Structural Equation Modeling of Social Networks: Specification,Estimation, and Application

Psychologists are interested in whether friends and couples share similar personalities or not. However, no statistical models are readily available to test the association between personalities and social relations in the literature. In this study, we develop a statistical model for analyzing social network data with the latent personality traits as covariates. Because the model contains a measurement model for the latent traits and a structural model for the relationship between the network and latent traits, we discuss it under the general framework of structural equation modeling (SEM). In our model, the structural relation between the latent variable(s) and the outcome variable is no longer linear or generalized linear. To obtain model parameter estimates, we propose to use a two-stage maximum likelihood (ML) procedure. This modeling framework is evaluated through a simulation study under representative conditions that would be found in social network data. Its usefulness is then demonstrated through an empirical application to a college friendship network.     

Do physical and relational aggression explain adolescents' friendship selection? The competing roles of network characteristics,gender, and social status

The role of physical and relational aggression in adolescents' friendship selection was examined in a longitudinal sample of 274 Chilean students from 5th and 6th grade followed over 1 year. Longitudinal social network modeling (SIENA) was used to study selection processes for aggression while influence processes were controlled for. Furthermore, the effects of network characteristics (i.e., reciprocity and transitivity), gender, and social status on friendship selection were examined. The starting assumption of this study was that selection effects based on aggression might have been overestimated in previous research as a result of failing to consider influence processes and alternative characteristics that steer friendship formation. The results show that selection effects of both physical and relational aggression disappeared when network effects, gender, and social status were taken into account. Particularly gender and perceived popularity appeared to be far more important determinants of friendship selection over time than aggression. Moreover, a peer influence effect was only found for relational aggression, and not for physical aggression. These findings suggest that similarity in aggression among befriended adolescents can be considered to be mainly a by-product rather than a leading dimension in friendship selection.     

Neural network modeling of risk assessment in child protective services

The advantages of using neural network methodology for the modeling of complex social science data are demonstrated, and neural network analysis is applied to Washington State Child Protective Services risk assessment data. Neural network modeling of the association between social worker overall assessment of risk and the 37 separate risk factors from the State of Washington Risk Assessment Matrix is shown to provide case classification results superior to linear or logistic multiple regression. The improvement in case prediction and classification accuracy is attributed to the superiority of neural networks for modeling nonlinear relationships between interacting variables; in this respect the mathematical framework of neural networks is a better approximation to the actual process of human decision making than linear, main effects regression. The implications of this modeling advantage for evaluating social science data within the framework of ecological theories are discussed.     

The Gaussian Graphical Model in Cross-Sectional and Time-Series Data

We discuss the Gaussian graphical model (GGM; an undirected network of partial correlation coefficients) and detail its utility as an exploratory data analysis tool. The GGM shows which variables predict one-another, allows for sparse modeling of covariance structures, and may highlight potential causal relationships between observed variables. We describe the utility in three kinds of psychological data sets: data sets in which consecutive cases are assumed independent (e.g., cross-sectional data), temporally ordered data sets (e.g., n = 1 time series), and a mixture of the 2 (e.g., n > 1 time series). In time-series analysis, the GGM can be used to model the residual structure of a vector-autoregression analysis (VAR), also termed graphical VAR. Two network models can then be obtained: a temporal network and a contemporaneous network. When analyzing data from multiple subjects, a GGM can also be formed on the covariance structure of stationary means—the between-subjects network. We discuss the interpretation of these models and propose estimation methods to obtain these networks, which we implement in the R packages graphicalVAR and mlVAR. The methods are showcased in two empirical examples, and simulation studies on these methods are included in the supplementary materials.     

A hierarchical state space approach to affective dynamics

Linear dynamical system theory is a broad theoretical framework that has been applied in various research areas such as engineering, econometrics and recently in psychology. It quantifies the relations between observed inputs and outputs that are connected through a set of latent state variables. State space models are used to investigate the dynamical properties of these latent quantities. These models are especially of interest in the study of emotion dynamics, with the system representing the evolving emotion components of an individual. However, for simultaneous modeling of individual and population differences, a hierarchical extension of the basic state space model is necessary. Therefore, we introduce a Bayesian hierarchical model with random effects for the system parameters. Further, we apply our model to data that were collected using the Oregon adolescent interaction task: 66 normal and 67 depressed adolescents engaged in a conflict-oriented interaction with their parents and second-to-second physiological and behavioral measures were obtained. System parameters in normal and depressed adolescents were compared, which led to interesting discussions in the light of findings in recent literature on the links between cardiovascular processes, emotion dynamics and depression. We illustrate that our approach is flexible and general: The model can be applied to any time series for multiple systems (where a system can represent any entity) and moreover, one is free to focus on various components of this versatile model.     

Individual and structural origins of friendship and social position among professionals

Friendship affects individual and organizational well-being through direct relations, social positions, and complex network structures. In this study, the authors use longitudinal data from 2 groups of master's of business administration students to increase understanding of how friendship networks develop. The authors propose and test a dynamic model in which attribute similarity facilitates dyadic friendship ties, as well as similar network centrality and social position; early friendship increases later similarity in structural position and centrality; and early structural similarity enhances the likelihood of future friendship. Findings largely supported the model, demonstrating how homophily and early social contacts can jointly shape maturing friendship networks.     

预设路径模型及其在认知心理学研究中的应用

预设路径模型(Fixed-links modeling)是在结构方程模型框架下发展出的用于分析心理学实验数据的统计模型。该类模型的主要特征是根据前期理论基础和实验设计,通过预先设定模型中显变量与潜变量之间的因素载荷矩阵实现对显变量方差的拆分。因素载荷矩阵的设定主要基于实验水平与所表征的心理过程之间的关系,选取相应函数来表征不同实验水平之间的变化趋势。该模型在表征工作记忆、注意和学习的内部加工过程及揭示不同认知过程的具体联系中发挥了重要作用。     

移动社交媒介使用行为对青少年友谊质量的影响:网络自我表露和网络社会支持的中介作用

随着移动智能设备和移动互联网的出现,移动社交媒介已日益成为人们网络活动和人际交往的新平台。为考察移动社交媒介使用行为、网络自我表露、网络社会支持和友谊质量之间的关系,本研究采用问卷法对473名青少年进行了调查。结果发现:(1)移动社交媒介使用行为会对网络自我表露和网络社会支持产生显著的直接正向影响,网络自我表露会对网络社会支持产生显著的直接正向影响,网络社会支持会对友谊质量产生显著的直接正向影响。(2)移动社交媒介使用行为可以通过网络社会支持的中介作用对友谊质量产生显著的间接影响,还可以通过网络自我表露和网络社会支持的链式中介作用对友谊质量产生显著的间接影响。     

多阶段混合增长模型的方法及研究现状

多阶段混合增长模型(PGMM)可对发展过程中的阶段性及群体异质性特征进行分析,在能力发展、行为发展及干预、临床心理等研究领域应用广泛。PGMM可在结构方程模型和随机系数模型框架下定义,通常使用基于EM算法的极大似然估计和基于马尔科夫链蒙特卡洛模拟的贝叶斯推断两种方法进行参数估计。样本量、测量时间点数、潜在类别距离等因素对模型及参数估计有显著影响。未来应加强PGMM与其它增长模型的比较研究;在相同或不同的模型框架下研究数据特征、类别属性等对参数估计方法的影响。     

The contribution of extracurricular activities to adolescent friendships: new insights through social network analysis

Extracurricular activities are settings that are theorized to help adolescents maintain existing friendships and develop new friendships. The overarching goal of the current investigation was to examine whether coparticipating in school-based extracurricular activities supported adolescents' school-based friendships. We used social network methods and data from the National Longitudinal Study of Adolescent Health to examine whether dyadic friendship ties were more likely to exist among activity coparticipants while controlling for alternative friendship processes, namely dyadic homophily (e.g., demographic and behavioral similarities) and network-level processes (e.g., triadic closure). Results provide strong evidence that activities were associated with current friendships and promoted the formation of new friendships. These associations varied based on school level (i.e., middle vs. high school) and activity type (i.e., sports, academic, arts). Results of this study provide new insight into the complex relations between activities and friendship that can inform theories of their developmental outcomes.     

Dynamic Social Networks in Recovery Homes

Acute treatment aftercare in the form of sober living environments—i.e., recovery houses—provide an inexpensive and effective medium-term treatment alternative for many with substance use disorders. Limited evidence suggests that house-situated social relationships and associated social support are critical determinants of how successful these residential experiences are for their members, but little is known about the mechanisms underlying these relationships. This study explored the feasibility of using dynamic social network modeling to understand house-situated longitudinal associations among individual Alcoholics Anonymous related recovery behaviors, length of residence, dyadic interpersonal trust, and dyadic confidant relationship formation processes. Trust and confidant relationships were measured 3 months apart in U.S. urban-area recovery houses, all of which were part of a network of substance use recovery homes. A stochastic actor-based model was successfully estimated from this data set. Results suggest that confidant relationships are predicted by trust, while trust is affected by recovery behaviors and length of residence. Conceptualizing recovery houses as a set of independent, evolving social networks that can be modeled jointly appears to be a promising direction for research.     

Parameters, Predictions, and Evidence in Computational Modeling: A Statistical View Informed by ACT-R

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a "model fixed, data variable" paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood-based, "data fixed, model variable" paradigm in which models are treated as stochastic processes in experiments with participant-to-participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT-R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof-of-concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data.     

Capturing the interplay among within- and between-person processes using multilevel modeling techniques

Multilevel modeling is an excellent way to analyze nested or clustered data of the type commonly collected through investigations into the linkages between psychological functioning and relationship processes. This article describes two especially relevant applications of multilevel modeling. The first application, growth curve analysis, is already familiar to many researchers and involves modeling individuals’ change trajectories over time and relating the derived change parameters to person-level characteristics or phenomena. The purpose of this paper is to emphasize a second application, multilevel process analysis, which involves modeling within-subject characteristics other than change over a representation of time. Multilevel analysis of within-subject processes is particularly well-suited for hypotheses common to clinical psychology investigations, yet has received substantially less attention in the literature than its growth curve counterpart. Types of research questions and methodologies that can be addressed within the multilevel process analysis framework are described. Finally, aspects of multilevel process analysis are demonstrated with daily diary data collected from wives who reported on their marital happiness and depressed mood for 3 weeks.     

The Importance of Perceived Care and Connectedness with Friends and Parents for Adolescent Social Anxiety

Nonclinical social anxiety in adolescence can be highly problematic, as it likely affects current and especially new social interactions. Relationships with significant others, such as close friends, mothers, and fathers, could aid socially anxious adolescents’ participation in social situations, thereby helping reduce feelings of social anxiety. We examined whether making friends as well as high friendship quality help reduce social anxiety over time, and whether friends’, mothers’, and fathers’ care interact in reducing social anxiety. Using longitudinal data from 2,194 participants in a social network (48% girls; M_age = 13.58) followed for 3 years, we estimated friendship selection and influence processes via a continuous time‐modeling approach using SIENA. We controlled for the effects of depressive symptoms, self‐esteem, gender, age, and family structure. Our findings suggest that perceived care by friends mediated the effect of making friends on social anxiety. Perceptions of mother and father, as well as friend care and connectedness, respectively, did not interact in decreasing social anxiety. Nonetheless, care and connectedness with mothers, fathers, and friends jointly predicted decreases in social anxiety. Caring relationships with friends and parents each play a role in mutually protecting early adolescents against increasing in social anxiety over time.     

Bayesian Factor Analysis as a Variable-Selection Problem: Alternative Priors and Consequences

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor-loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, Muthén & Asparouhov proposed a Bayesian structural equation modeling (BSEM) approach to explore the presence of cross loadings in CFA models. We show that the issue of determining factor-loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov's approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike-and-slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set is used to demonstrate our approach.     

Adolescent Friendship as a Dynamic System: Entropy and Deviance in the Etiology and Course of Male Antisocial Behavior

A dynamic systems framework was applied to understand the influence of friendship on antisocial behavior from childhood (age 9-10) through adulthood (age 24-25) for Oregon Youth Study males (N = 206). Boys were videotaped interacting with a friend at ages 14, 16, and 18, and deviant content and interpersonal processes were independently coded. Conditional dyadic interpersonal processes were studied as a communication system and summarized by an index of information entropy (F. Attneave, 1959). High entropy scores represent disorganized, unpredictable patterns of interaction, whereas low entropy scores reflect an organized dialogue. Conversations of early-onset antisocial boys and their best friends were less organized and included more deviant content than those of well-adjusted controls. Prediction analyses, however, revealed an interaction between entropy and deviant talk. Consistent with expectation, males with well-organized interactions (i.e., low entropy) but elevated levels of deviant content were most likely to continue antisocial behavior into adulthood. Findings suggest that individual risk for maladaptation may be amplified by early adolescent friendship dynamics organized around deviance.     

Improved Insight into and Prediction of Network Dynamics by Combining VAR and Dimension Reduction

To understand within-person psychological processes, one may fit VAR(1) models (or continuous-time variants thereof) to multivariate time series and display the VAR(1) coefficients as a network. This approach has two major problems. First, the contemporaneous correlations between the variables will frequently be substantial, yielding multicollinearity issues. In addition, the shared effects of the variables are not included in the network. Consequently, VAR(1) networks can be hard to interpret. Second, crossvalidation results show that the highly parametrized VAR(1) model is prone to overfitting. In this article, we compare the pros and cons of two potential solutions to both problems. The first is to impose a lasso penalty on the VAR(1) coefficients, setting some of them to zero. The second, which has not yet been pursued in psychological network analysis, uses principal component VAR(1) (termed PC-VAR(1)). In this approach, the variables are first reduced to a few principal components, which are rotated toward simple structure; then VAR(1) analysis (or a continuous-time analog) is applied to the rotated components. Reanalyzing the data of a single participant of the COGITO study, we show that PC-VAR(1) has the better predictive performance and that networks based on PC-VAR(1) clearly represent both the lagged and the contemporaneous variable relations.     

How Animals Classify Friends and Foes

A model of stimulus equivalence, which describes how non-similarity-based categories are formed, is used to describe aspects of animal social and communicative interactions such as kinship, friendship, coalitions, territorial behavior, and referential calling. Although this model was originally designed to deal with stimulus relations in linguistic behavior, it can be readily applied to understanding the cognitive mechanisms that underlie social as well as nonsocial categorizations in numerous taxa. This approach provides a new, parsimonious, and experimentally based understanding of how animals without language deal with problems of classification in their environment.     

A data–information–knowledge cycle for modeling driving behavior

When talking about automation, “autonomous vehicles”, often abbreviated as AVs, come to mind. In transitioning from the “driver” mode to the different automation levels, there is an inevitable need for modeling driving behavior. This often happens through data collection from experiments and studies, but also information extraction, a key step in behavioral modeling. Particularly, naturalistic driving studies and field operational trials are used to collect meaningful data on drivers’ interactions in real–world conditions. On the other hand, information extraction methods allow to predict or mimic driving behavior, by using a set of statistical learning methods. In simple words, the way to understand drivers’ needs and wants in the era of automation can be represented in a data–information cycle, starting from data collection, and ending with information extraction. To develop this cycle, this research reviews studies with keywords “data collection”, “information extraction”, “AVs”, while keeping the focus on driving behavior. The resulting review led to a screening of about 161 papers, out of which about 30 were selected for a detailed analysis. The analysis included an investigation of the methods and equipment used for data collection, the features collected, the size and frequency of the data along with the main problems associated with the different sensory equipment; the studies also looked at the models used to extract information, including various statistical techniques used in AV studies. This paved the way to the development of a framework for data analytics and fusion, allowing the use of highly heterogeneous data to reach the defined objectives; for this paper, the example of impacts of AVs on a network level and AV acceptance is given. The authors suggest that such a framework could be extended and transferred across the various transportation sectors.