Pub Date : 2024-07-27DOI: 10.35566/jbds/ogasawara2
Haruhiko Ogasawara
Kristof’s theorem gives the global maximum and minimum of the trace of some matrix products without using calculus or Lagrange multipliers with various applications in psychometrics and multivariate analysis. However, the underutilization has been seen irrespective of its great use in practice. This may partially be due to the lengthy and involved proof of the theorem. In this tutorial, some known or new lemmas are rephrased or provided to understand the essential points in the proof. ten Berge’s generalized Kristof theorem is also addressed. Then, the modified Kristof and ten Berge theorems using parent orthonormal matrices are shown, which may be of use to see the properties of the Kristof and ten Berge theorems.
{"title":"Rephrasing the Lengthy and Involved Proof of Kristof’s Theorem: A Tutorial with Some New Findings","authors":"Haruhiko Ogasawara","doi":"10.35566/jbds/ogasawara2","DOIUrl":"https://doi.org/10.35566/jbds/ogasawara2","url":null,"abstract":"Kristof’s theorem gives the global maximum and minimum of the trace of some matrix products without using calculus or Lagrange multipliers with various applications in psychometrics and multivariate analysis. However, the underutilization has been seen irrespective of its great use in practice. This may partially be due to the lengthy and involved proof of the theorem. In this tutorial, some known or new lemmas are rephrased or provided to understand the essential points in the proof. ten Berge’s generalized Kristof theorem is also addressed. Then, the modified Kristof and ten Berge theorems using parent orthonormal matrices are shown, which may be of use to see the properties of the Kristof and ten Berge theorems.\u0000 ","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":"76 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Katharine Daniel, Robert Moulder, Matthew Southward, Jennifer Cheavens, Steven Boker
Intensive longitudinal data collected via ecological momentary assessment (EMA) are often sampled with unequal time spacing between surveys. Given the popularity of EMA data, it is important to understand whether time series methods are robust to such time interval misspecification. The present study demonstrates via simulation that stability and spread—two metrics for quantifying different aspects of transitioning behavior within multivariate binary time series data—are unbiased when applied to data that are collected along an off/on burst sampling schedule, a between-person random sampling schedule, and a within-person random sampling schedule. These results held in randomly generated data with differing numbers of time series variables (k=10 and k=20) and in data simulated based on the proportions of observed data from a prior EMA study. Further, stability and spread demonstrated approximately 95% coverage for all between- and within-person random sampling schedules. However, coverage for stability and spread was poor in the off/on burst sampling schedules (around 67%). We also applied these transition metrics—which measure repetitiveness and diversity of transitions, respectively—to a foundational EMA dataset that was among the first to show that adults regularly use many different emotion regulation strategies throughout their daily life citep{heiy2014back}. As hypothesized, we found a stronger positive relation between mood and higher stability/lower spread in emotion regulation among people with fewer depressive symptoms than those with more depressive symptoms. Taken together, stability and spread appear to be appropriate metrics to use with data collected using common unequal time spacing conditions and can be used to uncover theoretically consistent insights in real psychosocial data.
通过生态瞬时评估（EMA）收集的密集纵向数据通常是在调查时间间隔不等的情况下采样的。鉴于 EMA 数据的普及性，了解时间序列方法是否对这种时间间隔错误规范具有鲁棒性非常重要。本研究通过仿真证明，稳定性和传播度--这两个用于量化多元二元时间序列数据中过渡行为不同方面的指标--在应用于按照离/开突发抽样计划、人与人之间随机抽样计划和人与人之间随机抽样计划收集的数据时是无偏的。这些结果适用于随机生成的具有不同数量时间序列变量（k=10 和 k=20）的数据，以及基于先前 EMA 研究中观察到的数据比例模拟的数据。此外，在所有人与人之间和人与人之间的随机抽样计划中，稳定性和传播性的覆盖率约为 95%。然而，在关/开突发采样计划中，稳定性和扩散的覆盖率较低（约为 67%）。我们还将这些过渡度量指标--它们分别测量过渡的重复性和多样性--应用于一个基础性的EMA数据集，该数据集是最早显示成年人在日常生活中经常使用多种不同的情绪调节策略的数据集之一（citep{heiy2014back}）。正如假设的那样，我们发现与抑郁症状较多的人相比，抑郁症状较少的人的情绪与情绪调节的较高稳定性/较低分散性之间存在更强的正相关关系。综合来看，稳定性和扩散性似乎是使用常见的不等时间间隔条件收集数据的合适指标，可用于在真实的社会心理数据中发现理论上一致的见解。
{"title":"Stability and Spread: Transition Metrics that are Robust to Time Interval Misspecification","authors":"Katharine Daniel, Robert Moulder, Matthew Southward, Jennifer Cheavens, Steven Boker","doi":"10.35566/jbds/daniel","DOIUrl":"https://doi.org/10.35566/jbds/daniel","url":null,"abstract":"Intensive longitudinal data collected via ecological momentary assessment (EMA) are often sampled with unequal time spacing between surveys. Given the popularity of EMA data, it is important to understand whether time series methods are robust to such time interval misspecification. The present study demonstrates via simulation that stability and spread—two metrics for quantifying different aspects of transitioning behavior within multivariate binary time series data—are unbiased when applied to data that are collected along an off/on burst sampling schedule, a between-person random sampling schedule, and a within-person random sampling schedule. These results held in randomly generated data with differing numbers of time series variables (k=10 and k=20) and in data simulated based on the proportions of observed data from a prior EMA study. Further, stability and spread demonstrated approximately 95% coverage for all between- and within-person random sampling schedules. However, coverage for stability and spread was poor in the off/on burst sampling schedules (around 67%). We also applied these transition metrics—which measure repetitiveness and diversity of transitions, respectively—to a foundational EMA dataset that was among the first to show that adults regularly use many different emotion regulation strategies throughout their daily life citep{heiy2014back}. As hypothesized, we found a stronger positive relation between mood and higher stability/lower spread in emotion regulation among people with fewer depressive symptoms than those with more depressive symptoms. Taken together, stability and spread appear to be appropriate metrics to use with data collected using common unequal time spacing conditions and can be used to uncover theoretically consistent insights in real psychosocial data.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":"8 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141356163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-12DOI: 10.35566/jbds/marcoulides
Katerina M. Marcoulides, Laura Trinchera
A novel algorithmic modeling method is proposed to determine dissimilarities between subjects for longitudinal data clustering using natural cubic smoothing splines. Although various modeling techniques have to date been suggested for conducting such analyses, a major problem with many of these approaches is that they often impose overly restrictive assumptions. As a consequence, potentially problematic interpretations of data clustering regarding both the number and the nature of the growth trajectory patterns can occur. The proposed method is shown to be highly effective in identifying heterogeneity of growth trajectories in settings with data exhibiting complex nonlinear longitudinal patterns and without imposing potentially problematic constraints on the model.
{"title":"A Novel Approach for Identifying Unobserved Heterogeneity in Longitudinal Growth Trajectories Using Natural Cubic Smoothing Splines","authors":"Katerina M. Marcoulides, Laura Trinchera","doi":"10.35566/jbds/marcoulides","DOIUrl":"https://doi.org/10.35566/jbds/marcoulides","url":null,"abstract":"A novel algorithmic modeling method is proposed to determine dissimilarities between subjects for longitudinal data clustering using natural cubic smoothing splines. Although various modeling techniques have to date been suggested for conducting such analyses, a major problem with many of these approaches is that they often impose overly restrictive assumptions. As a consequence, potentially problematic interpretations of data clustering regarding both the number and the nature of the growth trajectory patterns can occur. The proposed method is shown to be highly effective in identifying heterogeneity of growth trajectories in settings with data exhibiting complex nonlinear longitudinal patterns and without imposing potentially problematic constraints on the model.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":"122 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140986514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maya E O'Neil, David C Cameron, Kate Clauss, Danielle Krushnic, William Baker-Robinson, Sara Hannon, Tamara Cheney, Josh Kaplan, Lawrence Cook, Meike Niederhausen, Miranda Pappas, David Cifu
Background: Although posttraumatic stress disorder (PTSD) is common following traumatic brain injury (TBI), the specific associations between these conditions is difficult to elucidate in part due to the diverse methodologies, small samples, and limited longitudinal data in the extant literature. Objective: Conduct a proof-of-concept study demonstrating our ability to compile patient-level TBI data from shared studies in the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System to address these shortcomings and improve our understanding of TBI outcomes including the rates PTSD comorbidity. Method: We searched the FITBIR database for shared studies reporting rates of probable PTSD among participants with no TBI, history of mild TBI, or history of moderate/severe TBI. We merged and harmonized data across the relevant studies and analyzed rates of probable PTSD across TBI history and severity categories. Results: Four FITBIR studies with 2,312 participants included PTSD outcome data. The final sample for comparative analyses comprised 1,633 participants from two studies with TBI group comparison data. Approximately 79% had a history of mild TBI and 32-37% screened positive for probable PTSD. Participants with a history of mild TBI had 2.8 greater odds of probable PTSD compared to those without TBI (95% CI: 2.0, 3.7). Conclusions: Only two FITBIR studies reported data examining PTSD outcomes for mild TBI as of January 2021. The analyses are consistent with prior literature, suggesting mild TBI is associated with higher rates of probable PTSD than no TBI. This study developed the methods, shared the harmonization and analysis code, and publicly shared the TBI and PTSD meta-dataset back to FITBIR for dissemination through their website, allowing future research teams to update these and other, related analyses as more studies are contributed to and shared via the FITBIR platform.
{"title":"A Proof-of-Concept Study Demonstrating How FITBIR Datasets Can be Harmonized to Examine Posttraumatic Stress Disorder-Traumatic Brain Injury Associations","authors":"Maya E O'Neil, David C Cameron, Kate Clauss, Danielle Krushnic, William Baker-Robinson, Sara Hannon, Tamara Cheney, Josh Kaplan, Lawrence Cook, Meike Niederhausen, Miranda Pappas, David Cifu","doi":"10.35566/jbds/oneil","DOIUrl":"https://doi.org/10.35566/jbds/oneil","url":null,"abstract":"Background: Although posttraumatic stress disorder (PTSD) is common following traumatic brain injury (TBI), the specific associations between these conditions is difficult to elucidate in part due to the diverse methodologies, small samples, and limited longitudinal data in the extant literature. \u0000Objective: Conduct a proof-of-concept study demonstrating our ability to compile patient-level TBI data from shared studies in the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System to address these shortcomings and improve our understanding of TBI outcomes including the rates PTSD comorbidity. \u0000Method: We searched the FITBIR database for shared studies reporting rates of probable PTSD among participants with no TBI, history of mild TBI, or history of moderate/severe TBI. We merged and harmonized data across the relevant studies and analyzed rates of probable PTSD across TBI history and severity categories. \u0000Results: Four FITBIR studies with 2,312 participants included PTSD outcome data. The final sample for comparative analyses comprised 1,633 participants from two studies with TBI group comparison data. Approximately 79% had a history of mild TBI and 32-37% screened positive for probable PTSD. Participants with a history of mild TBI had 2.8 greater odds of probable PTSD compared to those without TBI (95% CI: 2.0, 3.7). \u0000Conclusions: Only two FITBIR studies reported data examining PTSD outcomes for mild TBI as of January 2021. The analyses are consistent with prior literature, suggesting mild TBI is associated with higher rates of probable PTSD than no TBI. This study developed the methods, shared the harmonization and analysis code, and publicly shared the TBI and PTSD meta-dataset back to FITBIR for dissemination through their website, allowing future research teams to update these and other, related analyses as more studies are contributed to and shared via the FITBIR platform.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":"3 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140654610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research introduces the concept of the loss aversion distribution, a pioneering framework designed for the analysis of consumer behavior. Departing from the conventions of traditional exponential models, this innovative approach incorporates a non-memoryless characteristic, which modulates the consumer's response to loss aversion throughout the product's life cycle. This modulation is achieved by a variable exponent influenced by the parameter $b$, representing the psychological impact of loss aversion, and the constant $k$, which reflects the market value of the good at the time of manufacture. Together, these parameters adeptly encapsulate the dynamic nature of consumer loss aversion from the moment of manufacture to the point of expiry. The model elucidates an initial muted response from consumers at the onset of ownership, which then intensifies during the mid-life cycle of the product, before ultimately diminishing as the product approaches its expiry. Through a meticulous derivative analysis of the probability density function, the study delineates the distribution's key properties, including its monotonicity, boundedness within the interval [0, 1], and its adherence to non-negativity. This framework not only enhances our comprehension of consumer behavior in relation to perishable goods but also paves the way for further investigations into psychometrics and the intricacies of loss aversion modeling.
{"title":"Loss Aversion Distribution: The Science Behind Loss Aversion Exhibited by Sellers of Perishable Good","authors":"Daniel Koh","doi":"10.35566/jbds/koh","DOIUrl":"https://doi.org/10.35566/jbds/koh","url":null,"abstract":"This research introduces the concept of the loss aversion distribution, a pioneering framework designed for the analysis of consumer behavior. Departing from the conventions of traditional exponential models, this innovative approach incorporates a non-memoryless characteristic, which modulates the consumer's response to loss aversion throughout the product's life cycle. This modulation is achieved by a variable exponent influenced by the parameter $b$, representing the psychological impact of loss aversion, and the constant $k$, which reflects the market value of the good at the time of manufacture. Together, these parameters adeptly encapsulate the dynamic nature of consumer loss aversion from the moment of manufacture to the point of expiry. The model elucidates an initial muted response from consumers at the onset of ownership, which then intensifies during the mid-life cycle of the product, before ultimately diminishing as the product approaches its expiry. Through a meticulous derivative analysis of the probability density function, the study delineates the distribution's key properties, including its monotonicity, boundedness within the interval [0, 1], and its adherence to non-negativity. This framework not only enhances our comprehension of consumer behavior in relation to perishable goods but also paves the way for further investigations into psychometrics and the intricacies of loss aversion modeling.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":" 114","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140385882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This tutorial offers an exploration of advanced Bayesian methodologies for compositional data analysis, specifically the Bayesian Lasso and Bayesian Spike-and-Slab Lasso (SSL) techniques. Our focus is on a novel Bayesian methodology that integrates Lasso and SSL priors, enhancing both parameter estimation and variable selection for linear regression with compositional predictors. The tutorial is structured to streamline the learning process, breaking down complex analyses into a series of straightforward steps. We demonstrate these methods using R and JAGS, employing simulated datasets to illustrate key concepts. Our objective is to provide a clear and comprehensive understanding of these sophisticated Bayesian techniques, preparing readers to adeptly navigate and apply these methods in their own compositional data analysis endeavors.
{"title":"A Tutorial on Bayesian Linear Regression with Compositional Predictors Using JAGS","authors":"Yunli Liu, Xin Tong","doi":"10.35566/jbds/tongliu","DOIUrl":"https://doi.org/10.35566/jbds/tongliu","url":null,"abstract":"This tutorial offers an exploration of advanced Bayesian methodologies for compositional data analysis, specifically the Bayesian Lasso and Bayesian Spike-and-Slab Lasso (SSL) techniques. Our focus is on a novel Bayesian methodology that integrates Lasso and SSL priors, enhancing both parameter estimation and variable selection for linear regression with compositional predictors. The tutorial is structured to streamline the learning process, breaking down complex analyses into a series of straightforward steps. We demonstrate these methods using R and JAGS, employing simulated datasets to illustrate key concepts. Our objective is to provide a clear and comprehensive understanding of these sophisticated Bayesian techniques, preparing readers to adeptly navigate and apply these methods in their own compositional data analysis endeavors.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":"348 5-6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140490852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-10DOI: 10.35566/jbds/v3n2/orourke
Holly O'Rourke, Da Eun Han
Recent work has demonstrated how to calculate conditional mediated effects for mediation models with zero-inflated count outcomes in a non-causal framework (O’Rourke & Vazquez, 2019); however, those formulas do not distinguish between logistic and count portions of the data distribution when calculating mediated effects separately for zeroes and counts. When calculating conditional mediated effects for the counts in a zero-inflated count outcome Y, the b path should use the partial derivative of the log-linear regression equation for X and M predicting Y. When calculating conditional mediated effects for the zeroes, the b path should use the partial derivative of the logistic regression equation for X and M predicting Y instead of the log-linear equation. This paper presents adjustments to the analytical formulas of conditional mediated effects for mediation with zero-inflated count outcomes when zeroes and counts are differentially predicted. Using a Monte Carlo simulation, we also empirically show that these adjustments produce different results than when the distributional form of zeroes is ignored.
{"title":"Considering the Distributional Form of Zeroes When Calculating Mediation Effects with Zero-Inflated Count Outcomes","authors":"Holly O'Rourke, Da Eun Han","doi":"10.35566/jbds/v3n2/orourke","DOIUrl":"https://doi.org/10.35566/jbds/v3n2/orourke","url":null,"abstract":"Recent work has demonstrated how to calculate conditional mediated effects for mediation models with zero-inflated count outcomes in a non-causal framework (O’Rourke & Vazquez, 2019); however, those formulas do not distinguish between logistic and count portions of the data distribution when calculating mediated effects separately for zeroes and counts. When calculating conditional mediated effects for the counts in a zero-inflated count outcome Y, the b path should use the partial derivative of the log-linear regression equation for X and M predicting Y. When calculating conditional mediated effects for the zeroes, the b path should use the partial derivative of the logistic regression equation for X and M predicting Y instead of the log-linear equation. This paper presents adjustments to the analytical formulas of conditional mediated effects for mediation with zero-inflated count outcomes when zeroes and counts are differentially predicted. Using a Monte Carlo simulation, we also empirically show that these adjustments produce different results than when the distributional form of zeroes is ignored.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":" 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135141756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This literature review explores the use of machine learning-based approaches for the diagnosis and treatment of dyslexia, a learning disorder that affects reading and spelling skills. Various machine learning models, such as artificial neural networks (ANNs), support vector machines (SVMs), and decision trees, have been used to classify individuals as either dyslexic or non-dyslexic based on functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data. These models have shown promising results for early detection and personalized treatment plans. However, further research is needed to validate these approaches and identify optimal features and models for dyslexia diagnosis and treatment.
{"title":"Predicting Dyslexia with Machine Learning: A Comprehensive Review of Feature Selection, Algorithms, and Evaluation Metrics","authors":"Velmurugan S","doi":"10.35566/jbds/v3n1/s","DOIUrl":"https://doi.org/10.35566/jbds/v3n1/s","url":null,"abstract":"This literature review explores the use of machine learning-based approaches for the diagnosis and treatment of dyslexia, a learning disorder that affects reading and spelling skills. Various machine learning models, such as artificial neural networks (ANNs), support vector machines (SVMs), and decision trees, have been used to classify individuals as either dyslexic or non-dyslexic based on functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) data. These models have shown promising results for early detection and personalized treatment plans. However, further research is needed to validate these approaches and identify optimal features and models for dyslexia diagnosis and treatment.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47245030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.35566/jbds/v3n1/wyman
Austin Wyman, Zhiyong Zhang
Emotion recognition application programming interface (API) is a recent advancement in computing technology that synthesizes computer vision, machine-learning algorithms, deep-learning neural networks, and other information to detect and label human emotions. The strongest iterations of this technology are produced by technology giants with large, cloud infrastructure (i.e., Google, and Microsoft), bolstering high true positive rates. We review the current status of applications of emotion recognition API in psychological research and find that, despite evidence of spatial, age, and race bias effects, API is improving the accessibility of clinical and educational research. Specifically, emotion detection software can assist individuals with emotion-related deficits (e.g., Autism Spectrum Disorder, Attention Deficit-Hyperactivity Disorder, Alexithymia). API has been incorporated in various computer-assisted interventions for Autism, where it has been used to diagnose, train, and monitor emotional responses to one's environment. We identify AP's potential to enhance interventions in other emotional dysfunction populations and to address various professional needs. Future work should aim to address the bias limitations of API software and expand its utility in subfields of clinical, educational, neurocognitive, and industrial-organizational psychology.
{"title":"API Face Value","authors":"Austin Wyman, Zhiyong Zhang","doi":"10.35566/jbds/v3n1/wyman","DOIUrl":"https://doi.org/10.35566/jbds/v3n1/wyman","url":null,"abstract":"Emotion recognition application programming interface (API) is a recent advancement in computing technology that synthesizes computer vision, machine-learning algorithms, deep-learning neural networks, and other information to detect and label human emotions. The strongest iterations of this technology are produced by technology giants with large, cloud infrastructure (i.e., Google, and Microsoft), bolstering high true positive rates. We review the current status of applications of emotion recognition API in psychological research and find that, despite evidence of spatial, age, and race bias effects, API is improving the accessibility of clinical and educational research. Specifically, emotion detection software can assist individuals with emotion-related deficits (e.g., Autism Spectrum Disorder, Attention Deficit-Hyperactivity Disorder, Alexithymia). API has been incorporated in various computer-assisted interventions for Autism, where it has been used to diagnose, train, and monitor emotional responses to one's environment. We identify AP's potential to enhance interventions in other emotional dysfunction populations and to address various professional needs. Future work should aim to address the bias limitations of API software and expand its utility in subfields of clinical, educational, neurocognitive, and industrial-organizational psychology.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47350702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-13DOI: 10.35566/jbds/v3n1/marvin
Luca Marvin, Haiyan Liu, S. Depaoli
Bayesian growth curve modeling is a popular method for studying longitudinal data. In this study, we discuss a flexible extension, the Bayesian piecewise growth curve model (BPGCM), which allows the researcher to break up a trajectory into phases joined at change points called knots. By fitting BPGCMs, the researcher can specify three or more phases of growth without concern for model identification. Our goal is to provide substantive researchers with a guide for implementing this important class of models. We present a simple application of Bayesian linear BPGCMs to childrens' math achievement. Our tutorial includes Mplus code, strategies for specifying knots, and how to interpret model selection and fit indices. Extensions of the model are discussed.
{"title":"Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories","authors":"Luca Marvin, Haiyan Liu, S. Depaoli","doi":"10.35566/jbds/v3n1/marvin","DOIUrl":"https://doi.org/10.35566/jbds/v3n1/marvin","url":null,"abstract":"Bayesian growth curve modeling is a popular method for studying longitudinal data. In this study, we discuss a flexible extension, the Bayesian piecewise growth curve model (BPGCM), which allows the researcher to break up a trajectory into phases joined at change points called knots. By fitting BPGCMs, the researcher can specify three or more phases of growth without concern for model identification. Our goal is to provide substantive researchers with a guide for implementing this important class of models. We present a simple application of Bayesian linear BPGCMs to childrens' math achievement. Our tutorial includes Mplus code, strategies for specifying knots, and how to interpret model selection and fit indices. Extensions of the model are discussed.","PeriodicalId":93575,"journal":{"name":"Journal of behavioral data science","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43379689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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