Handbook of Longitudinal Research - Design, Measurement, and Analysis

Front Cover

Handbook of Longitudinal Research: Design, Measurement, and Analysis

Copyright Page

Table of Contents

List of Contributors

Preface

Part I Longitudinal Research Design

Chapter 1 Introduction: Longitudinal research design and analysis

1 Longitudinal and cross-sectional designs for research

2 Designs for longitudinal research

3 Measurement issues in longitudinal research

4 Descriptive and causal analysis in longitudinal research

5 Description and measurement of qualitative change

6 Timing of qualitative change: event history analysis

7 Panel analysis, structural equation models, and multilevel models

8 Time series analysis and deterministic dynamic models

9 Conclusion

References

Chapter 2 Using national census data to study change

1 Introduction

2 Official uses of census information

3 Public and research use of census information

4 The census as a source for longitudinal analysis

5 Data availability

6 Aggregate and microdata

7 Questions

8 Uses of census data in research

9 The potential of longitudinal analysis of census data

10 Historical context and the politics of numbers

11 A final note

References

Chapter 3 Repeated cross-sectional research: the general social surveys

1 Introduction

2 Organization

3 Data collection: 1972–2004

4 Publications by the user community

5 Teaching and other uses

6 Contributions to knowledge

7 Summary

References

Chapter 4 Structuring the National Crime Victim Survey for use in longitudinal analysis

1 Introduction

2 NCVS procedures

3 Individual-level longitudinal analyses using the NCVS

4 Example: Prediction of violent crime victimization

5 Summary and discussion

Glossary

References

Chapter 5 The Millennium Cohort Study and mature national birth cohorts in Britain

1 Introduction

2 The heritage of birth cohort studies in Britain

3 The ESRC Millennium Cohort Study

4 Findings and scope for analysis of the Millennium Cohort

5 Conclusion

Glossary

References

Chapter 6 Retrospective longitudinal research: the German Life History Study

1 Introduction and overview

2 Origins, goals and institutional contexts of the German Life History Study

3 Surveys and methods: sampling, data collection, data editing

4 Autobiographical memory and retrospective measurement

5 Substantive areas and major findings

6 Data access and documentation

Acknowledgements

Glossary

References

Part II Measurement Issues in Longitudinal Research

Chapter 7 Respondent recall

1 Introduction

2 Respondent recall – issues of memory

3 Respondent recall – issues in longitudinal research design

4 Research evidence

5 Research techniques for improving respondent recall

6 Conclusion

Glossary

References

Chapter 8 A review and summary of studies on panel conditioning

1 Introduction

2 Theoretical and analytic principles

3 Conditioning and changes in behavior

4 Conditioning and changes in the process for reporting behaviors

5 Conditioning and reports of attitudes, opinions and subjective phenomena

6 Summary and discussion

References

Chapter 9 Reliability issues in longitudinal research

1 Introduction

2 Reliability issues in longitudinal research

3 A framework for examining structural stability in longitudinal research

4 Regression to the mean

5 Unreliability of change scores and the regression fallacy

6 Concluding remarks

References

Chapter 10 Orderly change in a stable world: The antisocial trait as a chimera

1 Introduction

2 Stable but changing

3 Two developmental models

4 Analyses of qualitative shifts

5 The trait as a chimera

6 Implications

Acknowledgments

References

Chapter 11 Minimizing panel attrition

1 Introduction

2 Longitudinal survey design and attrition

3 The process of attrition and techniques for maximizing response

4 The use of incentives to minimize attrition

5 Conclusion and best practice guidelines

References

Chapter 12 Nonignorable nonresponse in longitudinal studies

1 Introduction

2 MAR, pattern mixture and selection models

3 Empirical application: methods

4 Empirical applications: results

5 Discussion

References

Part III Descriptive and Causal Analysis in Longitudinal Research

Chapter 13 Graphical techniques for exploratory and confirmatory analyses of longitudinal data

1 Introduction

2 Graphical exploration of longitudinal data

3 Graphical model-checking based on residuals

4 Conclusion

Software

Acknowledgements

References

Chapter 14 Separating age, period, and cohort effects in developmental and historical research

1 Age and period as alternative dimensions of time

2 Age, period, and cohort as explanatory variables

3 Cohort as a unit of analysis

4 Illustration of the dummy variable regression analysis of age, period, and cohort effects

5 Period effects: Changes over time

6 Age effects: life cycle and developmental changes

7 Conclusion

Author’s note

References

Chapter 15 An introduction to pooling cross-sectional and time series data

1 Introduction

2 Three pooling problems

3 Fixed effects or random effects: three considerations

4 Estimation issues in fixed effects models

5 A practical example: welfare spending and crime

6 Simple extensions

7 Summary and additional readings

References

Chapter 16 Dynamic models and cross-sectional data: the consequences of dynamic misspecification

1 Introduction

2 General dynamic linear structural equation model

3 Quasi-dynamic model

4 Autocorrelated model

5 Dynamic autocorrelated model

6 A First-order dynamic model

7 Conclusion

References

Chapter 17 Causal analysis with nonexperimental panel data

1 Introduction

2 Causal analysis with panel data

3 Qualitative outcomes

4 Quantitative (interval-level) outcomes

5 Independent cross-sections

6 Software

References

Chapter 18 Causal inference in longitudinal experimental research

1 Introduction

2 Experimental research with only one follow-up measurement

3 Experimental research with more than one follow-up measurement

4 General recommendation

References

Part IV Description and Measurement of Qualitative Change

Chapter 19 Analyzing longitudinal qualitative observational data

1 Analyzing longitudinal qualitative observational data

2 Final comments

Glossary

References

Chapter 20 Configural frequency analysis of longitudinal data

1 Introduction

2 CFA—a tutorial

3 CFA of longitudinal data

4 CFA of symmetry patterns

5 Discussion

References

Chapter 21 Analysis of longitudinal categorical data using optimal scaling techniques

1 Introduction

2 Optimal scaling

3 Analyzing longitudinal data using optimal scaling techniques: two strategies

4 Example

5 Extensions

6 Software

7 Concluding remarks

References

Chapter 22 An introduction to latent class analysis

1 Introduction

2 Model for LCA

3 Model fit

4 Model comparisons

5 Unconstrained LCA

6 Multiple groups LCA

7 Scaling models

8 Covariate LCA

9 Software notes

References

Chapter 23 Latent class models in longitudinal research

1 Introduction

2 The mixture latent Markov model

3 The most important special cases

4 Application to NYS data

5 Discussion

Appendix A: Baum-Welch algorithm for the mixture latent Markov model

Appendix B: Examples of Latent GOLD syntax files

References

Part V Timing of Qualitative Change: Event History Analysis

Chapter 24 Nonparametric methods for event history data: descriptive measures

1 Introduction

2 Single spell data with censoring

3 Single spell data with competing risks

4 Multistate data

5 Recurrent event data

6 Current status data on recurrent events

7 Backward recurrent times

8 Summary

References

Chapter 25 The Cox proportional hazards model, diagnostics, and extensions

1 Introduction

2 Cox proportional hazards model

3 Cox model residuals

4 Covariate functional form

5 Proportional hazards assumption

6 Other diagnostics

7 Interpreting a Cox model

8 Cox modeling extensions and sources of dependence

9 Conclusion

References

Chapter 26 Parametric event history analysis: an application to the analysis of recidivism

1 Introduction

2 Problems of conventional methods in the analysis of event history data: recidivism as an example

3 Parametric versus nonparametric event history methods

4 Multivariate prediction of survival time: an example of log-normal and log-logistic event history analysis

References

Chapter 27 Discrete-time survival analysis: predicting whether, and if so when, an event occurs

1 Introduction

2 Measuring time and recording event occurrence

3 Descriptive analysis of discrete-time survival data

4 Modeling event occurrence as a function of predictors

5 Extensions of the basic discrete-time hazard model

6 Is survival analysis really necessary?

Glossary

References

Part VI Panel Analysis, Structural Equation Models, and Multilevel Models

Chapter 28 Generalized estimating equations for longitudinal panel analysis

1 Introduction

2 Generalized linear models

3 The independence model

4 Subject-specific (SS) versus population-averaged (PA) models

5 Estimating the working correlation matrix

References

Chapter 29 Linear panel analysis

1 Introduction

2 Unobserved heterogeneity models for linear panel analysis

3 Dynamic panel analysis

4 Structural equation panel models

5 Dynamic panel models with unobserved heterogeneity

6 Conclusion

Acknowledgement

Data and software

References

Chapter 30 Panel analysis with logistic regression

1 Measuring change in categorical dependent variables

2 Logistic regression for conditional and unconditional change in two-wave panel models

3 The subject-specific model for a two-wave panel

4 Estimation of the conditional logistic regression model

5 The fixed effects model using conditional logistic regression

6 Unconditional logistic regression for the unconditional change model

7 Logistic regression for the conditional change model

8 Extensions to polytomous dependent variables

9 Multiwave logistic regression panel models

10 Conclusion

Software

References

Chapter 31 Latent growth curve models

Author notes

References

Chapter 32 Multilevel growth curve analysis for quantitative outcomes

1 Introduction

2 Research design and data management

3 Building the multilevel growth model

4 Conclusion

Software

Glossary

References

Chapter 33 Multilevel analysis with categorical outcomes

1 Specifying the relationship between the categorical dependent variable and time

2 Multilevel logistic regression models for repeated measures data

3 Multilevel logistic regression for prevalence of marijuana use

4 The population averaged model for prevalence of marijuana use

5 The unit-specific model for prevalence of marijuana use

6 Extensions and contrasts

7 Conclusion: multilevel logistic regression for longitudinal data analysis

Software

References

Part VII Time Series Analysis and Deterministic Dynamic Models

Chapter 34 A brief introduction to time series analysis

1 Introduction

2 Describing or modeling the outcome as a function of time

3 Describing or modeling the outcome as a function of present and past random shocks

4 Describing or modeling the outcome as a function of past values of the outcome (plus at)

5 The ARIMA(p,d,q) model

6 Example: IBM stock prices

7 Example: homicides in three midwestern states

8 Extensions to the simple univariate model

9 Forecasting

10 Conclusion

Software

Bibliographic note

References

Chapter 35 Spectral analysis

1 Introduction

2 A simple periodic model and harmonic analysis

3 Periodogram analysis

4 Tests for hidden periodic components

5 The spectrum of time series and its estimation

6 Relationships between two times series and cross-spectrum

7 Some mathematical detail

References

Chapter 36 Time-series techniques for repeated cross-section data

1 The simple ordinary least squares (OLS) method

2 Autoregressive (maximum likelihood) models

3 The lagged endogenous variable OLS method

4 Box-Jenkins (ARIMA) methods

5 An application of different time series to the same set of data: or how different assumptions can produce different conclusions

6 Which techniques? Assessing relative strengths and weaknesses

7 Conclusion

Appendix: effects of differencing on two hypothetical time series

References

Chapter 37 Differential equation models for longitudinal data

1 Second order equations

2 Some methods for estimating parameters

3 Latent differential equations

4 Multivariate second order LDE

5 LDE model extensions

6 Limitations and recommendations

7 Conclusions

Author note

References

Chapter 38 Nonlinear dynamics, chaos, and catastrophe theory

1 Nonlinear dynamics

2 Competition and cooperation

3 Chaos theory

4 Catastrophe theory

5 The future of nonlinear modeling in the social sciences

Glossary

References

Index