Staying Connected: An Examination of Relationship Maintenance Behaviors in Long-Distance Relationships

ABSTRACT

Romantic long-distance relationships (LDRs) are becoming increasingly prevalent as individuals seek educational and employment opportunities across the globe. LDRs create unique challenges for couples, potentially impacting couple and individual well-being. It is important to understand efforts that LDR partners make to stay engaged, known as relationship maintenance behaviors (RMBs), and whether common RMBs facilitate or hinder relational and individual functioning. This study has two goals: (a) to examine whether RMBs predict relationship satisfaction and (b) to investigate whether relationship satisfaction mediates the association between RMBs and individual functioning. Eighty-seven adults in LDRs participated in our online survey. Results indicated that RMBs predicted relationship satisfaction, some negatively, and that relationship satisfaction was a mediator between RMBs and individual well-being.

KEYWORDS:

• communication
• couples
• long distance relationships
• relationship maintenance behaviors

Notes

The first 34 participants did not receive a gift card as funding was not available at that time.

Race/ethnicity categories were not mutually exclusive, therefore percentages do not add up to 100.

In the original article, Merolla (2012) tested both a 9-factor and 10-factor model, with results suggesting similar model fit and that either model would be acceptable for use in empirical investigations (Andrew Merolla, personal communication, January 12, 2015). The nine-factor model was chosen for the current study because of the stronger theoretical basis and also because it is more parsimonious.

We controlled for relevant demographic characteristics (relationship length, number of miles separated, and frequency of visits), but none of these control variables were significant (p > 0.05). Some participants were missing data on the control variables (n = 6), and it was not possible to impute values for these variables because they were demographic factors (i.e., no other variables could be used as their predictors). Therefore, we present the model without demographic control variables to retain the greatest sample size and power for our model, as well as to avoid the result biases associated with listwise deletion.

The value in parentheses represents the numerator degrees of freedom for the models. When using multiple imputation for regression analyses, the denominator degrees of freedom approach infinity due to the large number of model iterations. This value no longer carries the same meaning as in typical regression analyses and is therefore omitted here.