Longitudinal Analysis (Statistical Associates Blue Book Series 39)
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Longitudinal Analysis (Statistical Associates Blue Book Series 39)
An introductory graduate level text on longitudinal analysis using SPSS, SAS, and Stata.
328 pages
Longitudinal 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 modeling
Structural equation modeling (SEM), used for growth curve analysis and modeling change in structural relationships across a limited number of time periods.
Overview13 Comparing time series procedures13 GLM (OLS regression or ANOVA) with time as a variable13 Time series analysis (ex., ARIMA14 Repeated measures GLM14 Generalized estimating equations (GEE)14 Population-averaged panel data regression14 Random effects panel data regression15 Linear mixed models (LMM)15 Generalized linear mixed models (GLMM)15 Structural equation modeling15 GLMM-SEM15 Key concepts and terms16 Types of time-related data16 Statistical procedures for different types of data collected over time18 Time series analysis19 Overview19 Key Terms and Concepts19 Simple time series design20 Time series effects20 Serial dependence20 Stationarity20 Differencing21 Specification21 Autocorrelation21 Decomposition22 Model order22 Exponential Smoothing23 Overview23 Weighting23 Example24 Sequence charts24 Requesting exponential smoothing in SPSS26 Exponential smoothing model types: Simple27 Exponential smoothing model types: Holt's linear trend30 Exponential smoothing model types: Brown's linear trend31 Exponential smoothing model types: Damped trend32 Exponential smoothing model types: Seasonal effects32 Transformation of the dependent variable33 Statistical output for time series analysis in SPSS33 Residual and partial residual autocorrelation36 Displaying forecast values37 Saving exponential smoothing values in SPSS38 ARIMA Models40 Overview40 Example40 Constants and predictors41 Stationarity41 ARIMA p, d, and q parameters46 Types of ARIMA models50 Unit roots52 ARIMA for the example data52 Forecasts54 Residual Analysis55 Seasonal ARIMA61 ARIMA Modeling: Intervention and transfer function analysis62 The SPSS "Expert Modeler"68 Overview68 The “Expert Modeler†interface68 Leading indicator (CCF) analysis71 Overview71 SPSS set-up71 CCF output72 Creating a leading indicator variable74 Assumptions of time series analysis75 Stationarity75 Normally distributed independent residuals with homogenous variance76 Inconsequential outliers76 Frequently asked questions about time series analysis76 How many time periods are needed?76 What should the researcher do about missing data?76 When I try to specify p, d, and q for an ARIMA model, should non-significant spikes be treated as zero?77 I suspect there is not a single trend line but rather the trend is different for different subgroups in my population. How do I handle this?77 How does one go about disentangling age, period, and cohort time series effects?79 Is there an acceptable ARIMA model for all data?79 What is an ARFIMA model?80 Regression time series models80 Curve fitting80 Curve Estimation dialog in SPSS80 and 248 more pages of topics.