The Goal Circuit Model: A Hierarchical Multi‐Route Model of the Acquisition and Control of Routine Sequential Action in Humans

Human control of action in routine situations involves a flexible interplay between (a) task‐dependent serial ordering constraints; (b) top‐down, or intentional, control processes; and (c) bottom‐up, or environmentally triggered, affordances. In addition, the interaction between these influences is modulated by learning mechanisms that, over time, appear to reduce the need for top‐down control processes while still allowing those processes to intervene at any point if necessary or if desired. We present a model of the acquisition and control of goal‐directed action that goes beyond existing models by operationalizing an interface between two putative systems—a routine and a non‐routine system—thereby demonstrating how explicitly represented goals can interact with the emergent task representations that develop through learning in the routine system. The gradual emergence of task representations offers an explanation for the transfer of control with experience from the non‐routine goal‐based system to the routine system. At the same time it allows action selection to be sensitive both to environmental triggers and to biasing from multiple levels within the goal system.     

Community Storytelling Network,Neighborhood Context,and Civic Engagement: A Multilevel Approach

From a communication infrastructure theory perspective, the current study examined individuals’ civic engagement (neighborhood belonging, collective efficacy, and civic participation) as influenced by 2 multilevel components of the communication infrastructure—an integrated connectedness to a storytelling network (ICSN) and the residential context—focusing on ethnic heterogeneity and residential stability. Our multilevel analyses show that ICSN is the most important individual‐level factor in civic engagement—neighborhood belonging, collective efficacy, and civic participation—after controlling for other individual‐level and neighborhood‐level factors. In both ethnically homogeneous and heterogeneous areas and in both stable and unstable areas, ICSN is an important factor in civic engagement. As contextual factors, residential stability positively affects neighborhood belonging and collective efficacy, and ethnic heterogeneity is negatively related to collective efficacy. Our data do not show any direct contextual effects of residential stability or ethnic heterogeneity on civic participation. However, our HLM analysis showed that the relative importance of ICSN for the likelihood of participation in civic activities is significantly higher in unstable or ethnically heterogeneous areas than in stable or ethnically homogeneous areas.     

Interpersonal Mistreatment of Women in the Workplace

The purpose of this paper is to introduce the work included in the special issue: Interpersonal Mistreatment of Women in the Workplace. In doing so, the authors develop a multilevel conceptual model, illustrating how the research included in the special issue address causes and consequences of interpersonal mistreatment at the micro, meso, and macro-level of analysis. In addition, the integrated model demonstrates how factors at different levels both influence and are influenced by those at other levels of analysis. Based on this collective work, the authors encourage researchers interested in addressing the mistreatment and marginalization of less powerful groups to consider the multilevel causes and consequences of such behavior. It is only through holistic examinations that researchers can we fully understand this insidious problem and encourage people—whether likely targets of mistreatment or not—to take a stand to end this treatment in workplaces and other society as a whole.     

Bayesian analysis of longitudinal multitrait–multimethod data with ordinal response variables

A new multilevel latent state graded response model for longitudinal multitrait–multimethod (MTMM) measurement designs combining structurally different and interchangeable methods is proposed. The model allows researchers to examine construct validity over time and to study the change and stability of constructs and method effects based on ordinal response variables. We show how Bayesian estimation techniques can address a number of important issues that typically arise in longitudinal multilevel MTMM studies and facilitates the estimation of the model presented. Estimation accuracy and the impact of between‐ and within‐level sample sizes as well as different prior specifications on parameter recovery were investigated in a Monte Carlo simulation study. Findings indicate that the parameters of the model presented can be accurately estimated with Bayesian estimation methods in the case of low convergent validity with as few as 250 clusters and more than two observations within each cluster. The model was applied to well‐being data from a longitudinal MTMM study, assessing the change and stability of life satisfaction and subjective happiness in young adults after high‐school graduation. Guidelines for empirical applications are provided and advantages and limitations of a Bayesian approach to estimating longitudinal multilevel MTMM models are discussed.     

A Computationally Efficient State Space Approach to Estimating Multilevel Regression Models and Multilevel Confirmatory Factor Models

Although the state space approach for estimating multilevel regression models has been well established for decades in the time series literature, it does not receive much attention from educational and psychological researchers. In this article, we (a) introduce the state space approach for estimating multilevel regression models and (b) extend the state space approach for estimating multilevel factor models. A brief outline of the state space formulation is provided and then state space forms for univariate and multivariate multilevel regression models, and a multilevel confirmatory factor model, are illustrated. The utility of the state space approach is demonstrated with either a simulated or real example for each multilevel model. It is concluded that the results from the state space approach are essentially identical to those from specialized multilevel regression modeling and structural equation modeling software. More importantly, the state space approach offers researchers a computationally more efficient alternative to fit multilevel regression models with a large number of Level 1 units within each Level 2 unit or a large number of observations on each subject in a longitudinal study.     

Social Contexts and Building Social Capital for Collective Action: Three Case Studies of Volunteers in the Context of HIV and AIDS in South Africa

Social capital is increasingly conceptualised in academic and policy literature as a panacea for a range of health and development issues, particularly in the context of HIV. In this paper, we conceptualise social capital as an umbrella concept capturing processes including networks, norms, trust and relationships that open up opportunities for participation and collective action that allow communities to address issues of common concern. We specifically outline social capital as comprising three distinct forms: bonding, bridging and linking social capital. Rather than presenting original data, we draw on three well‐documented and previously published case studies of health volunteers in South Africa. We explore how social contexts shape the possibility for the emergence and sustainability of social capital. We identify three cross‐cutting contextual factors that are critical barriers to the emergence of social capital: poverty, stigma and the weakness of external organisations' abilities to support small groups. Our three case studies suggest that the assumption that social capital can be generated from the ground upwards is not reasonable. Rather, there needs to be a greater focus on how those charged with supporting small groups—non‐governmental organisations, bureaucracies and development agencies—can work to enable social capital to emerge. Copyright © 2014 John Wiley & Sons, Ltd.     

Psychological momentum: The phenomenology of goal pursuit

Psychological momentum (PM) is thought to be a force that influences judgment, emotion, and performance. Based on a review of the extant literature, we elucidate two distinct approaches that researchers have adopted in their study of PM: the input‐centered approach and the output‐centered approach . Consistent with the input‐centered approach, we conceptualize PM as a process whereby temporal and contextual PM‐like stimuli (i.e., perceptual velocity, perceptual mass, perceptual historicity, and perceptually interconnected timescales)—initially perceived as an impetus—are extrapolated to imagined future outcomes through mental simulation. In turn, and consistent with the output‐centered approach, we posit that mental simulation elicits experiential (e.g., perceptual, cognitive, emotional) and behavioral states that govern goal pursuit, and that the pursuit of goals further influences perceptions of self, environment, and action quality. In all, we suggest that PM is interdependently linked to perceptions and behaviors in the sense that PM both influences and is influenced by changes in self‐perceptions, environmental perceptions, and behavior, and we conclude by linking the PM construct to recent work on prospection.     

Understanding the emergence of state goal orientation in organizational work groups: the role of leadership and multilevel climate perceptions

This article attends to a broad range of practically significant employee motivations and provides insight into how to enhance individual-level performance by examining individual-level state goal orientation emergence in organizational work groups. Leadership and multilevel climate processes are theorized to parallel each dimension of state goal orientation to cue and ultimately induce the corresponding achievement focus among individual work group members. It is argued that the patterns of leader behavior, which elucidate the leader's achievement priority, shape group members' psychological and work group climate to embody this priority. Resulting multilevel climate perceptions signal and compel group members to adopt the ascribed form of state goal orientation. The quality of the leader-member exchange relationship is viewed as a means to clarify leader messages in the formation of group members' psychological climate and internalize these cues in the emergence of state goal orientation. Considerations for future research and practice are discussed.     

Ignoring Clustering in Confirmatory Factor Analysis: Some Consequences for Model Fit and Standardized Parameter Estimates

In many situations, researchers collect multilevel (clustered or nested) data yet analyze the data either ignoring the clustering (disaggregation) or averaging the micro-level units within each cluster and analyzing the aggregated data at the macro level (aggregation). In this study we investigate the effects of ignoring the nested nature of data in confirmatory factor analysis (CFA). The bias incurred by ignoring clustering is examined in terms of model fit and standardized parameter estimates, which are usually of interest to researchers who use CFA. We find that the disaggregation approach increases model misfit, especially when the intraclass correlation (ICC) is high, whereas the aggregation approach results in accurate detection of model misfit in the macro level. Standardized parameter estimates from the disaggregation and aggregation approaches are deviated toward the values of the macro- and micro-level standardized parameter estimates, respectively. The degree of deviation depends on ICC and cluster size, particularly for the aggregation method. The standard errors of standardized parameter estimates from the disaggregation approach depend on the macro-level item communalities. Those from the aggregation approach underestimate the standard errors in multilevel CFA (MCFA), especially when ICC is low. Thus, we conclude that MCFA or an alternative approach should be used if possible.     

Complicating Narratives: Defining and Deconstructing Ethical Challenges in Community Psychology

In this concluding essay, we review the case studies presented in this Special Issue and examine whether community psychology has a distinctive approach to defining and resolving the core ethical canons of the Belmont Report (1979): Respect for Persons, Beneficence, and Justice. For two of these Principles—Respect for Persons and Beneficence—community psychologists elaborate upon and extend their definitions to consider their meaning in community‐based, social justice‐oriented research. The field's approach to Respect for Persons is multilevel in nature; in addition to respecting individuals and their diverse identities, we also have obligations to respect our community partnerships, the communities with whom we work, and the populations and cultures represented in our work. Similarly, for community psychologists, Beneficence is a multilevel construct that considers risks and benefits at the group, community, and cultural levels of analysis. With respect to Justice, community psychologists’ views of our ethical responsibilities are qualitatively different in meaning from the original Belmont Report and from disciplinary‐specific interpretations of this principle in ethical guidance documents from psychology, sociology, and evaluation. Our valuing of social change demands that we contribute to individual and group empowerment and liberation, and in so doing, that we avoid collusion with oppressive systems. Thus, we define our ethical responsibilities for promoting Justice as more action‐oriented than do other disciplines. The essay closes with an exploration of future directions for developing a comprehensive ethical framework for community psychology.     

On the use of multilevel modeling as an alternative to items analysis in psycholinguistic research

The use of multilevel modeling is presented as an alternative to separate item and subject ANOVAs (F1 x F2) in psycholinguistic research. Multilevel modeling is commonly utilized to model variability arising from the nesting of lower level observations within higher level units (e.g., students within schools, repeated measures within individuals). However, multilevel models can also be used when two random factors are crossed at the same level, rather than nested. The current work illustrates the use of the multilevel model for crossed random effects within the context of a psycholinguistic experimental study, in which both subjects and items are modeled as random effects within the same analysis, thus avoiding some of the problems plaguing current approaches.     

Person‐specific versus multilevel autoregressive models: Accuracy in parameter estimates at the population and individual levels

This paper compares the multilevel modelling (MLM) approach and the person‐specific (PS) modelling approach in examining autoregressive (AR) relations with intensive longitudinal data. Two simulation studies are conducted to examine the influences of sample heterogeneity, time series length, sample size, and distribution of individual level AR coefficients on the accuracy of AR estimates, both at the population level and at the individual level. It is found that MLM generally outperforms the PS approach under two conditions: when the sample has a homogeneous AR pattern, namely, when all individuals in the sample are characterized by AR processes with the same order; and when the sample has heterogeneous AR patterns, but a multilevel model with a sufficiently high order (i.e., an order equal to or higher than the maximum order of individual AR patterns in the sample) is fitted and successfully converges. If a lower‐order multilevel model is chosen for heterogeneous samples, the higher‐order lagged effects are misrepresented, resulting in bias at the population level and larger prediction errors at the individual level. In these cases, the PS approach is preferable, given sufficient measurement occasions (T ≥ 50). In addition, sample size and distribution of individual level AR coefficients do not have a large impact on the results. Implications of these findings on model selection and research design are discussed.     

Explaining Differences in Subjective Well‐Being Across 33 Nations Using Multilevel Models: Universal Personality,Cultural Relativity,and National Income

This multinational study simultaneously tested three prominent hypotheses—universal disposition, cultural relativity, and livability—that explained differences in subjective well‐being across nations. We performed multilevel structural equation modeling to examine the hypothesized relationships at both individual and cultural levels in 33 nations. Participants were 6,753 university students (2,215 men; 4,403 women; 135 did not specify), and the average age of the entire sample was 20.97 years (SD = 2.39). Both individual‐ and cultural‐level analyses supported the universal disposition and cultural relativity hypotheses by revealing significant associations of subjective well‐being with Extraversion, Neuroticism, and independent self‐construal. In addition, interdependent self‐construal was positively related to life satisfaction at the individual level only, whereas aggregated negative affect was positively linked with aggregate levels of Extraversion and interdependent self‐construal at the cultural level only. Consistent with the livability hypothesis, gross national income (GNI) was related to aggregate levels of negative affect and life satisfaction. There was also a quadratic relationship between GNI and aggregated positive affect. Our findings reveal that universal disposition, cultural self‐construal, and national income can elucidate differences in subjective well‐being, but the multilevel analyses advance the literature by yielding new findings that cannot be identified in studies using individual‐level analyses alone.     

Effectively Analyzing Change over Time in Laboratory Research on Stress and Health: A Multilevel Modeling Approach

Stress and health researchers often utilize standardized laboratory stress tasks to evaluate the physical and psychological consequences of challenging experiences. These laboratory sessions usually include multiple measurements of physical and psychological responses collected over time. Multilevel modeling allows researchers to make use of all available data points to model the trajectory of change over time, and within distinct task periods such as baseline, stressor, and recovery. To effectively predict future health outcomes it is important to examine both stress‐related reactivity and recovery. In this paper, we review the analytic approaches used in recent laboratory stress research and note that many recent articles have aggregated multiple responses, used difference scores, or conducted repeated measures analysis of variance (ANOVA). Relatively few studies used a multilevel modeling approach. We highlight the advantages of a multilevel modeling approach and provide an example for using this approach as an alternative to repeated measures ANOVA and difference scores.     

How to fit and interpret multilevel models using SPSS

Hierarchic or multilevel models are used to analyse data when cases belong to known groups and sample units are selected both from the individual level and from the group level. In this work, the multilevel models most commonly discussed in the statistic literature are described, explaining how to fit these models using the SPSS program (any version as of the 11 th ) and how to interpret the outcomes of the analysis. Five particular models are described, fitted, and interpreted: (1) one-way analysis of variance with random effects, (2) regression analysis with means-as-outcomes, (3) one-way analysis of covariance with random effects, (4) regression analysis with random coefficients, and (5) regression analysis with means- and slopes-as-outcomes. All models are explained, trying to make them understandable to researchers in health and behaviour sciences.     

Helping Teams to Help Themselves: Comparing two Team‐Led Debriefing Methods

Team‐based structures have become more widely used in organizations. Therefore, it is important for team members to perform well in their current team and to build skills and enthusiasm for working on future teams. This study examined team debriefing, an intervention in which team members reflect on recent experiences to prepare for subsequent tasks. Prior researchers have shown that facilitated team debriefs work, but they have not examined how to enable teams to conduct their own debriefs or studied how debriefs affect individual level outcomes. Therefore, we compared 2 team‐led debriefing techniques: (a) an unguided debrief and (b) a guided debrief designed to incorporate lessons learned from prior debriefs. We collected data from 174 business students who were members of 35 teams from 9 sections of a Strategic Management course. Class sections were randomly assigned to one of the debriefing conditions, and teams completed 4 business cases over 10 weeks. A multilevel design was employed and a multistage model building approach was used to test the hypotheses using hierarchical linear modeling techniques. Results of this cluster randomized, quasi‐experimental design suggest that the team‐led guided debrief intervention resulted in superior team processes as compared to the unguided debriefing method. Team processes, in turn, related significantly to greater team performance and increased individual readiness for teamwork and enthusiasm for teaming. Implications for future research and practice are discussed.     

Multilevel Mixture Factor Models

Factor analysis is a statistical method for describing the associations among sets of observed variables in terms of a small number of underlying continuous latent variables. Various authors have proposed multilevel extensions of the factor model for the analysis of data sets with a hierarchical structure. These Multilevel Factor Models (MFMs) have in common that—as in multilevel regression analysis—variation at the higher level is modeled using continuous random effects. In this article, we present an alternative multilevel extension of factor analysis which we call the Multilevel Mixture Factor Model (MMFM). It is based on the assumption that higher level units belong to latent classes that differ in terms of the parameters of the factor model specified for the lower level units. We demonstrate the added value of MMFM compared with MFM, both from a theoretical and applied perspective, and we illustrate the complementarity of the two approaches with an empirical application on students' satisfaction with the University of Florence. The multilevel aspect of this application is that students are nested within study programs, which makes it possible to cluster these programs based on their differences in students' satisfaction.     

Factorial Structure of the Family Values Scale From a Multilevel-Multicultural Perspective

In cross-cultural research, there is a tendency for researchers to draw inferences at the country level based on individual-level data. Such action implicitly and often mistakenly assumes that both the measuring instrument and its underlying construct(s) are operating equivalently across both levels. Based on responses from 5,482 college students sampled from 27 countries, we took a structural equation modeling approach to addressing this issue of level equivalence. Purposes of the study were: (a) to validate the hypothesized two-factor structure of the Family Values Scale (FV Scale; Georgas, 1999) within a multilevel framework that took individual- and country-level information into account; (b) to test equivalence of the FV Scale across individual and country levels; and (c) to evaluate relations between the FV Scale and three possibly important covariates—gender at the individual level, and affluence and religion at the country level. Implications of findings and importance of multilevel equivalence in cross-cultural research are discussed.     

Identifying ICT-Related Affective Events Across Life Domains and Examining their Unique Relationships with Employee Recovery

The purpose of this study was to investigate the role of information and communication technology (ICT) use for employees by exploring affective ICT events and their relation to employee recovery. We conducted two daily diary studies with knowledge workers in Germany. In study 1 (N = 153), we followed a qualitative approach by developing a taxonomy of negative and positive ICT events at work and at home. We found 11 negative (e.g., multichannel use, work e-mails in the evening) and 10 positive ICT event clusters (e.g., utilizing idle times, availability at home). In study 2 (N = 154), we examined how the occurrence of ICT-related events in the evening is related to detachment from work and sleep quality using a multilevel design. Study 2 provided a differentiated picture of relationships between evening ICT events and recovery. Overall, results indicate that work-related ICT events in the evening—even in cases where they are appraised as positive—are detrimental for employee recovery. Our study contributes to the refinement of the operationalization of ICT use for future analyses of ICT impacts and sheds light on the differential effects of specific types of ICT events. ICT use conceptualized as the occurrence of affective events helps to advance our understanding of the diversity of ICT-related experiences. The negative effects of handling work e-mails at home imply that organizations should engage in optimizing their “e-mail culture.”