scholarly journals Empirical Bayes MCMC estimation for modeling treatment processes, mechanisms of change, and clinical outcomes in small samples.

2014 ◽  
Vol 82 (5) ◽  
pp. 854-867 ◽  
Author(s):  
Timothy J. Ozechowski
Keyword(s):  
Clinical Outcomes ◽  
Empirical Bayes ◽  
Small Samples ◽  
Mechanisms Of Change ◽  
Treatment Processes ◽  
Mcmc Estimation

scholarly journals Prior sensitivity analysis in default Bayesian structural equation modeling

2017 ◽  
Author(s):  
Sara van Erp ◽  
Joris Mulder ◽  
Daniel L. Oberski
Keyword(s):  
Sensitivity Analysis ◽  
Structural Equation Modeling ◽  
Structural Equation ◽  
Empirical Bayes ◽  
Mean Squared Error ◽  
Small Samples ◽  
Equation Modeling ◽  
Model Parameters ◽  
Ml Estimation ◽  
Bayesian Structural Equation Modeling

Bayesian structural equation modeling (BSEM) has recently gained popularity because it enables researchers to fit complex models while solving some of the issues often encountered in classical maximum likelihood (ML) estimation, such as nonconvergence and inadmissible solutions. An important component of any Bayesian analysis is the prior distribution of the unknown model parameters. Often, researchers rely on default priors, which are constructed in an automatic fashion without requiring substantive prior information. However, the prior can have a serious influence on the estimation of the model parameters, which affects the mean squared error (MSE), bias, coverage rates, and quantiles of the estimates.In this paper, we investigate the performance of three different default priors: noninformative improper priors, vague proper priors, and empirical Bayes priors, with the latter being novel in the BSEM literature. Based on a simulation study, we find that these three default BSEM methods may perform very differently, especially with small samples. A careful prior sensitivity analysis is therefore needed when performing a default BSEM analysis. For this purpose, we provide a practical step-by-step guide for practitioners to conducting a prior sensitivity analysis in default BSEM. Our recommendations are illustrated using a well-known case study from the structural equation modeling literature and all code for conducting the prior sensitivity analysis is made available in the online supplemental material.

Using Loss Functions for DIF Detection: An Empirical Bayes Approach

2000 ◽  
Vol 25 (2) ◽  
pp. 225-247 ◽  
Author(s):  
Rebecca Zwick ◽  
Dorothy T. Thayer ◽  
Charles Lewis
Keyword(s):  
Empirical Bayes ◽  
Loss Functions ◽  
Small Samples ◽  
Type I ◽  
Type Ii ◽  
Classification Rules ◽  
Expected Loss ◽  
Detection Rule ◽  
Type Ii Errors ◽  
Better Than

We investigated a DIF flagging method based on loss functions. The approach builds on earlier research that involved the development of an empirical Bayes (EB) enhancement to Mantel-Haenszel (MH) DIF analysis. The posterior distribution of DIF parameters was estimated and used to obtain the posterior expected loss for the proposed approach and for competing classification rules. Under reasonable assumptions about the relative seriousness of Type I and Type II errors, the loss-function-based DIF detection rule was found to perform better than the commonly used "A, " "B, " and "C" DIF classification system, especially in small samples.

scholarly journals IMPROVED RELIABILITY ESTIMATES FOR SMALL SAMPLES USING EMPIRICAL BAYES TECHNIQUES

2009 ◽  
Vol 2009 (2) ◽  
pp. i-23 ◽  
Author(s):  
Hyeonjoo J. Oh ◽  
Hongwen Guo ◽  
Michael E. Walker
Keyword(s):  
Empirical Bayes ◽  
Small Samples ◽  
Reliability Estimates

Selecting Skills To Teach Communication Partners: Where Do I Start?

2012 ◽  
Vol 21 (4) ◽  
pp. 127-135 ◽  
Author(s):  
Cathy Binger ◽  
Jennifer Kent-Walsh
Keyword(s):  
Clinical Outcomes ◽  
Intervention Program ◽  
Communication Partners ◽  
Multiple Challenges

Abstract Clinicians and researchers long have recognized that teaching communication partners how to provide AAC supports is essential to AAC success. One way to improve clinical outcomes is to select appropriate skills to teach communication partners. Although this sometimes seems like it should be a straightforward component of any intervention program, deciding which skills to teach partners can present multiple challenges. In this article, we will troubleshoot common issues and discuss how to select skills systematically, resulting in the desired effects for both communication partners and clients.

Linguistic Interactions: A Therapeutic Consideration for Adults With Aphasia

2008 ◽  
Vol 17 (3) ◽  
pp. 93-98
Author(s):  
Lynn E. Fox
Keyword(s):  
Clinical Outcomes ◽  
Social Contexts ◽  
Interaction Models ◽  
Social Communities ◽  
Wernicke's Aphasia ◽  
Communication Challenges ◽  
Therapeutic Consideration ◽  
Patterns Of Interaction ◽  
Structural Language

Abstract Linguistic interaction models suggest that interrelationships arise between structural language components and between structural and pragmatic components when language is used in social contexts. The linguist, David Crystal (1986, 1987), has proposed that these relationships are central, not peripheral, to achieving desired clinical outcomes. For individuals with severe communication challenges, erratic or unpredictable relationships between structural and pragmatic components can result in atypical patterns of interaction between them and members of their social communities, which may create a perception of disablement. This paper presents a case study of a woman with fluent, Wernicke's aphasia that illustrates how attention to patterns of linguistic interaction may enhance AAC intervention for adults with aphasia.

The Complexity of Health Disparities: More Than Just Black–White Differences

2021 ◽  
pp. 1-10
Author(s):  
Charles Ellis ◽  
Molly Jacobs
Keyword(s):  
Health Disparities ◽  
Clinical Outcomes ◽  
The United States ◽  
Careful Examination ◽  
White Americans ◽  
Speech Language Pathology ◽  
Death Rates ◽  
The Social ◽  
Health Related ◽  
Language Pathology

Health disparities have once again moved to the forefront of America's consciousness with the recent significant observation of dramatically higher death rates among African Americans with COVID-19 when compared to White Americans. Health disparities have a long history in the United States, yet little consideration has been given to their impact on the clinical outcomes in the rehabilitative health professions such as speech-language pathology/audiology (SLP/A). Consequently, it is unclear how the absence of a careful examination of health disparities in fields like SLP/A impacts the clinical outcomes desired or achieved. The purpose of this tutorial is to examine the issue of health disparities in relationship to SLP/A. This tutorial includes operational definitions related to health disparities and a review of the social determinants of health that are the underlying cause of such disparities. The tutorial concludes with a discussion of potential directions for the study of health disparities in SLP/A to identify strategies to close the disparity gap in health-related outcomes that currently exists.

Debates in Dysphagia Management: How Do You Use Evidence-Based Practice in Your Dysphagia Patient Care?

2011 ◽  
Vol 20 (4) ◽  
pp. 121-123
Author(s):  
Jeri A. Logemann
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Clinical Outcomes ◽  
Clinical Judgment ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Method ◽  
Do So

Evidence-based practice requires astute clinicians to blend our best clinical judgment with the best available external evidence and the patient's own values and expectations. Sometimes, we value one more than another during clinical decision-making, though it is never wise to do so, and sometimes other factors that we are unaware of produce unanticipated clinical outcomes. Sometimes, we feel very strongly about one clinical method or another, and hopefully that belief is founded in evidence. Some beliefs, however, are not founded in evidence. The sound use of evidence is the best way to navigate the debates within our field of practice.

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