Reexamining personal, social, and cultural influences on compliance behavior in the United States, Poland, and Hong Kong

Researchers have extended the literature on strategies of gaining compliance with a request to incorporate cultural variations into the analytic framework. In the present investigation, the authors sought to go beyond previous studies of the factors increasing compliance rates by reexamining how researchers conceptualize and measure personal, social, and cultural influences on compliance behavior in the United States, Poland, and Hong Kong. The authors found that different levels of compliance were affected by culture, principles of influence, and the individual's personal orientation of idiocentrism/allocentrism (I/A). In the present study, the authors extended previous cross-cultural work by decomposing the I/A into 2 separate individual difference variables: normative perceptions and evaluative perceptions. The interaction of person and situation on compliance showed the power of situational demands and the strength of different aspects of personal collectivism. Different patterns of compliance at the culture level revealed the importance of culture in shaping this behavioral tendency. Thus, the authors' integration of personal, social, and cultural influences provided an interactive model to help researchers explain compliance more comprehensively.     

Emotion as a thermostat: representing emotion regulation using a damped oscillator model

The authors present in this study a damped oscillator model that provides a direct mathematical basis for testing the notion of emotion as a self-regulatory thermostat. Parameters from this model reflect individual differences in emotional lability and the ability to regulate emotion. The authors discuss concepts such as intensity, rate of change, and acceleration in the context of emotion, and they illustrate the strengths of this approach in comparison with spectral analysis and growth curve models. The utility of this modeling approach is illustrated using daily emotion ratings from 179 college students over 52 consecutive days. Overall, the damped oscillator model provides a meaningful way of representing emotion regulation as a dynamic process and helps identify the dominant periodicities in individuals' emotions.     

Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete extended Kalman filter

Many intensive longitudinal measurements are collected at irregularly spaced time intervals, and involve complex, possibly nonlinear and heterogeneous patterns of change. Effective modelling of such change processes requires continuous-time differential equation models that may be nonlinear and include mixed effects in the parameters. One approach of fitting such models is to define random effect variables as additional latent variables in a stochastic differential equation (SDE) model of choice, and use estimation algorithms designed for fitting SDE models, such as the continuous-discrete extended Kalman filter (CDEKF) approach implemented in the dynr R package, to estimate the random effect variables as latent variables. However, this approach's efficacy and identification constraints in handling mixed-effects SDE models have not been investigated. In the current study, we analytically inspect the identification constraints of using the CDEKF approach to fit nonlinear mixed-effects SDE models; extend a published model of emotions to a nonlinear mixed-effects SDE model as an example, and fit it to a set of irregularly spaced ecological momentary assessment data; and evaluate the feasibility of the proposed approach to fit the model through a Monte Carlo simulation study. Results show that the proposed approach produces reasonable parameter and standard error estimates when some identification constraint is met. We address the effects of sample size, process noise variance, and data spacing conditions on estimation results.     

Work values and the transition to work life: A longitudinal study

Research on career development has shown that work values play a key motivational role in job selection and career development. In the context of the current economic crisis, it is of particular relevance to examine the role of work values for employment in the transition from school to work. This longitudinal study examined the role of intrinsic (perceived importance of having a job that is interesting and matches one's own competences), rewards (having a good salary and high chance for promotion), and security (having a stable job) work values on subsequent employment status and person–job fit (how an individual's job matches one's own characteristics such as education and job preferences). Finnish participants reported their work values and background variables via questionnaire at ages 20 and 23 (Ns = 348 and 415 respectively). Intrinsic work values predicted a higher degree of person–job fit two years later. Rewards work values predicted lower chances of being unemployed; and security work values predicted higher chances of being unemployed later on. Family socio-economic status (SES) was not related to employment outcomes in this Finnish sample.     

Analytic Standard Errors for Exploratory Process Factor Analysis

Exploratory process factor analysis (EPFA) is a data-driven latent variable model for multivariate time series. This article presents analytic standard errors for EPFA. Unlike standard errors for exploratory factor analysis with independent data, the analytic standard errors for EPFA take into account the time dependency in time series data. In addition, factor rotation is treated as the imposition of equality constraints on model parameters. Properties of the analytic standard errors are demonstrated using empirical and simulated data.     

Exploring the Dynamics of Dyadic Interactions via Hierarchical Segmentation

In this article we present an exploratory tool for extracting systematic patterns from multivariate data. The technique, hierarchical segmentation (HS), can be used to group multivariate time series into segments with similar discrete-state recurrence patterns and it is not restricted by the stationarity assumption. We use a simulation study to describe the steps and properties of HS. We then use empirical data on daily affect from one couple to illustrate the use of HS for describing the affective dynamics of the dyad. First, we partition the data into three periods that represent different affective states and show different dynamics between both individuals’ affect. We then examine the synchrony between both individuals’ affective states and identify different patterns of coherence across the periods. Finally, we discuss the possibilities of using results from HS to construct confirmatory dynamic models with multiple change points or regime-specific dynamics.     

Anxiety and Depression in Children with HFASDs: Symptom Levels and Source Differences

The purpose of this study was to: (1) examine symptom levels of anxiety and depression in children with high-functioning autism spectrum disorders (HFASDs) compared with matched control children using child self-reports and parent ratings; and (2) examine source differences within the two condition groups. An overall multivariate effect indicated significantly elevated depression and anxiety symptoms for children with HFASDs based on parent reports; however no significant between-group differences based on child self-reports. Within-condition source comparisons (parent vs. child) revealed a significant multivariate effect indicating a significant difference in symptoms of depression and anxiety for the HFASD group but none for the control. Correlations between parent and child reports for the HFASD group suggested some positive association between child-reports and parent-reports for depressive symptoms only; however, the difference in average scores reflected a substantial discrepancy in the magnitude of symptoms by rater. Implications for clinical assessment and future research are provided.     

Neurobiological degeneracy: Supporting stability,flexibility and pluripotentiality in complex motor skill

This paper investigated neurobiological degeneracy of the motor system that emerged as a function of levels of environmental constraint. Fourteen participants performed a breaststroke-swimming task that required them to develop a specific biomechanically expert pattern and in turn provide the basis for a suitable task vehicle to study the functional role of movement variability. Inter-limb coordination was defined based on the computation of continuous relative phase between elbow and knee oscillators. Unsupervised cluster analysis on arm–leg coordination revealed the existence of different patterns of coordination when participants achieved the same task goal under different levels of environmental constraints (i.e. different amounts of forward resistances). In addition, clusters differed in terms of higher order derivatives (e.g., joint angular velocity, joint amplitude), suggesting an effective role for degeneracy in learning by allowing the exploration of the key relationships between motor organization and interacting constraints. There is evidence to suggest that neurobiological degeneracy supports the potential for motor re-organization to enhance motor learning.     

Practical Tools and Guidelines for Exploring and Fitting Linear and Nonlinear Dynamical Systems Models

A dynamical system is a system of variables that show some regularity in how they evolve over time. Change concepts described in most dynamical systems models are by no means novel to social and behavioral scientists, but most applications of dynamic modeling techniques in these disciplines are grounded on a narrow subset of—typically linear—theories of change. I provide practical guidelines, recommendations, and software code for exploring and fitting dynamical systems models with linear and nonlinear change functions in the context of four illustrative examples. Cautionary notes, challenges, and unresolved issues in utilizing these techniques are discussed.     

Distinguishing voluntary from involuntary attention to emotion

This study explored whether voluntary attention to emotion is distinguishable from involuntary attention to emotion. University students (N = 166) completed self-report questionnaires, designed for this study, intended to measure voluntary and involuntary attention to one’s own emotions. Moreover, participants completed questionnaires measuring other emotional constructs and distress. Finally, participants completed a dot probe task intended to obtain a behavioral measure of voluntary attention to emotion. Affect intensity was positively correlated with both voluntary and involuntary attention to emotion. As expected, dot probe emotional bias scores were associated with self-reported voluntary attention to emotion, but not with involuntary attention to emotion. Voluntary and involuntary attention to emotion were also differentially associated with clarity of emotion, anhedonic depression, and worry. The results of this study suggest it is important to distinguish between voluntary and involuntary attention to one’s own emotions.