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Age Period Cohort Analysis

Course Dates and Times

Monday 25 February – Friday 1 March, 14:00 – 17:30 (finishing slightly earlier on Friday)
15 hours over 5 days

Roula Nezi

University of Surrey

This course is designed to give you a deep knowledge of Age-Period-Cohort (APC) Models. These are models that help social scientists examine whether time-varying changes in social and political phenomena are due to the biological and social process of ageing (age effects), social or economic factors, for example an economic crisis (period effects) or the unique experience of a group, for example children socialised under an authoritarian regime (cohort effects).

What makes the analysis of those effects interesting and methodologically challenging is the so called 'identification problem'; The age-period-cohort conundrum refers to the problem of separating the effects of age, periods and cohorts.

By the end of the course you will understand the identification problem in the study of ageing and cohort analysis and the different methods developed to overcome the identification problem. You will also be familiar with the limitations of these methods and know which method to use, depending on your own research question.

We will pay special attention is to the Hierarchical Age-Period-Cohort-Model (HAPC).

In addition to the methods behind the study of APC models, you will also learn how to conduct your own APC models in R.

The overall goal of the course is to give you a general overview of APC models, familiarise you with the different methods of studying age-period-cohort effects, and teach you how to produce and interpret your own findings.

Tasks for ECTS Credits

2 credits (pass/fail grade) Attend at least 90% of course hours, participate fully in in-class activities, and carry out the necessary reading and/or other work prior to, and after, class.

3 credits (to be graded) As above, plus complete a take-home exam, distributed on Friday, due Monday (after class).

4 credits (to be graded) As above, plus complete a research paper due two weeks after the class, using your own data and APC method.

Instructor Bio

Roula Nezi is Lecturer in Political Science at the University of Surrey, UK. Her research follows two broad themes: the effect of policy change on political attitudes, and the impact of economic factors on political outcomes.

In recent years, Roula has conducted research in a wide range of comparative European projects on political representation, political behaviour, and political elites. She is currently leading a project on the support of authoritarian beliefs and the vote for rightwing populist parties among young cohorts in Europe, and a project on 'contagious populism' funded by the University of Surrey.

Her research has been published in the British Journal of Political Science, the Journal of European Social Policy, Europe-Asia Studies, and Electoral Studies.

Her research interests lie in public opinion and political attitudes, political representation, and political accountability. She is also interested in political methodology, including comparative analysis and survey design.

Twitter @roula_nezi

The aim of this course is to introduce you to the major methodological tools related to the study of ageing and cohort analysis.

In times of major political events, studies of Age-Period-Cohort are highly significant; APC models help us understand time-varying social and political changes, and to identify the trends and dynamics of those trends among the population.

This course will show you how to conduct such analysis. I will start by explaining the importance of studying aging, cohorts and time, and the importance of the identification problem.

Then I introduce the key methods and models in the study of APC models and the newest developments in the study of Age-Period-Cohort models.

By the end of this course, you will understand the key concepts and theories behind APC models, know how to apply different statistical methods related to the study of Age-Period-Cohorts and to produce your own findings.

I will introduce the concept of priors and Bayesian statistical inference for APC models, though we will not cover those topics extensively.

Day 1
I introduce you to the study of Age-Period-Cohorts and explain why APC models are important for the study of social change. We discuss the identification problem in the study of Age-Period-Cohort and how different statistical methods can help us overcome this problem. In our first lab session you will learn how to get your dataset ready for APC analyses; an important first step in the study of APC models.

Day 2
We examine the first generation of models in the study of APC models. These include the use of age-by-time period contingency tables, the use of proxy variables in the study of APC models, and the coefficient constraint approach. We’ll practice all these methods in the lab session.

Day 3
I introduce the Intrinsic Estimator, a method to estimate and capture the effects of age, period, and cohorts. We discuss the advantages of this method the statistical model, and criticism of it. During the lab session, we will practice on Intrinsic Estimator models.

Day 4
I introduce the Hierarchical APC Model. Hierarchical modelling is considered the most up-to-date method in the study of the APC models. These models take into consideration the multilevel structure of the data; individuals are nested in a cross-classification of periods and birth cohorts. In the lab we will learn how to run a Hierarchical APC Model.

Day 5
We talk about the use of priors (Bayesian statistical inference) in estimating APC effects and about non-parametric Generalised Additive Models (GAMs). GAMs allow researchers to plot nonparametric smoothed curves for the effect of year or of the cohort. This helps us observe differences among the estimated effects, and differences of those effects among different countries.

You should have a good understanding of multivariate linear regression and multilevel (hierarchical) models before you take this class. Good knowledge includes fixed and random effects and understanding of interaction terms.

If you don't already have a solid grasp of multilevel modelling, I recommend you first take a class in multilevel (hierarchical) models.

This class will not teach you multilevel modelling, but rather how to run a Hierarchical (HAPC) model for estimating Age-Period-Cohort effects. You will also acquire the tools and syntax needed to apply those models.

I personally use R and I strongly encourage the use of R in this class, though, some examples will be given in STATA as well.

Please bring your own laptop with an up-to-date version of R.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, contact the instructor before registering.

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.


This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.

Day Topic Details
Day 1 Why Age-period-cohort (APC) models?

90 min lecture
Why it is important to study APC models and the importance of the identification problem                       

90 min lab
First illustration and how to prepare your data

Day 2 The first generations of APC models and the Constrained Generalised Linear Model

90 min lecture
Descriptive APC analysis, the proxy variable approach, Constrained Generalised Linear Model                           

90 min lab
Empirical illustration

Day 3 Intrinsic Estimator

90 min lecture
The theory behind the Intrinsic Estimator                     

90 min lab
Empirical Illustration of the Intrinsic Estimator

Day 4 Hierarchical Age-Period-Cohort models (HAPC)

90 min lecture
The basics and advanced HAPC analyses                 

90 min lab 
Illustration of the HAPC model

Day 5 HAPC Continued and Generalised Additive Models

90 min lecture
Criticism of the HAPC model and the inclusion of priors, Non parametric Generalised Additive models                                                                   

90 min lab
Empirical Illustration of GAM models

Day Readings
Day 2

Yang, Y., & Land, K. C. (2016). Age-period-cohort analysis: New models, methods, and empirical applications. Chapman and Hall/CRC. (Chapters 3 and 4)

Preston, S. H., & Wang, H. (2006). Sex mortality differences in the United States: The role of cohort smoking patterns. Demography, 43(4), 631-646. Chicago

O’Brien, R. M. (2011). The age–period–cohort conundrum as two fundamental problems. Quality & Quantity, 45(6), 1429-1444.

Day 1

Ryder, N.B. (1985) The Cohort as a Concept in the Study of Social Change. In: Mason W.M., Fienberg S.E. (eds) Cohort Analysis in Social Research. Springer, New York, NY

Riley, M. (1987). On the Significance of Age in Sociology. American Sociological Review, 52(1), 1-14.

Mason, Karen , Oppenheim, William , M. Mason, H., H. Winsborough, W. Kenneth Poole.(1973) Some Methodological Issues in Cohort Analysis of Archival Data. American Sociological Review 38: 242-258

Day 3

Yang, Y., Fu, W. J., & Land, K. C. (2004). A methodological comparison of age‐period‐cohort models: the intrinsic estimator and conventional generalized linear models. Sociological methodology, 34 (1), 75-110.

Yang, Yang, Schulhofer-Wohl, Sam, Fu, Wenjiang, and Land, Kenneth. 2008. The Intrinsic Estimator for Age-Period-Cohort Analysis: What it is and How to Use it. American Journal of Sociology 113 (6):1697–1736.           

Day 4

Yang, Yang and Land, Kenneth. 2008. Age-Period-Cohort Analysis of Repeated Cross-Section Surveys: Fixed or Random Effects? Sociological Methods & Research 36 (3):297–326.

Frenk, S. M., Yang, Y. C., & Land, K. C. (2013). Assessing the Significance of Cohort and Period Effects in Hierarchical Age-Period-Cohort Models: Applications to Verbal Test Scores and Voter Turnout in U.S. Presidential Elections. Social Forces; a Scientific Medium of Social Study and Interpretation, 92(1), 221–248.

Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge university press. (Chapters 11,12,13)

Neundorf, Anja and Niemi, Richard G. 2014. Beyond Political Socialization: New Approaches to Age, Period, Cohort Analysis. Electoral Studies 33:1–6.

Day 5

Grasso, M. T., Farrall, S., Gray, E., Hay, C., & Jennings, W. (2017). Thatcher’s children, Blair’s babies, political socialization and trickle-down value change: An age, period and cohort analysis. British Journal of Political Science, 1-20.

Grasso, M. T. (2014). Age, period and cohort analysis in a comparative context: Political generations and political participation repertoires in Western Europe. Electoral Studies, 33, 63-76.

Andersen, R. (2009). Nonparametric methods for modelling nonlinearity in regression analysis. Annual Review of Sociology, 35, 67-85.

Bell, A., & Jones, K. (2015). Bayesian informative priors with Yang and Land's hierarchical age-period-cohort model. Quality and Quantity, 49(1), 255-266.

Yang, Y. (2006). 2. Bayesian Inference for Hierarchical Age-Period-Cohort Models of Repeated Cross-Section Survey Data. Sociological Methodology, 36(1), 39-74.

Tilley, J. (2002). Political Generations and Partisanship in the UK, 1964–1997. Journal of the Royal Statistical Society: Series A (Statistics in Society), 165(1), 121-135.

Software Requirements

Latest version of R (3.5.1) but will also provide examples in Stata

Hardware Requirements

PC, MAC and Linux are appropriate.


Mason, K. O., Mason, W. M., Winsborough, H. H., & Poole, W. K. (1973)
Some methodological issues in cohort analysis of archival data
American sociological review, 242-258

Mason, W. M., & Fienberg, S. (Eds.). (2012)
Cohort analysis in social research: Beyond the identification problem
Springer Science & Business Media

Glenn, N. D. (1976)
Cohort analysts' futile quest: Statistical attempts to separate age, period and cohort effects
American sociological review, 41(5), 900-904

Grasso, Maria Teresa
Generations, Political Participation and Social Change in Western Europe
London: Routledge 2016

Neundorf, Anja. 2010
Democracy in Transition: A Micro Perspective on System Change in Post-Socialist Societies
Journal of Politics 72(4):1096–1108

Dinas, E., & Stoker, L. (2014)
Age-Period-Cohort analysis: A design-based approach
Electoral Studies, 33, 28–40

Recommended Courses to Cover Before this One

Summer School

Applied Multilevel Regression Modelling

Winter School

Regression Refresher
Multilevel Regression Modelling

Recommended Courses to Cover After this One

Winter School

Introduction to Bayesian Inference