Attrition in developmental psychology: A review of modern missing data reporting and practices
JS Nicholson, PR Deboeck… - International Journal of …, 2017 - journals.sagepub.com
Inherent in applied developmental sciences is the threat to validity and generalizability due
to missing data as a result of participant drop-out. The current paper provides an overview of …
to missing data as a result of participant drop-out. The current paper provides an overview of …
Multiple imputation as a flexible tool for missing data handling in clinical research
CK Enders - Behaviour research and therapy, 2017 - Elsevier
The last 20 years has seen an uptick in research on missing data problems, and most
software applications now implement one or more sophisticated missing data handling …
software applications now implement one or more sophisticated missing data handling …
[图书][B] Applied missing data analysis
CK Enders - 2022 - books.google.com
The most user-friendly and authoritative resource on missing data has been completely
revised to make room for the latest developments that make handling missing data more …
revised to make room for the latest developments that make handling missing data more …
Reflections on partial least squares path modeling
CN McIntosh, JR Edwards… - Organizational …, 2014 - journals.sagepub.com
The purpose of the present article is to take stock of a recent exchange in Organizational
Research Methods between critics and proponents of partial least squares path modeling …
Research Methods between critics and proponents of partial least squares path modeling …
Missing data
RJA Little, N Schenker - Handbook of statistical modeling for the social …, 1995 - Springer
Studies in the social and behavioral sciences frequently suffer from missing data. For
instance, sample surveys often have some individuals who either refuse to participate or do …
instance, sample surveys often have some individuals who either refuse to participate or do …
Missing data methods for arbitrary missingness with small samples
D McNeish - Journal of Applied Statistics, 2017 - Taylor & Francis
Missing data are a prevalent and widespread data analytic issue and previous studies have
performed simulations to compare the performance of missing data methods in various …
performed simulations to compare the performance of missing data methods in various …
[图书][B] Ecological statistics: contemporary theory and application
The application and interpretation of statistics are central to ecological study and practice.
Ecologists are now asking more sophisticated questions than in the past. These new …
Ecologists are now asking more sophisticated questions than in the past. These new …
Introduction to latent class analysis with applications
M Porcu, F Giambona - The Journal of Early Adolescence, 2017 - journals.sagepub.com
Latent class analysis (LCA) is a statistical method used to group individuals (cases, units)
into classes (categories) of an unobserved (latent) variable on the basis of the responses …
into classes (categories) of an unobserved (latent) variable on the basis of the responses …
Analyzing structural equation models with missing data
CK Enders - Structural equation modeling: A second course, 2006 - books.google.com
A wealth of options exists for analyzing structural equation models (SEM) with missing data,
including the expectation maximization (EM) algorithm, full information maximum likelihood …
including the expectation maximization (EM) algorithm, full information maximum likelihood …
Using principal components as auxiliary variables in missing data estimation
To deal with missing data that arise due to participant nonresponse or attrition,
methodologists have recommended an “inclusive” strategy where a large set of auxiliary …
methodologists have recommended an “inclusive” strategy where a large set of auxiliary …