Home » Applied Linear Regression for Longitudinal Data
Product Image

Applied Linear Regression for Longitudinal Data

Frans E.S. Tan | Shahab Jolani
€117.99
Quantity:
In stock with our UK publisher. 14-28 days
Delivery/Collection within 10-20 working days
14 days return policy Shipping & Delivery
A01=Frans E.S. Tan
A01=Shahab Jolani
ANCOVA Approach
ANCOVA Model
Author_Frans E.S. Tan
Author_Shahab Jolani
Behavioral Sciences
Category=GPS
Category=JMA
Category=JMB
Category=PBT
Data Analysis
data visualization techniques
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
experimental study design
Gain Score Analysis
health research methods
Imputation Models
Linear Regression
Longitudinal Data Analysis
MAR Assumption
MAR Mechanism
MCAR Assumption
MCAR Mechanism
Missing Data
missing data analysis strategies
Missing Data Mechanism
Missing Observations
MNAR Scenario
Multiple Imputation
OLS Method
Pattern Mixture Models
Proximity Score
Proximity Study
Random Effects Models
Random Intercept Model
Reference Time Point
repeated measures
RS Model
sensitivity analysis
SPSS System File
statistical modeling
Stochastic Regression Imputation
Tau Group
Variance Covariance Structure

Product details

  • ISBN 9780367634315
  • Weight: 480g
  • Dimensions: 156 x 234mm
  • Publication Date: 09 Dec 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
Secure checkout Fast Shipping Easy returns
Description

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 Frans E.S. Tan | Shahab Jolani

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.

More from this author