Journal of Behavioral Data Science
type: channel
description: ISSN: 2575-8306 (Print) ISSN: 2574-1284 (Online) DOI: 10.35566/jbds The Journal of Behavioral Data Science is a peer-reviewed, open-access journal that aims to provide a free-of-charge-to-publish platform for researchers and practitioners in the area of data science and data analytics. The journal is committed to making high-quality research freely accessible to authors and readers. Publishing in the journal and accessing its content are completely free for both authors and readers. This allows for the widest possible dissemination of research, and promotes interdisciplinary collaboration and innovation in the field of behavioral data science. The average citation per article is 9.1 since the debut of the journal based on Google Scholar. JBDS is indexed by Scopus with the 2025 CiteScore of 4.0.
publisher: International Society for Data Science and Analytics
language: en
publication name: Journal of Behavioral Data Science
issn: 2574-1284
items: A Guide to Specifying Effects in Latent Change Score Models with Moderated Mediation
items: More Than a Model: The Compounding Impact of Behavioral Ambiguity and Task Complexity on Hate Speech Detection
items: Evaluating the Threat of Phantom Faces in Emotion Detection AI through Simulation
items: A Data Permutation Method for Testing Random Slopes of Bayesian Growth Curves
items: Machine Learning Approaches for Depression Detection on Social Media: A Systematic Review of Biases and Methodological Challenges.
items: An Innovation to Test Treatment X Pretest Interactions within Difference-in-Differences
items: Exploring the Impact of Social Media Usage and Sports Participation on High School Students’ Mental Health and Academic Confidence
items: Lord's Paradox Illustrated in Three-Wave Longitudinal Analyses: Cross Lagged Panel Models Versus Linear Latent Growth Models
items: A Tutorial on Supervised Machine Learning Variable Selection Methods in Classification for the Social and Health Sciences in R
items: Modeling Data with Measurement Errors but without Predefined Metrics: Fact versus Fallacy
items: greekLetters: Routines for Writing Greek Letters and Mathematical Symbols on the RStudio and RGui
items: Extending Latent Basis Growth Model to Explore Joint Development in the Framework of Individual Measurement Occasions
items: Rephrasing the Lengthy and Involved Proof of Kristof’s Theorem: A Tutorial with Some New Findings
items: Loss Aversion Distribution: The Science Behind Loss Aversion Exhibited by Sellers of Perishable Good
items: A Tutorial on Bayesian Linear Regression with Compositional Predictors Using JAGS
items: Stability and Spread: Transition Metrics that are Robust to Time Interval Misspecification
items: Conducting Meta-analyses of Proportions in R
items: Robust Bayesian growth curve modeling: A tutorial using JAGS
items: A Novel Approach for Identifying Unobserved Heterogeneity in Longitudinal Growth Trajectories Using Natural Cubic Smoothing Splines
items: Lasso and Group Lasso with Categorical Predictors: Impact of Coding Strategy on Variable Selection and Prediction
items: A Proof-of-Concept Study Demonstrating How FITBIR Datasets Can be Harmonized to Examine Posttraumatic Stress Disorder-Traumatic Brain Injury Associations
items: Considering the Distributional Form of Zeroes When Calculating Mediation Effects with Zero-Inflated Count Outcomes
items: API Face Value
items: On Some Known Derivations and New Ones for The Wishart Distribution: A Didactic
items: Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories
items: Predicting Dyslexia with Machine Learning: A Comprehensive Review of Feature Selection, Algorithms, and Evaluation Metrics
items: Bayesian IRT in JAGS: A Tutorial
items: A Tutorial on Bayesian Analysis of Count Data Using JAGS
items: Handling Ignorable and Non-ignorable Missing Data through Bayesian Methods in JAGS
items: A Tutorial on Bayesian Latent Class Analysis Using JAGS

A Guide to Specifying Effects in Latent Change Score Models with Moderated Mediation
type: item
description: Latent change score (LCS) models are discrete-time longitudinal models that concurrently investigate growth over time and dynamic (lagged) relations among variables. Bivariate LCS models can be extended to multivariate scenarios with mediators and moderators, and mediation paths can be constrained or freely estimated across time. We provide a decision-making guide for model specification based on variable scale of measurement and hypothesized change processes. We then simulate two examples to illustrate how LCS models can be specified to estimate moderated mediation effects where the indirect effect from mediation is conditional upon values of the time-invariant moderator. We provide simulated data and annotated Mplus and R lavaan code.
subject: Theory and Methods
  topic: section
subject: Latent Change
  topic: keywords
subject: Mediation
  topic: keywords
subject: Moderation
  topic: keywords
subject: Moderated Mediation
  topic: keywords
subject: Conditional Indirect Effects
  topic: keywords
subject: Statistics
  topic: subjects
subject: Quantitative Psychology
  topic: subjects
subject: Psychology
  topic: disciplines
creator: Holly O'Rourke
creator: Chanler D. Hilley
rights: Copyright (c) 2025 Journal of Behavioral Data Science
date: 2025-11-03
publication date: 2025-11-03
starting page: 1
ending page: 27
doi: 10.35566/jbds/orourke

More Than a Model: The Compounding Impact of Behavioral Ambiguity and Task Complexity on Hate Speech Detection
type: item
description: The automated detection of hate speech is a critical but difficult task due to its subjective, behavior-driven nature, which leads to frequent annotator disagreement. While advanced models (e.g., transformers) are state-of-the-art, it is unclear how their performance is affected by the methodological choice of label aggregation (e.g., majority vote vs. unanimous agreement) and task complexity. We conduct a 2x2 quasi-experimental study to measure the compounding impact of these two factors: Labeling Strategy (low-ambiguity ``Pure'' data vs. high-ambiguity ``Majority'' data) and Task Granularity (Binary vs. Multi-class). We evaluate five models (Logistic Regression, Random Forest, Light Gradient Boosting Machine [LightGBM], Gated Recurrent Unit [GRU], and A Lite BERT [ALBERT]) across four quadrants derived from the HateXplain dataset. We find that (1) ALBERT is the top-performing model in all conditions, achieving its peak F1-Score (0.8165) on the ``Pure'' multi-class task. (2) Label ambiguity is strongly associated with performance loss; ALBERT's F1-Score drops by $\approx$15.6\% (from 0.8165 to 0.6894) when trained on the higher-disagreement ``Majority'' data in the multi-class setting. (3) This negative effect is compounded by task complexity, with the performance drop being nearly twice as severe for the multi-class task as for the binary task. A sensitivity analysis confirmed this drop is not an artifact of sample size. We conclude that in HateXplain, behavioral label ambiguity is a more significant bottleneck to model performance than model architecture, providing strong evidence for a data-centric approach.
subject: Theory and Methods
  topic: section
subject: Hate Speech Detection
  topic: keywords
subject: Behavioral Data Science
  topic: keywords
subject: Label Ambiguity
  topic: keywords
subject: Transformer Models
  topic: keywords
subject: Text Classification
  topic: keywords
creator: Shuo Xu
creator: Hailiang Wang
creator: Yijun Gao
creator: Yixiang Li
creator: Meng-Ju Kuo
rights: Copyright (c) 2025 Journal of Behavioral Data Science
date: 2025-12-25
publication date: 2025-12-25
starting page: 43
ending page: 63
doi: 10.35566/jbds/xu2025

Evaluating the Threat of Phantom Faces in Emotion Detection AI through Simulation
type: item
description: Emotion detection AI is an emerging tool in the field of psychology that enables researchers to process large batches of images of human faces and obtain estimates of the emotions present within images. Some algorithms, such as Py-Feat, are even capable of detecting multiple faces within an image and providing differential estimates for each face. However, a known problem with multiple detection algorithms is that they sometimes mistakenly detect multiple faces when only a single face exists. In such cases, detection of the true face is still available to users and the false face can be ignored, but there may be artifacts of the false face within the true face that are biasing the estimation of emotions. The present study investigated whether the presence of a second face reduces the accuracy of emotion estimation in the first face. Using 1,438 images from the RAVDESS labeled emotion data set, we generated image with multiple faces under a variety of conditions (i.e., size, opacity, emotion similarity, and number of faces) and compared them against unaltered, single face versions of the images. There were meaningful differences in accuracy across between the single-face and multiple-face images, with similarity and number of faces being the most detrimental conditions for multiple-face accuracy. Findings suggest that it is highly important for researchers to remove extraneous faces within images in order to maximize the accuracy of emotion detection analysis.
subject: Theory and Methods
  topic: section
subject: Emotion Detection
  topic: keywords
subject: Emotion Recognition
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Distortion
  topic: keywords
subject: Phantom Faces
  topic: keywords
subject: Multiple Faces
  topic: keywords
creator: Austin Wyman
creator: Zhiyong Zhang
rights: Copyright (c) 2025 Journal of Behavioral Data Science
date: 2025-08-15
publication date: 2025-08-15
starting page: 28
ending page: 42
doi: 10.35566/jbds/wyman51

A Data Permutation Method for Testing Random Slopes of Bayesian Growth Curves
type: item
description: Growth curve analysis is a popular method for modeling individual development across time. Specifying growth curve models in a Bayesian framework affords researchers the flexibility of including previous information as prior distributions of parameters. However, common choices of prior distribution for modeling slope variance in a Bayesian growth curve framework make determining the existence of meaningful interindividual differences in intraindividual change across time difficult due to boundary values of these priors. Additionally, many current methods are either technically difficult to implement or are sensitive to model specification. We present a simple data permutation method that reliably distinguishes between longitudinal data with individual slope variation and those without slope variation. We show situations in that the proposed data permutation testing outperforms DIC based model comparison through Monte Carlo simulations and apply this data permutation method to data derived from the National Longitudinal Study of Adolescent to Adult Health.
subject: Theory and Methods
  topic: section
subject: Bayesian Growth Curve Modeling
  topic: keywords
subject: Random Slope Testing
  topic: keywords
subject: Permutation Testing
  topic: keywords
subject: Longitudinal Data Analysis
  topic: keywords
creator: Robert Moulder
creator: Xin Tong
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2025-06-25
publication date: 2025-06-25
starting page: 1
ending page: 22
doi: 10.35566/jbds/moulder

Machine Learning Approaches for Depression Detection on Social Media: A Systematic Review of Biases and Methodological Challenges.
type: item
description: The global rise in depression necessitates innovative detection methods for early intervention. Social media provides a unique opportunity to identify depression through user-generated posts. This systematic review evaluates machine learning (ML) models for depression detection on social media, focusing on biases and methodological challenges throughout the ML lifecycle. A search of PubMed, IEEE Xplore, and Google Scholar identified 47 relevant studies published after 2010. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was utilized to assess methodological quality and risk of bias. Significant biases impacting model reliability and generalizability were found. There is a predominant reliance on Twitter (63.8%) and English-language content (over 90%), with most studies focusing on users from the United States and Europe. Non-probability sampling methods (approximately 80%) limit representativeness. Only 23% of studies explicitly addressed linguistic nuances like negations, crucial for accurate sentiment analysis. Inconsistent hyperparameter tuning was observed, with only 27.7% properly tuning models. About 17% did not adequately partition data into training, validation, and test sets, risking overfitting. While 74.5% used appropriate evaluation metrics for imbalanced data, others relied on accuracy without addressing class imbalance, potentially skewing results. Reporting transparency varied, often lacking critical methodological details. These findings highlight the need to diversify data sources, standardize preprocessing protocols, ensure consistent model development practices, address class imbalance, and enhance reporting transparency. By overcoming these challenges, future research can develop more robust and generalizable ML models for depression detection on social media, contributing to improved mental health outcomes globally.
subject: Literature Review
  topic: section
subject: Mental illness
  topic: keywords
subject: Social media
  topic: keywords
subject: Bias evaluation with PROBAST
  topic: keywords
subject: Systematic review
  topic: keywords
subject: Machine learning and deep learning
  topic: keywords
creator: Yuchen Cao
creator: Jianglai Dai
creator: Zhongyan Wang
creator: Yeyubei Zhang
creator: Xiaorui Shen
creator: Yunchong Liu
creator: Yexin Tian
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2025-02-14
publication date: 2025-02-14
starting page: 67
ending page: 102
doi: 10.35566/jbds/caoyc

An Innovation to Test Treatment X Pretest Interactions within Difference-in-Differences
type: item
description: We introduce a way to test Treatment X Pretest interactions within difference-in-differences (DID). Mathematically adding a Treatment X Pretest interaction to DID transforms the treatment estimate to an ANCOVA-type estimate, which differs from DID's estimate and is often biased against at-risk cases. Dual-centered ANCOVA duplicates DID's treatment estimate and can test whether that estimate varies by pretest scores. To illustrate, we test a Treatment X Pretest interaction for the effects of therapy for depression using the Fragile Families and Child Wellbeing longitudinal dataset. After centering posttest and pretest outcome data on pretest group means, DID and ANCOVA estimates are both equivalent to the original DID treatment estimate. ANCOVA of these dual-centered data can then test a Treatment X Pretest interaction.
subject: Theory and Methods
  topic: section
subject: Difference-in-differences
  topic: keywords
subject: Treatment X Pretest interaction
  topic: keywords
subject: Longitudinal analyses
  topic: keywords
subject: Causal validity
  topic: keywords
subject: ANCOVA
  topic: keywords
creator: Robert Larzelere
creator: Hua Lin
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2025-03-04
publication date: 2025-03-04
starting page: 51
ending page: 66
doi: 10.35566/jbds/larzelere

Exploring the Impact of Social Media Usage and Sports Participation on High School Students’ Mental Health and Academic Confidence
type: item
description: This study investigates the effects of sports participation and social media use on high school students' mental health and self-perception, with a focus on understanding their unique contributions to happiness and academic confidence. Structural equation modeling was applied to analyze the relationships between sports participation, time spent on social media, and self-reported levels of happiness and confidence, while accounting for potential gender differences. The results indicate that sports participation is positively associated with happiness, but does not significantly affect academic confidence. In contrast, the use of social media is negatively associated with academic confidence, although it does not significantly impact happiness. Gender differences were observed, with female students reporting a lower level of happiness but a higher level of academic confidence. These findings suggest that while extracurricular activities, such as sports varsity involvement, can support students' well-being, the excessive use of social media apps may undermine their academic confidence.
subject: Application and Case Studies
  topic: section
subject: High school students
  topic: keywords
subject: Social media usage
  topic: keywords
subject: Sport participation
  topic: keywords
subject: Mental health
  topic: keywords
subject: Happiness
  topic: keywords
subject: Academic confidence
  topic: keywords
creator: Yilin Elaine Shan
creator: Xin Tong
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2024-12-04
publication date: 2024-12-04
starting page: 64
ending page: 79
doi: 10.35566/jbds/shan

Lord's Paradox Illustrated in Three-Wave Longitudinal Analyses: Cross Lagged Panel Models Versus Linear Latent Growth Models
type: item
description: Lord’s (1967) paradox showed that two basic ways to analyze change longitudinally can produce contradictory results in 2-occasion nonrandomized studies. This study extends that paradox to difference-score and ANCOVA-type residualized change score analyses across three waves of data for four corrective actions thought to be effective: corrective disciplinary actions by parents (timeout and reasoning) and corrective actions by professionals (psychotherapy and hospitalization). All significant findings indicated that these corrective actions were harmful according to cross-lagged panel models but beneficial according to linear latent growth models. One type of analysis may not generalize to the other type of analysis. These results are consistent with recent recognition that ANCOVA-type analyses are biased by invariant between-person differences, but difference-score analyses can have their own biases. Recognition of these biases is needed to discriminate between stronger and weaker causal evidence in longitudinal analyses.
subject: Theory and Methods
  topic: section
subject: Causal inference
  topic: keywords
subject: Cross-lagged panel analysis
  topic: keywords
subject: Latent growth modeling
  topic: keywords
creator: Hua Lin
creator: Robert Larzelere
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2024-12-18
publication date: 2024-12-18
starting page: 51
ending page: 63
doi: 10.35566/jbds/lin

A Tutorial on Supervised Machine Learning Variable Selection Methods in Classification for the Social and Health Sciences in R
type: item
description: With the increasing availability of large datasets in the behavioral and health sciences, the need for efficient and effective variable selection techniques has grown. While traditional methods like stepwise regression remain prevalent, numerous advanced techniques are available but underutilized in these fields. This tutorial aims to increase awareness and understanding of five variable selection methods available in the popular statistical software R: LASSO, Elastic Net, a penalized SVM classifier, random forest, and the genetic algorithm. Using a recent survey-based assessment dataset on misophonia diagnosis, we provide step-by-step guidance on variables selections and implementation of each method in the context of classification. We discuss the strengths, weaknesses, and performance of each technique, emphasizing the importance of selecting appropriate performance metrics. The associated code and data implemented in this tutorial are available on Open Science Framework and provide an interactive learning experience. We encourage social and health science researchers to adopt these advanced variable selection methods, leading to more robust, interpretable, and impactful models. This paper is written with the assumption that individuals have at least a basic understanding of R.
subject: Tutorials
  topic: section
subject: Machine Learning
  topic: keywords
subject: Variable selection
  topic: keywords
subject: Big data
  topic: keywords
subject: Data classification
  topic: keywords
subject: R
  topic: keywords
creator: Catherine Bain
creator: Dingjing Shi
creator: Yaser Banad
creator: Lauren Ethridge
creator: Jordan Norris
creator: Jordan Loeffelman
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2025-02-28
publication date: 2025-02-28
starting page: 103
ending page: 147
doi: 10.35566/jbds/bain

Modeling Data with Measurement Errors but without Predefined Metrics: Fact versus Fallacy
type: item
description: Data in social and behavioral sciences typically contain measurement errors and also do not have predefined metrics. Structural equation modeling (SEM) is commonly used to analyze such data. This article discuss issues in latent-variable modeling as compared to regression analysis with composite-scores. Via logical reasoning and analytical results as well as the analyses of two real datasets, several misconceptions related to bias and accuracy of parameter estimates, standardization of variables, and result interpretation are clarified. The results are expected to facilitate better understanding of the strength and limitations of SEM and regression analysis with weighted composites, and to advance social and behavioral data science.
subject: Theory and Methods
  topic: section
subject: Measurement error
  topic: keywords
subject: Attenuation
  topic: keywords
subject: Standardization
  topic: keywords
subject: Scales of latent variables
  topic: keywords
creator: Ke-Hai Yuan
creator: Zhiyong Zhang
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2024-08-18
publication date: 2024-08-18
starting page: 1
ending page: 28
doi: 10.35566/jbds/yuan

greekLetters: Routines for Writing Greek Letters and Mathematical Symbols on the RStudio and RGui
type: item
description: This is a brief description of the R package greekLetters. In short, greekLetters is a package for displaying Greek letters and various mathematical symbols in RStudio and RGui environments.
subject: Software
  topic: section
subject: R
  topic: keywords
subject: Statistical notation
  topic: keywords
subject: Mathematical symbols
  topic: keywords
subject: Greek letters
  topic: keywords
creator: Kévin Allan Sales Rodrigues
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2024-08-18
publication date: 2024-08-18
starting page: 80
ending page: 85
doi: 10.35566/jbds/rodrigues

Extending Latent Basis Growth Model to Explore Joint Development in the Framework of Individual Measurement Occasions
type: item
description: Longitudinal processes often exhibit nonlinear change patterns. Latent basis growth models (LBGMs) provide a versatile solution without requiring specific functional forms. Building on the LBGM specification for unequally-spaced waves and individual measurement occasions proposed by Liu and Perera (2023), we extend LBGMs to multivariate longitudinal outcomes. The extended models enable the analysis of nonlinear parallel longitudinal processes with unequally-spaced study waves in the framework of individual measurement occasions. We present the proposed models by simulation studies and real-world data analyses. Simulation studies demonstrate that the proposed model can provide unbiased and accurate estimates with target coverage probabilities for the parameters of interest. Real-world analyses of reading and mathematics scores demonstrate its effectiveness in analyzing joint developmental processes that vary in temporal patterns. Computational code is included.
subject: Theory and Methods
  topic: section
subject: Latent Basis Growth Model
  topic: keywords
subject: Parallel Nonlinear Longitudinal Processes
  topic: keywords
subject: Individual Measurement Occasions
  topic: keywords
subject: Simulation Studies
  topic: keywords
creator: Jin Liu
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2025-01-01
publication date: 2025-01-01
starting page: 1
ending page: 28
doi: 10.35566/jbds/jinliu

Rephrasing the Lengthy and Involved Proof of Kristof’s Theorem: A Tutorial with Some New Findings
type: item
description: 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.
subject: Theory and Methods
  topic: section
subject: von Neumann’s trace inequality
  topic: keywords
subject: Generalized Kristof theorem
  topic: keywords
subject: Suborthonormal
  topic: keywords
subject: Semiorthonormal
  topic: keywords
subject: Singular value decomposition
  topic: keywords
creator: Haruhiko Ogasawara
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2024-07-27
publication date: 2024-07-27
starting page: 29
ending page: 50
doi: 10.35566/jbds/ogasawara2

Loss Aversion Distribution: The Science Behind Loss Aversion Exhibited by Sellers of Perishable Good
type: item
description: 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.
subject: Theory and Methods
  topic: section
subject: Loss aversion
  topic: keywords
subject: Prospect theory
  topic: keywords
subject: Probability theory
  topic: keywords
creator: Daniel Koh
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2024-03-24
publication date: 2024-03-24
starting page: 63
ending page: 80
doi: 10.35566/jbds/koh

A Tutorial on Bayesian Linear Regression with Compositional Predictors Using JAGS
type: item
description: 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.
subject: Tutorials
  topic: section
subject: Bayesian analysis
  topic: keywords
subject: Compositional data
  topic: keywords
subject: Lasso
  topic: keywords
subject: Spike and slab lasso
  topic: keywords
creator: Yunli Liu
creator: Xin Tong
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2024-01-28
publication date: 2024-01-28
starting page: 81
ending page: 104
doi: 10.35566/jbds/tongliu

Stability and Spread: Transition Metrics that are Robust to Time Interval Misspecification
type: item
description: 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 (Heiy & Cheavens, 2014). 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.
subject: Theory and Methods
  topic: section
subject: Time series
  topic: keywords
subject: Ecological momentary assessment
  topic: keywords
subject: Transitions
  topic: keywords
subject: Binary data
  topic: keywords
subject: Emotion regulation
  topic: keywords
subject: Switching
  topic: keywords
creator: Katharine Daniel
creator: Robert Moulder
creator: Matthew Southward
creator: Jennifer Cheavens
creator: Steven Boker
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2024-06-11
publication date: 2024-06-11
starting page: 19
ending page: 44
doi: 10.35566/jbds/daniel

Conducting Meta-analyses of Proportions in R
type: item
description: Meta-analysis of proportions has been widely adopted across various scientific disciplines as a means to estimate the prevalence of phenomena of interest. However, there is a lack of comprehensive tutorials demonstrating the proper execution of such analyses using the R programming language. The objective of this study is to bridge this gap and provide an extensive guide to conducting a meta-analysis of proportions using R. Furthermore, we offer a thorough critical review of the methods and tests involved in conducting a meta-analysis of proportions, highlighting several common practices that may yield biased estimations and misleading inferences. We illustrate the meta-analytic process in five stages: (1) preparation of the R environment; (2) computation of effect sizes; (3) quantification of heterogeneity; (4) visualization of heterogeneity with the forest plot and the Baujat plot; and (5) explanation of heterogeneity with moderator analyses. In the last section of the tutorial, we address the misconception of assessing publication bias in the context of meta-analysis of proportions. The provided code offers readers three options to transform proportional data (e.g., the double arcsine method). The tutorial presentation is conceptually oriented and formula usage is minimal. We will use a published meta-analysis of proportions as an example to illustrate the implementation of the R code and the interpretation of the results.
subject: Tutorials
  topic: section
subject: Meta-analysis of proportions
  topic: keywords
subject: Heterogeneity
  topic: keywords
subject: Meta-regression
  topic: keywords
subject: Double arcsine transformation
  topic: keywords
subject: Baujat plot
  topic: keywords
creator: Naike Wang
rights: Copyright (c) 2023 Journal of Behavioral Data Science
date: 2023-11-07
publication date: 2023-11-07
starting page: 64
ending page: 126
doi: 10.35566/jbds/v3n2/wang

Robust Bayesian growth curve modeling: A tutorial using JAGS
type: item
description: Latent growth curve models (LGCM) are widely used in longitudinal data analysis, and robust methods can be used to model error distributions for non-normal data. This tutorial introduces how to model linear, non-linear, and quadratic growth curve models under the Bayesian framework and uses examples to illustrate how to model errors using t, exponential power, and skew-normal distributions. The code of JAGS models is provided and implemented by the R package runjags. Model diagnostics and comparisons are briefly discussed.
subject: Tutorials
  topic: section
subject: Robust Growth Curve Modeling
  topic: keywords
subject: Bayesian Estimation
  topic: keywords
subject: Structural Equation Modeling
  topic: keywords
subject: JAGS
  topic: keywords
creator: Ruoxuan Li
rights: Copyright (c) 2023 Journal of Behavioral Data Science
date: 2023-09-24
publication date: 2023-09-24
starting page: 43
ending page: 63
doi: 10.35566/jbds/v3n2/li

A Novel Approach for Identifying Unobserved Heterogeneity in Longitudinal Growth Trajectories Using Natural Cubic Smoothing Splines
type: item
description: 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.
subject: Theory and Methods
  topic: section
subject: Unobserved heterogeneity
  topic: keywords
subject: Latent class detection
  topic: keywords
subject: Natural cubic smoothing splines
  topic: keywords
creator: Katerina M. Marcoulides
creator: Laura Trinchera
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2024-05-12
publication date: 2024-05-12
starting page: 1
ending page: 18
doi: 10.35566/jbds/marcoulides

Lasso and Group Lasso with Categorical Predictors: Impact of Coding Strategy on Variable Selection and Prediction
type: item
description: Machine learning methods are being increasingly adopted in behavioral research. Lasso regression performs variable selection and regularization, and is particularly appealing to behavioral researchers because of its connection to linear regression. Researchers may expect properties of linear regression to translate to lasso, but we demonstrate that this assumption is problematic for models with categorical predictors. Specifically, we demonstrate that while the coding strategy used for categorical predictors does not impact the performance of linear regression, it does impact lasso’s performance. Group lasso is an alternative to lasso for models with categorical predictors. We investigate the discrepancy between lasso and group lasso models using a real data set: lasso performs different variable selection and has different prediction accuracy depending on the coding strategy, while group lasso performs consistent variable selection but has different prediction accuracy. Using a Monte Carlo simulation, we demonstrate a specific case where group lasso tends to include many variables when few are needed, leading to overfitting. We conclude with recommended solutions to this issue and future directions of exploration to improve the implementation of machine learning approaches in behavioral science. This project shows that when using lasso and group lasso with categorical predictors, the choice of coding strategy should not be ignored.
subject: Theory and Methods
  topic: section
subject: Lasso regression
  topic: keywords
subject: Categorical predictors
  topic: keywords
subject: Regularization
  topic: keywords
creator: Yihuan Huang
creator: Tristan Tibbe
creator: Amy Tang
creator: Amanda Montoya
rights: Copyright (c) 2023 Journal of Behavioral Data Science
date: 2024-01-26
publication date: 2024-01-26
starting page: 15
ending page: 42
doi: 10.35566/jbds/v3n2/montoya

A Proof-of-Concept Study Demonstrating How FITBIR Datasets Can be Harmonized to Examine Posttraumatic Stress Disorder-Traumatic Brain Injury Associations
type: item
description: 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.
subject: Theory and Methods
  topic: section
subject: Traumatic brain injury
  topic: keywords
subject: Posttraumatic stress disorder
  topic: keywords
subject: Evidence synthesis
  topic: keywords
subject: Data repository
  topic: keywords
subject: Meta-data
  topic: keywords
creator: Maya O'Neil
creator: David Cameron
creator: Kate Clauss
creator: Danielle Krushnic
creator: William Baker-Robinson
creator: Sara Hannon
creator: Tamara Cheney
creator: Josh Kaplan
creator: Lawrence Cook
creator: Meike Niederhausen
creator: Miranda Pappas
creator: David Cifu
rights: Copyright (c) 2024 Journal of Behavioral Data Science
date: 2024-04-25
publication date: 2024-04-25
starting page: 45
ending page: 62
doi: 10.35566/jbds/oneil

Considering the Distributional Form of Zeroes When Calculating Mediation Effects with Zero-Inflated Count Outcomes
type: item
description: 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.
subject: Theory and Methods
  topic: section
subject: Mediation analysis
  topic: keywords
subject: Count outcomes
  topic: keywords
subject: Zero-inflation
  topic: keywords
subject: ZIP
  topic: keywords
subject: ZINB
  topic: keywords
subject: Hurdle models
  topic: keywords
creator: Holly O'Rourke
creator: Da Eun Han
rights: Copyright (c) 2023 Journal of Behavioral Data Science
date: 2023-11-10
publication date: 2023-11-10
starting page: 1
ending page: 14
doi: 10.35566/jbds/v3n2/orourke

API Face Value
type: item
description: 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.
subject: Literature Review
  topic: section
subject: API
  topic: keywords
subject: Emotion Recognition
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: ASD
  topic: keywords
subject: ADHD
  topic: keywords
subject: Alexithymia
  topic: keywords
creator: Austin Wyman
creator: Zhiyong Zhang
rights: Copyright (c) 2022 Journal of Behavioral Data Science
date: 2023-07-13
publication date: 2023-07-13
starting page: 59
ending page: 69
doi: 10.35566/jbds/v3n1/wyman

On Some Known Derivations and New Ones for The Wishart Distribution: A Didactic
type: item
description: The proofs of the probability density function (pdf) of the Wishart distribution tend to be complicated with geometric viewpoints, tedious Jacobians and not self-contained algebra. In this paper, some known proofs and simple new ones for uncorrelated and correlated cases are provided with didactic explanations. For the new derivation of the uncorrelated case, an elementary direct derivation of the distribution of the Bartlett-decomposed matrix is provided. In the derivation of the correlated case from the uncorrelated one, simple methods including a new one are shown.
subject: Theory and Methods
  topic: section
subject: Jacobian
  topic: keywords
subject: Multivariate normality
  topic: keywords
subject: Probability density function (pdf)
  topic: keywords
subject: Triangular matrix
  topic: keywords
subject: Bartlett decomposition
  topic: keywords
creator: Haruhiko Ogasawara
rights: Copyright (c) 2022 Journal of Behavioral Data Science
date: 2023-06-21
publication date: 2023-06-21
starting page: 34
ending page: 58
doi: 10.35566/jbds/v3n1/ogasawara

Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories
type: item
description: 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.
subject: Theory and Methods
  topic: section
subject: Piecewise Growth Curve Models
  topic: keywords
subject: Bayesian SEM
  topic: keywords
subject: Model Selection
  topic: keywords
creator: Luca Marvin
creator: Haiyan Liu
creator: Sarah Depaoli
rights: Copyright (c) 2022 Journal of Behavioral Data Science
date: 2023-07-13
publication date: 2023-07-13
starting page: 1
ending page: 33
doi: 10.35566/jbds/v3n1/marvin

Predicting Dyslexia with Machine Learning: A Comprehensive Review of Feature Selection, Algorithms, and Evaluation Metrics
type: item
description: 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.
subject: Literature Review
  topic: section
subject: SVM
  topic: keywords
subject: EEG
  topic: keywords
subject: Dyslexia
  topic: keywords
creator: Velmurugan S
rights: Copyright (c) 2022 Journal of Behavioral Data Science
date: 2023-07-28
publication date: 2023-07-28
starting page: 70
ending page: 83
doi: 10.35566/jbds/v3n1/s

Bayesian IRT in JAGS: A Tutorial
type: item
description: Item response modeling is common throughout psychology and education in assessments of intelligence, psychopathology, and ability. The current paper provides a tutorial on estimating the two-parameter logistic and graded response models in a Bayesian framework as well as provide an introduction on evaluating convergence and model fit in this framework. Example data are drawn from depression items in the 2017 Wave of the National Longitudinal Survey of Youth and example code is provided for JAGS and implemented through R using the runjags package. The aim of this paper is to provide readers with the necessary information to conduct Bayesian IRT in JAGS.
subject: Tutorials
  topic: section
subject: Logistic Response Model
  topic: keywords
subject: Item Response Theory
  topic: keywords
subject: Bayesian Method
  topic: keywords
subject: JAGS Tutorial
  topic: keywords
creator: Kenneth McClure
rights: Copyright (c) 2022 Journal of Behavioral Data Science
date: 2023-03-27
publication date: 2023-03-27
starting page: 84
ending page: 107
doi: 10.35566/jbds/v3n1/mccure

A Tutorial on Bayesian Analysis of Count Data Using JAGS
type: item
description: In behavioral studies, the frequency of a particular behavior or event is often collected and the acquired data are referred to as count data. This tutorial introduces readers to Poisson regression models which is a more appropriate approach for such data. Meanwhile, count data with excessive zeros often occur in behavioral studies and models such as zero-inflated or hurdle models can be employed for handling zero-inflation in the count data. In this tutorial, we aim to cover the necessary fundamentals for these methods and equip readers with application tools of JAGS. Examples of the implementation of the models in JAGS from within R are provided for demonstration purposes.
subject: Tutorials
  topic: section
subject: Count data
  topic: keywords
subject: Zero-inflation
  topic: keywords
subject: Poisson regression
  topic: keywords
subject: ZIP model
  topic: keywords
subject: Hurdle model
  topic: keywords
creator: Sijing Shao
rights: Copyright (c) 2022 Journal of Behavioral Data Science
date: 2022-12-14
publication date: 2022-12-14
starting page: 156
ending page: 173
doi: 10.35566/jbds/v2n2/shao

Handling Ignorable and Non-ignorable Missing Data through Bayesian Methods in JAGS
type: item
description: With the prevalence of missing data in social science research, it is necessary to use methods for handling missing data. One framework in which data with missing values can still be used for parameter estimation is the Bayesian framework. In this tutorial, different missing data mechanisms including Missing Completely at Random, Missing at Random, and Missing Not at Random are introduced. Methods for estimating models with missing values under the Bayesian framework for both ignorable and non-ignorable missingness are also discussed. A structural equation model on data from the Advanced Cognitive Training for Independent and Vital Elderly study is used as an illustration on how to fit missing data models in JAGS.
subject: Tutorials
  topic: section
subject: Missing Data
  topic: keywords
subject: Bayesian Analysis
  topic: keywords
subject: Structural Equation Modeling
  topic: keywords
creator: Ziqian Xu
rights: Copyright (c) 2022 Journal of Behavioral Data Science
date: 2022-12-13
publication date: 2022-12-13
starting page: 99
ending page: 126
doi: 10.35566/jbds/v2n2/xu

A Tutorial on Bayesian Latent Class Analysis Using JAGS
type: item
description: This tutorial introduces readers to latent class analysis (LCA) as a model-based approach to understand the unobserved heterogeneity in a population. Given the growing popularity of LCA, we aim to equip readers with theoretical fundamentals as well as computational tools. We outline some potential pitfalls of LCA and suggest related solutions. Moreover, we demonstrate how to conduct frequentist and Bayesian LCA in R with real and simulated data. To ease learning, the analysis is broken down into a series of simple steps. Beyond the simple LCA, two extensions including mixed-model LCA and growth curve LCA are provided to aid readers’ transition to more advanced models. The complete R code and data set are provided.
subject: Tutorials
  topic: section
subject: Latent class analysis
  topic: keywords
subject: Mixture models
  topic: keywords
subject: Bayesian analysis
  topic: keywords
creator: Meng Qiu
rights: Copyright (c) 2022 Journal of Behavioral Data Science
date: 2022-12-04
publication date: 2022-12-04
starting page: 127
ending page: 155
doi: 10.35566/jbds/v2n2/qiu