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.
International Society for Data Science and Analytics
EmojiSentR: An R Package for Integrated Text and Emoji Sentiment Analysis
Although emojis are used by 92% of the world’s online population, appear in over one quarter of social media posts, and carry affect beyond words, most software packages for sentiment analysis ignore emoji semantics. Our newly developed R package, EmojiSentR, addresses this gap by merging emoji valence with word-level sentiment in a tidy R workflow. The package bundles an internal lexicon derived from the Novak-1200 dataset (1,200 glyphs 8 emotions) and a pipeline function, sentiment_analysis(), that (1) extracts emojis, (2) cleans residual text, (3) scores each channel separately, (4) leverages VADER’s (Valence Aware Dictionary and sEntiment Reasoner) simple negation, and (5) returns a weighted composite score. Furthermore, by adding Unicode-cleanup and Poppler-based PDF-to-text utilities, the package also broadens data-source coverage. In an illustrative example, on a corpus of 17,996 English tweets, EmojiSentR changed the predicted polarity in 20.7% of messages while adding only 769.4 ms of computation time per 1,000 posts. For instance, polarity reversals appeared in sarcastic laughter and uncertainty cues. The EmojiSentR package makes it simple and convenient to treat emojis as first-class sentiment carriers within R. Its modular design supports user-tunable weights, transparent lexicon updates, and forthcoming multilingual extensions, offering researchers a reproducible tool for analyzing emoji-rich text as well as PDF-based data sources.
Copyright (c) 2025 Journal of Behavioral Data Science
Zero-Frequency Cell Correction Strategies in Tetrachoric Correlation Estimation: Expanded Strategies and Multivariate Implications
Zero-frequency cells pose a challenge for tetrachoric correlation estimation, but investigation of correction strategies remains limited. This study evaluates several zero-cell correction strategies, including different values to add, different ways to add the value, and the use of unadjusted versus adjusted thresholds in the second stage in the two-stage procedure. These strategies are examined across different correlation sizes and thresholds, to estimate a single tetrachoric correlation and extended to multivariate applications involving a tetrachoric correlation matrix and a confirmatory factor analysis model for binary data. Using multiple evaluation criteria, we show how these strategies perform differently across correlation sizes and the pattern of thresholds. This study also introduces ways to improve computational efficiency for tetrachoric correlation simulation studies that leverage the discrete structure to reduce redundant computations.
Detecting and Evaluating Bias in Large Language Models: Concepts, Methods, and Challenges
Large Language Models (LLMs) are increasingly deployed in sensitive real-world contexts, yet concerns remain about their biases and the harms they can cause. Existing surveys mostly discuss sources of bias and mitigation techniques, but give less systematic attention to how bias in LLMs should be detected, measured, and reported. This survey addresses that gap. We present a structured review of methods for detecting and evaluating bias in LLMs. We first introduce the conceptual foundations, including representational versus allocational harms and taxonomies of bias. We then discuss how to design evaluations in practice: specifying measurement targets, choosing datasets and metrics, and reasoning about validity and reliability. Building on this, we review intrinsic methods that probe representations and likelihoods, and extrinsic methods that assess bias in classification, question answering, open-ended generation, and dialogue. We further highlight recent advances in counterfactual and certification-based evaluation, which aim to provide stronger guarantees on fairness metrics. Beyond English-centric settings, we survey cross-lingual and application-specific evaluations, intersectional bias analysis, and meta-level issues such as evaluator reliability, metric robustness, reproducibility, and governance. The review concludes by synthesizing best practices and offering a practitioner-oriented checklist, providing both a conceptual map and a practical toolkit for evaluating bias in LLMs.
Computational Approaches to Diabetes Risk Assessment: A Review of Data-Driven Techniques
Over 540 million people worldwide suffer from diabetes mellitus, making it a serious global health concern. The advancement of robust predictive models that surpass traditional risk assessment approaches has demonstrated significant potential due to machine learning techniques. This thorough analysis summarizes the state of the art in machine learning-based diabetes prediction systems by examining algorithmic approaches, dataset properties, and performance indicators. The analysis shows how advanced ensemble and deep learning techniques have replaced more conventional statistical methods in order to achieve better results. Critical drawbacks still exist, nonetheless, such as an excessive dependence on datasets with a restricted demographic, a lack of real-world validation, and inadequate model interpretability for clinical acceptability. Regulatory obstacles, population-specific dataset variability, and discrepancies between algorithmic performance and therapeutic impact are some of the main obstacles. In order to convert advancements into clinically useful systems, future priorities include creating representative datasets, putting explainable artificial intelligence (AI) into practice, and carrying out prospective clinical studies.
Determining Relative Importance with Independent Variable Groups: An Alternative Dominance Analysis Method
Grouping independent variables (IVs) in relative importance analyses like dominance analysis (DA) can reduce computation time and improve analysis feasibility. A side effect of grouping IVs is that determining the importance of individual IVs within groups is not possible. This work proposes an extension of DA where the researcher can group IVs yet still compare individual IVs. The proposed extension extends from Owen values, a variant of the Shapley value solution concept from Cooperative Game Theory. Owen values are used to generate within-group DA statistics and designations which can be used for relative importance analysis of individual IVs in IV groups. This manuscript provides an analytic example with data in the R statistical computing environment that shows how within-group DA statistics and designations are determined. The manuscript concludes by discussing how to group IVs into IV groups and potential extensions to the method to simplify the IV grouping process.
A Guide to Specifying Effects in Latent Change Score Models with Moderated Mediation
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.
More Than a Model: The Compounding Impact of Behavioral Ambiguity and Task Complexity on Hate Speech Detection
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.
Evaluating the Threat of Phantom Faces in Emotion Detection AI through Simulation
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.
A Data Permutation Method for Testing Random Slopes of Bayesian Growth Curves
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.
Copyright (c) 2024 Journal of Behavioral Data Science
Machine Learning Approaches for Depression Detection on Social Media: A Systematic Review of Biases and Methodological Challenges.
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.
An Innovation to Test Treatment X Pretest Interactions within Difference-in-Differences
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.
Exploring the Impact of Social Media Usage and Sports Participation on High School Students’ Mental Health and Academic Confidence
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.
Lord's Paradox Illustrated in Three-Wave Longitudinal Analyses: Cross Lagged Panel Models Versus Linear Latent Growth Models
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.
A Tutorial on Supervised Machine Learning Variable Selection Methods in Classification for the Social and Health Sciences in R
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.
Modeling Data with Measurement Errors but without Predefined Metrics: Fact versus Fallacy
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.
greekLetters: Routines for Writing Greek Letters and Mathematical Symbols on the RStudio and RGui
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.
Extending Latent Basis Growth Model to Explore Joint Development in the Framework of Individual Measurement Occasions
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.
Parallel Nonlinear Longitudinal Processes
Rephrasing the Lengthy and Involved Proof of Kristof’s Theorem: A Tutorial with Some New Findings
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.
Loss Aversion Distribution: The Science Behind Loss Aversion Exhibited by Sellers of Perishable Good
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.
A Tutorial on Bayesian Linear Regression with Compositional Predictors Using JAGS
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.
Stability and Spread: Transition Metrics that are Robust to Time Interval Misspecification
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.
Conducting Meta-analyses of Proportions in R
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.
Copyright (c) 2023 Journal of Behavioral Data Science
Robust Bayesian growth curve modeling: A tutorial using JAGS
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.
A Novel Approach for Identifying Unobserved Heterogeneity in Longitudinal Growth Trajectories Using Natural Cubic Smoothing Splines
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.
Lasso and Group Lasso with Categorical Predictors: Impact of Coding Strategy on Variable Selection and Prediction
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.
A Proof-of-Concept Study Demonstrating How FITBIR Datasets Can be Harmonized to Examine Posttraumatic Stress Disorder-Traumatic Brain Injury Associations
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.
Considering the Distributional Form of Zeroes When Calculating Mediation Effects with Zero-Inflated Count Outcomes
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.
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.
Copyright (c) 2022 Journal of Behavioral Data Science
On Some Known Derivations and New Ones for The Wishart Distribution: A Didactic
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.
Using Bayesian Piecewise Growth Curve Models to Handle Complex Nonlinear Trajectories
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.