Computational social science is growing up: why puberty consists of embracing measurement validation, theory development, and open science practices

Puberty is a phase in which individuals often test the boundaries of themselves and surrounding others and further define their identity – and thus their uniqueness compared to other individuals. Similarly, as Computational Social Science (CSS) grows up, it must strike a balance between its own practices and those of neighboring disciplines to achieve scientific rigor and refine its identity. However, there are certain areas within CSS that are reluctant to adopt rigorous scientific practices from other fields, which can be observed through an overreliance on passively collected data (e.g., through digital traces, wearables) without questioning the validity of such data. This paper argues that CSS should embrace the potential of combining both passive and active measurement practices to capitalize on the strengths of each approach, including objectivity and psychological quality. Additionally, the paper suggests that CSS would benefit from integrating practices and knowledge from other established disciplines, such as measurement validation, theoretical embedding, and open science practices. Based on this argument, the paper provides ten recommendations for CSS to mature as an interdisciplinary field of research

The co-evolution of emotional well-being with weak and strong friendship ties

Social ties are strongly related to well-being. But what characterizes this relationship? This study investigates social mechanisms explaining how social ties affect well-being through social integration and social influence, and how well-being affects social ties through social selection. We hypothesize that highly integrated individuals - those with more extensive and dense friendship networks - report higher emotional well-being than others. Moreover, emotional well-being should be influenced by the well-being of close friends. Finally, well-being should affect friendship selection when individuals prefer others with higher levels of well-being, and others whose well-being is similar to theirs. We test our hypotheses using longitudinal social network and well-being data of 117 individuals living in a graduate housing community. The application of a novel extension of Stochastic Actor-Oriented Models for ordered networks (ordered SAOMs) allows us to detail and test our hypotheses for weak- and strong-tied friendship networks simultaneously. Results do not support our social integration and social influence hypotheses but provide evidence for selection: individuals with higher emotional well-being tend to have more strong-tied friends, and there are homophily processes regarding emotional well-being in strong-tied networks. Our study highlights the two-directional relationship between social ties and well-being, and demonstrates the importance of considering different tie strengths for various social processes

Back to Basics:The Importance of Conceptual Clarification in Psychological Science

Although the lack of conceptual clarity has been observed to be a widespread and fundamental problem in psychology, conceptual clarification plays a mostly marginal role in psychological research. In this article, we argue that better conceptualization of psychological phenomena is needed to move psychology forward as a science. We first show how conceptual unclarity seeps through all aspects of psychological research, from everyday concepts to statistical measures. We then turn to recommendations on how to improve conceptual clarity in psychology, emphasizing the importance of seeing research as an iterative process in which it is necessary to revisit the phenomena that are the foundations of theories and models, as well as how they are conceptualized and measured

Treatment Effect Estimation from Observational Network Data using Augmented Inverse Probability Weighting and Machine Learning

Causal inference methods for treatment effect estimation usually assume independent experimental units. However, this assumption is often questionable because experimental units may interact. We develop augmented inverse probability weighting (AIPW) for estimation and inference of causal treatment effects on dependent observational data. Our framework covers very general cases of spillover effects induced by units interacting in networks. We use plugin machine learning to estimate infinite-dimensional nuisance components leading to a consistent treatment effect estimator that converges at the parametric rate and asymptotically follows a Gaussian distribution. We apply our AIPW method to the Swiss StudentLife Study data to investigate the effect of hours spent studying on exam performance accounting for the students' social network

Studying Daily Social Interaction Quantity and Quality in Relation to Depression Change: A Multi-Phase Experience Sampling Study

Day-to-day social life and mental health are intertwined. Yet, no study to date has assessed how the quantity and quality of social interactions in daily life are associated with changes in depressive symptoms. This study examines these links using multiple-timescale data (iSHAIB data set; N = 133), where the level of depressive symptoms was measured before and after three 21-day periods of event-contingent experience sampling of individuals’ interpersonal interactions ( T = 64,112). We find weak between-person effects for interaction quantity and perceiving interpersonal warmth of others on changes in depressive symptoms over the 21-day period, but strong and robust evidence for overwarming—a novel construct representing the self-perceived difference between one’s own and interaction partner’s level of interpersonal warmth. The findings highlight the important role qualitative aspects of social interactions may play in the progression of individuals’ depressive symptoms

Feedback About a Person’s Social Context - Personal Networks and Daily Social Interactions

The social context of a person, meaning their social relationships and daily social interactions, is an important factor for understanding their mental health. However, personalised feedback approaches to psychotherapy do not consider this factor sufficiently yet. Therefore, we developed an interactive feedback prototype focusing specifically on a person’s social relationships as captured with personal social networks (PSN) and daily social interactions as captured with experience sampling methodology (ESM). We describe the development of the prototype as well as two evaluation studies: Semi-structured interviews with students (N = 23) and a focus group discussion with five psychotherapy patients. Participants from both studies considered the prototype useful. The students considered participation in our study, which included social context assessment via PSN and ESM as well as a feedback session, insightful. However, it remains unclear how much insight the feedback procedure generated for the students beyond the insights they already gained from the assessments. The focus group patients indicated that in a clinical context, (social context) feedback may be especially useful to generate insight for the clinician and facilitate collaboration between patient and clinician. Furthermore, it became clear that the current feedback prototype requires explanations by a researcher or trained clinician and cannot function as a stand-alone intervention. As such, we discuss our feedback prototype as a starting point for future research and clinical implementation

Current and Ideal Team Roles: Relationships to Job Satisfaction and Calling

Successful teamwork is an important factor for positive outcomes at the organizational and the individual level. Best results should be expected when every team member can contribute his or her specific set of strengths and skills, with all of the necessary skills being present in a team. Recently a new model of team roles developed from a positive psychological perspective has been suggested comprising of seven informal team roles. The present study examines the relevance of role-fit between roles displayed in the current team and roles displayed in an ideal team on a person’s job satisfaction and calling. For this purpose, a sample of N = 342 employed participants who took part in an online survey were analyzed. Results show that most current team roles contribute to job satisfaction and calling, whereas only few relationships are found with ideal roles. Further, the interplay between current and ideal role behavior is relevant for job satisfaction in most team roles, but only for few roles with regard to calling. Thus, both current and ideal team roles are relevant for work-related outcomes; this information could potentially be used as a starting point for positive interventions at the workplace

What do centrality measures measure in psychological networks?

Centrality indices are a popular tool to analyze structural aspects of psychological networks. As centrality indices were originally developed in the context of social networks, it is unclear to what extent these indices are suitable in a psychological network context. In this article we critically examine several issues with the use of the most popular centrality indices in psychological networks: degree, betweenness, and closeness centrality. We show that problems with centrality indices discussed in the social network literature also apply to the psychological networks. Assumptions underlying centrality indices, such as presence of a flow and shortest paths, may not correspond with a general theory of how psychological variables relate to one another. Furthermore, the assumptions of node distinctiveness and node exchangeability may not hold in psychological networks. We conclude that, for psychological networks, betweenness and closeness centrality seem especially unsuitable as measures of node importance. We therefore suggest three ways forward: (a) using centrality measures that are tailored to the psychological network context, (b) reconsidering existing measures of importance used in statistical models underlying psychological networks, and (c) discarding the concept of node centrality entirely. Foremost, we argue that one has to make explicit what one means when one states that a node is central, and what assumptions the centrality measure of choice entails, to make sure that there is a match between the process under study and the centrality measure that is used.ISSN:0021-843XISSN:0096-851XISSN:0145-2339ISSN:0145-2347ISSN:1939-184

Reprint of: The Swiss StudentLife Study: Investigating the emergence of an undergraduate community through dynamic, multidimensional social network data

The Swiss StudentLife Study (SSL Study) is a longitudinal social network data collection conducted in three undergraduate student cohorts (N₁ = 226, N₂ = 261, N₃ = 660) in 2016−2019. The main goal of the study was to understand the emergence of informal student communities and their effects on different individual outcomes, such as well-being, motivation, and academic success. To this end, multiple dimensions of social ties were assessed, combining computer-based surveys, social sensors, social media data, and field experiments. The dynamics of these social networks were measured on various time scales. In this paper, we present the design and data collection strategy of the SSL Study. We discuss practical challenges and solutions related to the data collection in four areas that were key to the success of our project: study design, research ethics, communication, and population definition