A causal research pipeline and tutorial for psychologists and social scientists.

Causality is a fundamental part of the scientific endeavor to understand the world. Unfortunately, causality is still taboo in much of psychology and social science. Motivated by a growing number of recommendations for the importance of adopting causal approaches to research, we reformulate the typical approach to research in psychology to harmonize inevitably causal theories with the rest of the research pipeline.

Trying to outrun causality with machine learning: Limitations of model explainability techniques for exploratory research.

Machine learning explainability techniques have been proposed as a means for psychologists to "explain" or interrogate a model in order to gain an understanding of a phenomenon of interest. Researchers concerned with imposing overly restrictive functional form (e.g.

Longitudinal evidence over 2 years of the pandemic shows that poor mental health in people living with obesity may be underestimated.

It is well-documented that people living with obesity are at greater risk of poorer mental health outcomes. The aim of our study was twofold: First, to examine the longitudinal trajectories of depression and anxiety in people living with obesity over two years across eight waves of a UK national COVID-19 survey (March 2020-March 2022) using smoothing-splines mixed-effects models. Second, to investigate participation effects via a missingness analysis to check whether survey attrition over time was related to participant characteristics.

Toward a causal link between attachment styles and mental health during the COVID-19 pandemic.

Background: Recent research has shown that insecure attachment, especially attachment anxiety, is associated with poor mental health outcomes, especially during the COVID-19 pandemic. Other research suggests that insecure attachment may be linked to nonadherence to social distancing behaviours during the pandemic.

Aims: The present study aims to examine the causal links between attachment styles (secure, anxious, avoidant), mental health outcomes (depression, anxiety, loneliness) and adherence to social distancing behaviours during the first several months of the UK lockdown (between April and August 2020).

Misspecification and unreliable interpretations in psychology and social science.

The replicability crisis has drawn attention to numerous weaknesses in psychology and social science research practice. In this work we focus on three issues that cannot be addressed with replication alone, and which deserve more attention: Functional misspecification, structural misspecification, and unreliable interpretation of results. We demonstrate a number of possible consequences via simulation, and provide recommendations for researchers to improve their research practice.

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

Infidelity can be a disruptive event in a romantic relationship with a devastating impact on both partners' well-being. Thus, there are benefits to identifying factors that can explain or predict infidelity, but prior research has not utilized methods that would provide 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 across two studies (one individual and one dyadic, N = 1,295).

Spectral and cross-spectral analysis-A tutorial for psychologists and social scientists.

Social scientists have become increasingly interested in using intensive longitudinal methods to study social phenomena that change over time. Many of these phenomena are expected to exhibit cycling fluctuations (e.g.

Uncovering the Most Important Factors for Predicting Sexual Desire Using Explainable Machine Learning.

Background: Low sexual desire is the most common sexual problem reported with 34% of women and 15% of men reporting lack of desire for at least 3 months in a 12-month period. Sexual desire has previously been associated with both relationship and individual well-being highlighting the importance of understanding factors that contribute to sexual desire as improving sexual desire difficulties can help improve an individual's overall quality of life.

Aim: The purpose of the present study was to identify the most salient individual (eg, attachment style, attitudes toward sexuality, gender) and relational (eg, relationship satisfaction, sexual satisfaction, romantic love) predictors of dyadic and solitary sexual desire from a large number of predictor variables.

What can be learned from couple research: Examining emotional co-regulation processes in face-to-face interactions.

A crucial component of successful counseling and psychotherapy is the dyadic emotion co-regulation process between patient and therapist that unfolds moment to moment during therapy sessions. The major reason for the disappointing progress in understanding this process is the lack of appropriate methods to assess subjectively experienced emotions continuously during therapy sessions without disturbing the natural flow of the interaction. The resulting inability has forced the field to focus on patients' overall emotion ratings at the end of each session with limited predictive value of the dyadic interplay between patient and therapist's emotional states within each session.

Using spectral and cross-spectral analysis to identify patterns and synchrony in couples' sexual desire.

Sexual desire discrepancy is one of the most frequently reported sexual concerns for individuals and couples and has been shown to be negatively associated with sexual and relationship satisfaction. Sexual desire has increasingly been examined as a state-like construct that ebbs and flows, but little is known about whether there are patterns in the fluctuation of sexual desire. Utilizing spectral and cross-spectral analysis, we transformed 30 days of dyadic daily diary data for perceived levels of sexual desire for a non-clinical sample of 133 couples (266 individuals) into the frequency domain to identify shared periodic state fluctuations in sexual desire.