John E. Jackson
- Published in print:
- 1998
- Published Online:
- November 2003
- ISBN:
- 9780198294719
- eISBN:
- 9780191599361
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198294719.003.0032
- Subject:
- Political Science, Reference
Reviews methodological techniques available across the discipline of political science. Econometrics and political science methods include structural equation estimations, time‐series analysis, and ...
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Reviews methodological techniques available across the discipline of political science. Econometrics and political science methods include structural equation estimations, time‐series analysis, and non‐linear models. Alternative approaches analyse public preferences, political institutions, and path dependence political economy modelling. The drawbacks of these methods are examined by questioning their underlying assumptions and examining their consequences. While there is cause for concern, solace lies in the fact that these problems are also faced across other disciplines.Less
Reviews methodological techniques available across the discipline of political science. Econometrics and political science methods include structural equation estimations, time‐series analysis, and non‐linear models. Alternative approaches analyse public preferences, political institutions, and path dependence political economy modelling. The drawbacks of these methods are examined by questioning their underlying assumptions and examining their consequences. While there is cause for concern, solace lies in the fact that these problems are also faced across other disciplines.
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 ...
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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.
James Woodward
- Published in print:
- 2004
- Published Online:
- January 2005
- ISBN:
- 9780195155273
- eISBN:
- 9780199835089
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0195155270.003.0007
- Subject:
- Philosophy, Philosophy of Science
This chapter applies the ideas about intervention and invariance developed in previous chapters to so-called causal models of the sort used in the social, behavioral, and biomedical sciences. Both ...
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This chapter applies the ideas about intervention and invariance developed in previous chapters to so-called causal models of the sort used in the social, behavioral, and biomedical sciences. Both regression models and structural equation models are explored and a modularity condition is defended in connection with the latter. Connections with the Causal Markov condition are also briefly discussed.Less
This chapter applies the ideas about intervention and invariance developed in previous chapters to so-called causal models of the sort used in the social, behavioral, and biomedical sciences. Both regression models and structural equation models are explored and a modularity condition is defended in connection with the latter. Connections with the Causal Markov condition are also 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/9780195367621.003.0002
- 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.
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/9780195367621.003.0005
- 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).
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.
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.
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.
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.
Joseph Y. Halpern
- Published in print:
- 2016
- Published Online:
- May 2017
- ISBN:
- 9780262035026
- eISBN:
- 9780262336611
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262035026.003.0002
- Subject:
- Computer Science, Artificial Intelligence
Three variants of a definition of actual causality are introduced. These definition uses structural equations to model counterfactuals. The definition is shown to yield a plausible and elegant ...
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Three variants of a definition of actual causality are introduced. These definition uses structural equations to model counterfactuals. The definition is shown to yield a plausible and elegant account of causation that handles well examples that have caused problems for other definitions. Although transitivity is not transitive according to this definition, conditions sufficient to guarantee transitivity of causality are provided. Although the definition given assumes that everything is known, it is shown to easily extend to a situation where there is uncertainty modeled using probability. A notion of sufficient causality is also considered, as well as causality in nonrecursive models, where there are circular dependenciesLess
Three variants of a definition of actual causality are introduced. These definition uses structural equations to model counterfactuals. The definition is shown to yield a plausible and elegant account of causation that handles well examples that have caused problems for other definitions. Although transitivity is not transitive according to this definition, conditions sufficient to guarantee transitivity of causality are provided. Although the definition given assumes that everything is known, it is shown to easily extend to a situation where there is uncertainty modeled using probability. A notion of sufficient causality is also considered, as well as causality in nonrecursive models, where there are circular dependencies
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.
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).
Christian Büchel and Karl Friston
- Published in print:
- 2001
- Published Online:
- March 2012
- ISBN:
- 9780192630711
- eISBN:
- 9780191724770
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780192630711.003.0016
- Subject:
- Neuroscience, Techniques
This chapter shows that new methods for measuring effective connectivity allow us to characterize the interactions between brain regions which underlie the complex interactions among different ...
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This chapter shows that new methods for measuring effective connectivity allow us to characterize the interactions between brain regions which underlie the complex interactions among different processing stages of functional architectures. It reviews the basic concepts of effective connectivity in neuroimaging. The methods introduced to assess effective connectivity are multiple linear regression, covariance structural equation modelling and variable parameter regression. The first example demonstrates that non-linear interactions can be characterized using simple extensions of linear models, while in the second, structural equation modelling is introduced as a device that allows one to combine observed changes in cortical activity and anatomical models. Finally, the chapter concludes that the approach to neuroimaging data and regional interactions is an exciting endeavour, which is starting to attract more attention.Less
This chapter shows that new methods for measuring effective connectivity allow us to characterize the interactions between brain regions which underlie the complex interactions among different processing stages of functional architectures. It reviews the basic concepts of effective connectivity in neuroimaging. The methods introduced to assess effective connectivity are multiple linear regression, covariance structural equation modelling and variable parameter regression. The first example demonstrates that non-linear interactions can be characterized using simple extensions of linear models, while in the second, structural equation modelling is introduced as a device that allows one to combine observed changes in cortical activity and anatomical models. Finally, the chapter concludes that the approach to neuroimaging data and regional interactions is an exciting endeavour, which is starting to attract more attention.
Peter Miksza and Kenneth Elpus
- Published in print:
- 2018
- Published Online:
- March 2018
- ISBN:
- 9780199391905
- eISBN:
- 9780199391943
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780199391905.003.0014
- Subject:
- Music, Theory, Analysis, Composition, Performing Practice/Studies
This chapter presents structural equation modeling as a tool for conducting research regarding how collections of variables may be related to each other as well as to a particular outcome or even ...
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This chapter presents structural equation modeling as a tool for conducting research regarding how collections of variables may be related to each other as well as to a particular outcome or even multiple outcomes. Structural equation modeling refers to a collection of analytical techniques that can be used to model complex patterns of predictive relationships among a collection of both measured and latent variables. As a statistical tool, structural equation modeling combines the features of regression and factor analysis. The chapter offers conceptual illustrations and practical steps for carrying out structural equation modeling by describing mediation and moderation analyses in the context of music education research.Less
This chapter presents structural equation modeling as a tool for conducting research regarding how collections of variables may be related to each other as well as to a particular outcome or even multiple outcomes. Structural equation modeling refers to a collection of analytical techniques that can be used to model complex patterns of predictive relationships among a collection of both measured and latent variables. As a statistical tool, structural equation modeling combines the features of regression and factor analysis. The chapter offers conceptual illustrations and practical steps for carrying out structural equation modeling by describing mediation and moderation analyses in the context of music education research.
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.
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).
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.
Nancy Cartwright
- Published in print:
- 2017
- Published Online:
- July 2017
- ISBN:
- 9780198746911
- eISBN:
- 9780191809132
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780198746911.003.0008
- Subject:
- Philosophy, Metaphysics/Epistemology, Philosophy of Science
In ‘The Causal Structure of Mechanisms’, Peter Menzies develops a structural equations account of mechanism to show how mechanisms explain causal regularities. This chapter distinguishes two senses ...
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In ‘The Causal Structure of Mechanisms’, Peter Menzies develops a structural equations account of mechanism to show how mechanisms explain causal regularities. This chapter distinguishes two senses of mechanism: first, the process by which the cause produces the effect, and second, the underlying structure that affords this process. The chapter argues that representing mechanisms by structural equations is great for showing how the intermediate steps in the process can connect the beginning cause with the end effect. But this is just a ‘surface’ phenomenon. The causal possibilities described in the structural equations depend on an underlying structure that gives rise to them. So this leaves us still with the job of understanding and representing that underlying structure and coming to grips with the question of how it gives rise to those causal possibilities.Less
In ‘The Causal Structure of Mechanisms’, Peter Menzies develops a structural equations account of mechanism to show how mechanisms explain causal regularities. This chapter distinguishes two senses of mechanism: first, the process by which the cause produces the effect, and second, the underlying structure that affords this process. The chapter argues that representing mechanisms by structural equations is great for showing how the intermediate steps in the process can connect the beginning cause with the end effect. But this is just a ‘surface’ phenomenon. The causal possibilities described in the structural equations depend on an underlying structure that gives rise to them. So this leaves us still with the job of understanding and representing that underlying structure and coming to grips with the question of how it gives rise to those causal possibilities.
