Testing Latent Longitudinal Models of Social Ties and Depression Among the Elderly: A Comparison of Distribution-Free and Maximum Likelihood Estimates With Nonnormal Data

Using a latent-variable modeling approach, relationships between social ties and depression were studied in a sample of 201 older adults. Both positive and negative ties were related to concurrent depression, whereas only negative ties predicted future depression. Nonnormally distributed scores were...

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Bibliographic Details
Published in:Psychology and aging 1992-03, Vol.7 (1), p.107-118
Main Authors: Finch, John F, Zautra, Alex J
Format: Article
Language:English
Subjects:
Adult. Elderly
Aged
Bereavement
Biological and medical sciences
Depression - psychology
Developmental psychology
Disabled Persons - psychology
Female
Fundamental and applied biological sciences. Psychology
Human
Humans
Latent Variables
Life Change Events
Likelihood Functions
Longitudinal Studies
Major Depression
Male
Maximum Likelihood
Methodology
Personal Satisfaction
Personality Inventory - statistics & numerical data
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Risk Factors
Sick Role
Social Environment
Social Support
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Summary:Using a latent-variable modeling approach, relationships between social ties and depression were studied in a sample of 201 older adults. Both positive and negative ties were related to concurrent depression, whereas only negative ties predicted future depression. Nonnormally distributed scores were observed for several variables, and results based on maximum likelihood (ML), which assumes multivariate normality, were compared with those obtained using Browne's (1982 , 1984) arbitrary distribution function (ADF) estimator for nonnormal variables. Inappropriate use of ML with nonnormal data yielded model chi-square values that were too large and standard errors that were too small. ML also failed to detect the over-time effect of negative ties on depression. The results suggest that the negative functions of social networks may causally influence depression and illustrate the need to test distributional assumptions when estimating latent-variable models.
ISSN:0882-7974
1939-1498
DOI:10.1037/0882-7974.7.1.107