{"product_id":"latent-markov-models-for-longitudinal-data-practical-methods","title":"Latent Markov Models for Longitudinal Data - Practical Methods","description":"\u003cp\u003eIn this review of Latent Markov Models for Longitudinal Data the bottom line is clear: this book is a focused, practical guide for researchers who analyze categorical longitudinal data and want a rigorous introduction to latent Markov approaches. Written by Francesco Bartolucci, Alessio Farcomeni, and Fulvia Pennoni, the text combines theoretical background with applied examples and ready-to-run code, making it especially useful for practitioners who value reproducible workflows.\u003c\/p\u003e\n\n\u003ch2\u003eKey Features\u003c\/h2\u003e\n\u003cul\u003e\n \u003cli\u003e\n\u003cstrong\u003eComprehensive theory:\u003c\/strong\u003e The book lays out the essential background on latent variable models so readers gain a clear conceptual foundation for latent Markov methods.\u003c\/li\u003e\n \u003cli\u003e\n\u003cstrong\u003eApplied examples:\u003c\/strong\u003e Numerous examples from economics, education, and sociology demonstrate how latent Markov models are used on real categorical longitudinal data.\u003c\/li\u003e\n \u003cli\u003e\n\u003cstrong\u003eSoftware support:\u003c\/strong\u003e The R and MATLAB routines used for the examples are available on the authors website, allowing readers to reproduce analyses and adapt code to their datasets.\u003c\/li\u003e\n \u003cli\u003e\n\u003cstrong\u003eModel connections:\u003c\/strong\u003e The text explains how the Markov chain model and the latent class model combine into a coherent latent Markov paradigm, clarifying assumptions and interpretation.\u003c\/li\u003e\n \n \u003cli\u003e\n\u003cstrong\u003eEstimation focus:\u003c\/strong\u003e The authors introduce maximum likelihood estimation and practical implementation details so readers can apply methods to observed data.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eWho It's For\u003c\/h2\u003e\n\u003cp\u003eThe book is aimed at applied statisticians, social scientists, and quantitative researchers who work with repeated categorical measures and need a methodologically sound treatment of hidden-state models. Those who want worked examples and code to move from concept to implementation will find the book particularly helpful.\u003c\/p\u003e\n\u003cp\u003eIt is less suitable for readers seeking a general introductory textbook in probability or for those needing exhaustive mathematical proofs; the emphasis is on formulation, assumptions, and practical estimation rather than a purely theoretical monograph.\u003c\/p\u003e\n\n\u003ch2\u003ePros \u0026amp; Cons\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003ePros\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n \u003cli\u003eClear integration of latent class and Markov chain ideas helps readers understand model structure and assumptions.\u003c\/li\u003e\n \u003cli\u003ePractical examples across multiple fields show how to apply models to real datasets.\u003c\/li\u003e\n \u003cli\u003eAvailability of R and MATLAB routines makes replication and adaptation straightforward for practitioners.\u003c\/li\u003e\n \u003cli\u003eFocused treatment of maximum likelihood estimation aids applied implementation.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCons\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe book emphasizes applied formulation and estimation, so readers seeking deeper theoretical proofs or advanced mathematical derivations may need supplementary sources.\u003c\/li\u003e\n \u003cli\u003eExamples are centered on categorical longitudinal data, which limits direct coverage of continuous-measure cases.\u003c\/li\u003e\n\u003c\/ul\u003e\n\n\u003ch2\u003eSpecifications\u003c\/h2\u003e\n\u003ctable\u003e\n \u003ctr\u003e\n\u003ctd\u003eTitle\u003c\/td\u003e\n\u003ctd\u003eLatent Markov Models for Longitudinal Data\u003c\/td\u003e\n\u003c\/tr\u003e\n \u003ctr\u003e\n\u003ctd\u003eSeries\u003c\/td\u003e\n\u003ctd\u003eChapman \u0026amp; Hall\/CRC Statistics in the Social and Behavioral Sciences\u003c\/td\u003e\n\u003c\/tr\u003e\n \u003ctr\u003e\n\u003ctd\u003eAuthors\u003c\/td\u003e\n\u003ctd\u003eFrancesco Bartolucci, Alessio Farcomeni, Fulvia Pennoni\u003c\/td\u003e\n\u003c\/tr\u003e\n \u003ctr\u003e\n\u003ctd\u003eFocus\u003c\/td\u003e\n\u003ctd\u003eFormulation and practical use of latent Markov models for categorical longitudinal data\u003c\/td\u003e\n\u003c\/tr\u003e\n \u003ctr\u003e\n\u003ctd\u003eExamples\u003c\/td\u003e\n\u003ctd\u003eApplications in economics, education, sociology and other fields\u003c\/td\u003e\n\u003c\/tr\u003e\n \u003ctr\u003e\n\u003ctd\u003eCode\u003c\/td\u003e\n\u003ctd\u003eR and MATLAB routines available on the authors website\u003c\/td\u003e\n\u003c\/tr\u003e\n \u003ctr\u003e\n\u003ctd\u003eEstimation\u003c\/td\u003e\n\u003ctd\u003eMaximum likelihood estimation for latent Markov models\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e\n\n\u003ch2\u003eOur Verdict\u003c\/h2\u003e\n\u003cp\u003eLatent Markov Models for Longitudinal Data is a practical, well-focused resource for applied researchers who analyze categorical repeated measures and want reproducible methods. With clear treatment of model structure, numerous applied examples, and accessible code, it delivers strong practical value despite limited full theoretical development.\u003c\/p\u003e\n\n\u003ch2\u003eFrequently Asked Questions\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003eDoes the book include code to reproduce examples?\u003c\/strong\u003e\u003cbr\u003eThe R and MATLAB routines used for the examples are provided on the authors website so readers can reproduce analyses and adapt code.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat kinds of data are these models meant for?\u003c\/strong\u003e\u003cbr\u003eThe methods focus on categorical longitudinal data where latent states evolve over time and standard latent class and Markov ideas apply.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eIs the book theoretical or applied?\u003c\/strong\u003e\u003cbr\u003eThe emphasis is on applied formulation, assumptions, and maximum likelihood estimation with worked examples rather than exhaustive theoretical proofs.\u003c\/p\u003e","brand":"Francesco Bartolucci, Alessio Farcomeni, Fulvia Pennoni","offers":[{"title":"Default Title","offer_id":48252794699995,"sku":"1439817081","price":138.86,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0724\/1043\/1707\/files\/61plyn2PTFL._SL1360.jpg?v=1778265644","url":"https:\/\/gearmusthave.com\/products\/latent-markov-models-for-longitudinal-data-practical-methods","provider":"GearMustHave","version":"1.0","type":"link"}