Longitudinal Models In Marketing

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.

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 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.

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.

This book provides a theoretical foundation for the analysis of discrete data such as count and binary data in the longitudinal setup. Unlike the existing books, this book uses a class of auto-correlation structures to model the longitudinal correlations for the repeated discrete data that accommodates all possible Gaussian type auto-correlation models as special cases including the equi-correlation models. This new dynamic modelling approach is utilized to develop theoretically sound inference techniques such as the generalized quasi-likelihood (GQL) technique for consistent and efficient estimation of the underlying regression effects involved in the model, whereas the existing ‘working’ correlations based GEE (generalized estimating equations) approach has serious theoretical limitations both for consistent and efficient estimation, and the existing random effects based correlations approach is not suitable to model the longitudinal correlations. The book has exploited the random effects carefully only to model the correlations of the familial data. Subsequently, this book has modelled the correlations of the longitudinal data collected from the members of a large number of independent families by using the class of auto-correlation structures conditional on the random effects. The book also provides models and inferences for discrete longitudinal data in the adaptive clinical trial set up. The book is mathematically rigorous and provides details for the development of estimation approaches under selected familial and longitudinal models. Further, while the book provides special cares for mathematics behind the correlation models, it also presents the illustrations of the statistical analysis of various real life data. This book will be of interest to the researchers including graduate students in biostatistics and econometrics, among other applied statistics research areas. Brajendra Sutradhar is a University Research Professor at Memorial University in St. John’s, Canada. He is an elected member of the International Statistical Institute and a fellow of the American Statistical Association. He has published about 110 papers in statistics journals in the area of multivariate analysis, time series analysis including forecasting, sampling, survival analysis for correlated failure times, robust inferences in generalized linear mixed models with outliers, and generalized linear longitudinal mixed models with bio-statistical and econometric applications. He has served as an associate editor for six years for Canadian Journal of Statistics and for four years for the Journal of Environmental and Ecological Statistics. He has served for 3 years as a member of the advisory committee on statistical methods in Statistics Canada. Professor Sutradhar was awarded 2007 distinguished service award of Statistics Society of Canada for his many years of services to the society including his special services for society’s annual meetings.

The linear mixed model has become the main parametric tool for the analysis of continuous longitudinal data, as the authors discussed in their 2000 book. Without putting too much emphasis on software, the book shows how the different approaches can be implemented within the SAS software package. The authors received the American Statistical Association's Excellence in Continuing Education Award based on short courses on longitudinal and incomplete data at the Joint Statistical Meetings of 2002 and 2004.

The book features many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce the material. Weiss emphasizes continuous data rather than discrete data, graphical and covariance methods, and generalizations of regression rather than generalizations of analysis of variance.