Ross Jacobucci

Ross Jacobucci
University of Notre Dame | ND · Department of Psychology

Doctor of Philosophy

About

72
Publications
23,891
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970
Citations
Additional affiliations
August 2013 - present
University of Southern California
Position
  • PhD Student

Publications

Publications (72)
Article
A new method is proposed that extends the use of regularization in both lasso and ridge regression to structural equation models. The method is termed regularized structural equation modeling (RegSEM). RegSEM penalizes specific parameters in structural equation models, with the goal of creating easier to understand and simpler models. Although regu...
Article
Research suggesting nonsuicidal self-injury (NSSI) may belong in a distinct diagnostic category has led to the inclusion of NSSI disorder in the DSM–5 section for future study. There has been limited research, however, examining the validity of Criterion A (the frequency criterion). The current study aimed to examine the validity of the frequency c...
Article
In psychological research, class imbalance in binary outcome variables is a common occurrence, particularly in clinical variables (e.g., suicide outcomes). Class imbalance can present a number of difficulties for inference and prediction, prompting the development of a number of strategies that perform data augmentation through random sampling from...
Article
Background Although no severity specifiers are noted in the Diagnostic and Statistical Manual of Mental Disorders – 5 for other specified feeding or eating disorder (OSFED), shape/weight overvaluation is a proposed eating disorder (ED) severity specifier. We used structural equation modeling (SEM) Trees to empirically determine values of shape/weig...
Article
Background : Anhedonia has long been theorized to be a multidimensional construct, focusing on domains of reward stimuli and temporal relationship to reward. However, little empirical work has directly examined whether there is support for this assertion. Methods : The study used data from young adults from four independent samples (n = 2098). Par...
Article
The present study aimed to extend prior literature on single-item assessment by examining response consistency (1) between several commonly used single-item assessments of suicidal ideation, planning, and attempts, and (2) across three timeframes (past month, past year, and lifetime) commonly employed in the literature. Participants (N = 613) were...
Article
Sparse estimation through regularization is gaining popularity in psychological research. Such techniques penalize the complexity of the model and could perform variable/path selection in an automatic way, and thus are particularly useful in models that have small parameter-to-sample-size ratios. This paper gives a detailed tutorial of the R packag...
Article
Background The current study aimed to examine the concurrent and prospective relationships between the three hypothesized components of behavioral approach system (BAS) sensitivity: drive, reflecting the motivation to pursue one's desired goals; reward responsiveness, reflecting sensitivity to reward or reinforcement; and fun-seeking, reflecting th...
Preprint
Psychological science has seen a rise in the application of complex statistical models to ever larger datasets, while at the same time, had a renewed focus on applying research in a confirmatory manner. This presents a fundamental conflict for psychological researchers as more complex forms of modeling necessarily eschew as stringent of theoretical...
Article
Over the past 40 years there have been great advances in the analysis of individual change and the analyses of between-person differences in change. While conditional growth models are the dominant approach, exploratory models, such as growth mixture models and structural equation modeling trees, allow for greater flexibility in the modeling of bet...
Article
Background The COVID-19 pandemic has spurred the implementation of several public safety measures to contain virus spread, most notably socially distancing policies. Prior research has linked similar public safety measures (i.e., quarantine) with suicide risk, in addition to supporting the role of social connection in suicidal thoughts and behavior...
Article
Decision trees (DTs) is a machine learning technique that searches the predictor space for the variable and observed value that leads to the best prediction when the data are split into two nodes based on the variable and splitting value. The algorithm repeats its search within each partition of the data until a stopping rule ends the search. Missi...
Article
Suicide rates among military-connected populations have increased over the past 15 years. Meta-analytic studies indicate prediction of suicide outcomes is lacking. Machine-learning approaches have been promoted to enhance classification models for suicide-related outcomes. In the present study, we compared the performance of three primary machine-l...
Article
Objective As recent advances in suicide research have underscored the importance of studying distinct suicide outcomes (i.e., suicidal thinking vs. behavior), there is a need to consider the theoretical meaningfulness of our statistical approach(es). As an alternative to more popular statistical methods, we introduce ordinal regression, detailing s...
Article
Objective Text‐based responses may provide significant contributions to suicide risk prediction, yet research including text data is limited. This may be due to a lack of exposure and familiarity with statistical analyses for this data structure. Method The current study provides an overview of data processing and statistical algorithms for text d...
Article
Regularization methods such as the least absolute shrinkage and selection operator (LASSO) are commonly used in high dimensional data to achieve sparser solutions. Recently, methods such as regularized structural equation modeling (SEM) and penalized likelihood SEM have been proposed, trying to transfer the benefits of regularization to models comm...
Preprint
Full-text available
Text is a burgeoning data source for psychological researchers, but little methodological research has focused on adapting popular modeling approaches for text to the context of psychological research. One popular measurement model for text, topic modeling, uses a latent mixture model to represent topics underlying a body of documents. Recently, ps...
Article
The use of machine learning is increasing in clinical psychology, yet it is unclear whether these approaches enhance the prediction of clinical outcomes. Several studies show that machine-learning algorithms outperform traditional linear models. However, many studies that have found such an advantage use the same algorithm, random forests with the...
Article
Evidence suggests that the negative consequences of COVID-19 may extend far beyond its considerable death toll, having a significant impact on psychological well-being. Despite work highlighting the link between previous epidemics and elevated suicide rates, there is limited research on the relationship between the COVID-19 pandemic and suicidal th...
Article
Individuals typically experience changes in physical health and cognitive ability across the life span. Although these constructs dynamically relate to one another, the temporal ordering of dynamic changes in physical health and cognitive ability is not well-established. Therefore, we examined the temporal ordering of the dynamic, bidirectional rel...
Preprint
Student characteristics like grit, need for cognition, intellectual self-concept, mastery orientation, school value, and growth mindset are important predictors of academic achievement. Yet, it remains unclear to what extent these proposed measures provide additional theoretical or empirical utility over established measures of general personality,...
Article
We examined the within-domain structure of the five domains of personality measured by the Big Five Inventory-2 with data collected from an adolescent sample (N = 838). Three possible factor models were tested: a single factor, correlated facets, and a single factor with correlated residuals. We examined each model controlling for acquiescence, a r...
Article
Full-text available
Suicide is a serious public health problem; however, suicides are preventable with timely, evidence-based interventions. Social media platforms have been serving users who are experiencing real-time suicidal crises with hopes of receiving peer support. To better understand the helpfulness of peer support occurring online, this study characterizes t...
Article
Full-text available
Background Eating-disorder severity indicators should theoretically index symptom intensity, impairment, and level of needed treatment. Two severity indicators for binge-eating disorder (BED) have been proposed (categories of binge-eating frequency and shape/weight overvaluation) but have mixed empirical support including modest clinical utility. T...
Article
Machine learning (i.e., data mining, artificial intelligence, big data) has been increasingly applied in psychological science. Although some areas of research have benefited tremendously from a new set of statistical tools, most often in the use of biological or genetic variables, the hype has not been substantiated in more traditional areas of re...
Article
Recursive partitioning, also known as decision trees and classification and regression trees (CART), is a machine learning procedure that has gained traction in the behavioral sciences because of its ability to search for nonlinear and interactive effects, and produce interpretable predictive models. The recursive partitioning algorithm is greedy—s...
Preprint
Evidence suggests that the negative consequences of COVID-19 may extend far beyond its considerable death toll, having a significant impact on psychological well-being. Prior work has highlighted that previous epidemics are linked to elevated suicide rates, however, there is no research to date on the relationship between the COVID-19 pandemic and...
Article
Structural equation model trees (SEM Trees) allow for the construction of decision trees with structural equation models fit in each of the nodes. Based on covariate information, SEM Trees can be used to create distinct subgroups containing individuals with similar parameter estimates. Currently, the structural equation modeling component of SEM Tr...
Preprint
Machine learning (i.e., data mining, artificial intelligence, big data) has seen an increase in application in psychological science. Although some areas of research have benefited tremendously from a new set of statistical tools, most often in the use of biological or genetic variables, the hype has not been substantiated in more traditional areas...
Preprint
Regularization methods such as the least absolute shrinkage and selection operator (LASSO) are commonly used in high dimensional data to achieve sparser solutions. They are also becoming increasingly popular in social and behavioral research. Recently methods such as regularized structural equation modeling (SEM) and penalized likelihood SEM have b...
Preprint
Full-text available
Machine learning is being utilized at an increasing rate in clinical psychology. Applying machine learning comes with a number of challenges, both in deciding which algorithms to test, and how to evaluate the predictive performance. We focus on this last component, demonstrating across both a simulation and empirical example that the method researc...
Article
Background : The current study aimed to classify recent and lifetime suicide attempt history among youth presenting to medical settings using machine learning (ML) as applied to a behavioral health screen self-report survey. Methods : In the current study, 13,325 (mean age = 17.06, SD = 2.61) pediatric primary care patients from rural, semi-urban,...
Article
Data mining methods offer a powerful tool for psychologists to capture complex relations such as interaction and nonlinear effects without prior specification. However, interpreting and integrating information from data mining models can be challenging. The current research proposes a strategy to identify nonlinear and interaction effects by using...
Article
******** Limited number of free copies: https://www.tandfonline.com/eprint/ZUITGIZPWBFPI6H6SG7Y/full?target=10.1080/10705511.2019.1693273 ******* Correct detection of measurement bias could help researchers revise models or refine psychological scales. Measurement bias detection can be viewed as a variable-selection problem, in which biased items...
Article
Full-text available
According to the hopelessness theory of depression, some individuals have a cognitive vulnerability (i.e., negative cognitive style) that interacts with stressful life events to produce depression. A negative cognitive style is associated with a maladaptive cognitive response to stress (i.e., increased negative attributions); however, no study has...
Article
Objectives: Future time perspective (FTP), or the way individuals orient to and consider their futures, is fundamental to motivation and well-being across the life span. There is a relative paucity of studies, however, that explore its contributing factors in mid-to-later life, specifically. Therefore, uncovering which variables contribute to indi...
Article
Full-text available
Objective: Extant literature has generally conceptualized nonsuicidal self-injury severity in terms of its frequency, although more recently researchers have assessed nonsuicidal self-injury severity by the number of different methods used. There is limited evidence, however, regarding the interaction of these indices in the prediction of clinical...
Preprint
Data mining methods offer a powerful tool for psychologists to capture complex relations such as interaction and nonlinear effects without prior specification. However, interpreting and integrating information from data mining models can be challenging. The current research proposes a strategy to identify nonlinear and interaction effects by using...
Article
The latent change score framework allows for estimating a variety of univariate trajectory models, such as the no change, linear change, exponential forms of change, as well as multivariate trajectory models that allow for coupling between two or more constructs. A particularly attractive feature of these models is that it is easy to decompose and...
Article
Nonsuicidal self-injury (NSSI) is a growing public health concern, and there is an increasing need to better characterize and identify severe NSSI behavior. One readily accessible, yet understudied, avenue for improving the assessment of NSSI severity is through the examination of individual forms, or methods, of the behavior. The present study aim...
Article
Exploratory mediation analysis via regularization, or XMed, is a recently developed technique that allows one to identify potential mediators of a process of interest. However, as currently implemented, it can only be applied to continuous outcomes. We extend this method to allow application to dichotomous outcomes, including both mediators and dep...
Article
Full-text available
Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real-world behavioral data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This diff...
Article
Full-text available
Behavioral researchers have shown growing interest in structural equation model trees (SEM Trees), a new recursive partitioning-based technique for detecting population heterogeneity. In the present research, we conducted a large-scale simulation to investigate the performance of latent growth curve model (LGCM)-based SEM Trees for uncovering betwe...
Article
Objective: The NSSI disorder diagnostic criteria have been the focus of empirical study. However, criterion A (i.e., required frequency and timeframe), has received relatively limited attention. The current study aimed to examine the relationship between past 12-month NSSI frequency and eight NSSI behavior features among individuals with past 12-mo...
Article
Introduction: Suicide is a major public health concern. One consistently cited risk factor for suicide is childhood maltreatment, which also may play a role in the transition from suicidal ideation to suicidal behavior. Method: The current study aimed to examine the relationship between childhood maltreatment and suicide attempts during adolescenc...
Article
Background: Machine learning techniques offer promise to improve suicide risk prediction. In the current systematic review, we aimed to review the existing literature on the application of machine learning techniques to predict self-injurious thoughts and behaviors (SITBs). Method: We systematically searched PsycINFO, PsycARTICLES, ERIC, CINAHL, an...
Article
Research questions that address developmental processes are becoming more prevalent in psychology and other areas of social science. Growth models have become a popular tool to model multiple individuals measured over several time points. These types of models allow researchers to answer a wide variety of research questions, such as modeling inter-...
Article
In this article, we introduce nonlinear longitudinal recursive partitioning (nLRP) and the R package longRpart2 to carry out the analysis. This method implements recursive partitioning (also known as decision trees) in order to split data based on individual- (i.e., cluster) level covariates with the goal of predicting differences in nonlinear long...
Preprint
Full-text available
Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real world behavioural data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This pos...
Article
Individuals with a history of non-suicidal self-injury (NSSI) are at alarmingly high risk for suicidal ideation (SI), planning (SP), and attempts (SA). Given these findings, research has begun to evaluate the features of this multi-faceted behavior that may be most important to assess when quantifying risk for SI, SP, and SA. However, no studies ha...
Preprint
Full-text available
Decision Trees (DT), one method falling under the umbrella of Exploratory Data Mining (EDM; McArdle & Ritschard, 2013), has seen an increasing amount of use in psychological research. This can be attributed to its easily interpretable tree structure, along with its propensity to automatically capture interactions among predictors. However, the crea...
Article
Full-text available
Research in regularization, as applied to structural equation modeling (SEM), remains in its infancy. Specifically, very little work has compared regularization approaches across both frequentist and Bayesian estimation. The purpose of this study was to address just that, demonstrating both similarity and distinction across estimation frameworks, w...
Article
Full-text available
In this software review, we provide a brief overview of four R functions to estimate nonlinear mixed-effects programs: nlme (linear and nonlinear mixed-effects model), nlmer (from the lme4 package, linear mixed-effects models using Eigen and S4), saemix (stochastic approximation expectation maximization), and brms (Bayesian regression models using...
Article
Full-text available
Objective. Non-suicidal self-injury (NSSI) has been linked to many adverse outcomes, with more frequent NSSI increasing the likelihood of impairment, severity, and more serious self-harming behavior (e.g., suicidality; Andover & Gibb, 2010; Darke et al., 2010). Despite the determined importance of NSSI frequency in understanding the severity of one...
Article
As previously examined using dynamic longitudinal models (Jacobucci, Grimm, & Zelinski, in preparation) to study the trajectory of both cognition and health in the Health and Retirement Study, change often takes on a nonlinear form. Studying change using structural equation models allows for individual differences in both intercepts and slopes, how...
Article
Exploratory mediation analysis refers to a class of methods used to identify a set of potential mediators of a process of interest. Despite its exploratory nature, conventional approaches are rooted in confirmatory traditions, and as such have limitations in exploratory contexts. We propose a two-stage approach called exploratory mediation analysis...
Article
Full-text available
The regsem package in R, an implementation of regularized structural equation mod-eling (RegSEM; Jacobucci, Grimm, and McArdle 2016), was recently developed with the goal of incorporating various forms of penalized likelihood estimation in a broad array of structural equations models. The forms of regularization include both the ridge (Hoerl and Ke...
Article
Although finite mixture models have received considerable attention, particularly in the social and behavioral sciences, an alternative method for creating homogeneous groups, structural equation model trees (Brandmaier, von Oertzen, McArdle, & Lindenberger, 2013), is a recent development that has received much less application and consideration. I...
Article
The current study examined how age of non-suicidal self-injury (NSSI) onset relates to NSSI severity and suicidality using decision tree analyses (nonparametric regression models that recursively partition predictor variables to create groupings). Those with an earlier age of NSSI onset reported greater NSSI frequency, NSSI methods, and NSSI-relate...
Code
Goes over the basics of using the autoSEM package in R. autoSEM implements a number of heuristic search algorithms to modify and generate confirmatory factor analysis models.
Conference Paper
Full-text available
A new method is proposed that extends the use of regularization in both lasso and ridge regression to structural equation models. The method is termed regularized structural equation modeling (RegSEM). RegSEM penalizes specific parameters in structural equation models, with the goal of creating easier to understand and simpler models. Although regu...
Article
In this article, we describe a recent development in the analysis of attrition: using classification and regression trees (CART) and random forest methods to generate inverse sampling weights. These flexible machine learning techniques have the potential to capture complex nonlinear, interactive selection models, yet to our knowledge, their perform...
Article
Full-text available
Risk behavior in adolescence is widely researched and is an important focus for researchers, federal and state agencies, and schools; however, goals differ across these settings and as such the measurement strategies must also differ. Schools require more frequent and efficient measures that better align with educational goals of increasing positiv...

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