Applied Longitudinal Data Analysis - Modeling Change and by Judith D. Singer

By Judith D. Singer

Switch is continuing in way of life. babies move slowly after which stroll, childrens discover ways to learn and write, youngsters mature in myriad methods, the aged develop into frail and forgetful. past those common methods and occasions, exterior forces and interventions instigate and disrupt swap: attempt rankings could upward thrust after a training direction, drug abusers may possibly stay abstinent after residential therapy. by means of charting alterations through the years and investigating even if and while occasions take place, researchers exhibit the temporal rhythms of our lives. Applied Longitudinal information Analysis is a much-needed expert booklet for empirical researchers and graduate scholars within the behavioral, social, and biomedical sciences. It deals the 1st obtainable in-depth presentation of 2 of present day most well-liked statistical equipment: multilevel versions for person switch and hazard/survival types for occasion prevalence (in either discrete- and continuous-time). utilizing transparent, concise prose and actual info units from released reviews, the authors take you step-by-step via entire analyses, from basic exploratory screens that demonstrate underlying styles via refined necessities of complicated statistical models.

Applied Longitudinal facts Analysis deals readers a personal session consultation with the world over famous specialists and represents a distinct contribution to the literature on quantitative empirical methods.

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Extra info for Applied Longitudinal Data Analysis - Modeling Change and Event Occurrence

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But because the “fitting of little OLS regression models” approach is intuitive and easy to implement in a person-period data set, we find that it connects empirical researchers with their data in a direct and intimate way. To fit an exploratory OLS regression model to each person’s data, you must first select a specific functional form for that model. Not only is this decision crucial during exploratory analysis, it becomes even more important during formal model fitting. Ideally, substantive theory and past research will guide your choice.

All polynomials—including linear, quadratic, and cubic trajectories —are linear in the parameters; their average trajectory will always be a polynomial of the same order as the individual trajectories. The average of a set of straight lines will be a straight line; the average of a set of quadratics will be a quadratic. But many other common curves do not share this property. The average of a set of logistic curves, for example, is usually a smoothed-out step function. This means that you must exercise extreme caution when examining an average growth trajectory.

These between-person explorations can help identify variables that may ultimately prove to be important predictors of change. 5 concludes by examining the reliability and precision of exploratory estimates of change and comments on their implications for the design of longitudinal studies. Keywords: longitudinal data, individual change, exploratory analyses, longitudinal studies, outcome values Change is the nursery of music, joy, life, and Eternity. —John Donne Wise researchers conduct descriptive exploratory analyses of their data before fitting statistical models.

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