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What are Agile, Flexible, or Adaptable Employees and Students? A Typology of Dynamic Individual Differences in Applied Settings
Abstract
The applied psychology literature has discussed and used a variety of different definitions of dynamic individual differences. Descriptions like dynamic, agile, adaptive, or flexible can refer to a variety of different types of constructs. The present article contributes to the literature by presenting an organizing typology of dynamic constructs. We also conducted a literature review of four major applied journals over the last 15 years to validate the taxonomy and to use it to map what type of dynamic individual differences constructs are typically studied in the applied psychology literature. The typology includes six basic conceptualizations of dynamic individual differences: Variability constructs (inconsistency across situations), skill acquisition constructs (learning new skills), transition constructs (avoiding “loss” in behavior/skill after unforeseen change), reacquisition constructs (relearning after change), acceleration/deceleration constructs (losing or gaining energy by displaying the behavior), and integration/dissolution constructs (behavior becomes more or less uniform). We provide both verbal and statistical definitions for each of these constructs, and demonstrate how these conceptualizations can be operationalized in assessment and criterion measurement using R code and simulated data. We also show how researchers can test different dynamic explanations using likelihood-based R ² statistics.
... This view contrasts to earlier research in which leader identities have been treated as relatively stable intrapersonal attributes (e.g., Johnson et al., 2012;Kwok et al., 2018;Venus et al., 2019), which develop over longer periods such as weeks or months (Day & Sin, 2011;Middleton et al., 2019;Miscenko et al., 2017). For example, leader identities have been examined from the perspective of skill acquisition in response to training (Lang et al., 2021;Wallace et al., 2021) and predicted by individual differences (e.g., learning orientation, motivation to lead; Kwok et al., 2021;Middleton et al., 2019). Recent experience sampling studies (Jennings et al., 2021;Lanaj et al., 2021a, b;Lanaj et al., 2021a, b), however, begin to tap into short-term changes in individuals' leader identities, showing that leader identities are sensitive to contextual stimulation (e.g., self-reflection exercises; Jennings et al., 2021;Lanaj et al., 2021a, b). ...
... These dynamics can be quantified via the velocity and the acceleration at which change occurs. McClean et al., 2019;Lang et al., 2021;Xu et al., 2020 Velocity Velocity describes the rate at which change in leader identity occurs over a specified unit of time (e.g., day-today). As such, it indicates how quickly change in leader identity occurs over time. ...
... Building on these perspectives, we conceptualize leader identity dynamics as the intrapersonal changes in an individual's leader identity activation over time. We apply the term dynamics (or "ebb and flow"; McClean et al., 2019) to describe change processes over time (Lang et al., 2021) and specifically focus on describing and explaining small changes over shorter periods of time (Xu et al., 2020). Individuals are motivated to establish and re-establish both who they are (I am a leader) and who they are not (I am not a leader; Watson, 2009). ...
Recent theorizing challenges the notion that leadership, and especially leader identities, is static. Yet, we know little about the dynamics that characterize how leader identities change within individuals across short periods of time. The current work integrates theorizing on temporal dynamics in leadership research with event systems theory to describe and predict day-to-day shifts (i.e., unidirectional, sudden changes) and dynamic ebb and flow patterns (i.e., multidirectional, potentially nonlinear changes over multiple days) of individuals’ leader identities. Specifically, we argue that the experience of strong (i.e., novel, disruptive, extraordinary) daily events facilitates positive leader identity shifts, and that over time, the resulting identity ebb and flows are more pronounced in unfamiliar compared to familiar contexts. We collected experience sampling data from 69 young adults at a university in the UK across seven-day periods at three different time points during the academic year (1159 data points). Using dynamical systems modeling, we analyze the velocity (i.e., rate of change) and the acceleration (i.e., change in velocity) parameters of individuals’ leader identity dynamics. We find that (a) on a daily level, strong events prompt positive shifts in leader identity, and that (b) over time, chains of stronger and weaker events provoke similar patterns of leader identity ebb and flows. However, these relationships are not stronger in unfamiliar compared to familiar contexts. Our research informs the theoretical understanding of events and short-term leader identity dynamics. We discuss implications for theory and research, in particular how events can trigger leader identity formation.
... While in classic approaches to personality researchers describe the behaviors and mental patterns of individuals as average tendencies across situations, recent approaches contend that average trait scores are only one way of quantifying individuals' behavioral and mental tendencies. Another relevant quantification of individuals' behavioral and mental tendencies is to map individual differences in within-person variability in behaviors and mental states, that is, the variability trait (Fleeson and Gallagher 2009;Lievens et al. 2018;Lang et al. 2021). Until now negotiation research has focused on average traits and has shown that they do not help much in predicting negotiation outcomes, but what about a negotiator's variability trait? ...
... Due to the complex nature of negotiation tasks, identifying a trait that could predict negotiation outcomes requires to focus on an another aspect of personality. In recent personality theories, average tendencies across situations are only one way to look at individual differences in personality (Fleeson and Gallagher 2009;Fleeson and Jayawickreme 2015;Lang et al. 2021). These theories posit that individual differences in within-person variability in mental states and behaviors described by personality traits -that is, the variability trait (Fleeson and Gallagher 2009;Lievens et al. 2018;Lang et al. 2021) -should be an integral part of descriptive models of personality next to average traits (Fleeson and Gallagher 2009;Lang et al. 2021). ...
... In recent personality theories, average tendencies across situations are only one way to look at individual differences in personality (Fleeson and Gallagher 2009;Fleeson and Jayawickreme 2015;Lang et al. 2021). These theories posit that individual differences in within-person variability in mental states and behaviors described by personality traits -that is, the variability trait (Fleeson and Gallagher 2009;Lievens et al. 2018;Lang et al. 2021) -should be an integral part of descriptive models of personality next to average traits (Fleeson and Gallagher 2009;Lang et al. 2021). ...
Prior research on the value of personality traits for predicting negotiation outcomes is rather inconclusive. Building on prior research and in light of recent personality and negotiation theories, we discuss why the traditional approach to personality traits has had limited success and propose an alternative approach to predicting negotiation outcomes from personality assessments. More specifically, we argue that negotiations are tasks in which performance is conditioned by the ability to adjust one’s mental states and behaviors according to situational demands. We therefore hypothesize that it is especially individual differences in within-person variability in personality – that is, the variability trait – that can be expected to predict negotiation outcomes, rather than individual differences in average traits. We show in two empirical studies involving dyads that the variability trait is indeed a better predictor of economic gains and satisfaction than average traits. Implications for theory, education, and practice are discussed.
... While the overall theme of interpreting results with modesty and caution has been espoused by seminal texts such as Abelson (1995) and more recently by Wasserstein et al. (2019) and Bliese and Wang (2020), the message warrants repeating within the context of CEM. Indeed, a recent publication by Lang and colleagues on emergence raises several points about alternative processes and dynamic individual differences that mirror and expand upon concerns raised by Dishop (Lang, Runge, & De Fruyt, 2021). ...
... We also believe that studying autoregressive effects is a generally interesting area of research for psychology. A recent comprehensive review of literature in applied psychology has shown that few studies have focused on autoregressive effects (Lang, Runge, & De Fruyt, 2021). However, recent work in the area shows renewed interest and new software solutions for studying individual autoregressive effects (Hamaker et al., 2015;Lang, 2014;Zyphur et al., 2020). ...
Dishop (2022) identifies the consensus emergence model (CEM) as a useful tool for future research on emergence but argues that autoregressive models with positive autoregressive effects are an important alternative data-generating mechanism that researchers need to rule out. Here, we acknowledge that alternative data-generating mechanisms are possibility for most, if not all, nonexperimental designs and appreciate Dishop’s attempts to identify cases where the CEM could provide misleading results. However, in a series of independent simulations, we were unable to replicate two of three key analyses, and the results for the third analysis did not support the earlier conclusions. The discrepancies appear to originate from Dishop’s simulation code and what appear to be inconsistent model specifications that neither simulate the models described in the article nor include notable positive autoregressive effects. We contribute to the wider literature by suggesting four key criteria that researchers can apply to evaluate the possibility of alternative data-generating mechanisms: Theory, parameter recovery, fit to real data, and context. Applied to autoregressive effects and emergence data, these criteria reveal that (a) theory in psychology would generally suggest negative instead of positive autoregressive effects for behavior, (b) it is challenging to recover true autoregressive parameters from simulated data, and (c) that real data sets across a number of different contexts show little to no evidence for autoregressive effects. Instead, our analyses suggest that CEM results are congruent with the temporal changes occurring within groups and that autoregressive effects do not lead to spurious CEM results.
... Agile learning emerges as a solution, enabling educators to dynamically adjust their teaching methods and curriculum to align with the evolving needs of students. This approach aims to equip students with the skills and knowledge necessary to navigate the challenges and seize the opportunities presented by the future [1]. ...
This article explores the paradigm of agile learning within the realm of education and underscores its pivotal role in navigating the dynamic educational landscape. The study scrutinizes the distinctions between Agile methodology, particularly its Scrum and eduScrum applications, and conventional educational paradigms, evaluating their efficacy in educational contexts. Agile is delineated as a methodology entailing the segmentation of tasks into brief phases of work, characterized by frequent review and adaptation of plans. Furthermore, the article elucidates the application of the eduScrum approach, identified as an effective strategy for planning and orchestrating teamwork in education. Comparative analyses between the traditional educational approach and these “Agile” methodologies are presented, yielding pertinent conclusions.
... Traditional educational approaches often struggle to keep pace with these changes. Agile learning allows educators to adapt their teaching methods and curriculum to meet the evolving needs of students and prepare them for the challenges and opportunities of the future (Lang, et al. 2021).. • Personalized Learning Experiences: Every student is unique, with different learning styles, strengths, and interests. Agile learning acknowledges and accommodates these individual differences by offering personalized learning experiences. ...
Education is undergoing a profound transformation as the needs and expectations of students evolve in the fast-paced, technology-driven world. In this era of constant change, traditional educational approaches are becoming increasingly inadequate. To meet the dynamic requirements of students, educators are embracing agile learning methodologies that emphasize adaptability, collaboration, and personalized learning experiences. Agile learning in education is a flexible and student-centric approach that enables educators to tailor their teaching methods to address the changing needs of students. This article explores the concept of agile learning in education and its significance in adapting to the evolving educational landscape.
... A common theme across these topics is an emphasis on how individuals adapt, whether working in or leading teams and organizations. Conceptually, adaptation has been defined as a process, but it has not always been studied as such (Lang et al., 2021). In this sense, the COVID-19 pandemic provides a unique opportunity to study processes of adaptation and change in situ and in a naturalistic context. ...
In this Editorial, we first highlight several themes affected by the COVID-19 pandemic as a specific example of crisis or mega-event (Morgeson et al., 2015) and focus on issues that relate to the studies featured in this collection of papers. We then describe the core findings and discuss the implications of these studies, highlighting several themes addressed in one or more of the papers compiled in this special issue, before summarizing the focal studies and discussing their implications.
... Their framework explores how key concepts and findings from the personality dynamics literature can inform the design and implementation of behaviour-change interventions. Lang et al. (2021), in turn, provided a typology of basic conceptualisations of dynamic individual differences (e.g., variability, transition, acceleration). Of particular interest to applied researchers is that -in addition to proposing a new conceptual framework -the paper also provides several tutorials on how to formally test these concepts using the R language. ...
There have been tremendous advancements in technology-based assessments in new modes of data collection and the use of artificial intelligence. Traditional assessment techniques in the fields of psychology, business, education, and health need to be reconsidered. Yet, while technology is pervasive, its spread is not consistent due to national differences in economics and culture. Given these trends, this book offers an integrative consolidation of how technology is changing the face of assessments across different regions of the world. There are three major book sections: in foundations, core issues of computational models, passively sensed data, and privacy concerns are discussed; in global perspectives, the book identifies ways technology has changed how we assess human attributes across the world, and finally, in regional focus, the book surveys how different regions around the world have adopted technology-based assessments for their unique cultural and societal context.
O artigo analisa o papel do Instituto Ayrton Senna (IAS) no processo de implementação do Programa Ensino Médio Integral de Tempo Integral (EMITI) no âmbito da reforma do Ensino Médio no estado de Santa Catarina. O EMITI, na rede estadual analisada, contou com a produção de materiais sob assessoria do IAS e teve como centralidade a ideia de protagonismo juvenil associada ao conceito de trabalhabilidade. A análise documental teve como categorias centrais as proposições sobre protagonismo juvenil e trabalhabilidade, e, a partir dessas categorias, são discutidas as implicações para a formação da juventude, dentre as quais se destaca um processo por meio do qual o direito à educação como um direito social é descontruído ao se priorizar a subjetivação da educação como uma cultura de adaptação e tornar central a concepção da formação do jovem como um empresário de si.
A growing body of research has documented the negative relationship between mindfulness and ego depletion. However, most of this research has taken a static view while ignoring the effects of mindfulness temporal changes at work. Thus, it is unclear whether the relationship between mindfulness change and ego depletion is still negative when this factor is added into the mix. To address this issue, we examine mindfulness change at the within‐person level (mindfulness shift) and the between‐person level (mindfulness variability) and explore their distinctive impacts on ego depletion and subsequently on counterproductive work behaviour (CWB). Drawing on ego depletion theory and mindfulness research, we propose that an upshift in mindfulness is negatively related to employee ego depletion and CWB at the within‐person level, whereas high chronic mindfulness variability is positively related to employee ego depletion and CWB at the between‐person level. To test our hypotheses, we used an experience sampling methodology to collect three‐wave data within a day from 166 employees over 10 consecutive workdays. At the within‐person level, we observed a negative and indirect effect of an upshift in daily mindfulness on daily CWB via daily ego depletion. At the between‐person level, we found a positive and indirect effect of chronic mindfulness variability on chronic CWB via chronic ego depletion.
Objective:
Evidence supports the effectiveness of cuing people to analyse negative autobiographical experiences from self-distanced rather than self-immersed perspectives. However, the evidence on which this expectation resides is limited largely to static snapshots of mean levels of cognitive and emotional factors.
Methods:
Via a pre-registered, randomised controlled trial (N = 257), we examined the differential effectiveness of self-distanced relative to self-immersed reflections on mean levels and within-person variability of sleep duration and quality as well as psychological well-being over a 5-day working week.
Results:
Except for sleep quality, we found that reflecting from a psychologically distanced perspective, overall, was no more effective for mean levels and within-person variability of sleep duration, well-being, and stress-related factors than when the current self is fully immersed in the experiential reality of the event.
Conclusions:
We consider several substantive and methodological considerations (e.g., dosage, salience of stressor event) that require interrogation in future research via experimental and longitudinal observational methods. This article is protected by copyright. All rights reserved.
This article gives an overview of previous work on affect variability, discusses the methodological shortcomings of research on affect variability, and presents the results of an empirical study of intraindividual variability in primary emotions across time. The results of a daily assessment study using structural equation modeling and nonlinear regression analyses showed that intraindividual variability in affect is a multidimensional construct that is sufficiently stable to be considered a psychological trait and can be reliably measured by the intraindividual standard deviation. Intraindividual variability showed convergent validity with mean level scores and neuroticism but was sufficiently distinct to be considered a unique trait. This was particularly true of intraindividual variability in positive emotions; only about 10% of the variance could be accounted for by mean affect levels and the variables of the 5-factor model of personality.
There is intuitive and practical appeal to the idea of emergent resilience, that is, sustaining healthy levels of functioning or recovering quickly after some degree of deterioration following exposure to heightened risk or vulnerability. Scholars typically utilize mean levels of functioning indices to identify qualitatively distinct latent subgroups of individuals who share similar patterns of change over time. We propose and showcase an alternative, yet complementary operationalization of emergent resilience via temporal changes in within-person variability. Twenty-nine male personnel (26.25 ± 2.67 years) from the Australian Army who passed a 3-week Special Forces Selection Course provided device-based assessments of sleep functioning for seven nights immediately following course completion. Participants also provided a hair sample for cortisol analysis prior to and immediately after the selection course as an index of accumulated stress, and self-reported their adaptability prior to the 7-day monitoring period. We combined latent growth modeling with an exponential variance function to capture fluctuations around latent means and their change over time. Consistent with our conceptualization of “bounce back” emergent resilience, within-person variability in sleep duration decreased each night by around 10%, which reflects a meaningful small mean decrease over time. We also revealed differential effects of the predictor variables; biological stress primarily influenced the total sleep duration on the first night of the 7-day monitoring period, whereas adaptability largely affected temporal changes in the within-person residual variances. These findings underscore the importance of synergizing concept, operationalization, and method for the science of human resilience.
In memory of Dr. Dennis John McFarland, who passed away recently, our objective is to continue his efforts to compare psychometric networks and latent variable models statistically. We do so by providing a commentary on his latest work, which he encouraged us to write, shortly before his death. We first discuss the statistical procedure McFarland used, which involved structural equation modeling (SEM) in standard SEM software. Next, we evaluate the penta-factor model of intelligence. We conclude that (1) standard SEM software is not suitable for the comparison of psychometric networks with latent variable models, and (2) the penta-factor model of intelligence is only of limited value, as it is nonidentified. We conclude with a reanalysis of the Wechlser Adult Intelligence Scale data McFarland discussed and illustrate how network and latent variable models can be compared using the recently developed R package Psychonetrics. Of substantive theoretical interest, the results support a network interpretation of general intelligence. A novel empirical finding is that networks of intelligence replicate over standardization samples.
Item response theory (IRT) is a modeling approach that links responses to test items with underlying latent constructs through formalized statistical models. This article focuses on how IRT can be used to advance science and practice in organizations. We describe established applications of IRT as a scale development tool and new applications of IRT as a research and theory testing tool that enables organizational researchers to improve their understanding of workers and organizations. We focus on IRT models and their application in four key research and practice areas: testing, questionnaire responding, construct validation, and measurement equivalence of scores. In so doing, we highlight how novel developments in IRT such as explanatory IRT, multidimensional IRT, random item models, and more complex models of response processes such as ideal point models and tree models can potentially advance existing science and practice in these areas. As a starting point for readers interested in learning IRT and applying recent developments in IRT in their research, we provide concrete examples with data and R code.
Expected final online publication date for the Annual Review of Organizational Psychology and Organizational Behavior, Volume 8 is January 21, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Adapting to task changes in work settings frequently calls not only for shifting one’s thoughts and behaviors to the new demands, but also for dealing with outdated knowledge and skills. This article focuses on the role of control strategies in task adaptation and reports two experimental studies using an air traffic control simulation task. In both studies (N � 66 and 105 with k � 1,320 and 1,680 observations, respectively), all participants first learned and performed an initial version of the task then received instruction about control strategies, performed an altered version of the task with new execution rules, and finally worked on a memory test. Participants were instructed to either deliberately forget the
old rules, remember the old rules, or simply learn the new task (Study 2 only). Results from discontinuous growth curve modeling revealed that the directed forgetting in both studies and the control group in Study 2 showed higher performance in the simulation after the change relative to their performance before the change (transition adaptation). There were no relearning differences between the groups suggesting that these differences persisted throughout the task. However, the memory test at the end of the study revealed that the directed forgetting groups and the learning control group remembered less outdated task
execution rules in the memory test after the simulation than the remembering group. The findings suggest that different types of cognitive strategies have costs and benefits. Conceptual and practical implications of these findings are discussed.
Researchers are increasingly interested in the affect dynamics of individuals for describing and explaining personality and psychopathology. Recently, the incremental validity of more complex indicators of affect dynamics (IADs; e.g. autoregression) has been called into question (Dejonckheere et al., 2019), with evidence accumulating that these might convey little unique information beyond mean level and general variability of emotions. Our study extends the evidence for the construct validity of IADs by investigating their redundancy and uniqueness, split-half reliability based on indices from odd-numbered and even-numbered days, and association with big five personality traits. We used three diverse samples that assessed daily and momentary emotions, including community participants, individuals with personality pathology, and their significant others (total N = 1192, total number of occasions = 51 278). Mean and variability of affects had high reliability and distinct nomological patterns to big five personality traits. In contrast, more complex IADs exhibited substantial redundancies with mean level and general variability of emotions. When partialing out these redundancies by using residual variables, some of the more complex IADs had acceptable reliability , but only a few of these showed incremental associations with big five personality traits, indicating that IADs have limited validity using the current assessment practices.
Prestigious journals are widely admired for publishing quality scholarship, yet the primary indicators of journal prestige (i.e., impact factors) do not directly assess audience admiration. Moreover, the publication landscape has changed substantially in the last 20 years, with electronic publishing changing the way we consume scientific research. Given that it has been 18 years since the publication of the last journal prestige survey of SIOP members, the authors conducted a new survey and used these results to reflect on changing practices within industrial and organizational (I-O) psychology. SIOP members (n = 557) rated the prestige and relevance of I-O and management journals. Responses were analyzed according to job setting, and were compared to a survey conducted by Zickar and Highhouse (2001) in 2000. There was considerable consistency in prestige ratings across settings (i.e., management department vs. psychology department; academic vs. applied), especially among the top journals. There was considerable variance, however, in the perceived usefulness of different journals. Results also suggested considerable consistency across the two time periods, but with some increases in prestige among OB-oriented journals. Changes in the journal landscape are discussed, including the rise of OHP as a topic of concentration in I-O. We suggest that I-O programs will continue to attract the top researchers in talent management and OHP, which should result in the use of a broader set of journals for judging I-O program impact.
Methodological checklists for improving research quality and reporting consistency - Volume 13 Issue 1 - Lillian T. Eby, Kristen M. Shockley, Talya N. Bauer, Bryan Edwards, Astrid C. Homan, Russell Johnson, Jonas W. B. Lang, Scott B. Morris, Frederick L. Oswald
As proposed in a prominent developmental model, social anxiety has different manifestations: social fear, shy temperament, anxious cognitions, and avoidance of social situations. Drawing from this model, we used the network approach to psychopathology to gain a detailed understanding of specific social anxiety components and their associations. The current article investigated (a) how social anxiety components are interconnected within a network, and (b) the consistency of the network over time, in a community sample of children and adolescents. Data from 3 waves of a longitudinal study were used. At Time 1 (T1) the total sample comprised 331 participants (Mage = 13.34 years); at Time 3 (T3) there were 236 participants (Mage = 17.48 years). Social anxiety components were assessed with self-report questionnaires. Networks of 15 nodes (i.e., components) were estimated. Network analysis of T1 components revealed 4 communities: cognitive, social-emotional, avoidance of performance, and avoidance of interaction situations. There were no direct connections between the cognitive and behavioral communities; social-emotional nodes appeared to act as bridge components between the 2 communities. A similar pattern of component associations and communities was found in the T2 and T3 networks, and the longitudinal network incorporating node change trajectories. Networks were estimated on group-level observational data and conclusions about cause-effect relationships are tentative. Although the sample size decreased across the 3 waves, the reliability of parameter estimates were minimally affected. Findings attest to the potential value of applying the network approach to investigate the pattern of associations among social anxiety components in youth. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Networks are gaining popularity as an alternative to latent variable models for representing psychological constructs. Whereas latent variable approaches introduce unobserved common causes to explain the relations among observed variables, network approaches posit direct causal relations between observed variables. While these approaches lead to radically different understandings of the psychological constructs of interest, recent articles have established mathematical equivalences that hold between network models and latent variable models. We argue that the fact that for any model from one class there is an equivalent model from the other class does not mean that both models are equally plausible accounts of the data-generating mechanism. In many cases the constraints that are meaningful in one framework translate to constraints in the equivalent model that lack a clear interpretation in the other framework. Finally, we discuss three diverging predictions for the relation between zero-order correlations and partial correlations implied by sparse network models and unidimensional factor models. We propose a test procedure that compares the likelihoods of these models in light of these diverging implications. We use an empirical example to illustrate our argument.
Theories suggest that groups within organizations often develop shared values, beliefs, affect, behaviors, or agreed-on routines; however, researchers rarely study predictors of consensus emergence over time. Recently, a multilevel-methods approach for detecting and studying emergence in organizational field data has been described. This approach—the consensus emergence model—builds on an extended three-level multilevel model. Researchers planning future studies based on the consensus emergence model need to consider (a) sample size characteristics required to detect emergence effects with satisfactory statistical power and (b) how the distribution of the overall sample size across the levels of the multilevel model influences power. We systematically address both issues by conducting a power simulation for detecting main and moderating effects involving consensus emergence under a variety of typical research scenarios and provide an R-based tool that readers can use to estimate power. Our discussion focuses on the future use and development of multilevel methods for studying emergence in organizational research.
The dynamics of individual behavior are related to the dynamics of the social structures in which individuals are embedded. This implies that in order to study social mechanisms such as social selection or peer influence, we need to model the evolution of social networks and the attributes of network actors as interdependent processes. The stochastic actor-oriented model is a statistical approach to study network-attribute coevolution based on longitudinal data. In its standard specification, the coevolving actor attributes are assumed to be measured on an ordinal categorical scale. Continuous variables first need to be discretized to fit into such a modeling framework. This article presents an extension of the stochastic actor-oriented model that does away with this restriction by using a stochastic differential equation to model the evolution of a continuous attribute. We propose a measure for explained variance and give an interpretation of parameter sizes. The proposed method is illustrated by a study of the relationship between friendship, alcohol consumption, and self-esteem among adolescents.
Over the years, many studies have demonstrated a relation between emotion dynamics and psychological well-being¹. Because our emotional life is inherently time-dynamic2–6, affective scientists argue that, next to how positive or negative we feel on average, patterns of emotional change are informative for mental health7–10. This growing interest initiated a surge in new affect dynamic measures, each claiming to capture a unique dynamical aspect of our emotional life, crucial for understanding well-being. Although this accumulation suggests scientific progress, researchers have not always evaluated (a) how different affect dynamic measures empirically interrelate and (b) what their added value is in the prediction of psychological well-being. Here, we address these questions by analysing affective time series data from 15 studies (n = 1,777). We show that (a) considerable interdependencies between measures exist, suggesting that single dynamics often do not convey unique information, and (b) dynamic measures have little added value over mean levels of positive and negative affect (and variance in these affective states) when predicting individual differences in three indicators of well-being (life satisfaction, depressive symptoms and borderline symptoms). Our findings indicate that conventional emotion research is currently unable to demonstrate independent relations between affect dynamics and psychological well-being.
Job search represents a dynamic process through which job seekers must consistently engage in effective self‐regulation. Although scholars have increasingly begun to theorize and conceptualize the job search in this manner, little is known about what fosters effective self‐regulation week‐to‐week. In light of this theoretical gap, we integrate self‐regulation theory with the feedback literature to examine how feedback quality influences affective, cognitive, and behavioral regulatory processes in job search. Furthermore, we examine feedback self‐efficacy (i.e., how efficacious a job seeker feels with respect to processing and implementing feedback received during the job search) as a stable, person‐level moderator of these within‐person relationships. In a sample of job seekers surveyed once a week for seven weeks, results indicate that receiving high quality feedback has a direct influence on positive and negative affective reactions tied to the job search, influencing subsequent positive (i.e., metacognitive strategies) and negative (i.e., affective rumination) cognitive processes. Metacognitive strategies, in turn, impact both the number of résumés sent and hours spent job seeking each week. Moreover, lower feedback self‐efficacy amplifies the relationship between feedback quality and negative affective reactions. Our results highlight the importance of high quality feedback in helping job seekers effectively regulate week‐to‐week.
This article is protected by copyright. All rights reserved
Integrating functional leadership theory with models of the team creativity and innovation, we present a dynamic model of leadership emergence where leadership emergence is shaped by (a) the type of contributions members express (constructive contributions proposing new ideas, or supportive contributions affirming ideas with merit), (b) when those contributions are expressed (i.e., in the idea generation or idea enactment phase), and (c) the extent fellow teammates themselves are contributing in constructive or supportive ways in those phases. We tested our theoretical model in two studies involving simulated teams engaged in an innovation design challenge. In both studies, we found that constructive contributions were more strongly related to leadership emergence in the idea generation phase than in the idea enactment phase. Moreover, the impact of constructive contributions on leadership emergence in the idea generation phase was stronger when there was a “void”—that is, fellow teammates’ constructive contributions were low. Surprisingly, in both studies, we found consistent evidence that supportive contributions also enhanced leadership emergence in the idea generation phase, whereas the findings on supportive contributions and leadership emergence in the idea enactment phase were mixed. Overall, our model highlights the importance of integrating dynamic and contextualized aspects of teams into theories of leadership emergence and also sheds new light on the processes underlying emergent forms of leadership in the early phases of the innovation cycle.
Even though considerable work has demonstrated a robust positive relationship between general mental ability (GMA) and task performance, recent work indicates that the expected relationship may not hold in the context of adaptive performance. By integrating the concept of choking, or performing worse than expected, with goal theory, the present work advances a theoretical framework aimed at furthering our understanding of how and when GMA is most likely to meaningfully impact performance. Drawing on this perspective, we propose that the relationship between GMA and adaptive performance is uniquely dependent on the type of goal individuals are striving to achieve. Additionally, we note that the nature of this relationship may evolve as people gain experience dealing with unexpected changes. Results of a discontinuous growth model fit to data obtained from a stock market exercise generally indicate that compared with performance goals, do-your-best and learning goals strengthen the relationship between GMA and adaptive performance. Further, we find that performance goals seem to effectively neutralize the GMA-adaptive performance relationship by benefiting those lower on GMA while simultaneously hindering those with higher levels. In contrast, the relationship is largely positive when either a do-your-best or a learning goal is being pursued, particularly after individuals are exposed to a second change.
The development and promotion of social-emotional skills in childhood and adolescence contributes to subsequent well-being and positive life outcomes. However, the assessment of these skills is associated with conceptual and methodological challenges. This review discusses how social-emotional skill measurement in youth could be improved in terms of skills’ conceptualization and classification, and in terms of assessment techniques and methodologies. The first part of the review discusses various conceptualizations of social-emotional skills, demonstrates their overlap with related constructs such as emotional intelligence and the Big Five personality dimensions, and proposes an integrative set of social-emotional skill domains that has been developed recently. Next, methodological approaches that are innovative and may improve social-emotional assessments are presented, illustrated by concrete examples. We discuss how these innovations could advance social-emotional assessments, and demonstrate links to similar issues in related fields. We conclude the review by providing several concrete assessment recommendations that follow from this discussion.
Over time, groups can change in at least two important ways. First, they can display different trajectories (e.g., increases or decreases) on constructs of interest. Second, the configuration of group members’ responses within a group can change, such that the members become more or less similar to each other. Psychologists have historically been interested in understanding changes in groups over time; however, there is currently no comprehensive quantitative framework for studying and modeling group processes over time. We present a multilevel framework for such research—the multilevel group-process framework (MGPF). The MGPF builds on a statistical approach developed to capture whether individual members of a group develop a shared climate over time, but we extend the core ideas in two important ways. First, we describe how researchers can gain insights into group phenomena such as group leniency, group learning, groupthink, group extremity, group forming, group freezing, and group adjourning through modeling change in latent mean levels and consensus. Second, we present a sequence of model-testing steps that enable researchers to systematically study and contrast different group processes. We describe how the MGPF can lead to novel research questions and illustrate its use in two example data sets.
Researchers often mention the utility and need for R-squared measures of explained variance for multilevel models (MLMs). Although this topic has been addressed by methodologists, the MLM R-squared literature suffers from several shortcomings: (a) analytic relationships among existing measures have not been established so measures equivalent in the population have been redeveloped 2 or 3 times; (b) a completely full partitioning of variance has not been used to create measures, leading to gaps in the availability of measures to address key substantive questions; (c) a unifying approach to interpreting and choosing among measures has not been provided, leading to researchers’ difficulty with implementation; and (d) software has inconsistently and infrequently incorporated available measures. We address these issues with the following contributions. We develop an integrative framework of R-squared measures for MLMs with random intercepts and/or slopes based on a completely full decomposition of variance. We analytically relate 10 existing measures from different disciplines as special cases of 5 measures from our framework. We show how our framework fills gaps by supplying additional total and level-specific measures that answer new substantive research questions. To facilitate interpretation, we provide a novel and integrative graphical representation of all the measures in the framework; we use it to demonstrate limitations of current reporting practices for MLM R-squareds, as well as benefits of considering multiple measures from the framework in juxtaposition. We supply and empirically illustrate an R function, r2MLM, that computes all measures in our framework to help researchers in considering effect size and conveying practical significance.
This study examined P. L. Ackerman's (1988) model of skill acquisition within an applied setting. Differences were examined between jobs on the basis of task consistency: changes in performance variability across experience, learning curves, and stability of ability-performance correlations across experience. Results showed the degree of task consistency influenced the shape of learning curves, with jobs composed of primarily consistent tasks improving more rapidly and reaching asymptote sooner. In addition, trends in ability-performance correlations were moderated by the degree of task consistency within a job. Specifically, for jobs with primarily consistent tasks, general cognitive ability best predicted early performance whereas psychomotor ability best predicted later performance. In contrast, general cognitive ability was the strongest predictor across experience for jobs with primarily inconsistent tasks.
Three apperception theories that explain how people respond to Thematic Apperception Test cards are proposed: a simple apperception theory, an apperception theory with a dynamic component, and an apperception theory with 2 types of responses. Each theory is translated into an item response theory model and is applied to need for achievement (nAch) data. The analysis indicates that the best fitting model is provided by the apperception theory with 2 types of responses, also referred to as the drop-out apperception theory. The 1st type of response predicted by this theory is determined by the nAch level of the person and the achievement-response-eliciting value of the card; this response is diagnostic for the nAch level of the person. The 2nd type of response is not determined by the 2 aforementioned characteristics and is therefore not diagnostic of the person's nAch level. The results are cross-validated for need for power and need for affiliation.
Three experience-sampling studies explored the distributions of Big-Five-relevant states (behavior) across 2 to 3 weeks of everyday life. Within-person variability was high, such that the typical individual regularly and routinely manifested nearly all levels of all traits in his or her everyday behavior. Second, individual differences in central tendencies of behavioral distributions were almost perfectly stable. Third, amount of behavioral variability (and skew and kurtosis) were revealed as stable individual differences. Finally, amount of within-person variability in extraversion was shown to reflect individual differences in reactivity to extraversion-relevant situational cues. Thus, decontextualized and noncontingent Big-Five content is highly useful for descriptions of individuals' density distributions as wholes. Simultaneously, contextualized and contingent personality units (e.g., conditional traits, goals) are needed for describing the considerable within-person variation.
Personality researchers and clinical psychologists have long been interested in within-person variability in a given personality trait. Two critical methodological challenges that stymie current research on within-person variability are separating meaningful within-person variability from (a) true differences in trait level; and (b) careless responding (or person unreliability). To partly avoid these issues, personality researchers commonly only study within-person variability in personality states over time using the standard deviation (SD) across repeated measurements of the same items (typically across days)-a relatively resource-intensive approach. In this article, we detail an approach that allows researchers to measure another type of within-person variability. The described approach utilizes item-response theory (IRT) on the basis of Böckenholt's (2012) three-process model, and extracts a meaningful variability score from Likert-ratings of personality descriptions that is distinct from directional (trait) responding. Two studies (N = 577; N = 120-235) suggest that IRT variability generalizes across traits, has high split-half reliability, is not highly correlated with established indices of IRT person unreliability for directional trait responding, and correlates with within-person SDs from personality inventories and within-person SDs in a diary study with repeated measurements across days 20 months later. The implications and usefulness of IRT variability from personality descriptions as a conceptually clarified, efficient, and feasible assessment of within-person variability in personality ratings are discussed.
Variability indices are a key measure of interest across diverse fields, in and outside psychology. A crucial problem for any research relying on variability measures however is that variability is severely confounded with the mean, especially when measurements are bounded, which is often the case in psychology (e.g., participants are asked “rate how happy you feel now between 0 and 100?”). While a number of solutions to this problem have been proposed, none of these are sufficient or generic. As a result, conclusions on the basis of research relying on variability measures may be unjustified. Here, we introduce a generic solution to this problem by proposing a relative variability index that is not confounded with the mean by taking into account the maximum possible variance given an observed mean. The proposed index is studied theoretically and we offer an analytical solution for the proposed index. Associated software tools (in R and MATLAB) have been developed to compute the relative index for measures of standard deviation, relative range, relative interquartile distance and relative root mean squared successive difference. In five data examples, we show how the relative variability index solves the problem of confound with the mean, and document how the use of the relative variability measure can lead to different conclusions, compared with when conventional variability measures are used. Among others, we show that the variability of negative emotions, a core feature of patients with borderline disorder, may be an effect solely driven by the mean of these negative emotions.
In the last decade, there has been increased recognition that traits refer not only to between-person differences but also to meaningful within-person variability across situations (i.e., whole trait theory). So far, this broader more contemporary trait conceptualization has made few inroads into assessment practices. Therefore, this study focuses on the assessment and predictive power of people’s intraindividual variability across situations. In three studies (either in student or employee samples), both test-takers’ mean trait scores and the variability of their responses across multiple written job-related situations of a situational judgment test (SJT) were assessed. Results revealed that people’s intraindividual variability (a) was related to their self-rated functional flexibility, (b) predicted performance above their mean scores, and (c) predicted their actual personality state variability over 10 days. These results open opportunities for complementing traditional selection procedures with more dynamic indices in assessment.
The brms package implements Bayesian multilevel models in R using the probabilistic programming language Stan. A wide range of distributions and link functions are supported, allowing users to fit – among others – linear, robust linear, binomial, Poisson, survival, ordinal, zero-inflated, hurdle, and even non-linear models all in a multilevel context. Further modeling options include autocorrelation of the response variable, user defined covariance structures, censored data, as well as meta-analytic standard errors. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. In addition, model fit can easily be assessed and compared with the Watanabe-Akaike information criterion and leave-one-out cross-validation.
Employee unethical behavior continues to be an area of interest as real-world business scandals persist. We investigate what happens after people engage in unethical behavior. Drawing from emotion theories (e.g., Tangney & Dearing, 2002) and the self-presentation literature (e.g., Leary & Miller, 2000), we first argue that people are socialized to experience shame after moral violations (Hypothesis 1). People then manage their shame and try to protect their self-images by engaging in exemplification behaviors (i.e., self-sacrificial behaviors that give the attribution of being a dedicated person; Hypothesis 2). We also examine the moderating role of supervisor bottom-line mentality (BLM; i.e., a supervisor's singular focus on pursuing bottom-line outcomes) in relation to our theoretical model. We argue that high supervisor BLM intensifies the employee unethical behavior to shame relationship (Hypothesis 3) and results in heightened exemplification as a way to protect one's self-image by portraying the self as a dedicated person who is worthy of association (Hypothesis 4). We test our theoretical model across 2 experimental studies and 2 field studies. Although our results provide general support for Hypotheses 1, 2, and 3, our results produced mixed findings for Hypothesis 4. Theoretical and practical implications are discussed. (PsycINFO Database Record
Personality traits are most often assessed using global self-reports of one’s general patterns of thoughts, feelings, and behavior. However, recent theories have challenged the idea that global self-reports are the best way to assess traits. Whole Trait Theory postulates that repeated measures of a person’s self-reported personality states (i.e., the average of many state self-reports) can be an alternative and potentially superior way of measuring a person’s trait level (Fleeson & Jayawickreme, 2015). Our goal is to examine the validity of average state self-reports of personality for measuring between-person differences in what people are typically like. In order to validate average states as a measure of personality, we examine whether they are incrementally valid in predicting informant reports above and beyond global self-reports. In 2 samples, we find that average state self-reports tend to correlate with informant reports, although this relationship is weaker than the relationship between global self-reports and informant reports. Further, using structural equation modeling, we find that average state self-reports do not significantly predict informant reports independently of global self-reports. Our results suggest that average state self-reports may not contain information about between-person differences in personality traits beyond what is captured by global self-reports, and that average state self-reports may contain more self-bias than is commonly believed. We discuss the implications of these findings for research on daily manifestations of personality and the accuracy of self-reports.
Past research on the effects of work engagement on the family has demonstrated contrasting effects, with some suggesting that work engagement is beneficial for family life while others suggesting that it may be detrimental. In the present research, using a sample of 125 employees who responded to daily surveys both at work and at home for two consecutive weeks, we present a multilevel examination of the relationships of work engagement to family outcomes aimed at elucidating such work-family effects. Our findings revealed that employees’ daily work engagement experiences related positively, within individuals, to work-family interpersonal capitalization, which in turn, related positively to daily family satisfaction and to daily work-family balance. Our findings also indicate that both the relationship between daily work engagement and work-family interpersonal capitalization and the indirect effects of daily work engagement on the family outcomes were stronger for employees with higher intrinsic motivation than for those with lower intrinsic motivation. We discuss theoretical and practical implications of our findings and offer directions for future research.
Team cognition has been identified as a critical component of team performance and decision-making. However, theory and research in this domain continues to remain largely static; articulation and examination of the dynamic processes through which collectively held knowledge emerges from the individual- to the team-level is lacking. To address this gap, we advance and systematically evaluate a process-oriented theory of team knowledge emergence. First, we summarize the core concepts and dynamic mechanisms that underlie team knowledge-building and represent our theory of team knowledge emergence (Step 1). We then translate this narrative theory into a formal computational model that provides an explicit specification of how these core concepts and mechanisms interact to produce emergent team knowledge (Step 2). The computational model is next instantiated into an agent-based simulation to explore how the key generative process mechanisms described in our theory contribute to improved knowledge emergence in teams (Step 3). Results from the simulations demonstrate that agent teams generate collectively shared knowledge more effectively when members are capable of processing information more efficiently and when teams follow communication strategies that promote equal rates of information sharing across members. Lastly, we conduct an empirical experiment with real teams participating in a collective knowledge-building task to verify that promoting these processes in human teams also leads to improved team knowledge emergence (Step 4). Discussion focuses on implications of the theory for examining team cognition processes and dynamics as well as directions for future research. (PsycINFO Database Record
We develop and test an integrative formal model of motivation and decision making. The model, referred to as the extended multiple-goal pursuit model (MGPM*), is an integration of the multiple-goal pursuit model (Vancouver, Weinhardt, & Schmidt, 2010) and decision field theory (Busemeyer & Townsend, 1993). Simulations of the model generated predictions regarding the effects of goal type (approach vs avoidance), risk, and time sensitivity on prioritization. We tested these predictions in an experiment in which participants pursued different combinations of approach and avoidance goals under different levels of risk. The empirical results were consistent with the predictions of the MGPM*. Specifically, participants pursuing one approach and one avoidance goal shifted priority from the approach to the avoidance goal over time. Among participants pursuing two approach goals, those with low time sensitivity prioritized the goal with the larger discrepancy, whereas those with high time sensitivity prioritized the goal with the smaller discrepancy. Participants pursuing two avoidance goals generally prioritized the goal with the smaller discrepancy. Finally, all of these effects became weaker as the level of risk increased. We used quantitative model comparison to show that the MGPM* explained the data better than the original multiple-goal pursuit model, and that the major extensions from the original model were justified. The MGPM* represents a step forward in the development of a general theory of decision making during multiple-goal pursuit.
We examine changes in work adjustment among 179 expatriates from 3 multinational organizations from predeparture through the first 9 months of a new international assignment. Our 10-wave results challenge classic U-shaped theories of expatriate adjustment (e.g., Torbiorn, 1982). Consistent with uncertainty reduction theory, our results instead suggest that expatriates typically experience a gradual increase in work adjustment over time. Two resources that expatriates bring to their assignments (previous culture-specific work experience and core self-evaluations) moderate the trajectory of work adjustment. Trajectory of adjustment predicts Month 9 career instrumentality and turnover intention, as well as career advancement (job promotion) 1.5 years further. Implications for theory, as well as for changes in expatriate management practices, are discussed. (PsycINFO Database Record
This book presents a thorough overview of a model of human functioning based on the idea that behavior is goal-directed and regulated by feedback control processes. It describes feedback processes and their application to behavior, considers goals and the idea that goals are organized hierarchically, examines affect as deriving from a different kind of feedback process, and analyzes how success expectancies influence whether people keep trying to attain goals or disengage. Later sections consider a series of emerging themes, including dynamic systems as a model for shifting among goals, catastrophe theory as a model for persistence, and the question of whether behavior is controlled or instead 'emerges'. Three chapters consider the implications of these various ideas for understanding maladaptive behavior, and the closing chapter asks whether goals are a necessity of life. Throughout, theory is presented in the context of diverse issues that link the theory to other literatures.
Constructs that reflect differences in variability are of interest to many researchers studying workplace phenomena. The aggregation methods typically used to investigate “variability-based” constructs suffer from several limitations, including the inability to include Level 1 predictors and a failure to account for uncertainty in the variability estimates. We demonstrate how mixed-effects location-scale (MELS) and heterogeneous variance models, which are direct extensions of traditional mixed-effects (or multilevel) models, can be used to test mean (location)- and variability (scale)-related hypotheses simultaneously. The aims of this article are to demonstrate (a) how the MELS and heterogeneous variance models can be estimated with both nested cross-sectional and longitudinal data to answer novel research questions about constructs of interest to organizational researchers, (b) how a Bayesian approach allows for the inclusion of random intercepts and slopes when predicting both variability and mean levels, and finally (c) how researchers can use a multilevel approach to predict between-group heterogeneous variances. In doing so, this article highlights the added value of viewing variability as more than a statistical nuisance in organizational research.
In the present paper, we offer an integrative approach to personality that combines within-person and between- person differences. By drawing on the principles of dynamic systems theory, we present Personality Dynamics model – a novel framework that captures people's typical pattern of changes in personality states using three model parameters: baseline personality, reflecting the stable set point around which one's states fluctuate, per- sonality variability, or the extent to which one's personality states fluctuate across time and situations, and per- sonality attractor force, pertaining to the swiftness with which deviations of one's baseline are pulled back to the baseline. We argue that the dynamic approach to personality represented in the PersDyn model has the potential to integrate different perspectives on individual differences. We also demonstrate that the dynamic approach to personality offers a consensual paradigm of personality with the potential to advance our understanding and knowledge of individual differences, by detailing the factors and processes included in the model, as well as links to existing theories and applications in various research lines.
With empirical research on team resilience on the rise, there is a need for an integrative conceptual model that delineates the essential elements of this concept and offers a heuristic for the integration of findings across studies. To address this need, we propose a multilevel model of team resilience that originates in the resources of individual team members and emerges as a team‐level construct through dynamic person–situation interactions that are triggered by adverse events. In so doing, we define team resilience as an emergent outcome characterized by the trajectory of a team's functioning, following adversity exposure, as one that is largely unaffected or returns to normal levels after some degree of deterioration in functioning. This conceptual model offers a departure point for future work on team resilience and reinforces the need to incorporate inputs and process mechanisms inherent within dynamic interactions among individual members of a team. Of particular, importance is the examination of these inputs, process mechanisms and emergent states, and outcomes over time, and in the context of task demands, objectives, and adverse events.
Practitioner points
• Team resilience as a dynamic, multilevel phenomenon requires clarity on the individual‐ and team‐level factors that foster its emergence within occupational and organizational settings.
• An understanding of the nature (e.g., timing, chronicity) of adverse events is key to studying and intervening to foster team resilience within occupational and organizational settings.
Recent empirical work in several economic fields, particularly environmental and energy economics, has adapted the regression discontinuity (RD) framework to applications where time is the running variable and treatment begins at a particular threshold in time. In this guide for practitioners, we discuss several features of this regression discontinuity in time framework that differ from the more standard cross-sectional RD framework. First, many applications (particularly in environmental economics) lack cross-sectional variation and are estimated using observations far from the temporal threshold. This common empirical practice is hard to square with the assumptions of a cross-sectional RD, which is conceptualized for an estimation bandwidth shrinking even as the sample size increases. Second, estimates may be biased if the time-series properties of the data are ignored (for instance, in the presence of an autoregressive process), or more generally if short-run and long-run effects differ. Finally, tests for sorting or bunching near the threshold are often irrelevant, making the framework closer to an event study than a regression discontinuity design. Based on these features and motivated by hypothetical examples using air quality data, we offer suggestions for the empirical researcher wishing to use the RD in time framework.
This article presents dynamic structural equation modeling (DSEM), which can be used to study the evolution of observed and latent variables as well as the structural equation models over time. DSEM is suitable for analyzing intensive longitudinal data where observations from multiple individuals are collected at many points in time. The modeling framework encompasses previously published DSEM models and is a comprehensive attempt to combine time-series modeling with structural equation modeling. DSEM is estimated with Bayesian methods using the Markov chain Monte Carlo Gibbs sampler and the Metropolis–Hastings sampler. We provide a detailed description of the estimation algorithm as implemented in the Mplus software package. DSEM can be used for longitudinal analysis of any duration and with any number of observations across time. Simulation studies are used to illustrate the framework and study the performance of the estimation method. Methods for evaluating model fit are also discussed.
This special issue consists of selected papers arising from the interdisciplinary conference “The Making of Measurement“ held at the University of Cambridge on 23-24 July 2015. In this introduction, we seek ways to further productive interactions among historical, philosophical, and sociological approaches to the study of measurement without attempting to lay out a prescriptive program for a field of “measurement studies.“ We ask where science studies has led us, and answer: from the function to the making of measurement. We discuss whether there is anything privileged or exemplary about physical measurement, and alight upon models and metrology, two particular focuses of enquiry that emerge from our selection of papers. Those papers with a historical dimension complement an already well-developed body of historiography applied to measurement and metrology.
Calibration procedures establish a reliable relation between the final states (‘indications’) of a measurement process and features of the objects being measured (‘outcomes’). This article analyzes the inferential structure of calibration procedures. I show that calibration is a modelling activity, namely the activity of constructing, deriving predictions from, and testing theoretical and statistical models of a measurement process. Measurement outcomes are parameter value ranges that maximize the predictive accuracy and mutual coherence of such models, among other desiderata. This model-based view of calibration clarifies the source of objectivity of measurement outcomes, the nature of measurement accuracy, and the close relationship between measurement and prediction. Contrary to commonly held views, I argue that measurement standards are not necessary for calibration, although they are useful in maintaining coherence across large networks of measurement procedures.
It is fitting in a book written in honor of Jack Atkinson’s lifelong contribution to the study of personality and human motivation to consider the motivational determinants of cumulative achievement. In this chapter I will show how parts of the theoretical framework outlined by Atkinson can be filled in to answer the question of what determines cumulative achievement. Figure 1 (adapted from Atkinson and Birch, 1978) gives an overview of the richness of Atkinson’s theory, and will serve as an outline of this chapter.
We examined the emotional basis of work engagement by focusing on the relationship between arousal and work vigour. Drawing on the DynAff model of Kuppens, Oravecz, and Tuerlinckx (2010), we looked at three elements underlying the temporal dynamics of arousal: (1) the level of baseline arousal (i.e., the attractor state around which arousal fluctuates), (2) the amount of variability in arousal around this baseline, and (3) the swiftness with which people return to their arousal baseline once they deviated from it. We conducted a five-day experience sampling study, in which 88 employees reported on their momentary core affect (i.e., their momentary level of valence and arousal), while vigour was measured at the end of the study. Results showed that higher levels of baseline arousal were related to increased levels of vigour. Furthermore, we found that baseline arousal interacted with arousal variability in the sense that only people with low levels of baseline arousal and low levels of arousal variability experienced lower levels of vigour. Together, our findings suggest that, if we want to advance our understanding of the emotional basis of work engagement, we need to look into the temporal dynamics underlying it.
Networks have been recently proposed for modeling dynamics in several kinds of psychological phenomena, such as personality and psychopathology. In this work, we introduce techniques that allow disentangling between-subject networks, which encode dynamics that involve stable individual differences, from within-subject networks, which encode dynamics that involve momentary levels of certain individual characteristics. Furthermore, we show how networks can be simultaneously estimated in separate groups of individuals, using a technique called the Fused Graphical Lasso. This technique allows also performing meaningful comparisons among groups. The unique properties of each kind of network are discussed. A tutorial to implement these techniques in the “R” statistical software is presented, together with an example of application.
In this review, we outline how a methodologically based framework, the discontinuous growth model (DGM), can be used to advance research and theory on transitions. Our review focuses on identifying the types of hypotheses and research questions that can be specified and tested using this framework. Three parameters of the DGM are described: the pre-event covariate (TIMEpre), a transition covariate (TRANS), and a recovery covariate (RECOV). We discuss relevant parameters by analyzing the relative and absolute changes following a transition event. We illustrate the framework with a variety of studies from different contexts and address the difficulty of interpreting responses to events without TIMEpre data. In addition, we discuss the role of large longitudinal databases as sources for advancing research and theory surrounding transitions, particularly for rare and unexpected events. Finally, we discuss ways in which transition research can inform our understanding of individual, team, and organizational re...
Organizational researchers routinely have access to repeated measures from numerous time periods punctuated by one or more discontinuities. Discontinuities may be planned, such as when a researcher introduces an unexpected change in the context of a skill acquisition task. Alternatively, discontinuities may be unplanned, such as when a natural disaster or economic event occurs during an ongoing data collection. In this article, we build off the basic discontinuous growth model and illustrate how alternative specifications of time-related variables allow one to examine relative versus absolute change in transition and post-transition slopes. Our examples focus on interpreting time-varying covariates in a variety of situations (multiple discontinuities, linear and quadratic models, and models where discontinuities occur at different times). We show that the ability to test relative and absolute differences provides a high degree of precision in terms of specifying and testing hypotheses.