest conceptually acceptable fit is to connect y to x raised to a power (an exponent). This means that linear regression should be carried out on the logarithms of x and y, not on x and y. Various other constraints, such as forbidden areas and anchor points, lead to exponential, simple logistic, and more complex patterns.
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/9780199534661.003.0012
- Subject:
- Political Science, Comparative Politics, Political Economy
When data are scattered, Ordinary Least-Squares (OLS) regression produces two quite distinct regression lines – one for y versus x and another for x versus y – and both may differ appreciably from ...
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When data are scattered, Ordinary Least-Squares (OLS) regression produces two quite distinct regression lines – one for y versus x and another for x versus y – and both may differ appreciably from what your eyes tell you. If data are scattered, OLS regression of y against x will disconfirm a model that actually fits; thus good statistics can be death of good science. Standard OLS equations cannot form a system of interlocking models, because they are unidirectional and nontransitive. Scale-independent symmetric regression avoids these problems of OLS, offering a single reversible and transitive equation.Less
When data are scattered, Ordinary Least-Squares (OLS) regression produces two quite distinct regression lines – one for y versus x and another for x versus y – and both may differ appreciably from what your eyes tell you. If data are scattered, OLS regression of y against x will disconfirm a model that actually fits; thus good statistics can be death of good science. Standard OLS equations cannot form a system of interlocking models, because they are unidirectional and nontransitive. Scale-independent symmetric regression avoids these problems of OLS, offering a single reversible and transitive equation.
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/9780199534661.003.0015
- Subject:
- Political Science, Comparative Politics, Political Economy
Take seriously the introductory advice by most introductory texts of statistics: graph the data and look at the graph so as to make sure linear regression makes sense from a statistical viewpoint. ...
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Take seriously the introductory advice by most introductory texts of statistics: graph the data and look at the graph so as to make sure linear regression makes sense from a statistical viewpoint. Graph more than the data – graph the entire conceptually allowed area and anchor points so as to make sure linear regression makes sense from a substantive viewpoint. If using linear regression, report not only the regression coefficients and the intercept but also the ranges, mean values, and medians of all input variables. While symmetric regression has advantages over OLS, fully reported symmetric regression is still merely regression.Less
Take seriously the introductory advice by most introductory texts of statistics: graph the data and look at the graph so as to make sure linear regression makes sense from a statistical viewpoint. Graph more than the data – graph the entire conceptually allowed area and anchor points so as to make sure linear regression makes sense from a substantive viewpoint. If using linear regression, report not only the regression coefficients and the intercept but also the ranges, mean values, and medians of all input variables. While symmetric regression has advantages over OLS, fully reported symmetric regression is still merely regression.
Thomas J. Stohlgren
- Published in print:
- 2006
- Published Online:
- September 2007
- ISBN:
- 9780195172331
- eISBN:
- 9780199790395
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195172331.003.0013
- Subject:
- Biology, Plant Sciences and Forestry
This chapter presents selected examples of non-spatial statistical modeling of plant diversity including correlation and simple regression, multiple regression, path coefficient analysis, canonical ...
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This chapter presents selected examples of non-spatial statistical modeling of plant diversity including correlation and simple regression, multiple regression, path coefficient analysis, canonical correspondence analysis, regression tree analysis, and logistic regression. It provides more details and considerations in their applications. Data from published studies are used to illustrate typical applications and interpretations of results to address several commonly asked questions from students. Suggestions and recommendations are also provided, including: having clear analysis objectives in mind, testing multiple techniques, and considering spatial analysis following non-spatial modeling of plant diversity.Less
This chapter presents selected examples of non-spatial statistical modeling of plant diversity including correlation and simple regression, multiple regression, path coefficient analysis, canonical correspondence analysis, regression tree analysis, and logistic regression. It provides more details and considerations in their applications. Data from published studies are used to illustrate typical applications and interpretations of results to address several commonly asked questions from students. Suggestions and recommendations are also provided, including: having clear analysis objectives in mind, testing multiple techniques, and considering spatial analysis following non-spatial modeling of plant diversity.
Paolo Mauro, Nathan Sussman, and Yishay Yafeh
- Published in print:
- 2006
- Published Online:
- May 2006
- ISBN:
- 9780199272693
- eISBN:
- 9780191603488
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0199272697.003.0005
- Subject:
- Economics and Finance, Financial Economics
This chapter presents a systematic attempt to identify the determinants of bond spreads. Using multivariate regression analysis, it relates emerging market bond spreads for 1870-1913 and for the ...
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This chapter presents a systematic attempt to identify the determinants of bond spreads. Using multivariate regression analysis, it relates emerging market bond spreads for 1870-1913 and for the modern period to macroeconomic variables and to categories of news articles. It is shown that past and present investors pay attention to both macroeconomic fundamentals and to information reflected in the news, especially regarding violent conflicts. News on institutional reforms rarely elicit investor response. Country-specific fundamentals and news play less of a role in determining bond spreads today than they did in the past.Less
This chapter presents a systematic attempt to identify the determinants of bond spreads. Using multivariate regression analysis, it relates emerging market bond spreads for 1870-1913 and for the modern period to macroeconomic variables and to categories of news articles. It is shown that past and present investors pay attention to both macroeconomic fundamentals and to information reflected in the news, especially regarding violent conflicts. News on institutional reforms rarely elicit investor response. Country-specific fundamentals and news play less of a role in determining bond spreads today than they did in the past.
Stephen Bazen
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780199576791
- eISBN:
- 9780191731136
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199576791.001.0001
- Subject:
- Economics and Finance, Econometrics
This book provides a presentation of the standard statistical techniques used by labour economists. It emphasizes both the input and the output of empirical analysis and covers five major topics ...
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This book provides a presentation of the standard statistical techniques used by labour economists. It emphasizes both the input and the output of empirical analysis and covers five major topics concerning econometric methods used in labour economics: regression and related methods, choice modelling, selectivity issues, duration analysis, and policy evaluation techniques. Each of these is presented in terms of model specification, possible estimation problems, diagnostic checking, and interpretation of the output. The book aims to provide guidance to practitioners on how to use the techniques and how to make sense of the results that are produced. It covers methods that are considered to be ‘standard’ tools in labour economics, but which are often given only a brief and highly technical treatment in econometrics textbooks.Less
This book provides a presentation of the standard statistical techniques used by labour economists. It emphasizes both the input and the output of empirical analysis and covers five major topics concerning econometric methods used in labour economics: regression and related methods, choice modelling, selectivity issues, duration analysis, and policy evaluation techniques. Each of these is presented in terms of model specification, possible estimation problems, diagnostic checking, and interpretation of the output. The book aims to provide guidance to practitioners on how to use the techniques and how to make sense of the results that are produced. It covers methods that are considered to be ‘standard’ tools in labour economics, but which are often given only a brief and highly technical treatment in econometrics textbooks.
Elinor Scarbrough and Eric Tanenbaum (eds)
- Published in print:
- 1998
- Published Online:
- November 2003
- ISBN:
- 9780198292371
- eISBN:
- 9780191600159
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198292376.001.0001
- Subject:
- Political Science, Reference
This volume is a collection of commissioned articles by 16 experts in social science methodology, each contribution introducing experienced social scientists to more advanced analytic techniques. The ...
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This volume is a collection of commissioned articles by 16 experts in social science methodology, each contribution introducing experienced social scientists to more advanced analytic techniques. The contributions explain the theoretical underpinnings of a particular technique, and illustrate the approach with a worked example. The techniques covered are the basic regression model and its extensions, linear structural equation modelling, log‐linear and latent class models, multi‐level modelling, and three extensions to modelling time series data. In these contributions, statistical notation is kept to a minimum; where necessary, it is consigned to footnotes or an appendix. Three final contributions introduce new developments in rational choice theory and discourse analysis.Less
This volume is a collection of commissioned articles by 16 experts in social science methodology, each contribution introducing experienced social scientists to more advanced analytic techniques. The contributions explain the theoretical underpinnings of a particular technique, and illustrate the approach with a worked example. The techniques covered are the basic regression model and its extensions, linear structural equation modelling, log‐linear and latent class models, multi‐level modelling, and three extensions to modelling time series data. In these contributions, statistical notation is kept to a minimum; where necessary, it is consigned to footnotes or an appendix. Three final contributions introduce new developments in rational choice theory and discourse analysis.
John G. Orme and Terri Combs-Orme
- Published in print:
- 2009
- Published Online:
- May 2009
- ISBN:
- 9780195329452
- eISBN:
- 9780199864812
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195329452.003.0003
- Subject:
- Social Work, Research and Evaluation
This chapter describes the use of multinomial logistic regression (also known as polytomous or nominal logistic or logit regression or the discrete choice model), a method for modeling relationships ...
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This chapter describes the use of multinomial logistic regression (also known as polytomous or nominal logistic or logit regression or the discrete choice model), a method for modeling relationships between a polytomous dependent variable and multiple independent variables. Polytomous variables have three or more unordered categories and are often called multicategorical or multinomial (the assumed underlying distribution). The chapter also discusses the testing and presentation of interactions and curvilinear relationships with multinomial logistic regression, as well as the assumptions of the model.Less
This chapter describes the use of multinomial logistic regression (also known as polytomous or nominal logistic or logit regression or the discrete choice model), a method for modeling relationships between a polytomous dependent variable and multiple independent variables. Polytomous variables have three or more unordered categories and are often called multicategorical or multinomial (the assumed underlying distribution). The chapter also discusses the testing and presentation of interactions and curvilinear relationships with multinomial logistic regression, as well as the assumptions of the model.
Steffen Kühnel
- Published in print:
- 1998
- Published Online:
- November 2003
- ISBN:
- 9780198292371
- eISBN:
- 9780191600159
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198292376.003.0004
- Subject:
- Political Science, Reference
Extending the regression model to path analysis, in which all the variables are randomly distributed and treating the variances and co‐variances as exogenous variables. The worked example ...
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Extending the regression model to path analysis, in which all the variables are randomly distributed and treating the variances and co‐variances as exogenous variables. The worked example demonstrates the use of PRELIS, LISREL 8, and GLIM software, and how to interpret the resulting statistics.Less
Extending the regression model to path analysis, in which all the variables are randomly distributed and treating the variances and co‐variances as exogenous variables. The worked example demonstrates the use of PRELIS, LISREL 8, and GLIM software, and how to interpret the resulting statistics.
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/9780199534661.003.0005
- Subject:
- Political Science, Comparative Politics, Political Economy
Most physics equations include few variables and at most one freely adjustable constant, which multiply or divide. In contrast, regression equations favored in social sciences often have many ...
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Most physics equations include few variables and at most one freely adjustable constant, which multiply or divide. In contrast, regression equations favored in social sciences often have many variables in additive–subtractive strings, with plenty of freely adjustable constants/coefficients. Physics equations are reversible and transitive; standard regression equations are unidirectional and nontransitive. Physics rarely offers alternate equations for the same phenomenon, with a different set of input variables and constants; this is frequent in social science regression analysis. Physics equations are presented with prediction in mind, while tables of regression coefficients in social sciences reflect postdiction and often preclude even that.Less
Most physics equations include few variables and at most one freely adjustable constant, which multiply or divide. In contrast, regression equations favored in social sciences often have many variables in additive–subtractive strings, with plenty of freely adjustable constants/coefficients. Physics equations are reversible and transitive; standard regression equations are unidirectional and nontransitive. Physics rarely offers alternate equations for the same phenomenon, with a different set of input variables and constants; this is frequent in social science regression analysis. Physics equations are presented with prediction in mind, while tables of regression coefficients in social sciences reflect postdiction and often preclude even that.
Stephen Bazen
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780199576791
- eISBN:
- 9780191731136
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199576791.003.0001
- Subject:
- Economics and Finance, Econometrics
This introductory chapter sets out the purpose of the book, which is to provide a practical guide to understanding and applying the standard econometric tools that are used in labour economics. ...
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This introductory chapter sets out the purpose of the book, which is to provide a practical guide to understanding and applying the standard econometric tools that are used in labour economics. Emphasis is placed on both the input and the output of empirical analysis, rather than the understanding of the origins and properties of estimators and tests, topics which are more than adequately covered in recent textbooks on microeconometrics. The basic idea developed in this book is that linear regression is an important starting point for empirical analysis in labour economics.Less
This introductory chapter sets out the purpose of the book, which is to provide a practical guide to understanding and applying the standard econometric tools that are used in labour economics. Emphasis is placed on both the input and the output of empirical analysis, rather than the understanding of the origins and properties of estimators and tests, topics which are more than adequately covered in recent textbooks on microeconometrics. The basic idea developed in this book is that linear regression is an important starting point for empirical analysis in labour economics.
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/9780199534661.003.0013
- Subject:
- Political Science, Comparative Politics, Political Economy
Models are tested with data, but also data are tested by agreement with conceptual models. When competing indices exist to measure the same phenomena, one should use the ones that agree with ...
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Models are tested with data, but also data are tested by agreement with conceptual models. When competing indices exist to measure the same phenomena, one should use the ones that agree with logically supported prediction. These indices need not be philosophically “truer” measures of the underlying concepts, but they are more useful for prediction. The choice between two accepted ways to measure cabinet duration and three ways to measure the number of parties illustrates this advice. Clearest results emerge when symmetric regression is used for testing.Less
Models are tested with data, but also data are tested by agreement with conceptual models. When competing indices exist to measure the same phenomena, one should use the ones that agree with logically supported prediction. These indices need not be philosophically “truer” measures of the underlying concepts, but they are more useful for prediction. The choice between two accepted ways to measure cabinet duration and three ways to measure the number of parties illustrates this advice. Clearest results emerge when symmetric regression is used for testing.
