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  Subjects -> PSYCHOLOGY (Total: 983 journals)
Showing 601 - 174 of 174 Journals sorted alphabetically
New School Psychology Bulletin     Open Access  
Nigerian Journal of Guidance and Counselling     Full-text available via subscription   (Followers: 2)
Nordic Psychology     Hybrid Journal  
O Que Nos Faz Pensar : Cadernos do Departamento de Filosofia da PUC-Rio     Open Access  
OA Autism     Open Access   (Followers: 4)
Occupational Health Science     Hybrid Journal  
Online Readings in Psychology and Culture     Open Access  
Open Journal of Medical Psychology     Open Access  
Open Mind     Open Access   (Followers: 1)
Open Neuroimaging Journal     Open Access  
Open Psychology Journal     Open Access  
Organisational and Social Dynamics: An International Journal of Psychoanalytic, Systemic and Group Relations Perspectives     Full-text available via subscription   (Followers: 5)
Organizational Psychology Review     Hybrid Journal   (Followers: 15)
Orientación y Sociedad : Revista Internacional e Interdisciplinaria de Orientación Vocacional Ocupacional     Open Access  
Paidéia (Ribeirão Preto)     Open Access  
Pain     Hybrid Journal   (Followers: 60)
Papeles del Psicólogo     Open Access  
Pastoral Psychology     Hybrid Journal   (Followers: 3)
Peace and Conflict : Journal of Peace Psychology     Full-text available via subscription   (Followers: 6)
Pensamiento Psicologico     Open Access  
Pensando Familias     Open Access  
Pensando Psicología     Open Access  
People and Animals : The International Journal of Research and Practice     Open Access  
Perception     Full-text available via subscription   (Followers: 14)
Perceptual and Motor Skills     Full-text available via subscription   (Followers: 8)
Persona     Open Access  
Persona : Jurnal Psikologi Indonesia     Open Access  
Persona Studies     Open Access  
Personality and Social Psychology Bulletin     Hybrid Journal   (Followers: 148)
Personality and Social Psychology Review     Hybrid Journal   (Followers: 49)
Personality Disorders: Theory, Research, and Treatment     Full-text available via subscription   (Followers: 18)
Personnel Assessment and Decisions     Open Access  
Personnel Psychology     Hybrid Journal   (Followers: 53)
Perspectives interdisciplinaires sur le travail et la santé     Open Access   (Followers: 3)
Perspectives on Behavior Science     Hybrid Journal  
Perspectives On Psychological Science     Hybrid Journal   (Followers: 36)
Perspectives Psy     Full-text available via subscription   (Followers: 1)
Phenomenology & Practice     Open Access   (Followers: 2)
Phenomenology and the Cognitive Sciences     Hybrid Journal   (Followers: 4)
Philosophical Psychology     Hybrid Journal   (Followers: 19)
Philosophy, Psychiatry, & Psychology     Full-text available via subscription   (Followers: 11)
Physiology & Behavior     Hybrid Journal   (Followers: 14)
physiopraxis     Hybrid Journal  
PiD - Psychotherapie im Dialog     Hybrid Journal   (Followers: 1)
Poiésis     Open Access  
Policy Insights from the Behavioral and Brain Sciences     Full-text available via subscription   (Followers: 4)
Political Psychology     Hybrid Journal   (Followers: 42)
Porn Studies     Hybrid Journal   (Followers: 6)
PPmP - Psychotherapie Psychosomatik Medizinische Psychologie     Hybrid Journal   (Followers: 1)
Practice Innovations     Full-text available via subscription  
Pragmatic Case Studies in Psychotherapy     Open Access   (Followers: 1)
Pratiques Psychologiques     Full-text available via subscription  
Praxis der Kinderpsychologie und Kinderpsychiatrie     Hybrid Journal  
Problems of Psychology in the 21st Century     Open Access  
Professional Psychology : Research and Practice     Full-text available via subscription   (Followers: 6)
Progress in Brain Research     Full-text available via subscription   (Followers: 2)
Psic : Revista de Psicologia da Vetor Editora     Open Access  
Psico     Open Access  
Psicoanalisi     Full-text available via subscription  
Psicobiettivo     Full-text available via subscription  
Psicoespacios     Open Access  
Psicogente     Open Access  
Psicol?gica Journal     Open Access  
Psicologia     Open Access  
Psicologia     Open Access  
Psicologia : Teoria e Pesquisa     Open Access  
Psicologia : Teoria e Prática     Open Access  
Psicologia da Educação     Open Access  
Psicologia della salute     Full-text available via subscription  
Psicología desde el Caribe     Open Access  
Psicologia di Comunità. Gruppi, ricerca-azione, modelli formativi     Full-text available via subscription  
Psicologia e Saber Social     Open Access   (Followers: 1)
Psicologia e Saúde em Debate     Open Access  
Psicologia em Pesquisa     Open Access  
Psicologia em Revista     Open Access  
Psicologia Ensino & Formação     Open Access  
Psicologia Hospitalar     Open Access  
Psicologia Iberoamericana     Open Access   (Followers: 1)
Psicologia para América Latina     Open Access  
Psicologia USP     Open Access   (Followers: 1)
Psicología, Conocimiento y Sociedad     Open Access  
Psicologia, Saúde e Doenças     Open Access  
Psicooncología     Open Access   (Followers: 1)
Psicoperspectivas     Open Access  
Psicoterapia e Scienze Umane     Full-text available via subscription  
Psikis : Jurnal Psikologi Islami     Open Access  
Psikohumaniora : Jurnal Penelitian Psikologi     Open Access  
Psisula : Prosiding Berkala Psikologi     Open Access  
Psocial : Revista de Investigación en Psicología Social     Open Access  
Psych     Open Access   (Followers: 1)
PsyCh Journal     Hybrid Journal   (Followers: 2)
PSYCH up2date     Hybrid Journal   (Followers: 2)
Psych. Pflege Heute     Hybrid Journal   (Followers: 1)
Psychê     Open Access  
Psyche: A Journal of Entomology     Open Access   (Followers: 6)
Psychiatrie et violence     Open Access  
Psychiatrie und Psychotherapie up2date     Hybrid Journal   (Followers: 2)
Psychiatrische Praxis     Hybrid Journal   (Followers: 1)
Psychiatry, Psychology and Law     Hybrid Journal   (Followers: 357)
Psychoanalysis and History     Hybrid Journal   (Followers: 3)
Psychoanalysis, Self and Context     Hybrid Journal   (Followers: 4)
Psychoanalytic Dialogues: The International Journal of Relational Perspectives     Hybrid Journal   (Followers: 8)
Psychoanalytic Inquiry: A Topical Journal for Mental Health Professionals     Hybrid Journal   (Followers: 7)
Psychoanalytic Perspectives     Hybrid Journal   (Followers: 6)
Psychoanalytic Psychology     Full-text available via subscription   (Followers: 3)
Psychoanalytic Psychotherapy     Hybrid Journal   (Followers: 13)
Psychoanalytic Review The     Full-text available via subscription   (Followers: 7)
Psychoanalytic Social Work     Hybrid Journal   (Followers: 10)
Psychoanalytic Study of the Child     Hybrid Journal   (Followers: 1)
Psychodynamic Practice: Individuals, Groups and Organisations     Hybrid Journal   (Followers: 6)
Psychodynamic Psychiatry     Full-text available via subscription   (Followers: 8)
Psychogeriatrics     Hybrid Journal   (Followers: 1)
Psychologia : Advances de la Disciplina     Open Access  
Psychologica     Open Access  
Psychologica Belgica     Open Access   (Followers: 1)
Psychological Assessment     Full-text available via subscription   (Followers: 12)
Psychological Bulletin     Full-text available via subscription   (Followers: 207)
Psychological Medicine     Hybrid Journal   (Followers: 19)
Psychological Perspectives: A Semiannual Journal of Jungian Thought     Hybrid Journal   (Followers: 1)
Psychological Reports     Hybrid Journal  
Psychological Research     Hybrid Journal   (Followers: 9)
Psychological Research on Urban Society     Open Access  
Psychological Review     Full-text available via subscription   (Followers: 183)
Psychological Science     Hybrid Journal   (Followers: 248)
Psychological Science and Education     Open Access   (Followers: 1)
Psychological Science and Education psyedu.ru     Open Access   (Followers: 1)
Psychological Science In the Public Interest     Hybrid Journal   (Followers: 17)
Psychological Studies     Hybrid Journal   (Followers: 3)
Psychological Thought     Open Access   (Followers: 2)
Psychological Trauma: Theory, Research, Practice, and Policy     Full-text available via subscription   (Followers: 20)
Psychologie Clinique     Full-text available via subscription  
Psychologie du Travail et des Organisations     Hybrid Journal  
Psychologie Française     Full-text available via subscription  
Psychologie in Erziehung und Unterricht     Full-text available via subscription   (Followers: 2)
Psychologische Rundschau     Hybrid Journal   (Followers: 2)
Psychology     Open Access   (Followers: 6)
Psychology     Open Access  
Psychology & Health     Hybrid Journal   (Followers: 32)
Psychology & Sexuality     Hybrid Journal   (Followers: 15)
Psychology and Aging     Full-text available via subscription   (Followers: 16)
Psychology and Developing Societies     Hybrid Journal  
Psychology and Law     Open Access   (Followers: 3)
Psychology and Psychotherapy: Theory, Research and Practice     Full-text available via subscription   (Followers: 18)
Psychology in Russia: State of the Art     Free   (Followers: 2)
Psychology in Society     Open Access  
Psychology Learning & Teaching     Full-text available via subscription   (Followers: 10)
Psychology of Addictive Behaviors     Full-text available via subscription   (Followers: 15)
Psychology of Aesthetics, Creativity and the Arts     Full-text available via subscription   (Followers: 15)
Psychology of Consciousness : Theory, Research, and Practice     Full-text available via subscription   (Followers: 7)
Psychology of Language and Communication     Open Access   (Followers: 14)
Psychology of Leaders and Leadership     Full-text available via subscription  
Psychology of Learning and Motivation     Full-text available via subscription   (Followers: 11)
Psychology of Men and Masculinity     Full-text available via subscription   (Followers: 24)
Psychology of Music     Hybrid Journal   (Followers: 21)
Psychology of Popular Media Culture     Full-text available via subscription   (Followers: 6)
Psychology of Religion and Spirituality     Full-text available via subscription   (Followers: 16)
Psychology of Sexual Orientation and Gender Diversity     Full-text available via subscription   (Followers: 12)
Psychology of Violence     Full-text available via subscription   (Followers: 16)
Psychology of Well-Being : Theory, Research and Practice     Open Access   (Followers: 20)
Psychology of Women Quarterly     Hybrid Journal   (Followers: 8)
Psychology Research and Behavior Management     Open Access   (Followers: 6)
Psychology, Community & Health     Open Access   (Followers: 3)
Psychology, Crime & Law     Hybrid Journal   (Followers: 27)
Psychology, Health & Medicine     Hybrid Journal   (Followers: 16)
Psychology, Public Policy, and Law     Full-text available via subscription   (Followers: 13)
Psychometrika     Hybrid Journal   (Followers: 7)
Psychomusicology : Music, Mind, and Brain     Full-text available via subscription   (Followers: 6)
Psychoneuroendocrinology     Hybrid Journal   (Followers: 14)
Psychonomic Bulletin & Review     Full-text available via subscription   (Followers: 18)
Psychopathology     Full-text available via subscription   (Followers: 4)
Psychopharmacology     Hybrid Journal   (Followers: 15)
Psychophysiology     Hybrid Journal   (Followers: 7)
psychopraxis. neuropraxis     Hybrid Journal   (Followers: 1)
Psychosis: Psychological, Social and Integrative Approaches     Hybrid Journal   (Followers: 8)
Psychosomatic Medicine     Hybrid Journal   (Followers: 12)
Psychosomatic Medicine and General Practice     Open Access   (Followers: 1)
Psychosomatics     Hybrid Journal   (Followers: 9)
Psychotherapeut     Hybrid Journal   (Followers: 4)
Psychotherapy and Politics International     Hybrid Journal   (Followers: 5)
Psychotherapy and Psychosomatics     Partially Free   (Followers: 11)
Psychotherapy in Australia     Full-text available via subscription   (Followers: 1)
Psychotherapy Research     Hybrid Journal   (Followers: 18)
PsychTech & Health Journal     Open Access   (Followers: 8)
Psyecology - Bilingual Journal of Environmental Psychology     Hybrid Journal   (Followers: 3)
Psyke & Logos     Open Access   (Followers: 4)
Psykhe (Santiago)     Open Access  
Quaderni di Gestalt     Full-text available via subscription  
Quaderns de Psicologia     Open Access  
Qualitative Psychology     Full-text available via subscription   (Followers: 6)
Qualitative Research in Psychology     Hybrid Journal   (Followers: 17)
Qualitative Studies     Open Access   (Followers: 12)
Quality and User Experience     Hybrid Journal   (Followers: 2)
Quantitative Methods for Psychology     Open Access   (Followers: 1)
Quarterly Journal of Experimental Psychology     Hybrid Journal   (Followers: 22)
Race and Social Problems     Hybrid Journal   (Followers: 10)
Reading Psychology     Hybrid Journal   (Followers: 6)
Rehabilitation Psychology     Full-text available via subscription   (Followers: 9)
Religion, Brain & Behavior     Hybrid Journal   (Followers: 10)
Research in Autism Spectrum Disorders     Hybrid Journal   (Followers: 27)
Research in Psychology and Behavioral Sciences     Open Access   (Followers: 2)

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Psychometrika
Journal Prestige (SJR): 2.374
Citation Impact (citeScore): 2
Number of Followers: 7  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1860-0980 - ISSN (Online) 0033-3123
Published by Springer-Verlag Homepage  [2469 journals]
  • Commentary on “Extending the Basic Local Independence Model to
           Polytomous Data” by Stefanutti, de Chiusole, Anselmi, and Spoto

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      Abstract: Abstract The Polytomous Local Independence Model (PoLIM) by Stefanutti, de Chiusole, Anselmi, and Spoto, is an extension of the Basic Local Independence Model (BLIM) to accommodate polytomous items. BLIM, a model for analyzing responses to binary items, is based on Knowledge Space Theory, a framework developed by cognitive scientists and mathematical psychologists for modeling human knowledge acquisition and representation. The purpose of this commentary is to show that PoLIM is simply a paraphrase of a DINA model in cognitive diagnosis for polytomous items. Specifically, BLIM is shown to be equivalent to the DINA model when the BLIM-items are conceived as binary single-attribute items, each with a distinct attribute; thus, PoLIM is equivalent to the DINA for polytomous single-attribute items, each with a distinct attribute.
      PubDate: 2022-06-17
       
  • Bayesian Dynamic Borrowing of Historical Information with Applications to
           the Analysis of Large-Scale Assessments

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      Abstract: Abstract The purpose of this paper is to demonstrate and evaluate the use of Bayesian dynamic borrowing (Viele et al, in Pharm Stat 13:41-54, 2014) as a means of systematically utilizing historical information with specific applications to large-scale educational assessments. Dynamic borrowing via Bayesian hierarchical models is a special case of a general framework of historical borrowing where the degree of borrowing depends on the heterogeneity among historical data and current data. A joint prior distribution over the historical and current data sets is specified with the degree of heterogeneity across the data sets controlled by the variance of the joint distribution. We apply Bayesian dynamic borrowing to both single-level and multilevel models and compare this approach to other historical borrowing methods such as complete pooling, Bayesian synthesis, and power priors. Two case studies using data from the Program for International Student Assessment reveal the utility of Bayesian dynamic borrowing in terms of predictive accuracy. This is followed by two simulation studies that reveal the utility of Bayesian dynamic borrowing over simple pooling and power priors in cases where the historical data is heterogeneous compared to the current data based on bias, mean squared error, and predictive accuracy. In cases of homogeneous historical data, Bayesian dynamic borrowing performs similarly to data pooling, Bayesian synthesis, and power priors. In contrast, for heterogeneous historical data, Bayesian dynamic borrowing performed at least as well, if not better, than other methods of borrowing with respect to mean squared error, percent bias, and leave-one-out cross-validation.
      PubDate: 2022-06-10
       
  • On Reverse Shrinkage Effects and Shrinkage Overshoot

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      Abstract: Abstract Given a squared Euclidean norm penalty, we examine some less well-known properties of shrinkage estimates. In particular, we highlight that it is possible for some components of the shrinkage estimator to be placed further away from the prior mean than the original estimate. An analysis of this effect is provided within three different modeling settings—encompassing linear, logistic, and ordinal regression models. Additional simulations show that the outlined effect is not a mathematical artefact, but likely to occur in practice. As a byproduct, they also highlight the possibilities of sign reversals (“overshoots”) for shrinkage estimates. We point out practical consequences and challenges, which might arise from the observed effects with special emphasis on psychometrics.
      PubDate: 2022-06-05
       
  • Sequential Generalized Likelihood Ratio Tests for Online Item Monitoring

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      Abstract: Abstract The study presents statistical procedures that monitor functioning of items over time. We propose generalized likelihood ratio tests that surveil multiple item parameters and implement with various sampling techniques to perform continuous or intermittent monitoring. The procedures examine stability of item parameters across time and inform compromise as soon as they identify significant parameter shift. The performance of the monitoring procedures was validated using simulated and real-assessment data. The empirical evaluation suggests that the proposed procedures perform adequately well in identifying the parameter drift. They showed satisfactory detection power and gave timely signals while regulating error rates reasonably low. The procedures also showed superior performance when compared with the existent methods. The empirical findings suggest that multivariate parametric monitoring can provide an efficient and powerful control tool for maintaining the quality of items. The procedures allow joint monitoring of multiple item parameters and achieve sufficient power using powerful likelihood-ratio tests. Based on the findings from the empirical experimentation, we suggest some practical strategies for performing online item monitoring.
      PubDate: 2022-06-04
       
  • An Extended GFfit Statistic Defined on Orthogonal Components of
           Pearson’s Chi-Square

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      Abstract: Abstract The Pearson and likelihood ratio statistics are commonly used to test goodness of fit for models applied to data from a multinomial distribution. The goodness-of-fit test based on Pearson’s Chi-squared statistic is sometimes considered to be a global test that gives little guidance to the source of poor fit when the null hypothesis is rejected, and it has also been recognized that the global test can often be outperformed in terms of power by focused or directional tests. For the cross-classification of a large number of manifest variables, the GFfit statistic focused on second-order marginals for variable pairs i, j has been proposed as a diagnostic to aid in finding the source of lack of fit after the model has been rejected based on a more global test. When data are from a table formed by the cross-classification of a large number of variables, the common global statistics may also have low power and inaccurate Type I error level due to sparseness in the cells of the table. The sparseness problem is rarely encountered with the GFfit statistic because it is focused on the lower-order marginals. In this paper, a new and extended version of the GFfit statistic is proposed by decomposing the Pearson statistic from the full table into orthogonal components defined on marginal distributions and then defining the new version, \(GFfit_{\perp }^{(ij)}\) , as a partial sum of these orthogonal components. While the emphasis is on lower-order marginals, the new version of \(GFfit_{\perp }^{(ij)}\) is also extended to higher-order tables so that the \(GFfit_{\perp }\) statistics sum to the Pearson statistic. As orthogonal components of the Pearson \(X^2\) statistic, \(GFfit_{\perp }^{(ij)}\) statistics have advantages over other lack-of-fit diagnostics that are currently available for cross-classified tables: the \(GFfit_{\perp }^{(ij)}\) generally have higher power to detect lack of fit while maintaining good Type I error control even if the joint frequencies are very sparse, as will be shown in simulation results; theoretical results will establish that \(GFfit_{\perp }^{(ij)}\) statistics have known degrees of freedom and are asymptotically independent with known joint distribution, a property which facilitates less conservative control of false discovery rate (FDR) or familywise error rate (FWER) in a high-dimensional table which would produce a large number of bivariate lack-of-fit diagnostics. Computation of \(GFfit_{\perp }^{(ij)}\) statistics is also computationally stable. The extended \(GFfit_{\perp }^{(ij)}\) statistic can be applied to a variety of models for cross-classified tables. An application of the new GFfit statistic as a diagnostic for a latent variable model is presented.
      PubDate: 2022-06-03
       
  • Erratum to: A Response-Time-Based Latent Response Mixture Model for
           Identifying and Modeling Careless and Insufficient Effort Responding in
           Survey Data

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      PubDate: 2022-06-01
       
  • Book Review of the Handbook of Graphical Models

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      PubDate: 2022-06-01
       
  • Transformer-Based Deep Neural Language Modeling for Construct-Specific
           Automatic Item Generation

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      Abstract: Abstract Algorithmic automatic item generation can be used to obtain large quantities of cognitive items in the domains of knowledge and aptitude testing. However, conventional item models used by template-based automatic item generation techniques are not ideal for the creation of items for non-cognitive constructs. Progress in this area has been made recently by employing long short-term memory recurrent neural networks to produce word sequences that syntactically resemble items typically found in personality questionnaires. To date, such items have been produced unconditionally, without the possibility of selectively targeting personality domains. In this article, we offer a brief synopsis on past developments in natural language processing and explain why the automatic generation of construct-specific items has become attainable only due to recent technological progress. We propose that pre-trained causal transformer models can be fine-tuned to achieve this task using implicit parameterization in conjunction with conditional generation. We demonstrate this method in a tutorial-like fashion and finally compare aspects of validity in human- and machine-authored items using empirical data. Our study finds that approximately two-thirds of the automatically generated items show good psychometric properties (factor loadings above .40) and that one-third even have properties equivalent to established and highly curated human-authored items. Our work thus demonstrates the practical use of deep neural networks for non-cognitive automatic item generation.
      PubDate: 2022-06-01
       
  • Better Information From Survey Data: Filtering Out State Dependence Using
           Eye-Tracking Data

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      Abstract: Abstract Ideally, survey respondents read and understand survey instructions, questions, and response scales, and provide answers that carefully reflect their beliefs, attitudes, or knowledge. However, respondents may also arrive at their responses using cues or heuristics that facilitate the production of a response, but diminish the targeted information content. We use eye-tracking data as covariates in a Bayesian switching-mixture model to identify different response behaviors at the item–respondent level. The model distinguishes response behaviors that are predominantly influenced either positively or negatively by the previous response, and responses that reflect respondents’ preexisting knowledge and experiences of interest. We find that controlling for multiple types of adaptive response behaviors allows for a more informative analysis of survey data and respondents.
      PubDate: 2022-06-01
       
  • Modeling Conditional Dependence of Response Accuracy and Response Time
           with the Diffusion Item Response Theory Model

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      Abstract: Abstract In this paper, we propose a model-based method to study conditional dependence between response accuracy and response time (RT) with the diffusion IRT model (Tuerlinckx and De Boeck in Psychometrika 70(4):629–650, 2005, https://doi.org/10.1007/s11336-000-0810-3; van der Maas et al. in Psychol Rev 118(2):339–356, 2011, https://doi.org/10.1080/20445911.2011.454498). We extend the earlier diffusion IRT model by introducing variability across persons and items in cognitive capacity (drift rate in the evidence accumulation process) and variability in the starting point of the decision processes. We show that the extended model can explain the behavioral patterns of conditional dependency found in the previous studies in psychometrics. Variability in cognitive capacity can predict positive and negative conditional dependency and their interaction with the item difficulty. Variability in starting point can account for the early changes in the response accuracy as a function of RT given the person and item effects. By the combination of the two variability components, the extended model can produce the curvilinear conditional accuracy functions that have been observed in psychometric data. We also provide a simulation study to validate the parameter recovery of the proposed model and present two empirical applications to show how to implement the model to study conditional dependency underlying data response accuracy and RTs.
      PubDate: 2022-06-01
       
  • An Empirical Q-Matrix Validation Method for the Polytomous G-DINA Model

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      Abstract: Abstract A number of empirically based Q-matrix validation methods are available in the literature, all of which were developed for cognitive diagnosis models (CDMs) involving dichotomous attributes. However, in many applications, it is more instructionally relevant to classify students into more than two categories (e.g., no mastery, basic mastery, and advanced mastery). To extend the practical utility of CDMs, methods for validating the Q-matrix for CDMs that measure polytomous attributes are needed. This study focuses on validating the Q-matrix of the generalized deterministic input, noisy, “and” gate model for polytomous attributes (pG-DINA). The pGDI, an extension of the G-DINA model discrimination index, is proposed for polytomous attributes. The pGDI serves as the basis of a validation method that can be used not only to identify potential misspecified q-entries, but also to suggest more appropriate attribute-level specifications. The theoretical properties of the pGDI are underpinned by several mathematical proofs, whereas its practical viability is examined using simulation studies covering various conditions. The results show that the method can accurately identify misspecified q-entries and suggest the correct attribute-level specifications, particularly when high-quality items are involved. The pGDI is applied to a proportional reasoning test that measures several polytomous attributes.
      PubDate: 2022-06-01
       
  • A Response-Time-Based Latent Response Mixture Model for Identifying and
           Modeling Careless and Insufficient Effort Responding in Survey Data

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      Abstract: Abstract Careless and insufficient effort responding (C/IER) can pose a major threat to data quality and, as such, to validity of inferences drawn from questionnaire data. A rich body of methods aiming at its detection has been developed. Most of these methods can detect only specific types of C/IER patterns. However, typically different types of C/IER patterns occur within one data set and need to be accounted for. We present a model-based approach for detecting manifold manifestations of C/IER at once. This is achieved by leveraging response time (RT) information available from computer-administered questionnaires and integrating theoretical considerations on C/IER with recent psychometric modeling approaches. The approach a) takes the specifics of attentive response behavior on questionnaires into account by incorporating the distance–difficulty hypothesis, b) allows for attentiveness to vary on the screen-by-respondent level, c) allows for respondents with different trait and speed levels to differ in their attentiveness, and d) at once deals with various response patterns arising from C/IER. The approach makes use of item-level RTs. An adapted version for aggregated RTs is presented that supports screening for C/IER behavior on the respondent level. Parameter recovery is investigated in a simulation study. The approach is illustrated in an empirical example, comparing different RT measures and contrasting the proposed model-based procedure against indicator-based multiple-hurdle approaches.
      PubDate: 2022-06-01
       
  • Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel
           Poisson Regression Model: An Application to Adolescent Alcohol Use with
           Spatial Covariates

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      Abstract: Abstract In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The model partitions individuals’ data into two phases, known as regimes, with: (1) a zero-inflation regime that is used to accommodate high instances of zeros (non-drinking) and (2) a multilevel Poisson regression regime in which variations in individuals’ log-transformed average rates of alcohol use are captured by means of an autoregressive process with exogenous predictors and a person-specific intercept. The times at which individuals are in each regime are unknown, but may be estimated from the data. We assume that the regime indicator follows a first-order Markov process as related to exogenous predictors of interest. The forecast performance of the proposed model was evaluated using a Monte Carlo simulation study and further demonstrated using substance use and spatial covariate data from the Colorado Online Twin Study (CoTwins). Results showed that the proposed model yielded better forecast performance compared to a baseline model which predicted all cases as non-drinking and a reduced ZIMLP model without the RS structure, as indicated by higher AUC (the area under the receiver operating characteristic (ROC) curve) scores, and lower mean absolute errors (MAEs) and root-mean-square errors (RMSEs). The improvements in forecast performance were even more pronounced when we limited the comparisons to participants who showed at least one instance of transition to drinking.
      PubDate: 2022-06-01
       
  • A Systematic Study into the Factors that Affect the Predictive Accuracy of
           Multilevel VAR(1) Models

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      Abstract: Abstract The use of multilevel VAR(1) models to unravel within-individual process dynamics is gaining momentum in psychological research. These models accommodate the structure of intensive longitudinal datasets in which repeated measurements are nested within individuals. They estimate within-individual auto- and cross-regressive relationships while incorporating and using information about the distributions of these effects across individuals. An important quality feature of the obtained estimates pertains to how well they generalize to unseen data. Bulteel and colleagues (Psychol Methods 23(4):740–756, 2018a) showed that this feature can be assessed through a cross-validation approach, yielding a predictive accuracy measure. In this article, we follow up on their results, by performing three simulation studies that allow to systematically study five factors that likely affect the predictive accuracy of multilevel VAR(1) models: (i) the number of measurement occasions per person, (ii) the number of persons, (iii) the number of variables, (iv) the contemporaneous collinearity between the variables, and (v) the distributional shape of the individual differences in the VAR(1) parameters (i.e., normal versus multimodal distributions). Simulation results show that pooling information across individuals and using multilevel techniques prevent overfitting. Also, we show that when variables are expected to show strong contemporaneous correlations, performing multilevel VAR(1) in a reduced variable space can be useful. Furthermore, results reveal that multilevel VAR(1) models with random effects have a better predictive performance than person-specific VAR(1) models when the sample includes groups of individuals that share similar dynamics.
      PubDate: 2022-06-01
       
  • A Lasso and a Regression Tree Mixed-Effect Model with Random Effects for
           the Level, the Residual Variance, and the Autocorrelation

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      Abstract: Abstract Research in psychology is experiencing a rapid increase in the availability of intensive longitudinal data. To use such data for predicting feelings, beliefs, and behavior, recent methodological work suggested combinations of the longitudinal mixed-effect model with Lasso regression or with regression trees. The present article adds to this literature by suggesting an extension of these models that—in addition to a random effect for the mean level—also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After introducing the extended mixed-effect location scale (E-MELS), the extended mixed-effect location-scale Lasso model (Lasso E-MELS), and the extended mixed-effect location-scale tree model (E-MELS trees), we show how its parameters can be estimated using a marginal maximum likelihood approach. Using real and simulated example data, we illustrate how to use E-MELS, Lasso E-MELS, and E-MELS trees for building prediction models to forecast individuals’ daily nervousness. The article is accompanied by an R package (called mels) and functions that support users in the application of the suggested models.
      PubDate: 2022-06-01
       
  • Two Filtering Methods of Forecasting Linear and Nonlinear Dynamics of
           Intensive Longitudinal Data

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      Abstract: Abstract With the advent of new data collection technologies, intensive longitudinal data (ILD) are collected more frequently than ever. Along with the increased prevalence of ILD, more methods are being developed to analyze these data. However, relatively few methods have yet been applied for making long- or even short-term predictions from ILD in behavioral settings. Applications of forecasting methods to behavioral ILD are still scant. We first establish a general framework for modeling ILD and then extend that frame to two previously existing forecasting methods: these methods are Kalman prediction and ensemble prediction. After implementing Kalman and ensemble forecasts in free and open-source software, we apply these methods to daily drug and alcohol use data. In doing so, we create a simple, but nonlinear dynamical system model of daily drug and alcohol use and illustrate important differences between the forecasting methods. We further compare the Kalman and ensemble forecasting methods to several simpler forecasts of daily drug and alcohol use. Ensemble forecasts may be more appropriate than Kalman forecasts for nonlinear dynamical systems models, but further forecasting evaluation methods must be put into practice.
      PubDate: 2022-06-01
       
  • Erratum to: Two Filtering Methods of Forecasting Linear and Nonlinear
           Dynamics of Intensive Longitudinal Data

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      PubDate: 2022-04-13
      DOI: 10.1007/s11336-022-09862-w
       
  • Forecasting Intra-individual Changes of Affective States Taking into
           Account Inter-individual Differences Using Intensive Longitudinal Data
           from a University Student Dropout Study in Math

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      Abstract: Abstract The longitudinal process that leads to university student dropout in STEM subjects can be described by referring to (a) inter-individual differences (e.g., cognitive abilities) as well as (b) intra-individual changes (e.g., affective states), (c) (unobserved) heterogeneity of trajectories, and d) time-dependent variables. Large dynamic latent variable model frameworks for intensive longitudinal data (ILD) have been proposed which are (partially) capable of simultaneously separating the complex data structures (e.g., DLCA; Asparouhov et al. in Struct Equ Model 24:257–269, 2017; DSEM; Asparouhov et al. in Struct Equ Model 25:359–388, 2018; NDLC-SEM, Kelava and Brandt in Struct Equ Model 26:509–528, 2019). From a methodological perspective, forecasting in dynamic frameworks allowing for real-time inferences on latent or observed variables based on ongoing data collection has not been an extensive research topic. From a practical perspective, there has been no empirical study on student dropout in math that integrates ILD, dynamic frameworks, and forecasting of critical states of the individuals allowing for real-time interventions. In this paper, we show how Bayesian forecasting of multivariate intra-individual variables and time-dependent class membership of individuals (affective states) can be performed in these dynamic frameworks using a Forward Filtering Backward Sampling method. To illustrate our approach, we use an empirical example where we apply the proposed forecasting method to ILD from a large university student dropout study in math with multivariate observations collected over 50 measurement occasions from multiple students ( \(N = 122\) ). More specifically, we forecast emotions and behavior related to dropout. This allows us to predict emerging critical dynamic states (e.g., critical stress levels or pre-decisional states) 8 weeks before the actual dropout occurs.
      PubDate: 2022-04-02
      DOI: 10.1007/s11336-022-09858-6
       
  • Control Theory Forecasts of Optimal Training Dosage to Facilitate
           Children’s Arithmetic Learning in a Digital Educational Application

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      Abstract: Abstract Education can be viewed as a control theory problem in which students seek ongoing exogenous input—either through traditional classroom teaching or other alternative training resources—to minimize the discrepancies between their actual and target (reference) performance levels. Using illustrative data from \(n=784\) Dutch elementary school students as measured using the Math Garden, a web-based computer adaptive practice and monitoring system, we simulate and evaluate the outcomes of using off-line and finite memory linear quadratic controllers with constraintsto forecast students’ optimal training durations. By integrating population standards with each student’s own latent change information, we demonstrate that adoption of the control theory-guided, person- and time-specific training dosages could yield increased training benefits at reduced costs compared to students’ actual observed training durations, and a fixed-duration training scheme. The control theory approach also outperforms a linear scheme that provides training recommendations based on observed scores under noisy and the presence of missing data. Design-related issues such as ways to determine the penalty cost of input administration and the size of the control horizon window are addressed through a series of illustrative and empirically (Math Garden) motivated simulations.
      PubDate: 2022-03-15
      DOI: 10.1007/s11336-021-09829-3
       
  • Guest Editors’ Introduction to the Special Issue on Forecasting with
           Intensive Longitudinal Data

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      PubDate: 2022-03-01
      DOI: 10.1007/s11336-022-09850-0
       
 
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