science researchersintroduction to panel m科学researchersintroduction面板m

Longitudinal Data Analysis for Social Science ResearchersIntroduction to Panel Modelswww.longitudinal.stir.ac.ukSIMPLE TABLE PANEL DATABEWARE BEWAREWomen in their 20s in 1991 Ten Years Later Traditionally used in social mobility work Can be made more exotic for example by incorporating techniques from loglinear modelling (there is a large body of methodological literature in this area)Change Score Less likely to use this approach in mainstream social science Understanding this will help you understand the foundation of more complex panel models (especially this afternoon)Yi 1 = bXi1 + ei1 Vector of explanatory variables and estimatesIndependent identifiably distributed errorOutcome 1 for individual iYi 1 = bXi1 + ei1 Vector of explanatory variables and estimatesIndependent identifiably distributed errorOutcome 1 for individual iEQUATION FOR TIME POINT 2Yi 1 = bXi1 + ei1 Yi 2 = bXi2 + ei2Considered together conventional regression analysis in NOT appropriate Yi 2 - Yi 1 = b(Xi2-Xi1) + (ei2 - ei1) Change in ScoreHere the b is simply a regression on the difference or change in scores. Women in 20s H.H. Income Month Before Interview (Wfihhmn)WAVE A WAVE BMEAN 1793.50 1788.15S.D. 1210.26 1171.36MEDIAN 1566.34 1587.50SKEWNESS 1.765 1.404PERCENTILES25% 914.43 950.5175% 2339.39 2353.85r =.679*A Simple Scatter PlotYi 2 - Yi 1 = b(Xi2-Xi1) + (ei2 - ei1) Change in ScoreHere the b is simply a regression on the difference or change in scores. Difference or change in scores(bfihhmn afihhmn) Models for Multiple MeasuresPanel ModelsPID WAVE SEX AGE Y001 1 1 20 1001 2 1 21 1001 3 1 22 1001 4 1 23 0001 5 1 24 0001 6 1 25 1Models for Multiple Repeated Measures“Increasingly, I tend to think of these approaches as falling under the general umbrella of generalised linear modelling (glm). This allows me to think about longitudinal data analysis simply as an extension of more familiar statistical models from the regression family. It also helps to facilitate the interpretation of results.”Some Common Panel Models(STATA calls these Cross-sectional time series!)Binary Y Logit xtlogit(Probit xtprobit)Count Y Poisson xtpoisson(Neg bino xtnbreg)Continuous Y Regression xtregModels for Multiple MeasuresAs social scientists we are often substantively interested in whether a specific event has occurred.Therefore for the next 30 minutes I will mostly be concentrating on models for binary outcomesRecurrent events are merely outcomes that can take place on a number of occasions. A simple example is unemployment measured month by month. In any given month an individual can either be employed or unemployed. If we had data for a calendar year we would have twelve discrete outcome measures (i.e. one for each month).Consider a binary outcome or two-state event0 = Event has not occurred1 = Event has occurredIn the cross-sectional situation we are used to modelling this with logistic regression.A study for six months0 = Unemployed; 1 = WorkingUNEMPLOYMENT AND RETURNING TO WORK STUDY Months1 2 3 4 5 6obs 0 0 0 0 0 0Constantly unemployedMonths1 2 3 4 5 6obs 1 1 1 1 1 1Constantly employedMonths1 2 3 4 5 6obs 1 0 0 0 0 0Employed in month 1 then unemployedMonths1 2 3 4 5 6obs 0 0 0 0 0 1Unemployed but gets a job in month sixHere we have a binary outcome so could we simply use logistic regression to model it?Yes and No We need to think about this issue.POOLED CROSS-SECTIONAL LOGIT MODELx it is a vector of explanatory variables and b is a vector of parameter estimates We could fit a pooled cross-sectional model to our recurrent events data.This approach can be regarded as a nave solution to our data analysis problem. We need to consider a number of issues.