Quantitative Psychology
The 88th Annual Meeting of the Psychometric Society, Maryland, USA, 2023
Page
%P
-
Chapter and Conference Paper
Assessing Cross-Level Interactions in Clustered Data Using CATE Estimation Methods
Treatment effect heterogeneity is a critical issue in causal inference, as a one-size-fits-all approach is not sufficient and can even be detrimental for many treatments and interventions. In environments wher...
-
Chapter and Conference Paper
Assessing the Effects of a Yearly Renewable Education Program Through Causal Mediation Analysis
When education programs are renewed yearly, participation in such programs can vary over time, resulting in multiple patterns of participation. One such example is the national Head Start program administered ...
-
Chapter and Conference Paper
Exploring Conceptual Differences Among Nonparametric Estimators of Treatment Heterogeneity in the Context of Clustered Data
One aim of educational research is to evaluate interventions developed to improve student learning and behavioral outcomes. Estimating an intervention’s treatment effect is one way to evaluate its efficacy. Th...
-
Book and Conference Proceedings
-
Article
Open AccessLow diffusion capacity predicts poor prognosis in extensive stage small cell lung cancer: a single-center analysis of 10 years
Poor pulmonary function and chronic obstructive pulmonary disease (COPD) are associated with poorer overall survival (OS) in non-small-cell lung cancer (NSCLC) patients. Few studies have investigated the assoc...
-
Chapter and Conference Paper
Comparing Parametric and Nonparametric Methods for Heterogeneous Treatment Effects
Efforts to estimate treatment effects and draw causal inferences based on observational data are increasingly relevant with the abundance of such data in the social and behavioral sciences. Although the averag...
-
Book and Conference Proceedings
-
Article
Open AccessEvaluating sensitivity to classification uncertainty in latent subgroup effect analyses
Increasing attention is being given to assessing treatment effect heterogeneity among individuals belonging to qualitatively different latent subgroups. Inference routinely proceeds by first partitioning the i...
-
Article
Open AccessPrescribers’ satisfaction with delivering medications for opioid use disorder
Expanding access to medications for opioid use disorder (MOUD), such as buprenorphine and extended release (XR) naltrexone, is critical to addressing the US opioid epidemic, but little is known about prescribe...
-
Chapter and Conference Paper
Where the Choice of Model Leads Us: An Empirical Comparison of Dyadic Data Analysis Frameworks
Dyadic data require unique approaches due to their innate ‘interdependence’. The Actor-Partner Interdependence Model (APIM; Kenny, J Soc Pers Relat 13: 279–294, 1996) and Common Fate Model (CFM; Kenny and La V...
-
Book and Conference Proceedings
-
Article
Open AccessNIATx-TI versus typical product training on e-health technology implementation: a clustered randomized controlled trial study protocol
Substance use disorders (SUDs) lead to tens-of-thousands of overdose deaths and other forms of preventable deaths in the USA each year. This results in over $500 billion per year in societal and economic costs...
-
Article
Open AccessPhysicians’ satisfaction with providing buprenorphine treatment
Buprenorphine is a critically important treatment for addressing the opioid epidemic, but there are virtually no studies of physicians’ job satisfaction with providing buprenorphine. Physicians’ job satisfacti...
-
Chapter and Conference Paper
Specifying Multilevel Mixture Selection Models in Propensity Score Analysis
Causal inference with observational data is challenging, as the assignment to treatment is often not random and people may have different reasons to receive or to be assigned to the treatment. Moreover, the an...
-
Chapter and Conference Paper
Measuring the Heterogeneity of Treatment Effects with Multilevel Observational Data
Multilevel latent class analysis and mixture propensity score models have been implemented to account for heterogeneous selection mechanisms and for proper causal inference with observational multilevel data (...
-
Article
Open AccessTest of a workforce development intervention to expand opioid use disorder treatment pharmacotherapy prescribers: protocol for a cluster randomized trial
Overdoses due to non-medical use of prescription opioids and other opiates have become the leading cause of accidental deaths in the USA. Buprenorphine and extended-release naltrexone are key evidence-based ph...
-
Article
Open AccessLipiodol nanoemulsions stabilized with polyglycerol-polycaprolactone block copolymers for theranostic applications
Polyglycerol is an attractive hydrophilic building block of amphiphilic copolymers for biomedical and pharmaceutical applications due to its biocompatibility, facile chemical modification, and anti-fouling act...
-
Chapter and Conference Paper
Causal Inference with Observational Multilevel Data: Investigating Selection and Outcome Heterogeneity
Causal inference with observational data is challenging, as the assignment to treatment is not random, and people may have different reasons to receive or be assigned to the treatment. The multilevel structure...
-
Article
Open AccessImplementing buprenorphine in addiction treatment: payer and provider perspectives in Ohio
Buprenorphine is under-utilized in treating opioid addiction. Payers and providers both have substantial influence over the adoption and use of this medication to enhance recovery. Their views could provide in...
-
Chapter and Conference Paper
Multilevel Propensity Score Methods for Estimating Causal Effects: A Latent Class Modeling Strategy
Despite their appeal, randomized experiments cannot always be conducted, for example, due to ethical or practical reasons. In order to remove selection bias and draw causal inferences from observational data, ...
