G93.2314
Fall, 2009
Longitudinal Statistics
Professor David Greenberg
COURSE DESCRIPTION/SYLLABUS
Longitudinal Statistics is an elective sequel to the Sociology Department's introductory
graduate-level statistics course. It would be helpful to have had the Department’s course,
Advanced Multivariate Statistics, but this is not necessary. The course will focus on a range of
methods used to analyze data collected at multiple times. The main emphases will be on cohort
analysis, time series, panel data and event history (survival) analysis. Illustrative examples
will be drawn from the various social science and policy evaluation literature. We will be
using a number of statistical packages, including SPSS, Stata, HLM and LISREL. The
course grade will be based partly on homework assignments (50%), and partly on a term
paper (50%). For the paper, students will choose a longitudinal date set, and analyze it using
the methods taught in the course. There will be no exam.
Prospective students from outside the Sociology Department should consult the instructor
regarding their preparation for this course. Auditors are welcome with consent of the
instructor whether or not they meet all the prerequisites, provided there are empty seats.
Students who anticipate doing quantitative research are strongly advised to take this course.
OFFICE LOCATION, OFFICE HOURS, ETC.
Puck Building: 295 Mercer St. 4th floor (office 4117)
tel. (212) 998-8345 or ext. 88345 from a campus phone
e-mail: [email protected] or [email protected]
Office hours: Mondays and Wednesdays: 3:45-4:45 PM and by appointment.
Class Hours: The class will meet Tuesdays between between 6:20 and 9 P.M. in 25 W. 4 St.
C-6 (in the basement). We will not necessarily go until 9 PM at every session, but on some
evenings we may. There will be no separate lab session. Instruction for the computer
programs will be provided in class.
BlackBoard Site: I will use the NYU Blackboard Site to send out e-mail messages to the class,
and to post data sets, handouts and any other course materials that can be easily posted. To
access it, go to your NYU Home account: http://home.nyu.edu. Log-in using your NYU ID
and password. You will see a section devoted to ACADEMICS. Click on that, and you will be
able to access the Blackboard site for this course. Handouts will be in the Documents section
of the site.
Fourth Floor Lab. Data sets will be posted on the Blackboard site for the course, and on the
townhall drive in the Sociology Department computer lab at 295 Lafayette, 4th Floor under
the name of Greenberg. SPSS, Stata, HLM (student version) and LISREL (student version)
are available on both networks. Because this lab is primarily for the use of NYU graduate
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students, students from other institutions may use it on a trial basis only. If this is impossible,
you may find it more convenient to use any ACF lab. You should bring a diskette to class with
you so that you can download data sets. Alternately, you may want to e-mail the data sets to
yourself. Of course, you are welcome to acquire the statistical programs yourself. Student
versions of HLM5 and LISREL can be downloaded free from www.ssicentral.com. Students
can purchase the current version of Stata through its GradPlan for $450.00. You can do this
through the Stata web site. Stata will forward your information to Diana Barnes in the
Politics Department, and you will then pick up the installation disks from her. She is at 19 W.
4 St. Room 214, tel. (212) 992-9675.
Textbooks
Students should not purchase texts until after the first day of class.
Main Textbooks
Charles Ostrom, Jr. Time Series Analysis: Regression Techniques
(Sage, 1990)
David McDowall, Richard McCleary, E. E. Meidinger and R. A. Hay, Jr., Interrupted Time
Series (Sage, 1980).
Patrick T. Brandt and John T. Williams, Multiple Time Series Models (Sage, 2007).
Paul D. Allison, Fixed Effects Regression Models (Sage, 2009).
Steven Finkel, Causal Analysis with Panel Data (Sage, 1995)
Mario A. Cleves, William W. Gould, Roberto G. Gutierrez and Yulia Marchenko, An
Introduction to Survival Analysis Using Stata, 2nd ed. (Stata Press). (CGGM below).
These books are available at Shakespeare and Company bookstore, on Broadway at Waverly
Place, in the basement. They will also be placed on 2-hour reserve in Bobst Library.
Handouts will be distributed each week. These will be a major text for the course.
Additional readings will be placed on reserve in the Sociology Department.
Recommended Supplementary Texts
If you do not own an econometrics textbook, it can be a good idea
to purchase one, e.g. William Greene, Econometric Analysis (Prentice-Hall) or Jack
Johnston and John DiNardo, Econometric Methods (McGraw-Hill). For the time series part,
Terence C. Mills and Raphael N. Markellos, The Econometric Modelling of Financial Time Series,
3rd ed. (Cambridge University Press, 2008). For the panel analysis part of the course, Ronald
C. Kessler and David F. Greenberg, Linear Panel Analysis: Models of Quantitative Change
(Academic Press) is out of print, but used copies can be found on-line. Edward E. Frees,
Longitudinal and Panel Data: Analysis and Applications in the Social Sciences (Cambridge U. Press,
2004) surveys many approaches. For event history analysis, Janet Box-Steffensmeier and
Bradford S. Jones, Event History Modeling: A Guide for Social Scientists (Cambridge University
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Press, 2004) (BSJ below. For those needing a quick review of calculus and matrix algebra, I
recommend Timothy M. Hagle, Basic Math for Social Scientists (Sage, 1995). More extensive
coverage of these topics, as well as treatment of probability theory, can be found in Jeff Gill,
Essential Mathematics for Political and Social Research (Cambridge University Press).
Two of these books: Box-Steffensmeier and Jones, Event History Modeling and Hagle’s Basic
Math are available at Shakespeare & Co.
Provisional Schedule
Sept. 8. Introduction to Longitudinal Data. Strengths and Limitations of Longitudinal Data
compared to Cross-Sectional Analyses. Age-period-cohort Effects.
Readings for advantages of longitudinal data analysis over
cross-sectional methods:
Richard B. Davies, “From Cross-Sectional to Longitudinal
Analysis.” Pp. 2-40 in
Angela Dale and Richard B. Davies,
Analyzing Social and Political Change: A Casebook
of
Methods. Thousand Oaks, CA: Sage, 1994.
Readings for Age-Period-Cohort Analysis:
Karen Oppenheim Mason, William M. Mason, H. H. Winsborough
and W.
Kenneth Poole, “Some Methodological Issues in Cohort Analysis of Archival Data,”
American Sociological Review 38 (1973): 242-58.
David Knoke and Michael Hout, “Social and Demographic
Factors in American
Political Party Affiliations, 19521972,” American Sociological Review 39
(1974):700-713.
Norval D. Glenn, “Cohort analysts’ Futile Quest: Statistical
Attempts to Separate
Age, Period and Cohort Effects,”
American Sociological Review 41 (1976): 900-904.
TIME SERIES
Sept. 15. Introduction to Time Series. Missing Data, smoothing techniques. Structural
stability or change? Introduction to serially correlated errors in regression analysis.
Visualizing time series in SPSS and Stata.
Readings: Ostrom, chs. 1,2
Larry W. Isaac and Larry J. Griffin, “Ahistoricism
in TimeSeries Analyses of Historical Process: Critique, Redirection, and
Illustrations from U.S. Labor History,” American Sociological
Review 54 (1989):873-990.
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Nathaniel Beck, “Time-Varying Parameter Regression
Journal of Political Science 27
(1983): 557-600.
Models,” American
Serially correlated errors in time series. Causes, consequences, remedies. Estimating models
involving serial correlation in SPSS and Stata.
Readings: Ostrom, chap. 3
Sept. 22. Lagged variables: single equation models. Introduction to ARCH/GARCH models.
Examples in Stata.
Readings: Ostrom, chap. 4
Larry Isaac and William R. Kelly, “Racial Insurgency, the State, and
Welfare Expansion: Local and National Evidence from the Postwar
United States, American Journal of Sociology 86.6 (May 1981): 134886.
Edward T. Jennings, Jr., “Racial Insurgency, the State,
and Welfare
Expansion: A Critical Comment and Reanalysis,” American Journal of
Sociology 88 (May 1983): 1220-1236.
Sept. 29. ARIMA models I. Univariate models. Examples in SPSS and Stata.
Readings: TBA
Applications:
R. P. Li and W. R. Thompson, “The Stochastic Process
of Alliance
Formation Behavior,” American Political
Science Review 72 (1978): 1288-1303.
. P. Quinn and R. Jacobson, “Industrial Policy
Through Restrictions
on Capital Flows,” American Journal of Political Science 33 (1989):
700-736.
Oct. 6. ARIMA models II. Seasonality. Intervention models. Transfer
Functions.
Readings: David McDowall et al., Interrupted Time Series, chspters to be announced.
Applications:
J. Alt, “Political Parties, World Demand, and
Political Science Review
79 (1986)P 1016-1040.
Unemployment,” American
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K. Rasler and W. Thompson, “War and the Economic Growth of
American Journal of Political Science 29 (1985): 513-48.
the Major Powers,”
B. D. Wood and R. W. Waterman, “The Dynamics of Control of
American Politica Science Review 85 (1991): 801-828.
Bureaucracy,”
October 13. Nonstationary time series. Unit root tests. Cointegration and error correction
models. Fractional integration. Vector autoregression (VAR models).
Readings: Jeff B. Cromwell et al., Multivariate Tests for Time
1-4.
Series Models, chs.
papers on cointegration and error correction models by Charles W.
Ostrom, Jr. and Renée M. Smith,
John T. Williams, Nathaniel Beck,
Renée M. Smith, and Robert H. Durr, in Political Analysis vol. 4
(1992).
Applications:
R. Reuveny and H. Kang, “International Trade,
Political
Conflict/Cooperation, and Granger Causality,” American
Journal of Political Science 40 (1996): 943-970.
PANEL DATA
Oct. 20 Pooled analysis of independent cross-sections. Differencein-difference
models. Static panel data. Fixed effects and random effects models. Hausman test.
Time-series cross- sections, panel-corrected standard errors, general estimating
equations, instrumental variable estimation.
Readings: Paul D. Allison, Fixed Effects Regression Models,
chs. 1-3
David F. Greenberg and Julie Phillips, “A Comparison of Methods for
Analyzing Criminological
Panel Data,” Journal of
Quantitative Criminology 24.1
(March 2008): 51-72.
James A. Stimson, “Regression in Space and Time: A
Statistical
Essay,” American Journal of Political Science 29 (1985):914-47.
Nathaniel Beck and Jonathan N. Katz, “What To Do
(and Not To
Do) with Time-Series Cross-Section
Data,” American Political Science Review 89
(1995):
634-47.
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Oct. 27. Autoregressive panel models of change. Introduction to
method of moments, and Arellano-Bond
estimation in Stata.
Readings: Finkel, chaps. 1-6. [or Kessler and Greenberg if you
hands on a copy]
David R. Rogosa, “A Critique of Cross-Lagged
Psychological Bulletin 88 (1980):
245-58.
LISREL. Generalized
can get your
Correlations,”
Applications:
Blair Wheaton, “The Sociogenesis of Psychological
Disorder: Reexamining the Causal Issue
with Longitudinal Data,” American Sociological
Review 43 (1978): 383-403.
David F. Greenberg, Ronald Kessler and Charles Logan, “A Panel Analysis of
Crime Rates and Arrest Rates," American Sociological Review 44 (1979) 843-50.
Nov. 3. Latent Growth Curve Models (multi-level modeling approach and structural equation
modeling approach). Applications in HLM and LISREL. Finite Mixture Modeling.
Readings: Anthony S. Bryk and Stephen Raudenbusch, “Application of Hierarchical
Linear Models to Assessing Change,” Psychological Bulletin 101 (1987): 147-58.
John B. Willett and Aline G. Sayer, “Using Covariance
Structure Analysis to Detect
Correlates and Predictors of Individual Changes Over Time,” Psychological Bulletin
116 (1994): 363-81.
Amy V. D’Unger, Kenneth C. Land, Patricia L. McCall and Daniel S. Nagin, “How
Many Latent Classes of Delinquent/Criminal Careers? Results from Mixed Poisson
Regression Analyses,” American Journal of Sociology 103 (1998): 1593-1630.
David F. Greenberg and Michael Ezell, “Criminal Career Trajectories: Discrete or
Continuous?”
Nov. 10. Count and Discrete Dependent Variables. Incomplete Panels and Selection Bias.
Markov Chains.
Readings: Paul D. Allison, Fixed Effects Regression Models, ch. 4.
EVENT HISTORY ANALYSIS
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Nov. 17 Rationale for Special Techniques for the analysis of durations. Static measures of
survival. Lifetable analysis. Probability density functions, hazard rates, cumulative hazard
rates, survival rates. Kaplan-Meier and Nelson-Aalen estimator. Tests of hypotheses.
Readings for Introduction to Event History Analysis
BSJ chs. 1, 2, 3 (optional)
CGG, chaps. 1-7. Also scan chs. 12-13.
Parametric Methods: exponential, Weibull, Gompertz, long-normal, log-logistic and gamma
distributions. Sickle models. Model selection criteria. Parametric methods for comparing two
survival functions. Explanatory models.
Applications of parametric methods (read one or two):
D. Scott Bennett, “Integrating and Teting Models of
Rivalry Duration,” American Journal of Political
Science 42 (Oct.
1998):
R. Bergstrom and P.-A. Edin, “Time Aggregation and the
Distributional
Shape of Unemployment Duration,”
Journal of Applied Econometrics 7 (Jan.Mar.
1992): 5-30.
Paul V. Warwick, “Rising Hazards: An Underlying Dynamic
Of
Parliamentary Government,” American Journal of
Political Science 36 (Nov.
1992): 857-76.
Nov. 24. Proportional hazards models. Accelerated failure time models. Cox regression. Timevarying covariates. Model Diagnostics.
Discrete-time models
Readings: BSJ, chs. 4-9 (optional).
CGG, chs. 9-11
Paul D. Allison, “Discrete-Time Methods for the
Analysis of Event Histories.” Pp. 61-98 in
Samuel Leinhardt (ed.),
Sociological Methodology 1982. San Francisco: Jossey-Bass.
Applications of Proportional Hazard Models and Discrete Time
two):
Models (read one or
Proportional Hazard Models
Henry Bienen and Nicolas van de Walle, “A Proportional
Hazard Model of
Leadership Duration,” Journal of
Politics 54 (August 1992): 685-717.
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Lawrence F. Katz and Bruce D. Meyer, “Unemployment
Insurance, Recall
Expectations, and Unemployment
Outcomes,” Quarterly Journal of Economics
105 (Nov.
1990): 973-1002.
C. Michelle Piskulick, “Toward a Comprehensive Model of
Welfare Exits: The
Case of AFDC,” American Journal
of Political Science 37 (Feb. 1993): 165-85.
Paul Warwick, “Economic Trends and Government Survival
in West
European Parliamentary Democracies,”
American Political Science Review 86
(Dec.
1992): 875-87.
Discrete Time Models
Frances Stokes Berry and William D. Berry, “State Lottery
Adoptions as Policy
Innovations: An Event History
Analysis,” American Political Science Review 84
(June
1990): 394-415.
Kurt Dassek and Eric Reinhardt, “Domestic Strife and the
Initiation of Violence
at Home and Abroad,” American
Journal of Political Science 43 (Jan. 1999).
Mark J. Gasiorowski, “Economic Crisis and Political Regime
Change: An
Event History Analysis,” American Political
Science Review 89 (Dec. 1995): 88297.
Ryken Grattet, Valerie Jenness and Theodore R. Curry, “The
Homogenization and Differentiation of Hate Crime Law in the United
States, 1978-1995: Innovation and Diffusion in the Criminalization of Bigotry,”
American Journal of Political Science 39 (April, 1998): 286-307.
Christopher Z. Mooney and Lee Mei-Hsien, “Legislative
Morality in the
American States: The Case of Pre-Roe Abortion Reform,” American Journal of
Political Science 39 (August, 1995): 599-627.
Dec. 1. Special topics: Unmeasured heterogeneiety (frailty
events. Competing risks.
models). Repeated
Readings: BSJ. chs. 9-10 (optional).
CGG, ch.15
Applications:
Daniel Diermeier and Randy T. Stevenson, “Cabinet Survival
and
Competing Risks,” American Journal of Political Science 36 (Feb. 1992): 12246.
Dec 8, 15. Student presentations.
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