Neuware - This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas.

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Bibliographic Details

Title: Applied Linear Regression for Longitudinal Data : With an Emphasis on Missing Observations
Publisher: Taylor & Francis Ltd Dez 2022
Publication Date: 2022
Language: English
ISBN 10: 0367634317
ISBN 13: 9780367634315
Binding: Buch
Condition: Neu
Dimensions: 236 millimeters width by 160 millimeters height by 17 millimeters depth
Weight: 490 grams

About this title

Synopsis

This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following:

Provides datasets and examples online
Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis
Conceptualises the analysis of comparative (experimental and observational) studies

It is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background.

About the Author

Frans E.S. Tan is an associate professor (retired) of methodology and statistics at Maastricht University, The Netherlands.

Shahab Jolani is an assistant professor of methodology and statistics at Maastricht University, The Netherlands.

"About this title" may belong to another edition of this title.

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