A Caution About Mechanical Solutions to the Identification Problem in Cohort Analysis: Comment on Sasaki and Suzuki

1989 ◽  
Vol 95 (3) ◽  
pp. 754-761 ◽  
Author(s):  
Norval D. Glenn
Keyword(s):  
Cohort Analysis ◽  
Identification Problem

Cohort Analysis in Social Research: Beyond the Identification Problem.

1986 ◽  
Vol 81 (395) ◽  
pp. 870
Author(s):  
Norval D. Glenn ◽  
William M. Mason ◽  
Stephen E. Fienberg
Keyword(s):  
Social Research ◽  
Cohort Analysis ◽  
Identification Problem

scholarly journals Cohort's Analysis in eCommerce

2021 ◽  
pp. 203-206
Author(s):  
Manali M Walanj
Keyword(s):  
Data Structures ◽  
Cohort Analysis ◽  
Identification Problem ◽  
Hierarchical Bayes ◽  
Outcome Variable ◽  
Cross Sectional ◽  
Problem Resolution ◽  
Linear Dependency ◽  
Temporal Dimensions ◽  
Cohort Analyses

Cohort analysis treats an outcome variable as a function of cohort membership, age, and period. The linear dependency of the three temporal dimensions always creates an identification problem. Resolution of this problem requires external knowledge that is often difficult to acquire. Most satisfactory is the introduction of variables held to measure the dimensions that underlie at least one of age, period and cohort. Such measured, substantive variables can provide direct tests of cohort-based explanations. A Promising path for future technical development is a hierarchical Bayes approach, which treats appropriately defined cohort, age, and period contrasts as randomly distributed and allows for their dependence on substantive, measured variables. Models that include age, period, and cohort can also include interactions between these dimensions, but not all such interactions are identified. This extends the realism of cohort models, since many phenomena seem to require specifications that allow for interactions between two or more of age, period, and cohort. Panel studies and cross-sectional studies with retrospective information not only support cohort analyses, they engender them. These longitudinal data structures do not, however, provide the basis for a solution to the identification problem.[5]

scholarly journals The Liberalization of American Attitudes to Homosexuality and the Impact of Age, Period, and Cohort Effects

Social Forces ◽  
2021 ◽  
Author(s):  
David Ekstam
Keyword(s):  
Side Information ◽  
Cohort Analysis ◽  
Model Identification ◽  
Identification Problem ◽  
Cohort Effects ◽  
General Level ◽  
Social Tolerance ◽  
Socialization Theory ◽  
Statistical Identification ◽  
The Impact

Abstract Prior analyses of age, period, and cohort effects in American attitudes to homosexuality have resulted in conflicting findings. I show that this is due to insufficient attention to the statistical identification problem facing such analyses. By means of more than four decades worth of survey data and two attitudinal measures taping social tolerance of homosexuality, I demonstrate that the conflicting results of prior research can be explained by differences in the implicit and unsubstantiated assumptions made to ensure model identification. To make up for the lack of attention to these assumptions in prior work, I discuss which age, period, and cohort effects we might expect to see based on prior knowledge about the case at hand, socialization theory, and research on how aging affects outgroup attitudes. On that basis, I also discuss which conclusions about age, period, and cohort effects we can actually draw in the case at hand. On a more general level, this article joins a growing literature that cautions against age-period-cohort analysis that does not give sufficient attention to theoretical expectations and side information when making the identifying assumptions on which the analysis must unavoidably rest.

scholarly journals Generations and Political Engagement

2021 ◽  
Author(s):  
Miroslav Nemčok ◽  
Hanna Wass
Keyword(s):  
Political Behavior ◽  
Voter Turnout ◽  
Generational Differences ◽  
Cohort Analysis ◽  
Political Engagement ◽  
Identification Problem ◽  
The Body ◽  
Linear Functions ◽  
Related Factors ◽  
Definition Of

The concept of “generation” constitutes a useful tool to understand the world of politics. Trends in political behavior typical for the youngest generation are indicative for future development. In a wider perspective, large differences between generations also reveal potential for intergenerational conflict and a shift in the entire political paradigm. Four important topics need to be addressed in order to properly understand the body of research studying specifics of political behavior across generations and the use of generation as an analytical tool: (a) conceptual definition of generation, (b) its distinction from other time-related concepts, (c) methodological challenges in applying the time-related factors in research, and (d) understanding the wider implications of these factors for individuals’ political behavior which has already been identified in the scholarship. A political generation is formed among cohorts who experience the same event(s) during their formative years and become permanently influenced by them. Therefore, members of the same generation share similar socialization experiences which create a sense of group belonging and shape the attitudes and behavior throughout their lives. This definition of political generation is distinctive among the three time-related factors—age, period, and cohort—each of which has a well-grounded and distinctive theoretical underpinning. However, a truly insightful examination of the time-related development in political engagement needs to utilize hybrid models that interact with age and period or cohort and period. This imposes a challenge known as identification problem—age (years since birth), period (year), and cohort (year of birth) are perfect linear functions of each other and therefore conventional statistical techniques cannot disentangle their effects. Despite extraordinary effort and outstanding ideas, this issue has not been resolved yet in a fully reliable and hence satisfactory manner. Regardless of methodological issues, the literature is already able to provide important findings resulting from cohort analysis of political engagement. This scholarship includes two major streams: The first focuses on voter turnout, exploring whether nonvoting among the youngest generation is a main reason for the turnout decline in contemporary democracies. The second stream examines the generational differences in political engagement and concludes that low electoral participation among the youngest generation may be explained by young people being more engaged with noninstitutionalized forms of political participation (e.g., occupations, petitions, protests, and online activism).

scholarly journals Addressing the Identification Problem in Age-period-cohort Analysis

Epidemiology ◽  
2012 ◽  
Vol 23 (4) ◽  
pp. 583-593 ◽  
Author(s):  
Yu-Kang Tu ◽  
Nicole Krämer ◽  
Wen-Chung Lee
Keyword(s):  
Cohort Analysis ◽  
Identification Problem

Ethnogeriatrics: Development and Application of the Cohort Analysis Tool

2014 ◽  
Vol 21 (2) ◽  
pp. 37-52
Author(s):  
Melen McBride
Keyword(s):  
Healthcare Professionals ◽  
Cohort Analysis ◽  
Analysis Tool ◽  
Speech Language Pathology ◽  
Clinical Tool ◽  
Research Activities ◽  
Key Concepts ◽  
Practice Teaching ◽  
Language Pathology ◽  
Significant Factors

Ethnogeriatrics is an evolving specialty in geriatric care that focuses on the health and aging issues in the context of culture for older adults from diverse ethnic backgrounds. This article is an introduction to ethnogeriatrics for healthcare professionals including speech-language pathologists (SLPs). This article focuses on significant factors that contributed to the development of ethnogeriatrics, definitions of some key concepts in ethnogeriatrics, introduces cohort analysis as a teaching and clinical tool, and presents applications for speech-language pathology with recommendations for use of cohort analysis in practice, teaching, and research activities.

Urban and rural millennials: Heterogeneity of the young adult generation

2019 ◽  
pp. 5-28
Author(s):  
Vadim V. Radaev
Keyword(s):  
Coming Out ◽  
Generational Differences ◽  
Cohort Analysis ◽  
Social Indicators ◽  
Well Being ◽  
Generational Cohort ◽  
Rural Infrastructure ◽  
Sociological Approach ◽  
Monitoring Survey

A sociological approach towards the generational cohort analysis is developed. A special emphasis is made upon the youngest adult generation of millennials coming out of their adolescence in the 2000s. A broad range of social indicators is used for empirical exploration of intra-generational differences between urban and rural millennials. Data were collected from the annual Russian Longitudinal Monitoring Survey (RLMS-HSE) in 2003—2016. Numerous significant differences have been revealed with regard to the educational level, family planning, use of modern gadgets and digital technologies, commitment to healthy lifestyles, and some values. Some practices are more widely spread among rural millennials, whereas other practices are more characteristic of urban millennials. Most of revealed differences are explained by the lower level of material well-being of rural millennials and lower quality of rural infrastructure.

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