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The Interpersonal Theory of Suicide has been foundational in guiding current suicide literature. Despite recent research underscoring fluctuations of suicidal ideation within hours, there have been few studies examining the key constructs of perceived burdensomeness and thwarted belongingness within an intensive framework. Thus, the current study aimed to add cumulative knowledge regarding the within-person relationship between perceived burdensomeness, thwarted belongingness, and active suicidal ideation as assessed within an ecological momentary assessment design.A final sample of 35 individuals with a past-year history of suicidal thoughts or behaviors completed brief surveys four times per day for 30 days.Findings highlighted that the addition of covariates may offer small improvements in modeling subsequent suicidal ideation, while controlling for SI at the prior time. Further, both thwarted belongingness and perceived burdensomeness were associated with next timepoint suicidal ideation, and their interaction added little incremental value.Findings demonstrate the potential importance of thwarted belongingness in predicting suicidal ideation. Further, results highlight that the main effects of thwarted belongingness and perceived burdensomeness, rather than their interaction, may be more important to consider in relation to active suicidal ideation.

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 just the positive cases, or from both the positive and negative cases. Through evaluation in benchmark datasets from computer science, these methods have shown marked improvements in predictive performance when the outcome is imbalanced. However, questions remain regarding generalizability to psychological data. To study this, we implemented a simulation study that tests a number of popular sampling strategies implemented in easy-to-use software, as well as in an empirical example focusing on the prediction of suicidal thoughts. In general, we found that while one sampling strategy demonstrated far worse performance even in comparison to no sampling, the other sampling methods performed similarly, evidencing slight improvements over no sampling. Further, we evaluated the sampling strategies across different forms of cross-validation, model fit metrics, and machine learning algorithms.

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). Participants completed multiple measures of anhedonia. RESULTS: We used rigorous conducted exploratory and confirmatory factor analyses on items from six commonly used anhedonia measures to examine dimensions underlying anhedonia. Results suggested a four-factor solution with factors reflecting social reward, social disinterest, status/achievement, and physical/natural reward. The identified factors reflected broad content domains of pleasure, but not specific reward processes. The four factors were modestly associated with one another, suggesting a weak common underlying anhedonia trait that manifests across multiple dimensions. Factor scores were associated with personality measures, reward-related indices, and depression symptoms, supporting the validity of the factors. LIMITATIONS: Participants were all young adults and we assessed anhedonia only at the level of self-report. CONCLUSION: Anhedonia is a multidimensional construct. However, the dimensions of anhedonia only distinguish domains of, but not temporal processes of anhedonia. Future work should continue to refine the structures underlying the construct of anhedonia through iterative theory- and data-driven research and examine associations between anhedonia and clinical outcomes.

Objective: Interpersonal difficulties are salient among those with a history of NSSI and precede NSSI urges and behaviors. Yet, limited research has focused on identifying which aspects of interpersonal stress may confer risk for NSSI. Method: The current study aimed to leverage data from two samples (combined n=206; n=114 with NSSI history) of participant-driven interviews regarding a recent interpersonal stressor to enhance the field’s knowledge of interpersonal difficulties in relation to NSSI risk. Results: Using topic modeling to extract thematic information, analyses identified four main topics: daily difficulties; family members; adjectives/verbal fillers; and friendship/romantic relationships. Relationships between the topics and three predictors (i.e., NSSI history, emotion dysregulation, sample) were examined. In one sample, the proportion of ‘adjectives/verbal fillers’ was greater for participants with a NSSI history and at higher levels of emotion dysregulation. Across samples, for participants with a NSSI history, ‘adjectives/verbal fillers’ and ‘friendship/romantic partners’ increased with levels of emotion dysregulation. Conclusion: Findings highlight a greater use of adjectives and verbal fillers among individuals with a history of NSSI and higher levels of emotion dysregulation. This pattern of language may serve as an indicator of a specific aspect of emotion regulation difficulties that confers risk for NSSI.

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 the motivation for pursuing novel rewards in a spontaneous manner, and NSSI urge severity. Methods A sample of 64 undergraduates with a history of repetitive NSSI completed an ecological momentary assessment protocol. During this period of time, participants reported on the BAS-constructs of drive, reward responsiveness, and fun-seeking, as well as NSSI urge severity on a momentary basis at three random intervals each day for a period of ten-days. Results Drive and reward responsiveness, but not fun-seeking, were concurrently positively associated with NSSI urge severity. However, no associations between BAS facets and prospective NSSI urges were found. Limitations This study was limited by its use of single items to assess the BAS-constructs of drive, reward responsiveness, and fun-seeking. Conclusions Our findings indicate that feeling strongly impacted by rewards and having a strong sense of drive toward goal attainment may represent cognitive risk states that are associated with increased within-person NSSI risk. However, their lack of prospective prediction may suggest that these cognitive states are associated only on a momentary basis with NSSI urges and may not confer risk.

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 package regsem, which implements regularization for structural equation models. Example R code is also provided to highlight the key arguments of implementing regularized structural equation models in this package. The tutorial ends by discussing remedies of some known drawbacks of a popular type of regularization, computational methods supported by the package that can improve the selection result, and some other practical issues such as dealing with missing data and categorical variables.

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. Missing data can be problematic in DTs because of an inability to place an observation with a missing value into a node based on the chosen splitting variable. Moreover, missing data can alter the selection process because of its inability to place observations with missing values. Simple missing data approaches (e.g., listwise deletion, majority rule, and surrogate split) have been implemented in DT algorithms; however, more sophisticated missing data techniques have not been thoroughly examined. We propose a modified multiple imputation approach to handling missing data in DTs, and compare this approach with simple missing data approaches as well as single imputation and a multiple imputation with prediction averaging via Monte Carlo Simulation. This study evaluated the performance of each missing data approach when data were MAR or MCAR. The proposed multiple imputation approach and surrogate splits had superior performance with the proposed multiple imputation approach performing best in the more severe missing data conditions. We conclude with recommendations for handling missing data in DTs.

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 data, guided by an empirical example of 947 online participants who completed both open-ended items and traditional self-report measures. We give an introduction to a number of text-based statistical approaches, including dictionary-based methods, topic modeling, word embeddings, and deep learning. RESULTS We analyze responses from the open-ended question "How do you feel today?", detailing characteristics of the responses, as well as predicting past-year suicidal ideation. CONCLUSIONS We see the analysis of text from social media, open-ended questions, and other text sources (i.e., medical records) as an important form of complementary assessment to traditional scales, shedding insight on what we are missing in our current set of questionnaires, which may ultimately serve to improve both our understanding and prediction of suicide.

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 a deductive data mining approach that in essence consists of comparing increasingly complex data mining models. The proposed approach is applied to 3 empirical data sets with details on how to interpret each step and model comparison, along with simulations providing a proof of concept. Annotated example code is also provided. Ultimately, the proposed deductive data mining approach provides a novel perspective on exploring interactions and nonlinear effects with the goal of model explanation and confirmation. Limitations of the current approach and future directions are also considered. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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 relationship between physical health and memory across ages 50-87 with Bivariate Dual Change Score Models (BDCSM). Employing a model-comparison approach, we tested whether inclusion of specific directional coupling parameters resulted in a meaningful improvement in model fit, controlling for education, gender, and race. The current sample included 9,103 individuals who participated in Waves 4-11 (1998-2012) of the Health and Retirement Study. Results indicated that both memory and physical health declined across ages 50-87. Furthermore, level of memory at a given time point was positively associated with subsequent change in physical health, meaning higher memory was linked to less decline in physical health by the subsequent time point. The opposite effect, namely physical health predicting memory, was much weaker. Age differences were also evident in the bidirectional coupling model, indicating that old-old individuals (i.e., ages 75-87) exhibited a much stronger coupling effect from memory to change in physical health than younger individuals (i.e., ages 50-74). In conclusion, memory buffers decline in physical health across mid-to-later life, and this effect is especially strong at older ages. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

BackgroundEating-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. This project uses structural equation model (SEM) trees – a form of exploratory data mining – to empirically determine the precise levels of binge-eating frequency and/or shape/weight overvaluation that most significantly differentiate BED severities.MethodsParticipants were 788 adults with BED enrolled in BED treatment studies. Participants completed interviews and self-report measures assessing eating-disorder and comorbid symptoms. SEM Tree analyses were performed by specifying an outcome model of BED severity and then recursively partitioning the outcome model into subgroups. Subgroups were split based on empirically determined values of binge-eating frequency and/or shape/weight overvaluation. SEM Forests also quantified which variable contributed more improvement in model fit.ResultsSEM Tree analyses yielded five subgroups, presented in ascending order of severity: overvaluation ConclusionsShape/weight overvaluation differentiated BED severity more strongly than binge-eating frequency. Findings indicate a nuanced potential BED severity indicator scheme, based on a combination of cognitive and behavioral eating-disorder symptoms. These results inform BED classification and may allow for the provision of more specific and need-matched treatment formulations.

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, and urban areas of Pennsylvania and 12,001 (mean age = 15.79, SD = 1.40) pediatric patients from an urban children's hospital emergency department were included in the analyses. We used two methods of ML (decision trees, random forests) to (a) generate algorithms to classify suicide attempt history, and (b) validate generated algorithms within and across samples to assess model performance. We also employed ridge regression to evaluate performance of the ML approaches. Results Our findings demonstrate that ML approaches did not enhance our ability to classify lifetime or recent suicide attempt history among youth across medical care settings, suggesting that relationships may be mainly linear and non-interactive. In line with prior research, a history of suicide planning, active suicidal ideation, passive suicidal ideation, and nonsuicidal self-injury emerged as relatively important correlates of suicide attempt. Limitations The cross-sectional nature of the current study prevents us from determining the extent to which the important variables identified confer risk for future suicidal behavior. Conclusions The present study underscores the importance of suicide risk screenings that focus on the assessment of active and passive suicidal ideation and suicide planning, in addition to nonsuicidal self-injury, across pediatric medical settings.

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 u...

Concerns of measurement error often motivate researchers to aggregate item information, using simple heuristics (e.g., sum scores) or latent variable methods, to mitigate unwanted effects such as parameter bias and attenuation. These approaches are often invoked without acknowledging that many scales in practice likely fail to possess the necessary properties for these models to be sufficient (i.e., positive conditional association and vanishing conditional dependence). We argue that measures which are not psychometrically homogeneous likely contain item specific effects particularly when examined in conjunction with external variables. We demonstrate this using a clinical empirical example assessing risk factors for suicidal ideation and show that measures constructed in alignment with principles of psychometric homogeneity are most appropriately modeled at the scale (or subscale) level while other measures should be considered at the item level. As a result, latent variable applications to such instruments are susceptible to interpretational confounding. The effects of interpretational confounding on R2, root mean square error, and model parameters are evaluated in a small simulation study. We conclude that item specific effects are not uncommon in practice and impact both explanatory and predictive research. Our findings suggest that classical approaches to addressing measurement error are insufficient to fully capture the breadth of instruments implemented in practice. Careful consideration of both the scale construction process and roles of scale items in the broader psychological theory are necessary prior to the application of traditional measurement methods.

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 ...

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 assessed whether this cognitive vulnerability is also associated with a maladaptive endocrine (e.g., cortisol) response to stress. If shown to be related, individual differences in cognitive style may potentially explain why the literature on the association between cortisol stress reactivity and depression is mixed, as cortisol responses to stress may vary as a function of attributional style. The aim of the present study was to provide a preliminary test of whether cognitive vulnerability was related to cortisol reactivity to an acute laboratory stressor among a sample of young adults (n = 20; Mage = 23.1 years; 10 females). Negative cognitive style and depressive symptoms were assessed via the Cognitive Style Questionnaire and the Patient Health Questionnaire, respectively. All participants also completed the Trier Social Stress Test (TSST). Salivary cortisol was collected before, during, and after the TSST. Results showed a significant association between negative cognitive style and cortisol stress reactivity, such that a greater negative cognitive style was related to a larger cortisol response to the TSST. Post hoc analyses revealed that this association was moderated by gender (i.e., effect observed in males only). Cortisol responses to the TSST, in general, were lower among females, but this relationship was not moderated by cognitive style. These findings may be related to underlying gender differences in stress vulnerability, which may have clinical implications for understanding the interactive effect of cognitive and neuroendocrine processes on vulnerability and resiliency to depression.

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 traject...

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 dependent variables. Simulation results show that XMed can achieve the same sensitivity as more conventional methods for mediation analysis such as the Sobel test, percentile bootstrap, and bias-corrected bootstrap, but in general requires only half the sample size to do so. We demonstrate the implementation of this approach using an illustrative example examining the relationship between youth behavioral/emotional problems and alcohol use.

Psychological science is experiencing a rise in the application of complex statistical models and, simultaneously, 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 constraints. In this paper, I argue that this is less of a conflict, and more a result of a continued adherence to applying the overly simplistic labels of exploratory and confirmatory. These terms mask a distinction between exploratory/confirmatory research practices and modeling. Further, while many researchers recognize that this dichotomous distinction is better represented as a continuum, this only creates additional problems. Finally, I argue that while a focus on preregistration helps clarify the distinction, psychological research would be better off replacing the terms exploratory and confirmatory with additional levels of detail regarding the goals of the study, modeling details, and scientific method.

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 between-person differences in change. We continue to push for greater flexibility in the modeling of individual change and its determinants by combining growth mixture modeling with structural equation modeling trees to evaluate how measured covariates predict class membership using a recursive partitioning algorithm. This approach, referred to as growth mixture modeling with membership trees, is illustrated with longitudinal reading data from the Early Childhood Longitudinal Study with the MplusTrees package in R.

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/weight overvaluation that differentiate OSFED severity. We additionally tested whether the SEM Tree-defined thresholds or a clinical cutoff for shape/weight overvaluation differentiated severity more meaningfully. Methods Participants were 690 females with OSFED presenting to residential ED treatment. SEM Tree analyses specified an outcome model of OSFED severity and then recursively partitioning the outcome model into severity groups. The SEM Tree-defined and clinical cutoff severity groups were compared on clinical characteristics. Results SEM Trees identified one split that occurred at value 5.12 on our shape/weight overvaluation items from the Eating Disorder Examination Questionnaire. The subgroup with higher overvaluation had significantly greater intensity of ED and depressive symptoms and longer lengths of stay. The subgroups created from the shape/weight overvaluation clinical-cut off value of 4 differed on the same clinical characteristics as the SEM Tree-derived groups, with the exception of laxative use frequency. Effect sizes were larger for the clinical cutoff as compared to the SEM Tree severity specification scheme. Limitations These cross-sectional data were used from a predominately white and female residential treatment sample; this likely skewed the subgroups and may limit generalizability. Conclusions Shape/weight overvaluation can meaningfully differentiate OSFED severity. The clinical cutoff slightly outperformed the empirically determined thresholds for shape/weight overvaluation.

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 specific forms that are well-aligned to examine outcomes specific to suicide research. METHOD Ordinal regression models allow for assessment of the influences of covariates on the experience of lower (i.e., suicidal ideation) to higher (i.e., suicidal planning) suicide risk outcomes. RESULTS As an empirical application, we fit a sequential ordinal regression model with 17 theoretically selected covariates and modeled category specific effects for each covariate. CONCLUSIONS Results detailed from depression and presence of nonsuicidal self-injury demonstrate the utility of ordinal regression in consideration of transitions across suicide outcomes. Ordinal regression models may be particularly informative in identifying risk factors unique to each suicide outcome, which has the potential to meaningfully inform theoretical models of suicide and suicide risk prediction.

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 commonly used in social and behavioral research. These methods allow researchers to estimate large models even in the presence of small sample sizes. However, some drawbacks of the LASSO, such as high false positive rates (FPRs) and inconsistency in selection results, persist at the same time. We propose the application of stability selection, a method based on repeated resampling of the data to select stable coefficients, to regularized SEM as a mechanism to overcome these limitations. Across 2 simulation studies, we find that stability selection greatly improves upon the LASSO in selecting the correct paths, specifically through reducing the number of false positives. We close the article by demonstrating the application of stability selection in 2 empirical examples and presenting several future research directions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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, psychologists have studied relationships between these topics and other psychological measures by using estimates of the topics as regression predictors along with other manifest variables. While similar two-stage approaches involving estimated latent variables are known to yield biased estimates and incorrect standard errors, two-stage topic modeling approaches have received limited statistical study and, as we show, are subject to the same problems. To address these problems, we proposed a novel statistical model-supervised latent Dirichlet allocation with covariates (SLDAX)-that jointly incorporates a latent variable measurement model of text and a structural regression model to allow the latent topics and other manifest variables to serve as predictors of an outcome. Using a simulation study with data characteristics consistent with psychological text data, we found that SLDAX estimates were generally more accurate and more efficient. To illustrate the application of SLDAX and a two-stage approach, we provide an empirical clinical application to compare the application of both the two-stage and SLDAX approaches. Finally, we implemented the SLDAX model in an open-source R package to facilitate its use and further study. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

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 response bias in which respondents tend to agree/disagree regardless of item content, using two approaches: acquiescence factor and within-person centering of item-level responses. Across each domain, results indicated both the correlated facets and correlated residuals models demonstrated acceptable fit. Accounting for acquiescent responding was generally associated with improved model fit. However, consistent with past struggles in measuring open-mindedness in adolescents, the correlated residuals model with acquiescence as a factor for open-mindedness failed to converge. Regularized structural equation modeling was conducted on this model for open-mindedness and suggested certain residual covariances that contributed to estimation difficulties should be constrained to zero. Advantages of models are discussed with implications for studying the Big Five personality domains in adolescents. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

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 recruited from an online community, Amazon Mechanical Turk (mTurk). Participants were administered three sets of four distinct single-items assessing suicidal ideation, suicidal planning, and suicide attempt history, respecitvely. Items were drawn from well-known large-scale studies (e.g., National Comorbidity Survey; World Health Organization Mental Health Survey Initiative, Youth Risk Behavior Survey) and commonly used suicide risk assessments (i.e., Self-Injurious Thoughts and Behaviors Interview). Through examinations of intraclass correlations and confirmatory factor analyses, findings suggested mixed response agreement across most outcomes and timeframes. Response inconsistency among items assessing suicidal ideation and among items assessing suicidal planning were partly attributed to minor, yet important, language differences. Given findings that even minor language changes in suicidal ideation and planning items may inflate or restrict prevalence estimates in a meaningful way, it will be important for researchers and clinicians alike to pay close attention to the wording of single items in designing research studies, interpreting findings, and assessing patient risk.

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, such as the Big Five. Here, we examine whether character measures display incremental validity over and above the Big Five Inventory and measures of fluid intelligence when predicting academic performance in a large sample (N = 1,583) of 3rd through 8th grade students. The results indicate that multiple character measures demonstrate incremental prediction, particularly need for cognition (ΔR2=6.5% over personality, ΔR2=2.5% over personality and fluid intelligence) and school value (ΔR2=4.2% over personality). Applying regularization techniques to identify a parsimonious solution, both coefficients for need for cognition and school value remained non-zero and accounted for unique variance. The incremental predictive power of individual character measures was primarily due to common variance across the character measures (ΔR2=11.5% over personality, 9.3% over personality and fluid intelligence). Educationally-contextualized measures have utility for predicting achievement in addition to decontextualized behavioral and ability measures.

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-searching for the variable and the splitting value that maximizes outcome homogeneity. Thus, the algorithm can be overly sensitive to chance associations in the data, particularly in small samples. In an effort to limit chance associations, we propose and evaluate a reliability-based cost function for recursive partitioning. The reliability-based cost function increases the likelihood of selecting variables that are more reliable, which should have more consistent associations with the outcome of interest. Two reliability-based cost functions are proposed, evaluated through simulation, and compared to the CART algorithm. Results indicate that reliability-based cost functions can be beneficial, particularly with smaller samples and when more reliable variables are important to the prediction, but can overlook important associations between the outcome and lower reliability predictors. The use of these cost functions was illustrated using data on depression and suicidal ideation from the National Longitudinal Survey of Youth.

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 thoughts and behaviors. Utilizing an online survey, the current study aimed to better understand the presence, and extent, of the association between COVID-19-related experiences and past-month suicidal thoughts and behaviors among adults in the United States recruited via Amazon Mechanical Turk (n = 907). Results support an association between several COVID-19-related experiences (i.e., general distress, fear of physical harm, effects of social distancing policies) and past-month suicidal ideation and suicide attempts. Further, a significant proportion of those with recent suicidal ideation explicitly link their suicidal thoughts to COVID-19. Exploratory analyses highlight a potential additional link between COVID-19 and suicidal behavior, suggesting that a portion of individuals may be intentionally exposing themselves to the virus with intent to kill themselves. These findings underscore the need for suicide risk screening and access to mental health services during the current pandemic. Particular attention should be paid to employing public health campaigns to disseminate information on such services to reduce the enormity of distress and emotional impairment associated with COVID-19 in the United States.

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 of research. We offer an explanation for this phenomena: namely that poor measurement prevents machine learning algorithms from accurately modeling nonlinear relationships, if they exist. This is showcased across a set of simulated examples, demonstrating that model selection between a machine learning algorithm and regression depends on the measurement quality, regardless of sample size. We conclude with a set of recommendations and a discussion of ways to better integrate machine learning with statistics as traditionally practiced in psychological science.

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 researchers choose to evaluate prediction with machine learning can have large consequences for the substantive conclusions. More specifically, we demonstrate that one method of evaluation that has been used repeatedly in clinical research, the optimism corrected bootstrap, can result in extremely biased results when paired with specific machine learning algorithms. We conclude with providing recommendations for researchers and a discussion of additional best practices.

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 adolescence (N = 4834; 52.1% female; 67.5% Caucasian). Data from the U.S. National Longitudinal Study of Adolescent Health were utilized. Forty-six theoretically-relevant risk factors were explored as potential mediators of this relationship using an exploratory mediation data analytic method. Results Results demonstrated a significant childhood maltreatment - suicide attempt relationship only among females. After considering demographics and suicidal ideation, having received counseling in the previous 12 months was the most influential mediator, followed by having a friend attempt suicide in the previous 12 months. Conclusions These findings highlight potential gender differences in the relationship between childhood maltreatment and later suicide attempts, and, moreover, the importance of assessing for recent exposure to peer suicidal behavior in suicide risk assessments.

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- and intra-individual differences and variability in longitudinal process (Molenaar 2004). The recently published book, Growth Modeling: Structural Equation and Multilevel Modeling Approaches (Grimm, Ram & Estabrook 2017), provides a solid foundation for both beginners and more advanced researchers interested in longitudinal data analysis by juxtaposing both the multilevel and structural equation modeling frameworks for several different models. By providing both sufficient technical background and practical coding examples in a variety of both commercial and open-source software, this book should serve as an excellent reference tool for behavioral and methodological researchers interested in growth modeling.

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’s behavior, there is still a need to identify which constructs may be influential in predicting frequency. The current study aimed to fill this gap by identifying which correlates are most important in relation to NSSI frequency through two exploratory data mining methods. METHOD: Seven hundred twelve undergraduate students with a history of NSSI completed self-report measures of NSSI behavior, suicidality, cognitive-affective deficits, and psychopathology symptomology. RESULTS: Both exploratory data mining methods, lasso regression and random forests, demonstrated number of NSSI methods to be the factor with the most importance in relation to lifetime NSSI frequency. Once this variable was removed, suicide plan and depressive symptomology were significant correlates across methods. CONCLUSIONS: The current findings support the literature between NSSI frequency and NSSI methods, but also implicate suicide plans, an often-overlooked factor, and depression in NSSI severity.

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 have examined the wide range of NSSI characteristics simultaneously when determining which NSSI features are most salient to suicide risk. The current study utilized three exploratory data mining techniques (elastic net regression, decision trees, random forests) to address these gaps in the literature. Undergraduates with a history of NSSI (N = 359) were administered measures assessing demographic variables, depression, and 58 NSSI characteristics (e.g., methods, frequency, functions, locations, scarring) as well as current SI, current SP, and SA history. Results suggested that depressive symptoms and the anti-suicide function of NSSI were the most important features of NSSI for predicting SI and SP. The most important features in predicting SA were the anti-suicide function of NSSI, NSSI-related medical treatment, and scarring. Overall, results suggest that NSSI functions, scarring, and medical lethality may be more important to assess than commonly regarded NSSI severity indices when ascertaining suicide risk.

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, while specifically highlighting more recent developments in Bayesian regularization. This is accomplished through the use of two empirical examples that demonstrate both ridge and lasso approaches across both frequentist and Bayesian estimation, along with detail regarding software implementation. We conclude with a discussion of future research, advocating for increased evaluation and synthesis across both Bayesian and frequentist frameworks.

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 Stan). We briefly describe the approaches used, provide a sample code, and highlight strengths and weaknesses of each.

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 via regularization (XMed) to better address these concerns. We demonstrate that this approach is able to correctly identify mediators more often than conventional approaches and that its estimates are unbiased. Finally, this approach is illustrated through an empirical example examining the relationship between college acceptance and enrollment.

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 criterion of NSSI disorder through the use of an exploratory data mining method, structural equation modeling trees, as a way to determine a NSSI frequency that optimally discriminates pathological NSSI from normative behavior among undergraduate students (n = 3,559), 428 who engaged in NSSI in the previous year. The model included psychopathology symptomology found to be comorbid with NSSI and cognitive-affective deficits commonly associated with NSSI. Results demonstrated a first split between individuals with 0 and 1 act of NSSI in the past year, as was expected. Among individuals with 1 or more previous acts, the optimal split was between those with 5 and 6 NSSI acts in the past year. Results from the current study suggest that individuals with 6 acts of NSSI in past year, compared with those with 5 acts or less, may represent a more severe group of self-injurers. These individuals reported higher levels of related psychopathology symptomology and cognitive-affective deficits, in addition to decreased quality of life. Findings have potential implications for the proposed frequency criteria of NSSI disorder and how pathological NSSI is characterized. (PsycINFO Database Record