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Pooseh S, Kalisch R, Köber G, Binder H, Timmer J. Intraindividual time-varying dynamic network of affects: linear autoregressive mixed-effects models for ecological momentary assessment. Front Psychiatry 2024; 15:1213863. [PMID: 38585485 PMCID: PMC10997345 DOI: 10.3389/fpsyt.2024.1213863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 02/21/2024] [Indexed: 04/09/2024] Open
Abstract
An interesting recent development in emotion research and clinical psychology is the discovery that affective states can be modeled as a network of temporally interacting moods or emotions. Additionally, external factors like stressors or treatments can influence the mood network by amplifying or dampening the activation of specific moods. Researchers have turned to multilevel autoregressive models to fit these affective networks using intensive longitudinal data gathered through ecological momentary assessment. Nonetheless, a more comprehensive examination of the performance of such models is warranted. In our study, we focus on simple directed intraindividual networks consisting of two interconnected mood nodes that mutually enhance or dampen each other. We also introduce a node representing external factors that affect both mood nodes unidirectionally. Importantly, we disregard the potential effects of a current mood/emotion on the perception of external factors. We then formalize the mathematical representation of such networks by exogenous linear autoregressive mixed-effects models. In this representation, the autoregressive coefficients signify the interactions between moods, while external factors are incorporated as exogenous covariates. We let the autoregressive and exogenous coefficients in the model have fixed and random components. Depending on the analysis, this leads to networks with variable structures over reasonable time units, such as days or weeks, which are captured by the variability of random effects. Furthermore, the fixed-effects parameters encapsulate a subject-specific network structure. Leveraging the well-established theoretical and computational foundation of linear mixed-effects models, we transform the autoregressive formulation to a classical one and utilize the existing methods and tools. To validate our approach, we perform simulations assuming our model as the true data-generating process. By manipulating a predefined set of parameters, we investigate the reliability and feasibility of our approach across varying numbers of observations, levels of noise intensity, compliance rates, and scalability to higher dimensions. Our findings underscore the challenges associated with estimating individualized parameters in the context of common longitudinal designs, where the required number of observations may often be unattainable. Moreover, our study highlights the sensitivity of autoregressive mixed-effect models to noise levels and the difficulty of scaling due to the substantial number of parameters.
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Affiliation(s)
- Shakoor Pooseh
- Center for Interdisciplinary Digital Sciences (CIDS), Technische Universität Dresden, Dresden, Germany
- Freiburg Center for Data Analysis and Modeling (FDM), Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Raffael Kalisch
- Leibniz Institute for Resilience Research (LIR), Mainz, Germany
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Göran Köber
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Harald Binder
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Jens Timmer
- Freiburg Center for Data Analysis and Modeling (FDM), Institute of Physics, University of Freiburg, Freiburg, Germany
- CIBSS-Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany
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2
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Veenman M, Janssen LHC, van Houtum LAEM, Wever MCM, Verkuil B, Epskamp S, Fried EI, Elzinga BM. A Network Study of Family Affect Systems in Daily Life. MULTIVARIATE BEHAVIORAL RESEARCH 2024; 59:371-405. [PMID: 38356299 DOI: 10.1080/00273171.2023.2283632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
Adolescence is a time period characterized by extremes in affect and increasing prevalence of mental health problems. Prior studies have illustrated how affect states of adolescents are related to interactions with parents. However, it remains unclear how affect states among family triads, that is adolescents and their parents, are related in daily life. This study investigated affect state dynamics (happy, sad, relaxed, and irritated) of 60 family triads, including 60 adolescents (Mage = 15.92, 63.3% females), fathers and mothers (Mage = 49.16). The families participated in the RE-PAIR study, where they reported their affect states in four ecological momentary assessments per day for 14 days. First, we used multilevel vector-autoregressive network models to estimate affect dynamics across all families, and for each family individually. Resulting models elucidated how family affect states were related at the same moment, and over time. We identified relations from parents to adolescents and vice versa, while considering family variation in these relations. Second, we evaluated the statistical performance of the network model via a simulation study, varying the percentage missing data, the number of families, and the number of time points. We conclude with substantive and statistical recommendations for future research on family affect dynamics.
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Affiliation(s)
- Myrthe Veenman
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
| | - Loes H C Janssen
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
| | | | - Mirjam C M Wever
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
| | - Bart Verkuil
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
| | - Sacha Epskamp
- Department of Psychology, National University of Singapore
| | - Eiko I Fried
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
| | - Bernet M Elzinga
- Department of Clinical Psychology, Faculty of Social Sciences, Leiden University
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3
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Ji L, Li Y, Potter LN, Lam CY, Nahum-Shani I, Wetter DW, Chow SM. Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data. Front Digit Health 2023; 5:1099517. [PMID: 38026834 PMCID: PMC10676222 DOI: 10.3389/fdgth.2023.1099517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 09/27/2023] [Indexed: 12/01/2023] Open
Abstract
Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.
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Affiliation(s)
- Linying Ji
- Department of Biobehavioral Health, The Pennsylvania State University, University Park, PA, United States
- Department of Psychology, Montana State University, Bozeman, MT, United States
| | - Yanling Li
- Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA, United States
| | - Lindsey N. Potter
- Center for Health Outcomes and Population Equity, Huntsman Cancer Institute, and Intermountain Healthcare Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States
| | - Cho Y. Lam
- Center for Health Outcomes and Population Equity, Huntsman Cancer Institute, and Intermountain Healthcare Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States
| | - Inbal Nahum-Shani
- Data-Science for Dynamic Decision-Making Center (d3c), Institute for Social Research, University of Michigan, Ann Arbor, MI, United States
| | - David W. Wetter
- Center for Health Outcomes and Population Equity, Huntsman Cancer Institute, and Intermountain Healthcare Department of Population Health Sciences, University of Utah, Salt Lake City, UT, United States
| | - Sy-Miin Chow
- Department of Human Development and Family Studies, The Pennsylvania State University, University Park, PA, United States
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4
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Rosenström TH, Czajkowski NO, Solbakken OA, Saarni SE. Direction of dependence analysis for pre-post assessments using non-Gaussian methods: a tutorial. Psychother Res 2023; 33:1058-1075. [PMID: 36706267 DOI: 10.1080/10503307.2023.2167526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 12/28/2022] [Indexed: 01/28/2023] Open
Abstract
OBJECTIVE We introduced methods for solving causal direction of dependence between variables observed in pre- and post-psychotherapy assessments, showing how to apply them and investigate their properties via simulations. In addition, we investigated whether changes in depressive symptoms drive changes in social and occupational functioning as suggested by the phase model of psychotherapy or vice versa, or neither. METHOD As a Gaussian (normal-distribution) model is unidentifiable here, we used an identifiable linear non-Gaussian structural vector autoregression model, conceptualizing instantaneous effects as during-psychotherapy causation and lagged effects as pre-treatment predictors of change. We tested six alternative estimators in six simulation settings that captured different real-world scenarios, and used real psychotherapy data from 1428 adult patients (Finnish Psychotherapy Quality Registry; assessments on Patient Health Questionnaire-9 and Social and Occupational Functioning Assessment Schedule). RESULTS The methodology was successful in identifying causal directions in simulated data. The real-data results provided no evidence for single direction of dependence, suggesting shared or reciprocal causation. CONCLUSIONS A powerful new tool was presented to investigate the process of psychotherapy using observational data. Application to patient data suggested that depression symptoms and functioning may reciprocate or reflect third variables instead of one predominantly driving the other during psychotherapy.
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Affiliation(s)
- Tom H Rosenström
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Nikolai O Czajkowski
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
| | - Ole André Solbakken
- Department of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway
| | - Suoma E Saarni
- Brain Center, Department of Psychiatry, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
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Moggia D, Bennemann B, Schwartz B, Hehlmann MI, Driver CC, Lutz W. Process-Based psychotherapy personalization: considering causality with continuous-time dynamic modeling. Psychother Res 2023; 33:1076-1095. [PMID: 37306112 DOI: 10.1080/10503307.2023.2222892] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 06/02/2023] [Indexed: 06/13/2023] Open
Abstract
Psychotherapy can be improved by integrating the study of mediators (how it works) and moderators (for whom it works). To demonstrate this integration, we studied the relationship between resource activation, problem-coping experiences and symptoms in cognitive-behavior therapy (CBT) for depression, to obtain preliminary insights on causal inference (which process leads to symptom improvement?) and prediction (which one for whom?). A sample of 715 patients with depression who received CBT was analyzed. Hierarchical Bayesian continuous time dynamic modeling was used to study the temporal dynamics between the variables analyzed within the first ten sessions. Depression and self-efficacy at baseline were examined as predictors of these dynamics. There were significant cross-effects between the processes studied. Under typical assumptions, resource activation had a significant effect on symptom improvement. Problem-coping experience had a significant effect on resource activation. Depression and self-efficacy moderated these effects. However, when system noise was considered, these effects may be affected by other processes. Resource activation was strongly associated with symptom improvement. To the extent of inferring causality, for patients with mild-moderate depression and high self-efficacy, promoting resource activation can be recommended. For patients with severe depression and low self-efficacy, promoting problem-coping experiences can be recommended.
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Park JJ, Fisher ZF, Chow SM, Molenaar PCM. Evaluating Discrete Time Methods for Subgrouping Continuous Processes. MULTIVARIATE BEHAVIORAL RESEARCH 2023:1-13. [PMID: 37590440 PMCID: PMC10873483 DOI: 10.1080/00273171.2023.2235685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Rapid developments over the last several decades have brought increased focus and attention to the role of time scales and heterogeneity in the modeling of human processes. To address these emerging questions, subgrouping methods developed in the discrete-time framework-such as the vector autoregression (VAR)-have undergone widespread development to identify shared nomothetic trends from idiographic modeling results. Given the dependence of VAR-based parameters on the measurement intervals of the data, we sought to clarify the strengths and limitations of these methods in recovering subgroup dynamics under different measurement intervals. Building on the work of Molenaar and collaborators for subgrouping individual time-series by means of the subgrouped chain graphical VAR (scgVAR) and the subgrouping option in the group iterative multiple model estimation (S-GIMME), we present results from a Monte Carlo study aimed at addressing the implications of identifying subgroups using these discrete-time methods when applied to continuous-time data. Results indicate that discrete-time subgrouping methods perform well at recovering true subgroups when the measurement intervals are large enough to capture the full range of a system's dynamics, either via lagged or contemporaneous effects. Further implications and limitations are discussed therein.
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Affiliation(s)
- Jonathan J Park
- Department of Human Development and Family Studies, The Pennsylvania State University
| | - Zachary F Fisher
- Department of Human Development and Family Studies, The Pennsylvania State University
| | - Sy-Miin Chow
- Department of Human Development and Family Studies, The Pennsylvania State University
| | - Peter C M Molenaar
- Department of Human Development and Family Studies, The Pennsylvania State University
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Coppersmith DD, Ryan O, Fortgang RG, Millner AJ, Kleiman EM, Nock MK. Mapping the timescale of suicidal thinking. Proc Natl Acad Sci U S A 2023; 120:e2215434120. [PMID: 37071683 PMCID: PMC10151607 DOI: 10.1073/pnas.2215434120] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 03/10/2023] [Indexed: 04/19/2023] Open
Abstract
This study aims to identify the timescale of suicidal thinking, leveraging real-time monitoring data and a number of different analytic approaches. Participants were 105 adults with past week suicidal thoughts who completed a 42-d real-time monitoring study (total number of observations = 20,255). Participants completed two forms of real-time assessments: traditional real-time assessments (spaced hours apart each day) and high-frequency assessments (spaced 10 min apart over 1 h). We found that suicidal thinking changes rapidly. Both descriptive statistics and Markov-switching models indicated that elevated states of suicidal thinking lasted on average 1 to 3 h. Individuals exhibited heterogeneity in how often and for how long they reported elevated suicidal thinking, and our analyses suggest that different aspects of suicidal thinking operated on different timescales. Continuous-time autoregressive models suggest that current suicidal intent is predictive of future intent levels for 2 to 3 h, while current suicidal desire is predictive of future suicidal desire levels for 20 h. Multiple models found that elevated suicidal intent has on average shorter duration than elevated suicidal desire. Finally, inferences about the within-person dynamics of suicidal thinking on the basis of statistical modeling were shown to depend on the frequency at which data was sampled. For example, traditional real-time assessments estimated the duration of severe suicidal states of suicidal desire as 9.5 h, whereas the high-frequency assessments shifted the estimated duration to 1.4 h.
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Affiliation(s)
| | - Oisín Ryan
- Department of Methodology and Statistics, Utrecht University, 3508 TCUtrecht, The Netherlands
| | | | - Alexander J. Millner
- Department of Psychology, Harvard University, Cambridge, MA02138
- Mental Health Research, Franciscan Children’s, Brighton, MA02135
| | - Evan M. Kleiman
- Department of Psychology, Rutgers, The State University of New Jersey, Piscataway, NJ08854
| | - Matthew K. Nock
- Department of Psychology, Harvard University, Cambridge, MA02138
- Mental Health Research, Franciscan Children’s, Brighton, MA02135
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA02114
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Abstract
A considerable amount of human behavior occurs within the context of sports. In recent years there have been notable advances in psychological science research applied to understanding athletic endeavor. This work has utilized a number of novel theoretical, methodological, and data analytic approaches. We review the current evidence related to developmental considerations, intrapersonal athlete factors, group processes, and the role of the coach in explaining how athletes function within the sport domain. This body of work sheds light on the diverse ways in which psychological processes contribute to athletic strivings. It also has the potential to spark interest in domains of psychology concerned with achievement as well as to encourage cross-domain fertilization of ideas.
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Affiliation(s)
- Mark R Beauchamp
- School of Kinesiology, University of British Columbia, Vancouver, Canada;
| | - Alan Kingstone
- Department of Psychology, University of British Columbia, Vancouver, Canada;
| | - Nikos Ntoumanis
- Danish Centre for Motivation and Behaviour Science, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark;
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9
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Haslbeck JMB, Ryan O, Robinaugh DJ, Waldorp LJ, Borsboom D. Modeling psychopathology: From data models to formal theories. Psychol Methods 2022; 27:930-957. [PMID: 34735175 PMCID: PMC10259162 DOI: 10.1037/met0000303] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Over the past decade, there has been a surge of empirical research investigating mental disorders as complex systems. In this article, we investigate how to best make use of this growing body of empirical research and move the field toward its fundamental aims of explaining, predicting, and controlling psychopathology. We first review the contemporary philosophy of science literature on scientific theories and argue that fully achieving the aims of explanation, prediction, and control requires that we construct formal theories of mental disorders: theories expressed in the language of mathematics or a computational programming language. We then investigate three routes by which one can use empirical findings (i.e., data models) to construct formal theories: (a) using data models themselves as formal theories, (b) using data models to infer formal theories, and (c) comparing empirical data models to theory-implied data models in order to evaluate and refine an existing formal theory. We argue that the third approach is the most promising path forward. We conclude by introducing the abductive formal theory construction (AFTC) framework, informed by both our review of philosophy of science and our methodological investigation. We argue that this approach provides a clear and promising way forward for using empirical research to inform the generation, development, and testing of formal theories both in the domain of psychopathology and in the broader field of psychological science. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
| | - Oisín Ryan
- Department of Methodology and Statistics
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10
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Ruissen GR, Zumbo BD, Rhodes RE, Puterman E, Beauchamp MR. Analysis of dynamic psychological processes to understand and promote physical activity behaviour using intensive longitudinal methods: a primer. Health Psychol Rev 2022; 16:492-525. [PMID: 34643154 DOI: 10.1080/17437199.2021.1987953] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Physical activity behaviour displays temporal variability, and is influenced by a range of dynamic psychological processes (e.g., affect) and shaped by various co-occurring events (e.g., social/environmental factors, interpersonal dynamics). Yet, most physical activity research tends not to examine the dynamic psychological processes implicated in adopting and maintaining physical activity. Intensive longitudinal methods (ILM) represent one particularly salient means of studying the complex psychological dynamics that underlie and result from physical activity behaviour. With the increased recent interest in using intensive longitudinal data to understand specific dynamic psychological processes, the field of exercise and health psychology is well-positioned to draw from state-of-the-art measurement and statistical approaches that have been developed and operationalised in other fields of enquiry. The purpose of this review is to provide an overview of some of the fundamental dynamic measurement and modelling approaches applicable to the study of physical activity behaviour change, as well as the dynamic psychological processes that contribute to such change.
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Affiliation(s)
- Geralyn R Ruissen
- School of Kinesiology, University of British Columbia, Vancouver, Canada
| | - Bruno D Zumbo
- Department of Educational and Counseling Psychology and Special Education, University of British Columbia, Vancouver, Canada
| | - Ryan E Rhodes
- School of Exercise Science, Physical and Health Education, University of Victoria, Victoria, Canada
| | - Eli Puterman
- School of Kinesiology, University of British Columbia, Vancouver, Canada
| | - Mark R Beauchamp
- School of Kinesiology, University of British Columbia, Vancouver, Canada
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11
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Borsboom D. Reflections on an emerging new science of mental disorders. Behav Res Ther 2022; 156:104127. [DOI: 10.1016/j.brat.2022.104127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 05/18/2022] [Indexed: 12/28/2022]
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12
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Marsman M, Rhemtulla M. Guest Editors' Introduction to The Special Issue "Network Psychometrics in Action": Methodological Innovations Inspired by Empirical Problems. PSYCHOMETRIKA 2022; 87:1-11. [PMID: 35397084 PMCID: PMC9021145 DOI: 10.1007/s11336-022-09861-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Affiliation(s)
- Maarten Marsman
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
- University of Amsterdam, Psychological Methods, Nieuwe Achtergracht 129B, PO Box 15906, 1001 NK, Amsterdam, The Netherlands.
| | - Mijke Rhemtulla
- Department of Psychology, University of California at Davis, Davis, California, USA
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Abstract
This commentary reflects on the articles included in the Psychometrika Special Issue on Network Psychometrics in Action. The contributions to the special issue are related to several possible future paths for research in this area. These include the development of models to analyze and represent interventions, improvement in exploratory and inferential techniques in network psychometrics, the articulation of psychometric theories in addition to psychometric models, and extensions of network modeling to novel data sources. Finally, network psychometrics is part of a larger movement in psychology that revolves around the analysis of human beings as complex systems, and it is timely that psychometricians start extending their rich modeling tradition to improve and extend the analysis of systems in psychology.
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Affiliation(s)
- Denny Borsboom
- Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands
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