Luis M. Cruz-Orive, Ana M. Insausti, Ricardo Insausti, and Dámaso Crespo
- Published in print:
- 2004
- Published Online:
- September 2009
- ISBN:
- 9780198505280
- eISBN:
- 9780191723766
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198505280.003.0002
- Subject:
- Neuroscience, Techniques
Downs syndrome is characterized by a triplication of chromosome 21 (HSA-21), and is the most frequent cause of mental retardation in the newborn. A special trisomic mouse (Ts65Dn) is a well known ...
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Downs syndrome is characterized by a triplication of chromosome 21 (HSA-21), and is the most frequent cause of mental retardation in the newborn. A special trisomic mouse (Ts65Dn) is a well known animal model for Downs syndrome. This chapter presents a new study that tries to detect possible differences between in Ts65Dn mice and controls in the total neuron number for each of four subpopulations, defined as expressing: calbindin, parvalbumin (Pv), glial fibrillary acidic protein (GFAP) in astrocytes, NADPH-diaphorase; within each of three well defined compartments of the hippocampus, namely: dentate gyrus (DG); union of CA1 and CA2; and CA3 (pyramidal layer). The problem; the material and statistical framework; estimation of neuron number by the classical Cavalieri-disector stereological design; statistical detection of group mean differences (univariate analysis); sample size predictions for further action; statistical detection of group mean differences; and study results are disussed.Less
Downs syndrome is characterized by a triplication of chromosome 21 (HSA-21), and is the most frequent cause of mental retardation in the newborn. A special trisomic mouse (Ts65Dn) is a well known animal model for Downs syndrome. This chapter presents a new study that tries to detect possible differences between in Ts65Dn mice and controls in the total neuron number for each of four subpopulations, defined as expressing: calbindin, parvalbumin (Pv), glial fibrillary acidic protein (GFAP) in astrocytes, NADPH-diaphorase; within each of three well defined compartments of the hippocampus, namely: dentate gyrus (DG); union of CA1 and CA2; and CA3 (pyramidal layer). The problem; the material and statistical framework; estimation of neuron number by the classical Cavalieri-disector stereological design; statistical detection of group mean differences (univariate analysis); sample size predictions for further action; statistical detection of group mean differences; and study results are disussed.
J. H. Abramson and Z. H. Abramson
- Published in print:
- 2001
- Published Online:
- September 2009
- ISBN:
- 9780195145250
- eISBN:
- 9780199864775
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195145250.003.0004
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
The exercises in this section deal with the assessment of associations between variables, with special reference to possible effects of shortcomings in the study methods, the strength and other ...
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The exercises in this section deal with the assessment of associations between variables, with special reference to possible effects of shortcomings in the study methods, the strength and other qualities of the association, its consistency, possible confounding effects, and the appraisal of causality. Specific topics include effects of misclassification, statistical significance and significance tests, methods of appraising the possibility and likely direction of confounding effects, measures of the strength of associations, synergism, the appraisal of associations in stratified data, the use of multivariate analysis (including multiple linear regression, multiple logistic regression analysis, and proportional hazards regression), the use of matched samples, synergism, risk factors and risk markers, and the appraisal of risk markers. A self-test concludes the section.Less
The exercises in this section deal with the assessment of associations between variables, with special reference to possible effects of shortcomings in the study methods, the strength and other qualities of the association, its consistency, possible confounding effects, and the appraisal of causality. Specific topics include effects of misclassification, statistical significance and significance tests, methods of appraising the possibility and likely direction of confounding effects, measures of the strength of associations, synergism, the appraisal of associations in stratified data, the use of multivariate analysis (including multiple linear regression, multiple logistic regression analysis, and proportional hazards regression), the use of matched samples, synergism, risk factors and risk markers, and the appraisal of risk markers. A self-test concludes the section.
Karen A. Randolph and Laura L. Myers
- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199764044
- eISBN:
- 9780199332533
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199764044.001.0001
- Subject:
- Social Work, Research and Evaluation
The complexity of social problems necessitates that social work researchers utilize multivariate statistical methods in their investigations. Having a thorough understanding of basic statistics can ...
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The complexity of social problems necessitates that social work researchers utilize multivariate statistical methods in their investigations. Having a thorough understanding of basic statistics can facilitate this process as multivariate methods have as their foundation many of these basic statistical procedures. In this pocket guide, the authors introduce readers to three of the more frequently used multivariate statistical methods in social work research—multiplelinear regression analysis,analysis of variance and covariance, and path analysis—with an emphasis on the basic statistics as important features of these methods. The primary intention is to help prepare entry level doctoral students and early career social work researchers in the use of multivariate statistical methods by offering a straightforward and easy to understand explanation of these methods and the basic statistics that inform them. The pocket guide begins with a review of basic statistics, hypothesis testing with inferential statistics, and bivariate analytic methods. Subsequent sections describe bivarate and multiple linear regression analyses, one-way and two-way analysis of variance (ANOVA) and covariance (ANCOVA), and path analysis. In each chapter, the authors introduce the various basic statistical procedures by providing definitions, formulas, descriptions of the underlying logic and assumptions of each procedure, and examples of how they have been applied in the social work research literature. The authors also explain estimation procedures and how to interpret results. Each chapter provides brief step-by-step instructions for conducting these statistical tests in Statistical Package for the Social Sciences (SPSS) and AMOS (SPSS, Inc. 2011), based on data from the National Educational Longitudinal Study of 1988 (NELS: 88). Finally, the book offers a companion website that provides more detailed instructions, as well as data sets and worked examples.Less
The complexity of social problems necessitates that social work researchers utilize multivariate statistical methods in their investigations. Having a thorough understanding of basic statistics can facilitate this process as multivariate methods have as their foundation many of these basic statistical procedures. In this pocket guide, the authors introduce readers to three of the more frequently used multivariate statistical methods in social work research—multiplelinear regression analysis,analysis of variance and covariance, and path analysis—with an emphasis on the basic statistics as important features of these methods. The primary intention is to help prepare entry level doctoral students and early career social work researchers in the use of multivariate statistical methods by offering a straightforward and easy to understand explanation of these methods and the basic statistics that inform them. The pocket guide begins with a review of basic statistics, hypothesis testing with inferential statistics, and bivariate analytic methods. Subsequent sections describe bivarate and multiple linear regression analyses, one-way and two-way analysis of variance (ANOVA) and covariance (ANCOVA), and path analysis. In each chapter, the authors introduce the various basic statistical procedures by providing definitions, formulas, descriptions of the underlying logic and assumptions of each procedure, and examples of how they have been applied in the social work research literature. The authors also explain estimation procedures and how to interpret results. Each chapter provides brief step-by-step instructions for conducting these statistical tests in Statistical Package for the Social Sciences (SPSS) and AMOS (SPSS, Inc. 2011), based on data from the National Educational Longitudinal Study of 1988 (NELS: 88). Finally, the book offers a companion website that provides more detailed instructions, as well as data sets and worked examples.
Patrick Dattalo
- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199773596
- eISBN:
- 9780199332564
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199773596.003.0001
- Subject:
- Social Work, Research and Evaluation
This chapter begins with an introduction to multivariate procedures, which allow social workers and other human services researchers to analyze multidimensional social problems and interventions in ...
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This chapter begins with an introduction to multivariate procedures, which allow social workers and other human services researchers to analyze multidimensional social problems and interventions in ways that minimize oversimplification. Examples of multivariate statistical procedures to predict and describe relationships include multivariate multiple regression (MMR), multivariate analysis of variance (MANOVA), and multivariate analysis of covariance (MANCOVA). Structural equation modeling (SEM) may be used for data simplification and reduction, description, and prediction. The discussion then turns to the rationale for multivariate analysis followed by a description of the organization and contents of this book.Less
This chapter begins with an introduction to multivariate procedures, which allow social workers and other human services researchers to analyze multidimensional social problems and interventions in ways that minimize oversimplification. Examples of multivariate statistical procedures to predict and describe relationships include multivariate multiple regression (MMR), multivariate analysis of variance (MANOVA), and multivariate analysis of covariance (MANCOVA). Structural equation modeling (SEM) may be used for data simplification and reduction, description, and prediction. The discussion then turns to the rationale for multivariate analysis followed by a description of the organization and contents of this book.
J. Mark Elwood
- Published in print:
- 2007
- Published Online:
- September 2009
- ISBN:
- 9780198529552
- eISBN:
- 9780191723865
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198529552.003.07
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
This chapter discusses the effects of chance variation that can be assessed by applying statistical tests. It is divided into three parts: the application of statistical tests and confidence limits ...
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This chapter discusses the effects of chance variation that can be assessed by applying statistical tests. It is divided into three parts: the application of statistical tests and confidence limits to a simple 2 x 2 table; applications to stratified and matched studies, and to multivariate analysis; and life-table methods for the consideration of the timing of outcome events in a cohort study or intervention study. Self-test questions are provided at the end of the chapter.Less
This chapter discusses the effects of chance variation that can be assessed by applying statistical tests. It is divided into three parts: the application of statistical tests and confidence limits to a simple 2 x 2 table; applications to stratified and matched studies, and to multivariate analysis; and life-table methods for the consideration of the timing of outcome events in a cohort study or intervention study. Self-test questions are provided at the end of the chapter.
Ricardo Otheguy and Ana Celia Zentella
- Published in print:
- 2012
- Published Online:
- January 2012
- ISBN:
- 9780199737406
- eISBN:
- 9780199918621
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199737406.003.0007
- Subject:
- Linguistics, Sociolinguistics / Anthropological Linguistics
Grouping categories are reconceptualized as independent variables. Pronoun rate is the dependent variable. The need for multivariate regression analysis is explained. The independent variables are ...
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Grouping categories are reconceptualized as independent variables. Pronoun rate is the dependent variable. The need for multivariate regression analysis is explained. The independent variables are ranked in terms of strength of influence on pronoun rate. The regression analysis confirms the statistically significant, unique contribution of differences in region, lect, exposure, generation, English, and SES. Problems of colinearity are addressed. Among the significant variables, regional differences are ranked highest. This is interpreted as evidence of strong continuity between the reference and bilingual lects. English is ranked higher than orientation (and also higher than cross‐orientation), which is interpreted as language contact being a stronger force for change than dialectal leveling. Explanations for these patterns are offered.Less
Grouping categories are reconceptualized as independent variables. Pronoun rate is the dependent variable. The need for multivariate regression analysis is explained. The independent variables are ranked in terms of strength of influence on pronoun rate. The regression analysis confirms the statistically significant, unique contribution of differences in region, lect, exposure, generation, English, and SES. Problems of colinearity are addressed. Among the significant variables, regional differences are ranked highest. This is interpreted as evidence of strong continuity between the reference and bilingual lects. English is ranked higher than orientation (and also higher than cross‐orientation), which is interpreted as language contact being a stronger force for change than dialectal leveling. Explanations for these patterns are offered.
Andrew Pickles and Bianca De Stavola
- Published in print:
- 2007
- Published Online:
- September 2009
- ISBN:
- 9780198528487
- eISBN:
- 9780191723940
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198528487.003.0008
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
This chapter begins by considering the range of different aspects of the disease process that life course analysis must address. Given the essential longitudinal nature of much life course analysis, ...
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This chapter begins by considering the range of different aspects of the disease process that life course analysis must address. Given the essential longitudinal nature of much life course analysis, it illustrates the population average and subject-specific modelling frameworks for longitudinal and multivariate data. The remainder of the chapter focuses on the tools — both conceptual and statistical — for tackling the problem of causal analysis. These include both traditional and more recently proposed methods, such as marginal structural models and instrumental variable methods, and approaches adopted from other disciplines, such as structural equation modelling. The chapter highlights both their specific strengths and their commonality. Two major concerns close the chapter, one relating to the implications for epidemiology of individual differences or non-uniform causal effects, and the second concerning the need to preserve objectivity within analysis.Less
This chapter begins by considering the range of different aspects of the disease process that life course analysis must address. Given the essential longitudinal nature of much life course analysis, it illustrates the population average and subject-specific modelling frameworks for longitudinal and multivariate data. The remainder of the chapter focuses on the tools — both conceptual and statistical — for tackling the problem of causal analysis. These include both traditional and more recently proposed methods, such as marginal structural models and instrumental variable methods, and approaches adopted from other disciplines, such as structural equation modelling. The chapter highlights both their specific strengths and their commonality. Two major concerns close the chapter, one relating to the implications for epidemiology of individual differences or non-uniform causal effects, and the second concerning the need to preserve objectivity within analysis.
Guo Shenyang
- Published in print:
- 2009
- Published Online:
- January 2010
- ISBN:
- 9780195337518
- eISBN:
- 9780199864256
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195337518.003.0003
- Subject:
- Social Work, Research and Evaluation
This chapter reviews the first type of multivariate model analyzing time-to-event data: the discrete-time models. The primary feature of this type of model is its approximation of hazard rate by ...
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This chapter reviews the first type of multivariate model analyzing time-to-event data: the discrete-time models. The primary feature of this type of model is its approximation of hazard rate by using probability estimated from a person-time data set. When the study focuses on a single event, the analyst uses a binary logistic regression to estimate the probability. When the study focuses on multiple events (i.e., termination of study time due to more than one reason), the analyst uses a multinomial logit regression to estimate multiple probabilities. The discrete-time model of multiple events is also known as competing-risks analysis. The chapter begins with an overview of the discrete-time models. It then describes data conversion and the binary logistic regression for analyzing a single event. Finally, it reviews issues related to data conversion and the multinomial logit model for analyzing multiple events.Less
This chapter reviews the first type of multivariate model analyzing time-to-event data: the discrete-time models. The primary feature of this type of model is its approximation of hazard rate by using probability estimated from a person-time data set. When the study focuses on a single event, the analyst uses a binary logistic regression to estimate the probability. When the study focuses on multiple events (i.e., termination of study time due to more than one reason), the analyst uses a multinomial logit regression to estimate multiple probabilities. The discrete-time model of multiple events is also known as competing-risks analysis. The chapter begins with an overview of the discrete-time models. It then describes data conversion and the binary logistic regression for analyzing a single event. Finally, it reviews issues related to data conversion and the multinomial logit model for analyzing multiple events.
Karen A. Randolph and Laura L. Myers
- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199764044
- eISBN:
- 9780199332533
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199764044.003.0001
- Subject:
- Social Work, Research and Evaluation
Chapter 1 defines important terms including basic statistics, multivariate analysis, and inferential statistics, as a way to introduce readers to the book’s premise—that a thorough understanding of ...
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Chapter 1 defines important terms including basic statistics, multivariate analysis, and inferential statistics, as a way to introduce readers to the book’s premise—that a thorough understanding of basic statistics is critical in the successful application of more advanced statistical methods. Readers are also introduced to assumptions and other requirements necessary for inferential statistical testing, making predictions about relationships between variables, and making causal inferences. The chapter then provides a description of each of the multivariate methods—multiple linear regression, Analysis of variance and Covariance, and path analysis—covered in the book. The chapter concludes with an overview of subsequent chapters and a description of the National Educational Longitudinal Study of 1988 (NELS: 88), which is the data set used for the book’s examples of each method.Less
Chapter 1 defines important terms including basic statistics, multivariate analysis, and inferential statistics, as a way to introduce readers to the book’s premise—that a thorough understanding of basic statistics is critical in the successful application of more advanced statistical methods. Readers are also introduced to assumptions and other requirements necessary for inferential statistical testing, making predictions about relationships between variables, and making causal inferences. The chapter then provides a description of each of the multivariate methods—multiple linear regression, Analysis of variance and Covariance, and path analysis—covered in the book. The chapter concludes with an overview of subsequent chapters and a description of the National Educational Longitudinal Study of 1988 (NELS: 88), which is the data set used for the book’s examples of each method.
Arend Lijphart
- Published in print:
- 1994
- Published Online:
- October 2011
- ISBN:
- 9780198273479
- eISBN:
- 9780191684050
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198273479.003.0005
- Subject:
- Political Science, Comparative Politics
This chapter discusses the result of the bivariate and multivariate analyses of the seventy electoral systems used in major democratic parliamentary elections in different countries. It explains the ...
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This chapter discusses the result of the bivariate and multivariate analyses of the seventy electoral systems used in major democratic parliamentary elections in different countries. It explains the bivariate relationships between the three dimensions of the electoral system and the disproportionality and party system characteristics. The analyses reveal that disproportionality does not necessarily lead to fewer elective parties in the proportional representation (PR) system and the differences in effective thresholds are considerably more consequential than the differences in electoral formula. The chapter also elaborates on the result of the regression analyses on the influence of the effective threshold and assembly size on disproportionality and party system variables.Less
This chapter discusses the result of the bivariate and multivariate analyses of the seventy electoral systems used in major democratic parliamentary elections in different countries. It explains the bivariate relationships between the three dimensions of the electoral system and the disproportionality and party system characteristics. The analyses reveal that disproportionality does not necessarily lead to fewer elective parties in the proportional representation (PR) system and the differences in effective thresholds are considerably more consequential than the differences in electoral formula. The chapter also elaborates on the result of the regression analyses on the influence of the effective threshold and assembly size on disproportionality and party system variables.
Ian Shrier and Russell J. Steele
- Published in print:
- 2009
- Published Online:
- February 2010
- ISBN:
- 9780199561629
- eISBN:
- 9780191722479
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199561629.003.10
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
Analyses used to unravel the multi-causal pathway leading to injury are more difficult than the commonly used univariate analyses. This chapter shows in detail the differences between univariate and ...
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Analyses used to unravel the multi-causal pathway leading to injury are more difficult than the commonly used univariate analyses. This chapter shows in detail the differences between univariate and multivariate analyses. Additionally, the chapter provides information, examples, and formulas to use multivariate analyses in practice.Less
Analyses used to unravel the multi-causal pathway leading to injury are more difficult than the commonly used univariate analyses. This chapter shows in detail the differences between univariate and multivariate analyses. Additionally, the chapter provides information, examples, and formulas to use multivariate analyses in practice.
David Linden
- Published in print:
- 2012
- Published Online:
- September 2012
- ISBN:
- 9780199596492
- eISBN:
- 9780191745669
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199596492.003.0010
- Subject:
- Neuroscience, Techniques, Development
The main techniques of functional neuroimaging and the application of multivariate pattern analysis for ‘brain reading’ can be applied to psychiatric questions in the following main ways: to ...
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The main techniques of functional neuroimaging and the application of multivariate pattern analysis for ‘brain reading’ can be applied to psychiatric questions in the following main ways: to ascertain the presence of particular mental states/symptoms; to find neural indicators of personality traits or abnormal behaviours; and to aid in the diagnosis and prognosis of mental illness. This chapter presents examples of these applications of modern neuroimaging techniques and discusses the potential inferences that can be drawn from the imaging results. This is followed by a section on the general limitation of imaging techniques in the verification of psychiatric symptoms and diagnoses, and an exposition of the issues of ethics and privacy that these developments may bring up.Less
The main techniques of functional neuroimaging and the application of multivariate pattern analysis for ‘brain reading’ can be applied to psychiatric questions in the following main ways: to ascertain the presence of particular mental states/symptoms; to find neural indicators of personality traits or abnormal behaviours; and to aid in the diagnosis and prognosis of mental illness. This chapter presents examples of these applications of modern neuroimaging techniques and discusses the potential inferences that can be drawn from the imaging results. This is followed by a section on the general limitation of imaging techniques in the verification of psychiatric symptoms and diagnoses, and an exposition of the issues of ethics and privacy that these developments may bring up.
David Sepkoski
- Published in print:
- 2012
- Published Online:
- February 2013
- ISBN:
- 9780226748559
- eISBN:
- 9780226748580
- Item type:
- chapter
- Publisher:
- University of Chicago Press
- DOI:
- 10.7208/chicago/9780226748580.003.0004
- Subject:
- Biology, Paleontology
Paleobiology has developed over time and it is now increasingly tending towards a reliance on quantification. Quantification became closely associated with theoretical work on evolution. Quantitative ...
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Paleobiology has developed over time and it is now increasingly tending towards a reliance on quantification. Quantification became closely associated with theoretical work on evolution. Quantitative paleobiology is still on the rise, with more and more researchers using structure-function relations to infer the ecology and behavior of long extinct organisms. It is impossible to avoid the suspicion that paleontologists' desire for greater analytical rigor was motivated partially by the perception that biologists (and geneticists in particular) would only take paleontology seriously when its data could be expressed numerically. Quantitative thinking found unique outlets in paleobiology, particularly in addressing problems involving large data sets and requiring complex multivariate analysis. Unquestionably, this approach would not have reached fruition before digital computers with large data storage capacities and reasonably accessible user interfaces became available.Less
Paleobiology has developed over time and it is now increasingly tending towards a reliance on quantification. Quantification became closely associated with theoretical work on evolution. Quantitative paleobiology is still on the rise, with more and more researchers using structure-function relations to infer the ecology and behavior of long extinct organisms. It is impossible to avoid the suspicion that paleontologists' desire for greater analytical rigor was motivated partially by the perception that biologists (and geneticists in particular) would only take paleontology seriously when its data could be expressed numerically. Quantitative thinking found unique outlets in paleobiology, particularly in addressing problems involving large data sets and requiring complex multivariate analysis. Unquestionably, this approach would not have reached fruition before digital computers with large data storage capacities and reasonably accessible user interfaces became available.
Arend Lijphart
- Published in print:
- 1994
- Published Online:
- October 2011
- ISBN:
- 9780198273479
- eISBN:
- 9780191684050
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198273479.003.0001
- Subject:
- Political Science, Comparative Politics
This introductory chapter explains the objective of this book, which is to analyse the operation and the political consequences of electoral systems, especially the degree of proportionality of their ...
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This introductory chapter explains the objective of this book, which is to analyse the operation and the political consequences of electoral systems, especially the degree of proportionality of their translation of votes into seats and their effects on party systems. Its focus is on the electoral systems that have been used in twenty-seven democratic countries from 1945 to 1990. These systems are those used in the majority of the free and democratic parliamentary elections ever conducted. The analyses employ two basic multivariate approaches, one is a comparable-cases strategy that focuses on within country variations and the other relies on a cross-sectional research design.Less
This introductory chapter explains the objective of this book, which is to analyse the operation and the political consequences of electoral systems, especially the degree of proportionality of their translation of votes into seats and their effects on party systems. Its focus is on the electoral systems that have been used in twenty-seven democratic countries from 1945 to 1990. These systems are those used in the majority of the free and democratic parliamentary elections ever conducted. The analyses employ two basic multivariate approaches, one is a comparable-cases strategy that focuses on within country variations and the other relies on a cross-sectional research design.
Patrick Dattalo
- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199773596
- eISBN:
- 9780199332564
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199773596.003.0006
- Subject:
- Social Work, Research and Evaluation
This chapter summarizes similarities and differences between multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), multivariate multiple regression (MMR), and ...
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This chapter summarizes similarities and differences between multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), multivariate multiple regression (MMR), and structural equation modeling (SEM). It offers suggestions to guide their differential use. It also compares and contrasts MANOVA and MANCOVA versus MMR, MANOVA and MANCOVA versus SEM, and MMR versus SEM.Less
This chapter summarizes similarities and differences between multivariate analysis of variance (MANOVA), multivariate analysis of covariance (MANCOVA), multivariate multiple regression (MMR), and structural equation modeling (SEM). It offers suggestions to guide their differential use. It also compares and contrasts MANOVA and MANCOVA versus MMR, MANOVA and MANCOVA versus SEM, and MMR versus SEM.
Robert A. Levine, Sarah E. Levine, Beatrice Schnell-Anzola, Meredith L. Rowe, and Emily Dexter
- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780195309829
- eISBN:
- 9780199932733
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195309829.003.0046
- Subject:
- Psychology, Developmental Psychology
In this chapter the literacy-mediation hypothesis – that the acquisition of academic literacy influences health literacy and health navigation skills – is tested in the four-country data and the ...
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In this chapter the literacy-mediation hypothesis – that the acquisition of academic literacy influences health literacy and health navigation skills – is tested in the four-country data and the UNICEF Nepal survey. The hypothesis is supported by multivariate analyses, not only in regard to the comprehension of printed health messages but also in the comprehension of radio messages and in producing an intelligible illness narrative – though both of the latter are oral communication tasks. These findings suggest that literacy instruction in school promotes a woman’s health literacy and navigation skills beyond those that involve reading and writing, and point to a more general ability to communicate in bureaucratic settings like schools and clinics and to a tendency to accept the authority of health professionals. The UNICEF Nepal survey shows health knowledge and media exposure to be involved in the causal sequence.Less
In this chapter the literacy-mediation hypothesis – that the acquisition of academic literacy influences health literacy and health navigation skills – is tested in the four-country data and the UNICEF Nepal survey. The hypothesis is supported by multivariate analyses, not only in regard to the comprehension of printed health messages but also in the comprehension of radio messages and in producing an intelligible illness narrative – though both of the latter are oral communication tasks. These findings suggest that literacy instruction in school promotes a woman’s health literacy and navigation skills beyond those that involve reading and writing, and point to a more general ability to communicate in bureaucratic settings like schools and clinics and to a tendency to accept the authority of health professionals. The UNICEF Nepal survey shows health knowledge and media exposure to be involved in the causal sequence.
Thomas A. Trikalinos, Louise B. Russell, and Gillian D. Sanders
- Published in print:
- 2016
- Published Online:
- November 2016
- ISBN:
- 9780190492939
- eISBN:
- 9780190492960
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780190492939.003.0009
- Subject:
- Public Health and Epidemiology, Public Health
This is a new chapter, highlighting the importance of interpreting, adjusting, and synthesizing evidence for informing cost-effectiveness analysis (CEA) models. The chapter goes beyond the original ...
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This is a new chapter, highlighting the importance of interpreting, adjusting, and synthesizing evidence for informing cost-effectiveness analysis (CEA) models. The chapter goes beyond the original Panel’s conception of evidence synthesis for CEAs by calling for analysts to undertake a pre-analytical phase (defining a question and identifying pertinent data from distinct sources), an analytical phase (positing and learning relationships across data from distinct sources), and a post-analytical phase (conjecturing on the implications of the learned relationships for the question at hand). Unlike systematic reviews and meta-analyses that aim to describe the state of the evidence and the distribution of effects from relevant studies, the goal of evidence synthesis for decision making is to obtain bias-corrected estimates of model parameters in the modeled setting. This invariably requires extra-empirical information, and places more demands on the analytic machinery.Less
This is a new chapter, highlighting the importance of interpreting, adjusting, and synthesizing evidence for informing cost-effectiveness analysis (CEA) models. The chapter goes beyond the original Panel’s conception of evidence synthesis for CEAs by calling for analysts to undertake a pre-analytical phase (defining a question and identifying pertinent data from distinct sources), an analytical phase (positing and learning relationships across data from distinct sources), and a post-analytical phase (conjecturing on the implications of the learned relationships for the question at hand). Unlike systematic reviews and meta-analyses that aim to describe the state of the evidence and the distribution of effects from relevant studies, the goal of evidence synthesis for decision making is to obtain bias-corrected estimates of model parameters in the modeled setting. This invariably requires extra-empirical information, and places more demands on the analytic machinery.
T.F.H. Allen and Thomas B. Starr
- Published in print:
- 2017
- Published Online:
- September 2018
- ISBN:
- 9780226489544
- eISBN:
- 9780226489711
- Item type:
- chapter
- Publisher:
- University of Chicago Press
- DOI:
- 10.7208/chicago/9780226489711.003.0010
- Subject:
- Biology, Ecology
Plant community data of species in places are compressed to give meaningful pattern in a lower dimension space. The outcome of analysis is affected by the context and transformation of the data. All ...
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Plant community data of species in places are compressed to give meaningful pattern in a lower dimension space. The outcome of analysis is affected by the context and transformation of the data. All this is a set of scaling operations that effect emergence in the data. Curvature in output can be distortion but is often the interaction between levels of analysis.Less
Plant community data of species in places are compressed to give meaningful pattern in a lower dimension space. The outcome of analysis is affected by the context and transformation of the data. All this is a set of scaling operations that effect emergence in the data. Curvature in output can be distortion but is often the interaction between levels of analysis.
Shylashri Shankar
- Published in print:
- 2009
- Published Online:
- October 2012
- ISBN:
- 9780195693201
- eISBN:
- 9780199081998
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195693201.001.0001
- Subject:
- Law, Human Rights and Immigration
Most experts agree that India's Supreme Court and lower courts' pro-active behaviour on social rights can be traced back to the immediate post-Emergency era. Post-Emergency, judges have become ...
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Most experts agree that India's Supreme Court and lower courts' pro-active behaviour on social rights can be traced back to the immediate post-Emergency era. Post-Emergency, judges have become ‘embedded negotiators’. Their judgments have carefully avoided conflict with the political wings while being mindful of their role as safe keepers of the rights of citizens. While the Court has sometimes been charged with judicial overreach, this book attempts to understand why certain choices were made by the Supreme Court judges and the circumstances in which they were made. Qualitative analysis of the constitutional and legal framework, landmark rulings, and dissenting opinion, along with a multivariate analysis of civil liberties and social rights cases are used. This book evaluates the judgments on preventive detention, anti-terror, health, and education cases and shows how judges seek legitimacy for their decisions.Less
Most experts agree that India's Supreme Court and lower courts' pro-active behaviour on social rights can be traced back to the immediate post-Emergency era. Post-Emergency, judges have become ‘embedded negotiators’. Their judgments have carefully avoided conflict with the political wings while being mindful of their role as safe keepers of the rights of citizens. While the Court has sometimes been charged with judicial overreach, this book attempts to understand why certain choices were made by the Supreme Court judges and the circumstances in which they were made. Qualitative analysis of the constitutional and legal framework, landmark rulings, and dissenting opinion, along with a multivariate analysis of civil liberties and social rights cases are used. This book evaluates the judgments on preventive detention, anti-terror, health, and education cases and shows how judges seek legitimacy for their decisions.
Ricardo Otheguy and Ana Celia Zentella
- Published in print:
- 2012
- Published Online:
- January 2012
- ISBN:
- 9780199737406
- eISBN:
- 9780199918621
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199737406.003.0008
- Subject:
- Linguistics, Sociolinguistics / Anthropological Linguistics
The study of internal (grammatical and discourse‐communicative) independent variables is distinguished from external social variables. Multivariate analysis is now logistic regression. Explanations ...
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The study of internal (grammatical and discourse‐communicative) independent variables is distinguished from external social variables. Multivariate analysis is now logistic regression. Explanations are offered for observed ranking of variables and constraints in reference lects. Variable hierarchies remain the same in bilingual lects, even into the second generation. Prediction is made and confirmed regarding constraint hierarchies keeping same ranking but undergoing reductions of range in the second generation. Rank differences in reference lects and rank changes in the second generation are studied in the variable based on person‐number inflections of the verb. Special attention is paid to ranking of second‐ and third‐person singular verbs. Results are analyzed in terms of evidence for continuity and contact‐induced change.Less
The study of internal (grammatical and discourse‐communicative) independent variables is distinguished from external social variables. Multivariate analysis is now logistic regression. Explanations are offered for observed ranking of variables and constraints in reference lects. Variable hierarchies remain the same in bilingual lects, even into the second generation. Prediction is made and confirmed regarding constraint hierarchies keeping same ranking but undergoing reductions of range in the second generation. Rank differences in reference lects and rank changes in the second generation are studied in the variable based on person‐number inflections of the verb. Special attention is paid to ranking of second‐ and third‐person singular verbs. Results are analyzed in terms of evidence for continuity and contact‐induced change.
