Longitudinal Analysis (Statistical Associates Blue Book Series) G. David Garson

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Published: February 5th 2013

Kindle Edition

380 pages


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Longitudinal Analysis (Statistical Associates Blue Book Series)  by  G. David Garson

Longitudinal Analysis (Statistical Associates Blue Book Series) by G. David Garson
February 5th 2013 | Kindle Edition | PDF, EPUB, FB2, DjVu, AUDIO, mp3, RTF | 380 pages | ISBN: | 10.27 Mb

An introductory graduate level text on longitudinal analysis using SPSS, SAS, and Stata.328 pagesLongitudinal analysis is an umbrella term for a variety of statistical procedures which deal with any type of data which is measured over time. SectionsMoreAn introductory graduate level text on longitudinal analysis using SPSS, SAS, and Stata.328 pagesLongitudinal analysis is an umbrella term for a variety of statistical procedures which deal with any type of data which is measured over time.

Sections of this volume group longitudinal analysis methods under the following categories:Time series analysis, often used for projecting economic or other time series, with or without additional independent variables. Includes ARIMA models.Linear regression models, which incorporate time as an independent variable.Panel data regression models,Repeated measures GLM, used to implement analysis of variance and regression models.General estimating equations analysis (GEE), used to implement nonlinear forms of regression modeling, including logistic and probit regression for repeated measures data.Linear mixed modeling (LMM), used for multilevel analysis where multiple time periods are treated as a data level.Generalized linear mixed models for longitudinal data (GLMM), used to implement nonlinear forms of linear mixed modelingStructural equation modeling (SEM), used for growth curve analysis and modeling change in structural relationships across a limited number of time periods.Overview13Comparing time series procedures13GLM (OLS regression or ANOVA) with time as a variable13Time series analysis (ex., ARIMA14Repeated measures GLM14Generalized estimating equations (GEE)14Population-averaged panel data regression14Random effects panel data regression15Linear mixed models (LMM)15Generalized linear mixed models (GLMM)15Structural equation modeling15GLMM-SEM15Key concepts and terms16Types of time-related data16Statistical procedures for different types of data collected over time18Time series analysis19Overview19Key Terms and Concepts19Simple time series design20Time series effects20Serial dependence20Stationarity20Differencing21Specification21Autocorrelation21Decomposition22Model order22Exponential Smoothing23Overview23Weighting23Example24Sequence charts24Requesting exponential smoothing in SPSS26Exponential smoothing model types: Simple27Exponential smoothing model types: Holts linear trend30Exponential smoothing model types: Browns linear trend31Exponential smoothing model types: Damped trend32Exponential smoothing model types: Seasonal effects32Transformation of the dependent variable33Statistical output for time series analysis in SPSS33Residual and partial residual autocorrelation36Displaying forecast values37Saving exponential smoothing values in SPSS38ARIMA Models40Overview40Example40Constants and predictors41Stationarity41ARIMA p, d, and q parameters46Types of ARIMA models50Unit roots52ARIMA for the example data52Forecasts54Residual Analysis55Seasonal ARIMA61ARIMA Modeling: Intervention and transfer function analysis62The SPSS Expert Modeler68Overview68The “Expert Modeler” interface68Leading indicator (CCF) analysis71Overview71SPSS set-up71CCF output72Creating a leading indicator variable74Assumptions of time series analysis75Stationarity75Normally distributed independent residuals with homogenous



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