scholarly journals Identifying the strongest self-report predictors of sexual satisfaction using machine learning

2022 ◽  
pp. 026540752110470
Author(s):  
Laura M. Vowels ◽  
Matthew J. Vowels ◽  
Kristen P. Mark
Keyword(s):  
Machine Learning ◽  
Relationship Satisfaction ◽  
Sexual Satisfaction ◽  
Predictor Variable ◽  
Well Being ◽  
Self Report ◽  
Future Research ◽  
Total N ◽  
Relationship Length ◽  
Two Samples

Sexual satisfaction has been robustly associated with relationship and individual well-being. Previous studies have found several individual (e.g., gender, self-esteem, and attachment) and relational (e.g., relationship satisfaction, relationship length, and sexual desire) factors that predict sexual satisfaction. The aim of the present study was to identify which variables are the strongest, and the least strong, predictors of sexual satisfaction using modern machine learning. Previous research has relied primarily on traditional statistical models which are limited in their ability to estimate a large number of predictors, non-linear associations, and complex interactions. Through a machine learning algorithm, random forest (a potentially more flexible extension of decision trees), we predicted sexual satisfaction across two samples (total N = 1846; includes 754 individuals forming 377 couples). We also used a game theoretic interpretation technique, Shapley values, which allowed us to estimate the size and direction of the effect of each predictor variable on the model outcome. Findings showed that sexual satisfaction is highly predictable (48–62% of variance explained) with relationship variables (relationship satisfaction, importance of sex in relationship, romantic love, and dyadic desire) explaining the most variance in sexual satisfaction. The study highlighted important factors to focus on in future research and interventions.

scholarly journals Identifying the Most Important Predictors of Sexual Satisfaction using Interpretable Machine Learning

2020 ◽  
Author(s):  
Laura Marika Vowels ◽  
Matthew J Vowels ◽  
Kristen P Mark
Keyword(s):  
Machine Learning ◽  
Relationship Satisfaction ◽  
Sexual Satisfaction ◽  
Learning Algorithm ◽  
Predictor Variable ◽  
Policy Makers ◽  
Total N ◽  
Two Samples ◽  
Game Theoretic ◽  
Variance Explained

Previous studies have found a number of different factors that are associated with sexual satisfaction but have been unable to estimate the relative importance of each predictor. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict sexual satisfaction across two samples (total N = 1846; includes 754 individuals forming 377 couples). We also used a game theoretic interpretation technique, which allowed us to estimate the size and direction of the effect of each predictor variable on the model outcome. The present study showed that sexual satisfaction is highly predictable (48-62% of variance explained) with relationship variables (relationship satisfaction, perception of love and sex, romantic love, dyadic desire) explaining the most variance in sexual satisfaction. The study enables researchers, policy-makers, and practitioners to target variables that are the most likely to improve sexual satisfaction in order to better people’s sexual lives.

Is Infidelity Predictable? Using Interpretable Machine Learning to Identify the Most Important Predictors of Infidelity

2020 ◽  
Author(s):  
Laura Marika Vowels ◽  
Matthew J Vowels ◽  
Kristen P Mark
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Large Body ◽  
Predictor Variable ◽  
Well Being ◽  
Relationship Length ◽  
Three Samples ◽  
Online Infidelity ◽  
Shapley Values ◽  
Game Theoretic

Infidelity is a common occurrence in relationships and can have a devastating impact on both partners’ well-being. A large body of literature have attempted to factors that can explain or predict infidelity but have been unable to estimate the relative importance of each predictor. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict in-person and online infidelity and intentions toward future infidelity across three samples (two dyadic samples; N = 1846). We also used a game theoretic explanation technique, Shapley values, which allowed us to estimate the effect size of each predictor variable on infidelity. The present study showed that infidelity was somewhat predictable overall with interpersonal factors (relationship satisfaction, love, desire, relationship length) being the most predictive. The results suggest that addressing relationship difficulties early in the relationship can help prevent future infidelity.

scholarly journals Romantic Relationship Length and its Perceived Quality: Mediating Role of Facebook-Related Conflict

2015 ◽  
Vol 11 (3) ◽  
pp. 395-405 ◽  
Author(s):  
H. M. Saidur Rahaman
Keyword(s):  
Relationship Satisfaction ◽  
Romantic Relationship ◽  
Online Survey ◽  
Negative Relationship ◽  
Self Report ◽  
Future Research ◽  
Mediation Analyses ◽  
Relationship Length ◽  
Self Report Data ◽  
The Relationship

The purpose of this study was to investigate how Facebook use is leading to negative relationship outcomes such as cheating and breakup by assessing users’ perceived relationship qualities. It was hypothesized that Facebook-related conflict will be negatively related with users’ relationship length and will also be negatively related with their perceived relationship satisfaction, commitment, and love. Facebook-related conflict further mediates the relationship between relationship length and perceived relationship satisfaction, commitment, and love. Self-report data were gathered from participants (N = 101) in an online survey by employing standard questionnaires. A set of regression and mediation analyses confirmed all the hypotheses of the study. That is, Facebook-related conflict mediates the relationship between relationship length and perceived relationship satisfaction, commitment, and love. Moreover, the magnitude of mediation was highest for relationship satisfaction. Implications for future research and contributions are discussed.

scholarly journals Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

2020 ◽  
Vol 117 (32) ◽  
pp. 19061-19071 ◽  
Author(s):  
Samantha Joel ◽  
Paul W. Eastwick ◽  
Colleen J. Allison ◽  
Ximena B. Arriaga ◽  
Zachary G. Baker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Individual Differences ◽  
Relationship Quality ◽  
Individual Difference ◽  
Large Scale ◽  
Well Being ◽  
Attachment Anxiety ◽  
Self Report ◽  
Individual Difference Variables ◽  
Partner Satisfaction

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships.

scholarly journals Screening Using the SDQ Total Difficulties Scale: An Analog Test of Three Cutoff Values

2019 ◽  
Author(s):  
Tyler L Renshaw
Keyword(s):  
Well Being ◽  
The Self ◽  
Self Report ◽  
Future Research ◽  
Subjective Well Being ◽  
Analog Test ◽  
Research And Practice ◽  
Cutoff Values ◽  
Dependent Variables ◽  
Strengths And Difficulties

This brief report presents an analog test of the relative classification validity of three cutoff values (CVs; 16, 18, and 20) derived from responses to the self-report version of the Strengths and Difficulties Questionnaire: Total Difficulties Scale. Results from Bayesian t-tests, using several school-specific subjective well-being indicators as dependent variables, yielded evidence suggesting all CV models effectively differentiated between students with lower and higher levels of risk. Evidence also indicated that the lowest CV (16) was more effective than the higher CVs (18, 20) at identifying students with greater levels of risk, and that the higher CVs functioned comparably well. Implications for future research and practice are noted.

scholarly journals Machine Learning Models of COVID-19 Cases in the United States: A Study of Initial Lockdown and Reopen Regimes

2021 ◽  
Vol 11 (23) ◽  
pp. 11227
Author(s):  
Arnold Kamis ◽  
Yudan Ding ◽  
Zhenzhen Qu ◽  
Chenchen Zhang
Keyword(s):  
United States ◽  
Machine Learning ◽  
Additive Model ◽  
Regression Tree ◽  
Predictor Variable ◽  
The United States ◽  
Predictor Variables ◽  
Future Research ◽  
Machine Learning Methods ◽  
Variance Explained

The purpose of this paper is to model the cases of COVID-19 in the United States from 13 March 2020 to 31 May 2020. Our novel contribution is that we have obtained highly accurate models focused on two different regimes, lockdown and reopen, modeling each regime separately. The predictor variables include aggregated individual movement as well as state population density, health rank, climate temperature, and political color. We apply a variety of machine learning methods to each regime: Multiple Regression, Ridge Regression, Elastic Net Regression, Generalized Additive Model, Gradient Boosted Machine, Regression Tree, Neural Network, and Random Forest. We discover that Gradient Boosted Machines are the most accurate in both regimes. The best models achieve a variance explained of 95.2% in the lockdown regime and 99.2% in the reopen regime. We describe the influence of the predictor variables as they change from regime to regime. Notably, we identify individual person movement, as tracked by GPS data, to be an important predictor variable. We conclude that government lockdowns are an extremely important de-densification strategy. Implications and questions for future research are discussed.

scholarly journals Structure and correlates of interpersonal problems and sensitivities

2019 ◽  
Author(s):  
Christopher James Hopwood ◽  
Evan Good
Keyword(s):  
Factor Model ◽  
Interpersonal Problems ◽  
Well Being ◽  
Future Research ◽  
Sources Of Information ◽  
Measurement Models ◽  
Two Samples ◽  
Problematic Behaviors ◽  
Interpersonal Dysfunction ◽  
Important Marker

ObjectiveInterpersonal dysfunction is an important marker of individual differences in personality and well‐being. Existing research on interpersonal dysfunction focuses primarily on the problematic behaviors of individuals without considering how sensitivity to others’ behavior impacts functioning. In this study, we test the structure and correlates of a model of relationship dysfunction that integrates the problems individuals bring to relationships with their sensitivities to others’ behavior. We specifically examine the conjoint structure of interpersonal problems and sensitivities using a circumplex framework and associations between dimensions derived from this structure and personality, well‐being, attachment, and response style variables.MethodWe evaluated competing measurement models and examined validity correlations of interpersonal problems and sensitivities in two samples (Study 1: N = 955; 79.2% women; Mage = 19.43; Study 2: N = 1,005; 72.1% women; Mage = 19.77).ResultsSix factors capturing general (nonspecific problems and sensitivities) and stylistic (warmth and dominance for both problems and sensitivities) variation in interpersonal dysfunction were empirically distinguishable and provided incremental information about external criteria.ConclusionsResults support problems and sensitivities as overlapping but distinct sources of information about interpersonal dysfunction, and they specifically suggest an integrative six‐factor model with considerable potential for future research.

scholarly journals Associations between self-rated physical Health and relationship satisfaction in couples with children

2020 ◽  
Vol 7 (2) ◽  
pp. 205510292095952
Author(s):  
Katherine JW Baucom ◽  
Jill Giresi ◽  
Richard E Heyman ◽  
Amy M Smith Slep
Keyword(s):  
Relationship Satisfaction ◽  
Physical Health ◽  
Physical Functioning ◽  
General Health ◽  
Community Sample ◽  
Self Report ◽  
Future Research ◽  
Relationship Processes ◽  
Individual Self

The degree to which individual self-rated physical health and concordance of self-rated physical health between partners are associated with relationship satisfaction was examined in a community sample of 399 couples with children. Couples completed self-report assessments of physical health (general health and physical functioning) and relationship satisfaction. Results suggest unique associations between partners’ general health and their own relationship satisfaction. Further, higher between-partner concordance in physical functioning was uniquely associated with higher relationship satisfaction in women. Understanding associations between health and relationship processes is crucial and has implications for future research on couple-based interventions to promote physical health.

Development and Validation of a Situational Social Avoidance Scale

1999 ◽  
Vol 85 (1) ◽  
pp. 114-120 ◽  
Author(s):  
F. Ishu Ishiyama
Keyword(s):  
Social Anxiety ◽  
Social Performance ◽  
Self Report ◽  
Social Avoidance ◽  
Total N ◽  
Two Samples ◽  
Performance Domains ◽  
Positive Correlations ◽  
Test Retest Reliability ◽  
Development And Validation

A 15-item self-report Situational Social Avoidance scale was developed and validated. Two samples of university students (total N = 407) provided data, evidencing high internal consistency (α=.89 for Sample 1, α = .92 for Sample 2) and test-retest reliability of .86 ( n = 65) over a 6.5-wk. interval. A factor-analysis yielded an interpretable 3-factor solution with three domains of social avoidance, (a) social performance, (b) socializing, and (c) self-assertion. Sample 2 showed a significant sex difference, with 138 women scoring higher, especially in the self-assertion and social performance domains. The scale had high positive correlations (from .60 to .78) with four frequently used social anxiety scales, and meaningful correlations with depression ( r = .36), self-esteem ( r = −.49), and self-critical cognition ( r = .50). Differential correlations were found between the scale's three factor-based subscales and the other social anxiety scales, suggesting different situational properties of the latter scales. Research implications and clinical use of the scale are discussed.

scholarly journals Relationships between condoms, hormonal methods, and sexual pleasure and satisfaction: an exploratory analysis from the Women's Well-Being and Sexuality Study

Sexual Health ◽  
2008 ◽  
Vol 5 (4) ◽  
pp. 321 ◽  
Author(s):  
Jenny A. Higgins ◽  
Susie Hoffman ◽  
Cynthia A. Graham ◽  
Stephanie A. Sanders
Keyword(s):  
Sexual Satisfaction ◽  
Contraceptive Use ◽  
Unwanted Pregnancy ◽  
Well Being ◽  
Sexual Pleasure ◽  
The Other ◽  
Relationship Length ◽  
Sexually Transmissible Infections ◽  
Sexuality Study ◽  
Risk Practices

Background: Little is known about how condoms and other contraceptives influence women’s sexual enjoyment, which could shape use patterns. Methods: Data from an online study of women’s sexual health and functioning were used to examine how three categories of contraceptive use – hormonal method only, condoms primarily, and dual use – could help predict decreased sexual pleasure associated with contraceptive method and overall sexual satisfaction in the past 4 weeks. Results: In analyses controlling for age, relationship length, and other variables, male condoms were most strongly associated with decreased pleasure, whether used alone or in conjunction with hormonal methods. Women who used hormonal methods alone were least likely to report decreased pleasure, but they also had significantly lower overall scores of sexual satisfaction compared with the other two groups. Dual users, or women who used both condoms and a hormonal method, reported the highest sexual satisfaction scores. Conclusions: Because male condoms were viewed by many of these women as decreasing sexual pleasure, sexual risk practices are likely to be affected. Although hormonal only users were highly unlikely to report decreased pleasure, they reported lower sexual satisfaction compared with the other two groups. Dual users, who had the highest sexual satisfaction scores, may have been the most sexually satisfied because they felt more fully protected against unwanted pregnancy and sexually transmissible infections – consistent with previous qualitative documentation of ‘eroticising safety.’ This exploratory study suggests that different contraceptives affect sexuality in various ways, warranting further research into these sexual dimensions and how they influence contraceptive practices.

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