Applied Statistics Commons^™

The Validity Of Online Patient Ratings Of Physicians, Jennifer L. Priestley, Yiyun Zhou, Robert Mcgrath

Jennifer L. Priestley

Background: Information from ratings sites are increasingly informing patient decisions related to health care and the selection of physicians.

Objective: The current study sought to determine the validity of online patient ratings of physicians through comparison with physician peer review.

Methods: We extracted 223,715 reviews of 41,104 physicians from 10 of the largest cities in the United States, including 1142 physicians listed as “America’s Top Doctors” through physician peer review. Differences in mean online patient ratings were tested for physicians who were listed and those who were not.

Results: Overall, no differences were found between the online patient ratings based …

Counting The Impossible: Sampling And Modeling To Achieve A Large State Homeless Count, Jennifer L. Priestley, Jane Massey

Jennifer L. Priestley

Objective: Using inferential statistics, we develop estimates of the homeless population of a geographically large and economically diverse state -- Georgia.

Methods: Multiple independent data sources (2000 U.S. Census, the 2006 Georgia County Guide, Georgia Chamber of Commerce) were used to develop Clusters of the 150 Georgia Counties. These clusters were used as "strata" to then execute traified sampling. Homeless counts were conducted within the sample counties, allowing for multiple regression models to be developed to generate predictions of homeless persons by county.

Results: In response to a mandate from the US Department of Housing and Urban Development, the State …

Network Structure And Inter-Organizational Knowledge Sharing Capability, Samaddar Subhashish, Jennifer Priestley

Jennifer L. Priestley

No abstract is currently available.

Knowledge Transfer In Multi-Organizational Networks: Influence Of Causal And Outcome Ambiguities, Jennifer Priestley

Jennifer L. Priestley

Informed by the general concept of ambiguity related to knowledge transfer, we first identify and develop the concept of outcome ambiguity as to explain the ambiguity related to inter-organizational knowledge transfer among network firms, which, we argue, is not addressed by the well-established concept of causal ambiguity [34] [46]. Based upon this discussion, we develop the first two of our six hypotheses. Subsequently, we discuss two types of inter-organizational networks and how causal ambiguity and outcome ambiguity would be expected to behave within these network types. This discussion will form the basis for the remaining four of our six hypotheses. …

Model Development Techniques And Evaluation Methods For Prediction And Classification Of Consumer Risk In The Credit Industry, Jennifer Priestley, Satish Nargundkar

Jennifer L. Priestley

In this chapter, we examine and compare the most prevalent modeling techniques in the credit industry, Linear Discriminant Analysis, Logistic Analysis and the emerging technique of Neural Network modeling. K-S Tests and Classification Rates are typically used in the industry to measure the success in predictive classification. We examine those two methods and a third, ROC Curves, to determine if the method of evaluation has an influence on the perceived performance of the modeling technique. We found that each modeling technique has its own strengths, and a determination of the “best” depends upon the evaluation method utilized and the costs …

Assessment Of Model Development Techniques And Evaluation Methods For Binary Classification In The Credit Industry, Satish Nargundkar, Jennifer Priestley

Jennifer L. Priestley

We examine and compare the most prevalent modeling techniques in the credit industry, Linear Discriminant Analysis, Logistic Analysis and the emerging technique of Neural Network modeling. K-S Tests and Classification Rates are typically used in the industry to measure the success in predictive classification. We examine those two methods and a third, ROC Curves, to determine if the method of evaluation has an influence on the perceived performance of the modeling technique. We found that each modeling technique has its own strengths, and a determination of the “best” depends upon the evaluation method utilized and the costs associated with misclassification.

Absorptive Capacity, Causal Ambiguity And Outcome Ambiguity: The Network Effect And Knowledge Transfer Difficulty Among Four Network Forms, Subhashish Samaddar, Jennifer Priestley

Jennifer L. Priestley

No abstract is currently available.