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Cited by 240

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

A Two‐Factor Model for Predicting When a Couple Will Divorce: Exploratory Analyses Using 14‐Year Longitudinal Data*

Gottman, John Mordechai, Levenson, Robert Wayne·2002·Family Process

Abstract

This article examines 14‐year longitudinal data and attempts to create a post hoc model that uses Time‐1 data to “predict” the length of time the marriage will last. The sample consists of the 21 couples (of 79 studied) who divorced over a 14‐year period. A two‐factor model is proposed. One factor is the amount of unregulated volatile positive and negative affect in the marriage, and this factor predicts a short marriage length for the divorcing couples. A second factor is called “neutral affective style,” and this factor predicts a long marriage length for the divorcing couples. This model is compared to a Time‐1 model of ailing marriage in which Time‐1 marital satisfaction is used to predict the timing of divorce.

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Crossref · 10.1111/j.1545-5300.2002.40102000083.x

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Metadata

Authors
Gottman, J. M., & Levenson, R. W.
Venue
Family Process
Publisher
Wiley
Published
2002-03
Volume
41
Issue
1
Pages
83-96
ISSN
  • 0014-7370 (ISSN-L)
  • 1545-5300
OpenAlex
W2069980741
Cited by
240
References
4
FWCI
4.87
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Two‐Factor Model for Predicting When a Couple Will Divorce: Exploratory Analyses Using 14‐Year Longitudinal Data*. https://app.cochat.ai/papers/10.1111/j.1545-5300.2002.40102000083.x
The paper
Gottman, J. M., & Levenson, R. W. (2002). A Two‐Factor Model for Predicting When a Couple Will Divorce: Exploratory Analyses Using 14‐Year Longitudinal Data*. Family Process. https://doi.org/10.1111/j.1545-5300.2002.40102000083.x

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.