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analysis of change analyzed assumed assumptions categorical variables causal analysis causal variables causes of change change in continuous change in discrete change in y(t change process Coleman components of change continuous time Markov continuous time model continuous variables counts of events cross-sectional data cross-sectional variation dependent difference equation differential equation model discrete or categorical discrete variables equilibrium formulation Event-history data events occur events to estimate example exponentially distributed framework govern the outcome independent variables individuals job shifts life-history data linear probability model log-linear model longitudinal methodology Markov Chain mathematical matrix model for change observed obtained occurrence of events outcome of events over-time data over-time variation p_(t panel data Poisson distribution Poisson process probabilities of moving probability distribution problem quantities rate of movement regression analysis relationship among variables result S^rensen sociology solution specification stochastic process models study of change test the models transition probabilities transition rates unit of analysis waiting