[{"_1":2,"_42":-5,"_43":-5},"loaderData",{"_3":4,"_10":11,"_40":-5,"_41":-5},"root",{"_5":-5,"_6":-5,"_7":-5,"_8":9},"user","shoppingCartId","toast","locale","en","routes/_store+/_readers+/email_author.$slug/route",{"_12":13,"_39":-5},"book",{"_14":15,"_16":17,"_18":19,"_20":21,"_22":23,"_24":25},"id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNTMyNjU","title","Longitudinal Data Analysis Using R","state","published","contactAuthorMethod","email","aboutText","

Longitudinal data is essential for understanding the world around us. It allows us to investigate changes over time and obtain better causal estimates. Nevertheless, this type of data is also more complex, making it difficult to manipulate, explore, and analyse.

This book covers all the key skills necessary for working with longitudinal data, employing a hands-on approach and real-world data. To ensure a solid foundation, it begins by introducing the basics of R, regression modelling, path analysis, and concepts of longitudinal data. It then covers how to efficiently prepare longitudinal data by importing, recoding and reshaping data. This is followed by a comprehensive introduction to data exploration using tables, summary statistics and visualisations. The book also provides an in-depth guide to state-of-the-art statistical models for analysing longitudinal data, including the multilevel model for change and the latent growth model. It discusses practical challenges to conducting longitudinal analysis, such as missing data, measurement error, separating age-period-cohort effects, presenting results, and following a reproducible workflow.

","authors",[26],{"_14":27,"_28":29,"_5":30},"Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6UHJvZHVjdEF1dGhvci81NjIyMA","name","Alexandru Cernat",{"_31":32},"avatar",{"_33":34,"_35":36,"_37":38},"url","https://d308did4coypzi.cloudfront.net/avatars/2017883/medium/Poza.jpg?1670946841","styleName","medium","exists",true,"currentUser","routes/_store+/_layout","routes/_store+/_readers+/_layout","actionData","errors"]

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