scholarly journals A training manual for event history analysis using longitudinal data

2019 ◽  
Author(s):  
Philippe Bocquier ◽  
Carren Ginsburg ◽  
Mark A. Collinson
Keyword(s):  
Longitudinal Data ◽  
Event History Analysis ◽  
Research Note ◽  
Event History ◽  
Data Repository ◽  
Training Manual ◽  
Full Potential ◽  
Micro Data ◽  
Cross Sectional ◽  
History Analysis

Abstract Objective: This research note reports on the activities of the Multi-centre Analysis of the Dynamics of Internal Migration And Health (MADIMAH) project aimed at collating and testing of a set of tools to conduct longitudinal event history analyses applied to standardised Health and Demographic Surveillance System (HDSS) datasets. The methods are illustrated using an example of longitudinal micro-data from the Agincourt HDSS, one of a number of open access datasets available through the INDEPTH iShare2 data repository. The research note documents the experience of the MADIMAH group in analysing HDSS data and demonstrates how complex analyses can be streamlined and conducted in an accessible way. These tools are aimed at aiding analysts and researchers wishing to conduct longitudinal data analysis of demographic events. Results: The methods demonstrated in this research note may successfully be applied by practitioners to longitudinal micro-data from HDSS, as well as retrospective surveys or register data. The illustrations provided are accompanied by detailed, tested computer programs, which demonstrate the full potential of longitudinal data to generate both cross-sectional and longitudinal standard descriptive estimates as well as more complex regression estimates.

scholarly journals A training manual for event history analysis using longitudinal data

2019 ◽  
Author(s):  
Philippe Bocquier ◽  
Carren Ginsburg ◽  
Mark A. Collinson
Keyword(s):  
Longitudinal Data ◽  
Event History Analysis ◽  
Research Note ◽  
Event History ◽  
Data Repository ◽  
Training Manual ◽  
Full Potential ◽  
Micro Data ◽  
Cross Sectional ◽  
History Analysis

Abstract Objective: This research note reports on the activities of the Multi-centre Analysis of the Dynamics of Internal Migration And Health (MADIMAH) project aimed at collating and testing of a set of tools to conduct longitudinal event history analyses applied to standardised Health and Demographic Surveillance System (HDSS) datasets. The methods are illustrated using an example of longitudinal micro-data from the Agincourt HDSS, one of a number of open access datasets available through the INDEPTH iShare2 data repository. The research note documents the experience of the MADIMAH group in analysing HDSS data and demonstrates how complex analyses can be streamlined and conducted in an accessible way. These tools are aimed at aiding analysts and researchers wishing to conduct longitudinal data analysis of demographic events. Results: The methods demonstrated in this research note may successfully be applied by practitioners to longitudinal micro-data from HDSS, as well as retrospective surveys or register data. The illustrations provided are accompanied by detailed, tested computer programs, which demonstrate the full potential of longitudinal data to generate both cross-sectional and longitudinal standard descriptive estimates as well as more complex regression estimates.

scholarly journals A training manual for event history analysis using longitudinal data

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Philippe Bocquier ◽  
Carren Ginsburg ◽  
Mark A. Collinson
Keyword(s):  
Longitudinal Data ◽  
Event History Analysis ◽  
Event History ◽  
Training Manual ◽  
History Analysis

State Lottery Adoptions as Policy Innovations: An Event History Analysis

1990 ◽  
Vol 84 (2) ◽  
pp. 395-415 ◽  
Author(s):  
Frances Stokes Berry ◽  
William D. Berry
Keyword(s):  
State Government ◽  
Event History Analysis ◽  
Organizational Innovation ◽  
Event History ◽  
Social Characteristics ◽  
Cross Sectional ◽  
State Lottery ◽  
History Analysis ◽  
Political Economic

Two types of explanations of state government innovation have been proposed: internal determinants models (which posit that the factors causing a state government to innovate are political, economic, and social characteristics of a state) and regional diffusion models (which point toward the role of policy adoptions by neighboring states in prompting a state to adopt). We show that the two are conceptually compatible, relying on Mohr's theory of organizational innovation. Then we develop and test a unified explanation of state lottery adoptions reflecting both internal and regional influences. The empirical results provide a great degree of support for Mohr's theory. For the empirical analysis, we rely on event history analysis, a form of pooled cross-sectional time series analysis, which we believe may be useful in a wide variety of subfields of political science. Event history analysis may be able to explain important forms of political behavior (by individuals, organizations, or governments) even if they occur only rarely.

Event History Analysis in Demography

1995 ◽  
Vol 49 (2) ◽  
pp. 355-357
Author(s):  
Johannes Huinink
Keyword(s):  
Event History Analysis ◽  
Event History ◽  
History Analysis

From source-oriented databases to event-history data files: a twelve-step action plan for the analysis of individual and household life histories

1998 ◽  
Vol 10 (1-3) ◽  
pp. 1-9
Author(s):  
Onno Boonstra ◽  
Maarten Panhuysen
Keyword(s):  
Life Histories ◽  
Event History Analysis ◽  
Action Plan ◽  
Event History ◽  
Flat File ◽  
Event History Data ◽  
History Analysis ◽  
Good Technique ◽  
Data Files ◽  
The Way

Population registers are recognised to be a very important source for demographic research, because it enables us to study the lifecourse of individuals as well as households. A very good technique for lifecourse analysis is event history analysis. Unfortunately, there are marked differences in the way the data are available in population registers and the way event history analysis expects them to be. The source-oriented approach of computing historical data calls for a ‘five-file structure’, whereas event history analysis only can handle fiat files. In this article, we suggest a series of twelve steps with which population register data can be transposed from a five-file structured database into a ‘flat file’ event history analysis dataset.

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