Biomedical subjects

Craig K Enders

Publications and source records attributed to Craig K Enders.

3 recordsLinked to original sources

PubMed2005

The varieties of religious development in adulthood: a longitudinal investigation of religion and rational choice.

The authors used growth mixture models to study religious development during adulthood (ages 27-80) in a sample of individuals who were identified during childhood as intellectually gifted. The authors identified 3 discrete trajectories of religious development: (a) 40% of participants belonged to a trajectory class characterized by increases in religiousness until midlife and declines in later adulthood; (b) 41% of participants belonged to a trajectory class characterized by very low religiousness in early adulthood and age-related decline; and (c) 19% of participants belonged to a trajectory class characterized by high religiousness in early adulthood and age-related increases. Gender, strength of religious upbringing, number of children, marrying, and agreeableness predicted membership in the trajectory classes. Results were largely consistent with the rational choice theory of religious involvement.

Adult↗

PubMed2003

Using the expectation maximization algorithm to estimate coefficient alpha for scales with item-level missing data.

A 2-step approach for obtaining internal consistency reliability estimates with item-level missing data is outlined. In the 1st step, a covariance matrix and mean vector are obtained using the expectation maximization (EM) algorithm. In the 2nd step, reliability analyses are carried out in the usual fashion using the EM covariance matrix as input. A Monte Carlo simulation examined the impact of 6 variables (scale length, response categories, item correlations, sample size, missing data, and missing data technique) on 3 different outcomes: estimation bias, mean errors, and confidence interval coverage. The 2-step approach using EM consistently yielded the most accurate reliability estimates and produced coverage rates close to the advertised 95% rate. An easy method of implementing the procedure is outlined.

Algorithms↗

PubMedDate not supplied

A primer on the use of modern missing-data methods in psychosomatic medicine research.

This paper summarizes recent methodologic advances related to missing data and provides an overview of two "modern" analytic options, direct maximum likelihood (DML) estimation and multiple imputation (MI). The paper begins with an overview of missing data theory, as explicated by Rubin. Brief descriptions of traditional missing data techniques are given, and DML and MI are outlined in greater detail; special attention is given to an "inclusive" analytic strategy that incorporates auxiliary variables into the analytic model. The paper concludes with an illustrative analysis using an artificial quality of life data set. Computer code for all DML and MI analyses is provided, and the inclusion of auxiliary variables is illustrated.

Data Collection↗