Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter. Written for non-mathematically-oriented readers, this text features: A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own data An emphasis on how the terminology, interpretation, and estimation of familiar general linear models relates to those of more complex models for longitudinal data Integrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers’ understanding of the overall model-building process Sample results sections for each example to provide useful templates for published reports Examples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and Mplus at www.PilesOfVariance.com to help readers apply the models to their own data The book opens with the building blocks of longitudinal analysis—general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces unconditional longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. Conditional longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data. Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book’s accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text.

Longitudinal models play a very important role in marketing model building, and there are some occasions when market research is conducted every day of the year. These longitudinal models can be useful to marketing managers to make many informed and important decisions for optimal allocation of resources to marketing mix variables. In particular, we cover three major applications of these principles adhering to Advertising tracking monitors, Brand Equity monitors, and sales promotion monitors. We introduce the reader to the basic principles and theory of econometrics in model building for analyzing sales and market share variables about marketing spending. Different functional forms are discussed in the book. And the readers are encouraged to use these functional forms to model the three monitors mentioned above. This book is targeted towards second-year MBA students and marketing/brand managers in companies to derive insights about the markets and competitors. This is followed up with different tools for forecasting companies' sales and market share. This book is useful for managers in durables and fast-moving consumer goods industries. This book addresses the need for when and where to make insights about marketing mix variables through econometric models. The author has 32 years of industry experience and is an expert in marketing models. The author has a Ph.D. in marketing from Purdue University. The book elucidates these theories without using complicated mathematical equations in simple-to-understand verbal models of complicated equations.

Longitudinal models play a very important role in marketing model building, and there are some occasions when market research is conducted every day of the year. These longitudinal models can be useful to marketing managers to make many informed and important decisions for optimal allocation of resources to marketing mix variables. In particular, we cover three major applications of these principles adhering to Advertising tracking monitors, Brand Equity monitors, and sales promotion monitors. We introduce the reader to the basic principles and theory of econometrics in model building for analyzing sales and market share variables about marketing spending. Different functional forms are discussed in the book. And the readers are encouraged to use these functional forms to model the three monitors mentioned above. This book is targeted towards second-year MBA students and marketing/brand managers in companies to derive insights about the markets and competitors. This is followed up with different tools for forecasting companies' sales and market share. This book is useful for managers in durables and fast-moving consumer goods industries. This book addresses the need for when and where to make insights about marketing mix variables through econometric models. The author has 32 years of industry experience and is an expert in marketing models. The author has a Ph.D. in marketing from Purdue University. The book elucidates these theories without using complicated mathematical equations in simple-to-understand verbal models of complicated equations.

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Most analyses were done with the MIXED procedure of the SAS software package, but the data analyses are presented in a software-independent fashion.

Longitudinal Analysis of Labor Market Data presents a set of papers by leading scholars on methods for analysing the longitudinal data that is available on numerous topics of interest to social scientists. Because many sources of longitudinal data record labour market phenomena such as unemployment, labour supply, earnings mobility, job turnover and participation in training programmes, all of the papers collected in this volume focus on models of the labour market. The main methodological points, however, are more general and apply to such diverse areas as demography, life science analysis and training evaluation, to name only a few, potential avenues of application. The book contains important methodological contributions to the emerging field of longitudinal analysis and is of interest to a wide range of social scientists.

Although many books currently available describe statistical models and methods for analyzing longitudinal data, they do not highlight connections between various research threads in the statistical literature. Responding to this void, Longitudinal Data Analysis provides a clear, comprehensive, and unified overview of state-of-the-art theory

This book is a practical guide for the analysis of longitudinal behavioural data. Longitudinal data consist of repeated measures collected on the same subjects over time.