Anthony Garratt, Kevin Lee, M. Hashem Pesaran, and Yongcheol Shin
- Published in print:
- 2006
- Published Online:
- September 2006
- ISBN:
- 9780199296859
- eISBN:
- 9780191603853
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0199296855.003.0001
- Subject:
- Economics and Finance, Econometrics
This chapter introduces the long-run structural approach to modelling. It makes brief comparisons with the key alternative approaches, namely large-scale simultaneous equation models, unrestricted ...
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This chapter introduces the long-run structural approach to modelling. It makes brief comparisons with the key alternative approaches, namely large-scale simultaneous equation models, unrestricted and structural VARs, dynamic stochastic general equilibrium models, and New Keynesian models. The strengths of the long-run structural modelling approach are summarized and the organization of the book is described.Less
This chapter introduces the long-run structural approach to modelling. It makes brief comparisons with the key alternative approaches, namely large-scale simultaneous equation models, unrestricted and structural VARs, dynamic stochastic general equilibrium models, and New Keynesian models. The strengths of the long-run structural modelling approach are summarized and the organization of the book is described.
Natasha K. Bowen and Shenyang Guo
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780195367621
- eISBN:
- 9780199918256
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195367621.001.0001
- Subject:
- Social Work, Research and Evaluation
Structural Equation Modeling (SEM) has long been used in social work research, but the writing on the topic is typically fragmented and highly technical. The chapters demonstrate two SEM programs ...
More
Structural Equation Modeling (SEM) has long been used in social work research, but the writing on the topic is typically fragmented and highly technical. The chapters demonstrate two SEM programs with distinct user interfaces and capabilities (Amos and Mplus) with enough specificity that readers can conduct their own analyses without consulting additional resources. Examples from social work literature highlight best practices for the specification, estimation, interpretation, and modification of structural equation models. Unlike most sources on SEM, this book provides clear guidelines on how to evaluate SEM output and how to proceed when model fit is not acceptable. Oftentimes, confirmatory factor analysis and general structure modeling are the most flexible, powerful, and appropriate choices for social work data.Less
Structural Equation Modeling (SEM) has long been used in social work research, but the writing on the topic is typically fragmented and highly technical. The chapters demonstrate two SEM programs with distinct user interfaces and capabilities (Amos and Mplus) with enough specificity that readers can conduct their own analyses without consulting additional resources. Examples from social work literature highlight best practices for the specification, estimation, interpretation, and modification of structural equation models. Unlike most sources on SEM, this book provides clear guidelines on how to evaluate SEM output and how to proceed when model fit is not acceptable. Oftentimes, confirmatory factor analysis and general structure modeling are the most flexible, powerful, and appropriate choices for social work data.
Qin Duo
- Published in print:
- 1997
- Published Online:
- November 2003
- ISBN:
- 9780198292876
- eISBN:
- 9780191596803
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198292872.003.0007
- Subject:
- Economics and Finance, History of Economic Thought, Econometrics
Looks at problems associated with econometric model construction for the period immediately after the formative phase, and tries to link up the previous chapters and to show what has been left ...
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Looks at problems associated with econometric model construction for the period immediately after the formative phase, and tries to link up the previous chapters and to show what has been left unsolved in the formation of econometrics. The structural modelling procedure explained only how to estimate and identify a priori given structural models, while many of the empirical studies involved searching for the appropriate structural models from the given data. This mismatch of the two sides gave rise to many problems and disputes, mostly in connection with the roles that modellers attributed to individual tools of testing, identification, and estimation in the integrated process of empirical model construction, as the procedure and the associated techniques spread and formed the core of orthodox econometrics. Revisits the issue of model construction with particular respect to the roles of testing, identification, and estimation, depicting how controversies arose as econometricians were swung back to more data‐based positions, away from the emphasis on a priori considerations; back to statistical results, away from reliance on economic theory; and back to dynamics, away from concerns over contemporaneous interdependency. The first section looks at modelling issues associated with hypothesis testing; the second examines problems about model formulation with respect to identification; the third turns to the estimation aspect of modelling; and the fourth leads the discourse to the focal issue of the probability approach underlying established econometrics by illustrating that most of the problems could be viewed as due to the incompleteness of the probability approach (as suggested in Chapter 1).Less
Looks at problems associated with econometric model construction for the period immediately after the formative phase, and tries to link up the previous chapters and to show what has been left unsolved in the formation of econometrics. The structural modelling procedure explained only how to estimate and identify a priori given structural models, while many of the empirical studies involved searching for the appropriate structural models from the given data. This mismatch of the two sides gave rise to many problems and disputes, mostly in connection with the roles that modellers attributed to individual tools of testing, identification, and estimation in the integrated process of empirical model construction, as the procedure and the associated techniques spread and formed the core of orthodox econometrics. Revisits the issue of model construction with particular respect to the roles of testing, identification, and estimation, depicting how controversies arose as econometricians were swung back to more data‐based positions, away from the emphasis on a priori considerations; back to statistical results, away from reliance on economic theory; and back to dynamics, away from concerns over contemporaneous interdependency. The first section looks at modelling issues associated with hypothesis testing; the second examines problems about model formulation with respect to identification; the third turns to the estimation aspect of modelling; and the fourth leads the discourse to the focal issue of the probability approach underlying established econometrics by illustrating that most of the problems could be viewed as due to the incompleteness of the probability approach (as suggested in Chapter 1).
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.
Qin Duo
- Published in print:
- 1997
- Published Online:
- November 2003
- ISBN:
- 9780198292876
- eISBN:
- 9780191596803
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198292872.003.0003
- Subject:
- Economics and Finance, History of Economic Thought, Econometrics
This chapter recounts the evolution of econometric models up to the 1940s, discussing the common criteria and principles used for model choice, and the generalization of model construction as ...
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This chapter recounts the evolution of econometric models up to the 1940s, discussing the common criteria and principles used for model choice, and the generalization of model construction as econometrics focused on the structural modelling procedure. The first section reviews the pre‐model period, and the second looks at the emergence of models and the structural method of model construction. The initial generalization (formalization) efforts of the model‐building strategy and criteria are dealt with in the third section. Concludes with the establishment of the structural modelling procedure (the maturity of simultaneous‐equations model formulation).Less
This chapter recounts the evolution of econometric models up to the 1940s, discussing the common criteria and principles used for model choice, and the generalization of model construction as econometrics focused on the structural modelling procedure. The first section reviews the pre‐model period, and the second looks at the emergence of models and the structural method of model construction. The initial generalization (formalization) efforts of the model‐building strategy and criteria are dealt with in the third section. Concludes with the establishment of the structural modelling procedure (the maturity of simultaneous‐equations model formulation).
Natasha K. Bowen and Shenyang Guo
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780195367621
- eISBN:
- 9780199918256
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195367621.003.0005
- Subject:
- Social Work, Research and Evaluation
This chapter describes how social workers use general structural equation models (general SEMs) and explains how to specify and test them. General SEMs include the measurement model of latent ...
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This chapter describes how social workers use general structural equation models (general SEMs) and explains how to specify and test them. General SEMs include the measurement model of latent variables and their indicators, as well as the structural model of directional relationships among latent variables. A measurement model becomes a general SEM when some or all of the correlational relationships among latent variables in the measurement model are respecified as directional relationships based on the researcher's substantive knowledge of the topic (i.e., theory and past research).Less
This chapter describes how social workers use general structural equation models (general SEMs) and explains how to specify and test them. General SEMs include the measurement model of latent variables and their indicators, as well as the structural model of directional relationships among latent variables. A measurement model becomes a general SEM when some or all of the correlational relationships among latent variables in the measurement model are respecified as directional relationships based on the researcher's substantive knowledge of the topic (i.e., theory and past research).
Patrick Dattalo
- Published in print:
- 2008
- Published Online:
- January 2009
- ISBN:
- 9780195315493
- eISBN:
- 9780199865475
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195315493.001.0001
- Subject:
- Social Work, Research and Evaluation
Sample size determination is an important and often difficult step in planning an empirical study. From a statistical perspective, sample size depends on the following factors: type of analysis to be ...
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Sample size determination is an important and often difficult step in planning an empirical study. From a statistical perspective, sample size depends on the following factors: type of analysis to be performed, desired precision of estimates, kind and number of comparisons to be made, number of variables to be examined, and heterogeneity of the population to be sampled. Other important considerations include feasibility, such as ethical limitations on access to a population of interest and the availability of time and money. The primary assumption of the book is that, within the context of ethical and practical limitations, efforts to obtain samples of appropriate size and quality remain an important and viable component of social science research. This text describes the following available approaches for estimating sample size in social work research and discusses their strengths and weaknesses: power analysis; heuristics or rules-of-thumb; confidence intervals; computer-intensive strategies; and ethical and cost considerations. In addition, strategies for mitigating pressures to increase sample size, such as emphasis on model parsimony (e.g., fewer dependent and independent variables), simpler study designs, an emphasis on replication, and careful planning of analyses are discussed. The book covers sample-size determination for advanced and emerging statistical strategies, such as structural equation modeling, multi-level analysis, repeated measures MANOVA, and repeated measures ANOVA which are not discussed in other texts.Less
Sample size determination is an important and often difficult step in planning an empirical study. From a statistical perspective, sample size depends on the following factors: type of analysis to be performed, desired precision of estimates, kind and number of comparisons to be made, number of variables to be examined, and heterogeneity of the population to be sampled. Other important considerations include feasibility, such as ethical limitations on access to a population of interest and the availability of time and money. The primary assumption of the book is that, within the context of ethical and practical limitations, efforts to obtain samples of appropriate size and quality remain an important and viable component of social science research. This text describes the following available approaches for estimating sample size in social work research and discusses their strengths and weaknesses: power analysis; heuristics or rules-of-thumb; confidence intervals; computer-intensive strategies; and ethical and cost considerations. In addition, strategies for mitigating pressures to increase sample size, such as emphasis on model parsimony (e.g., fewer dependent and independent variables), simpler study designs, an emphasis on replication, and careful planning of analyses are discussed. The book covers sample-size determination for advanced and emerging statistical strategies, such as structural equation modeling, multi-level analysis, repeated measures MANOVA, and repeated measures ANOVA which are not discussed in other texts.
Natasha K. Bowen and Shenyang Guo
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780195367621
- eISBN:
- 9780199918256
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195367621.003.0008
- Subject:
- Social Work, Research and Evaluation
This chapter distills elements of the best practices highlighted in previous chapters and provides concluding remarks about how to become a skillful and critical researcher with structural equation ...
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This chapter distills elements of the best practices highlighted in previous chapters and provides concluding remarks about how to become a skillful and critical researcher with structural equation modeling. Among these are that a sound SEM analysis should be guided by a theoretical model, and any subjective decision made by a user should have theoretical justification and rationale; when facing multiple choices in modeling, other things being equal, the user should choose the model that is most parsimonious; and the user should conduct sensitivity analyses to check violations of assumptions embedded in a model.Less
This chapter distills elements of the best practices highlighted in previous chapters and provides concluding remarks about how to become a skillful and critical researcher with structural equation modeling. Among these are that a sound SEM analysis should be guided by a theoretical model, and any subjective decision made by a user should have theoretical justification and rationale; when facing multiple choices in modeling, other things being equal, the user should choose the model that is most parsimonious; and the user should conduct sensitivity analyses to check violations of assumptions embedded in a model.
Natasha K. Bowen and Shenyang Guo
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780195367621
- eISBN:
- 9780199918256
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195367621.003.0002
- Subject:
- Social Work, Research and Evaluation
This chapter discusses a number of theoretical and statistical concepts and principles that are central to SEM. It introduces SEM notation and equations in the context of more familiar graphics and ...
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This chapter discusses a number of theoretical and statistical concepts and principles that are central to SEM. It introduces SEM notation and equations in the context of more familiar graphics and terminology. It explains the role of matrices in SEM analyses.Less
This chapter discusses a number of theoretical and statistical concepts and principles that are central to SEM. It introduces SEM notation and equations in the context of more familiar graphics and terminology. It explains the role of matrices in SEM analyses.
Jon Williamson
- Published in print:
- 2004
- Published Online:
- September 2007
- ISBN:
- 9780198530794
- eISBN:
- 9780191712982
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198530794.003.0010
- Subject:
- Mathematics, Logic / Computer Science / Mathematical Philosophy
The framework developed thus far in the book is extended to cope with the situation in which causal relations themselves are causes or effects. Examples of this phenomenon are presented and the ...
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The framework developed thus far in the book is extended to cope with the situation in which causal relations themselves are causes or effects. Examples of this phenomenon are presented and the formalism of recursive Bayesian nets is developed to handle these cases.Less
The framework developed thus far in the book is extended to cope with the situation in which causal relations themselves are causes or effects. Examples of this phenomenon are presented and the formalism of recursive Bayesian nets is developed to handle these cases.
Natasha K. Bowen and Shenyang Guo
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780195367621
- eISBN:
- 9780199918256
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195367621.003.0006
- Subject:
- Social Work, Research and Evaluation
Sometimes instead of getting results when they run an SEM analysis, researchers are confronted with discouraging messages about programming errors, data problems, or other causes of estimation ...
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Sometimes instead of getting results when they run an SEM analysis, researchers are confronted with discouraging messages about programming errors, data problems, or other causes of estimation failures. This chapter first summarizes possible causes of estimation failures. It then provides guidelines for interpreting the results of successful estimation procedures both statistically and substantively. Finally, it discusses strategies for improving fit when model test results are valid (i.e., the model ran and converged, all parameters estimates are within valid ranges, and no errors are reported by the program) but unsatisfactory (i.e., fit criteria are not met).Less
Sometimes instead of getting results when they run an SEM analysis, researchers are confronted with discouraging messages about programming errors, data problems, or other causes of estimation failures. This chapter first summarizes possible causes of estimation failures. It then provides guidelines for interpreting the results of successful estimation procedures both statistically and substantively. Finally, it discusses strategies for improving fit when model test results are valid (i.e., the model ran and converged, all parameters estimates are within valid ranges, and no errors are reported by the program) but unsatisfactory (i.e., fit criteria are not met).
Damien Fennell
- Published in print:
- 2011
- Published Online:
- September 2011
- ISBN:
- 9780199574131
- eISBN:
- 9780191728921
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199574131.003.0017
- Subject:
- Mathematics, Logic / Computer Science / Mathematical Philosophy
This chapter explores what the error term represents in structural models in econometrics and the assumptions about the error terms that are used for successful statistical and causal inference. The ...
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This chapter explores what the error term represents in structural models in econometrics and the assumptions about the error terms that are used for successful statistical and causal inference. The error term is of particular interest because it acts as a coverall term for parts of the system that are not fully known about and not explicitly modelled. The chapter attempts to bring some of the key assumptions imposed on the error term for different purposes (statistical and causal inference) and to ask to what extent the conditions imposed on the error term can be empirically tested in some way.Less
This chapter explores what the error term represents in structural models in econometrics and the assumptions about the error terms that are used for successful statistical and causal inference. The error term is of particular interest because it acts as a coverall term for parts of the system that are not fully known about and not explicitly modelled. The chapter attempts to bring some of the key assumptions imposed on the error term for different purposes (statistical and causal inference) and to ask to what extent the conditions imposed on the error term can be empirically tested in some way.
Donna Harrington
- Published in print:
- 2008
- Published Online:
- January 2009
- ISBN:
- 9780195339888
- eISBN:
- 9780199863662
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195339888.003.0001
- Subject:
- Social Work, Research and Evaluation
This chapter discusses the major uses of confirmatory factor analysis including measurement development, psychometric evaluation of measures, construct validation, testing method effects, and testing ...
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This chapter discusses the major uses of confirmatory factor analysis including measurement development, psychometric evaluation of measures, construct validation, testing method effects, and testing measurement invariance, and presents examples of these uses in the social work literature. Different types of validity are briefly defined and discussed. Confirmatory factor analysis is compared with three other common data analysis approaches: exploratory factor analysis, principal components analysis, and structural equation modeling. Software for conducting confirmatory factor analysis is briefly discussed.Less
This chapter discusses the major uses of confirmatory factor analysis including measurement development, psychometric evaluation of measures, construct validation, testing method effects, and testing measurement invariance, and presents examples of these uses in the social work literature. Different types of validity are briefly defined and discussed. Confirmatory factor analysis is compared with three other common data analysis approaches: exploratory factor analysis, principal components analysis, and structural equation modeling. Software for conducting confirmatory factor analysis is briefly discussed.
Natasha K. Bowen and Shenyang Guo
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780195367621
- eISBN:
- 9780199918256
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195367621.003.0004
- Subject:
- Social Work, Research and Evaluation
This chapter describes when and how to conduct a confirmatory factor analysis (CFA). CFA is a step in the scale development process, and it is also the first step in testing structural models. ...
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This chapter describes when and how to conduct a confirmatory factor analysis (CFA). CFA is a step in the scale development process, and it is also the first step in testing structural models. Therefore, all researchers using a latent variable analysis approach must have an understanding of CFA, whether or not they are developing and testing a new scale. CFA is also compared to exploratory factor analysis (EFA).Less
This chapter describes when and how to conduct a confirmatory factor analysis (CFA). CFA is a step in the scale development process, and it is also the first step in testing structural models. Therefore, all researchers using a latent variable analysis approach must have an understanding of CFA, whether or not they are developing and testing a new scale. CFA is also compared to exploratory factor analysis (EFA).
Peter Hagström and Gunnar Hedlund
- Published in print:
- 1999
- Published Online:
- November 2003
- ISBN:
- 9780198296041
- eISBN:
- 9780191596070
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198296045.003.0008
- Subject:
- Economics and Finance, Microeconomics
Looks at the limits of hierarchy in the internal structure of a firm and suggests an explanation as to why the concept of a simple hierarchy has been so readily accepted and is so seemingly ...
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Looks at the limits of hierarchy in the internal structure of a firm and suggests an explanation as to why the concept of a simple hierarchy has been so readily accepted and is so seemingly successful. The authors argue that the historically successful hierarchy has actually hidden a different underlying structure that is only now being revealed. Hierarchy ensures relative efficiency in a known, stable situation, but these are hardly the salient characteristics of the modern competitive environment, and firms are found to experiment with ways to deal with these new pressures in changes that range from ad hoc measures to radical structural transformations. The fundamental trade‐off here is one of (flexible) efficiency today and of positioning for tomorrow; one‐dimensional hierarchy could achieve that yesterday. The authors conclude that, in effect, there are, and always have been, three structural dimensions at play, namely position, action, and knowledge; these dimensions have coincided (or misalignments have not been apparent) in the past, but that is seen to be less and less the case nowadays, and nowhere is this more apparent than in the modern multinational corporation; this theoretically and historically derived three‐structural‐dimensions model is tried out on an illustrative firm case (Oticon A/S in Denmark).Less
Looks at the limits of hierarchy in the internal structure of a firm and suggests an explanation as to why the concept of a simple hierarchy has been so readily accepted and is so seemingly successful. The authors argue that the historically successful hierarchy has actually hidden a different underlying structure that is only now being revealed. Hierarchy ensures relative efficiency in a known, stable situation, but these are hardly the salient characteristics of the modern competitive environment, and firms are found to experiment with ways to deal with these new pressures in changes that range from ad hoc measures to radical structural transformations. The fundamental trade‐off here is one of (flexible) efficiency today and of positioning for tomorrow; one‐dimensional hierarchy could achieve that yesterday. The authors conclude that, in effect, there are, and always have been, three structural dimensions at play, namely position, action, and knowledge; these dimensions have coincided (or misalignments have not been apparent) in the past, but that is seen to be less and less the case nowadays, and nowhere is this more apparent than in the modern multinational corporation; this theoretically and historically derived three‐structural‐dimensions model is tried out on an illustrative firm case (Oticon A/S in Denmark).
Michel Mouchart and Federica Russo
- Published in print:
- 2011
- Published Online:
- September 2011
- ISBN:
- 9780199574131
- eISBN:
- 9780191728921
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199574131.003.0015
- Subject:
- Mathematics, Logic / Computer Science / Mathematical Philosophy
This chapter deals with causal explanation in quantitative‐oriented social sciences. In the framework of statistical modelling, we first develop a formal structural modelling approach which is meant ...
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This chapter deals with causal explanation in quantitative‐oriented social sciences. In the framework of statistical modelling, we first develop a formal structural modelling approach which is meant to shape causal explanation. Recursive decomposition and exogeneity are given a major role for explaining social phenomena. Then, based on the main features of structural models, the recursive decomposition is interpreted as a mechanism and exogenous variables as causal factors. Arguments from statistical methodology are first offered and then submitted to critical evaluation.Less
This chapter deals with causal explanation in quantitative‐oriented social sciences. In the framework of statistical modelling, we first develop a formal structural modelling approach which is meant to shape causal explanation. Recursive decomposition and exogeneity are given a major role for explaining social phenomena. Then, based on the main features of structural models, the recursive decomposition is interpreted as a mechanism and exogenous variables as causal factors. Arguments from statistical methodology are first offered and then submitted to critical evaluation.
Judea Pearl
- Published in print:
- 2011
- Published Online:
- September 2011
- ISBN:
- 9780199574131
- eISBN:
- 9780191728921
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199574131.003.0033
- Subject:
- Mathematics, Logic / Computer Science / Mathematical Philosophy
This chapter presents a general theory of causation based on the Structural Causal Model (SCM) described by Pearl (2000a). The theory subsumes and unifies current approaches to causation, including ...
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This chapter presents a general theory of causation based on the Structural Causal Model (SCM) described by Pearl (2000a). The theory subsumes and unifies current approaches to causation, including graphical, potential outcome, probabilistic, decision analytical, and structural equation models, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the chapter demonstrates how the theory engenders a coherent methodology for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (2) queries about probabilities of counterfactuals, and (3) queries about direct and indirect effects.Less
This chapter presents a general theory of causation based on the Structural Causal Model (SCM) described by Pearl (2000a). The theory subsumes and unifies current approaches to causation, including graphical, potential outcome, probabilistic, decision analytical, and structural equation models, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the chapter demonstrates how the theory engenders a coherent methodology for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (2) queries about probabilities of counterfactuals, and (3) queries about direct and indirect effects.
Patrick Dattalo
- Published in print:
- 2008
- Published Online:
- January 2009
- ISBN:
- 9780195315493
- eISBN:
- 9780199865475
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195315493.003.0002
- Subject:
- Social Work, Research and Evaluation
This chapter provides a brief description of the rationale and limitations of statistical power analysis, and presents important issues related to determining sample size for both commonly used and ...
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This chapter provides a brief description of the rationale and limitations of statistical power analysis, and presents important issues related to determining sample size for both commonly used and emerging statistical procedures in social work research. Procedures include difference between two means, difference between two proportions, odds ratio, relative risk, chi square, ANOVA, correlation, regression, logistic regression, structural equation modeling, and multi-level analysis.Less
This chapter provides a brief description of the rationale and limitations of statistical power analysis, and presents important issues related to determining sample size for both commonly used and emerging statistical procedures in social work research. Procedures include difference between two means, difference between two proportions, odds ratio, relative risk, chi square, ANOVA, correlation, regression, logistic regression, structural equation modeling, and multi-level analysis.
Natasha K. Bowen and Shenyang Guo
- Published in print:
- 2011
- Published Online:
- January 2012
- ISBN:
- 9780195367621
- eISBN:
- 9780199918256
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195367621.003.0001
- Subject:
- Social Work, Research and Evaluation
This introductory chapter first sets out the purpose of the book, which is to serve as a concise practical guide for the informed and responsible use of structural equation modeling (SEM). It is ...
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This introductory chapter first sets out the purpose of the book, which is to serve as a concise practical guide for the informed and responsible use of structural equation modeling (SEM). It is designed for social work faculty, researchers, and doctoral students who view themselves more as substantive experts than statistical experts, but who need to use SEM in their research. It is designed for social workers who desire a degree of analytical skill but have neither the time for coursework nor the patience to glean from the immense SEM literature the specifics needed to carry out an SEM analysis. The chapter then discusses what is SEM, the role of theory in SEM, the kinds of data that can or should be analyzed with SEM, and the research questions best answered by SEM.Less
This introductory chapter first sets out the purpose of the book, which is to serve as a concise practical guide for the informed and responsible use of structural equation modeling (SEM). It is designed for social work faculty, researchers, and doctoral students who view themselves more as substantive experts than statistical experts, but who need to use SEM in their research. It is designed for social workers who desire a degree of analytical skill but have neither the time for coursework nor the patience to glean from the immense SEM literature the specifics needed to carry out an SEM analysis. The chapter then discusses what is SEM, the role of theory in SEM, the kinds of data that can or should be analyzed with SEM, and the research questions best answered by SEM.
Rami Benbenishty and Ron Avi Astor
- Published in print:
- 2005
- Published Online:
- January 2009
- ISBN:
- 9780195157802
- eISBN:
- 9780199864393
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195157802.003.0008
- Subject:
- Social Work, Children and Families, Crime and Justice
This chapter presents structural equation models of how school context variables (risky peer behaviors, school policies, teachers' support and student participation) combined with victimization lead ...
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This chapter presents structural equation models of how school context variables (risky peer behaviors, school policies, teachers' support and student participation) combined with victimization lead to different kinds of students' subjective interpretations about their school: assessment of the severity of school violence, missing school due to fear, and a sense of safety. Key findings and implications include: (1) the amount of variance explained for the subjective assessment by within school variables alone is quite high; (2) different subjective interpretive outcomes are influenced by different types of victimization and school variables; (3) students' views of their school violence problem are influenced mainly by the risky peer behaviors and the response of the school staff to violent events; (4) non-attendance due to fear is influenced mainly by personal experiences with severe events of peer-violence; (5) a sense of safety is influenced mainly by a positive school climate; and (6) these patterns are true for all school levels studied and across ethnicity and gender.Less
This chapter presents structural equation models of how school context variables (risky peer behaviors, school policies, teachers' support and student participation) combined with victimization lead to different kinds of students' subjective interpretations about their school: assessment of the severity of school violence, missing school due to fear, and a sense of safety. Key findings and implications include: (1) the amount of variance explained for the subjective assessment by within school variables alone is quite high; (2) different subjective interpretive outcomes are influenced by different types of victimization and school variables; (3) students' views of their school violence problem are influenced mainly by the risky peer behaviors and the response of the school staff to violent events; (4) non-attendance due to fear is influenced mainly by personal experiences with severe events of peer-violence; (5) a sense of safety is influenced mainly by a positive school climate; and (6) these patterns are true for all school levels studied and across ethnicity and gender.
