Judith D. Singer and John B. Willett
- Published in print:
- 2003
- Published Online:
- September 2009
- ISBN:
- 9780195152968
- eISBN:
- 9780199864980
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195152968.001.0001
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, and the elderly become frail and forgetful. Beyond these natural ...
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Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, and the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. This book is concerned with behavioral, social, and biomedical sciences. It offers a presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using data sets from published studies, the book takes you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models.Less
Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, and the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. This book is concerned with behavioral, social, and biomedical sciences. It offers a presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using data sets from published studies, the book takes you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models.
Donna Harrington
- Published in print:
- 2008
- Published Online:
- January 2009
- ISBN:
- 9780195339888
- eISBN:
- 9780199863662
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195339888.001.0001
- Subject:
- Social Work, Research and Evaluation
Measures that are reliable, valid, and can be used across diverse populations are vital to social work research, but the development of new measures is an expensive and time-consuming process. An ...
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Measures that are reliable, valid, and can be used across diverse populations are vital to social work research, but the development of new measures is an expensive and time-consuming process. An array of existing measures can provide a cost-effective alternative, but in order to take this expedient step with confidence, researchers must ensure that the existing measure is appropriate for the new study. Confirmatory factor analysis (CFA) is one of the ways to do so. CFA has four primary functions—psychometric evaluation of measures, construct validation, testing method effects, and testing measurement invariance. This book provides an overview of the method, step-by-step guides to creating a CFA model and assessing its fit, and explanations of the requirements for using CFA, as well the book underscores the issues that are necessary to consider when using multiple groups or equivalent and multilevel models. Real-world examples, screenshots from the Amos software program that can be used to conduct CFA, and reading suggestions for each chapter form part of the book.Less
Measures that are reliable, valid, and can be used across diverse populations are vital to social work research, but the development of new measures is an expensive and time-consuming process. An array of existing measures can provide a cost-effective alternative, but in order to take this expedient step with confidence, researchers must ensure that the existing measure is appropriate for the new study. Confirmatory factor analysis (CFA) is one of the ways to do so. CFA has four primary functions—psychometric evaluation of measures, construct validation, testing method effects, and testing measurement invariance. This book provides an overview of the method, step-by-step guides to creating a CFA model and assessing its fit, and explanations of the requirements for using CFA, as well the book underscores the issues that are necessary to consider when using multiple groups or equivalent and multilevel models. Real-world examples, screenshots from the Amos software program that can be used to conduct CFA, and reading suggestions for each chapter form part of the book.
David Rueda
- Published in print:
- 2007
- Published Online:
- January 2008
- ISBN:
- 9780199216352
- eISBN:
- 9780191712241
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199216352.003.0003
- Subject:
- Political Science, Comparative Politics
This chapter has two main goals: to produce data that provide a complete picture of the preferences of insiders, outsiders, and upscale groups; and to test whether these preferences fit into the ...
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This chapter has two main goals: to produce data that provide a complete picture of the preferences of insiders, outsiders, and upscale groups; and to test whether these preferences fit into the partisanship model proposed in this book. The chapter proceeds as follows. The first section provides a brief explanation of the survey used in the analysis and the way insiders, outsiders, and upscale groups have been categorized. The second section offers a detailed explanation of the individual preferences implied in the insider-outsider model and an initial and descriptive assessment of their accuracy. The third section contains a systematic multilevel analysis of the individual preferences of insiders, outsiders, and upscale groups. The fourth section introduces the two macro factors which, in Chapter 2, were hypothesized to affect the differences between insiders and outsiders: job security and corporatism. The final section presents multilevel maximum likelihood models estimating the effects of job security and corporatism. The results corroborate the model's claims: lower levels of employment protection do indeed make insiders more like outsiders (i.e., more supportive of labour market policy). The results support an economic insider-outsider interpretation of the effects of corporatism on insider preferences.Less
This chapter has two main goals: to produce data that provide a complete picture of the preferences of insiders, outsiders, and upscale groups; and to test whether these preferences fit into the partisanship model proposed in this book. The chapter proceeds as follows. The first section provides a brief explanation of the survey used in the analysis and the way insiders, outsiders, and upscale groups have been categorized. The second section offers a detailed explanation of the individual preferences implied in the insider-outsider model and an initial and descriptive assessment of their accuracy. The third section contains a systematic multilevel analysis of the individual preferences of insiders, outsiders, and upscale groups. The fourth section introduces the two macro factors which, in Chapter 2, were hypothesized to affect the differences between insiders and outsiders: job security and corporatism. The final section presents multilevel maximum likelihood models estimating the effects of job security and corporatism. The results corroborate the model's claims: lower levels of employment protection do indeed make insiders more like outsiders (i.e., more supportive of labour market policy). The results support an economic insider-outsider interpretation of the effects of corporatism on insider preferences.
Judith D. Singer and John B. Willett
- Published in print:
- 2003
- Published Online:
- September 2009
- ISBN:
- 9780195152968
- eISBN:
- 9780199864980
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195152968.003.0003
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
This chapter introduces the multilevel model for change, demonstrating how it allows researchers to address within-person and between-person questions about change simultaneously. Although there are ...
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This chapter introduces the multilevel model for change, demonstrating how it allows researchers to address within-person and between-person questions about change simultaneously. Although there are several ways of writing the statistical model, a simple and common approach is adopted that has much substantive appeal. It specifies the multilevel model for change by simultaneously postulating a pair of subsidiary models—a level-1 submodel that describes how each person changes over time, and a level-2 model that describes how these changes differ across people.Less
This chapter introduces the multilevel model for change, demonstrating how it allows researchers to address within-person and between-person questions about change simultaneously. Although there are several ways of writing the statistical model, a simple and common approach is adopted that has much substantive appeal. It specifies the multilevel model for change by simultaneously postulating a pair of subsidiary models—a level-1 submodel that describes how each person changes over time, and a level-2 model that describes how these changes differ across people.
Judith D. Singer and John B. Willett
- Published in print:
- 2003
- Published Online:
- September 2009
- ISBN:
- 9780195152968
- eISBN:
- 9780199864980
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195152968.003.0005
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
This chapter demonstrates how to apply the multilevel model to complex data sets. Section 5.1 begins by illustrating what to do when the number of waves is constant but their spacing is irregular. ...
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This chapter demonstrates how to apply the multilevel model to complex data sets. Section 5.1 begins by illustrating what to do when the number of waves is constant but their spacing is irregular. Section 5.2 illustrates what to do when the number of waves per person differs as well; it also discusses the problem of missing data, the most common source of imbalance in longitudinal work. Section 5.3 demonstrates how to include time-varying predictors in your data analysis. Section 5.4 concludes by discussing why and how to adopt alternative representations for the main effect of TIME.Less
This chapter demonstrates how to apply the multilevel model to complex data sets. Section 5.1 begins by illustrating what to do when the number of waves is constant but their spacing is irregular. Section 5.2 illustrates what to do when the number of waves per person differs as well; it also discusses the problem of missing data, the most common source of imbalance in longitudinal work. Section 5.3 demonstrates how to include time-varying predictors in your data analysis. Section 5.4 concludes by discussing why and how to adopt alternative representations for the main effect of TIME.
Judith D. Singer and John B. Willett
- Published in print:
- 2003
- Published Online:
- September 2009
- ISBN:
- 9780195152968
- eISBN:
- 9780199864980
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195152968.003.0007
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
The previous chapters emphasized the fixed effects in the multilevel model for change. This chapter, in contrast, focuses on the model's random effects as embodied in its error covariance structure. ...
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The previous chapters emphasized the fixed effects in the multilevel model for change. This chapter, in contrast, focuses on the model's random effects as embodied in its error covariance structure. Section 7.1 begins by reviewing the “standard” multilevel model for change, expressed in composite form. Section 7.2 examines this model's random effects, demonstrating that the composite error term is indeed both heteroscedastic and autocorrelated, as preferred for longitudinal data. Section 7.3 compares several alternative error covariance structures and provide strategies for choosing among them.Less
The previous chapters emphasized the fixed effects in the multilevel model for change. This chapter, in contrast, focuses on the model's random effects as embodied in its error covariance structure. Section 7.1 begins by reviewing the “standard” multilevel model for change, expressed in composite form. Section 7.2 examines this model's random effects, demonstrating that the composite error term is indeed both heteroscedastic and autocorrelated, as preferred for longitudinal data. Section 7.3 compares several alternative error covariance structures and provide strategies for choosing among them.
Christopher J. Anderson and Russell J. Dalton
- Published in print:
- 2010
- Published Online:
- January 2011
- ISBN:
- 9780199599233
- eISBN:
- 9780191595790
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199599233.003.0011
- Subject:
- Political Science, Comparative Politics
This chapter reviews the findings of this book and discusses their implications for the study of voter behavior. It argues that the effect of political institutions on voter behavior is typically ...
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This chapter reviews the findings of this book and discusses their implications for the study of voter behavior. It argues that the effect of political institutions on voter behavior is typically indirect and contingent and that the most promixate contextual influences are typically different dimensions of the electoral supply in the form of party system polarization and fragmentation.Less
This chapter reviews the findings of this book and discusses their implications for the study of voter behavior. It argues that the effect of political institutions on voter behavior is typically indirect and contingent and that the most promixate contextual influences are typically different dimensions of the electoral supply in the form of party system polarization and fragmentation.
Russell J. Dalton and Christopher J. Anderson (eds)
- Published in print:
- 2010
- Published Online:
- January 2011
- ISBN:
- 9780199599233
- eISBN:
- 9780191595790
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199599233.001.0001
- Subject:
- Political Science, Comparative Politics
A large body of electoral studies and political party research argues that the institutional context defines incentives that shape citizen participation and voting choice. Based on the unique ...
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A large body of electoral studies and political party research argues that the institutional context defines incentives that shape citizen participation and voting choice. Based on the unique resources of the Comparative Study of Electoral System surveys, this volume provides the first systematic comparative analysis of how and why cross-national differences in political institutions and party systems shape individual citizens' attitudes and political behavior, including voter turnout, campaign participation, and vote choice. An international team of electoral scholars finds that countries' formal institutional characteristics and party systems have only a modest impact on citizen political choices compared to individual level factors. Furthermore, the formal institutional characteristics of electoral system that have been most emphasized by electoral studies researchers have less impact than characteristics of the party system that are separate from formal institutions. Advanced multilevel analyses demonstrate that contextual effects are more often indirect and interactive, and thus their effects are typically not apparent in single nation election studies. The results have the potential to reshape our understanding of how the institutional framework and context of election matters, and the limits of institutional design in shaping citizen electoral behavior.Less
A large body of electoral studies and political party research argues that the institutional context defines incentives that shape citizen participation and voting choice. Based on the unique resources of the Comparative Study of Electoral System surveys, this volume provides the first systematic comparative analysis of how and why cross-national differences in political institutions and party systems shape individual citizens' attitudes and political behavior, including voter turnout, campaign participation, and vote choice. An international team of electoral scholars finds that countries' formal institutional characteristics and party systems have only a modest impact on citizen political choices compared to individual level factors. Furthermore, the formal institutional characteristics of electoral system that have been most emphasized by electoral studies researchers have less impact than characteristics of the party system that are separate from formal institutions. Advanced multilevel analyses demonstrate that contextual effects are more often indirect and interactive, and thus their effects are typically not apparent in single nation election studies. The results have the potential to reshape our understanding of how the institutional framework and context of election matters, and the limits of institutional design in shaping citizen electoral behavior.
Dorothea Nitsch and Gita D Mishra
- Published in print:
- 2009
- Published Online:
- September 2009
- ISBN:
- 9780199231034
- eISBN:
- 9780191723841
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199231034.003.0010
- Subject:
- Public Health and Epidemiology, Public Health, Epidemiology
Intergenerational data necessarily reflect the time and place that the different generations of participants were living in. This chapter aims first to introduce simple concepts to provide an ...
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Intergenerational data necessarily reflect the time and place that the different generations of participants were living in. This chapter aims first to introduce simple concepts to provide an understanding of the founding assumptions and principles, before moving on to more complex analytic methods. As the objectives of analyses may vary substantially across intergenerational studies, there is no easy guideline for analyses, except perhaps that some a priori clarity on the main associations of interest is crucial. Since parents and their offspring are genetically related, intergenerational studies are to some extent genetically informative even if no genotyping was performed. Much of the analyses are concerned with identifying or unravelling the relationship between outcomes and genetic and environmental factors. Ways of handling missing data as well as approaches to deal with non-paternity are also discussed. Illustrative examples are drawn from the two cohort studies.Less
Intergenerational data necessarily reflect the time and place that the different generations of participants were living in. This chapter aims first to introduce simple concepts to provide an understanding of the founding assumptions and principles, before moving on to more complex analytic methods. As the objectives of analyses may vary substantially across intergenerational studies, there is no easy guideline for analyses, except perhaps that some a priori clarity on the main associations of interest is crucial. Since parents and their offspring are genetically related, intergenerational studies are to some extent genetically informative even if no genotyping was performed. Much of the analyses are concerned with identifying or unravelling the relationship between outcomes and genetic and environmental factors. Ways of handling missing data as well as approaches to deal with non-paternity are also discussed. Illustrative examples are drawn from the two cohort studies.
Joseph L. Schafer
- Published in print:
- 2006
- Published Online:
- March 2012
- ISBN:
- 9780195173444
- eISBN:
- 9780199847051
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195173444.003.0002
- Subject:
- Psychology, Social Psychology
The growth of semiparametric regression modeling through generalized estimating questions (GEE) is one of the most influential recent developments in statistical practice. GEE methods are attractive ...
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The growth of semiparametric regression modeling through generalized estimating questions (GEE) is one of the most influential recent developments in statistical practice. GEE methods are attractive both from a practical and theoretical perspective; they are easy to use, flexible, and make relatively weak assumptions about the distribution of the response of interest. They are closely linked to multilevel models and are commonly regarded as robust relatives of the linear mixed model characterized by Hedeker et al. Because of longstanding tensions existing between two different schools of statistical thought, some who handle longitudinal data may rely either on multilevel models or GEE but not both. The authors see them as complementary instead of referring to the two as rivals.Less
The growth of semiparametric regression modeling through generalized estimating questions (GEE) is one of the most influential recent developments in statistical practice. GEE methods are attractive both from a practical and theoretical perspective; they are easy to use, flexible, and make relatively weak assumptions about the distribution of the response of interest. They are closely linked to multilevel models and are commonly regarded as robust relatives of the linear mixed model characterized by Hedeker et al. Because of longstanding tensions existing between two different schools of statistical thought, some who handle longitudinal data may rely either on multilevel models or GEE but not both. The authors see them as complementary instead of referring to the two as rivals.
