SAS Macro BDM for Fitting the Dale Regression Model to Bivariate Ordinal Response Data

A SAS macro for fitting an extension of the Dale (1986) regression model to bivariate ordinal data is provided. The macro is described in detail and examples from Dale (1986) and McMillan, Hanson, Bedrick, and Lapham (2005) are discussed.

The ABC's of the real business cycle

Includes bibliographical references.The real business cycle approach to business cycle theory is viable and worthy of extended research. However, it does not resolve the dilemma facing business cycle theorists in coming up with one true explanation of the cycle. Throughout my research paper, I have clearly defined its shortcomings in terms of the non-recognition of money and a refusal to incorporate aggregate demand into the model. As a new theory, my purpose was to gain an enlightened view of what many feel is a revolutionary new way of business cycle thinking. I started with an explanation of what the theory holds as its premise and detailed the theory in action with the use of a simple real cycle model. My research was confined mainly to current economic periodicals along with a few books by real business cycle proponents. While I am a novice in the field of business cycle studies, I feel my paper offers a generous overview of the real business cycle and is accessible to both the layman and expert as well. Hopefully, the reader will be intrigued by my paper and will find it to be an adequate discussion of business cycle analysis

Perceptions of personalized professional development

The purpose of teacher professional development is to enhance teacher quality so that students may achieve at high levels. Many times, professional development is too general or not connected to teachers’ needs or learning preferences. The purpose of this study was to investigate and compare the perceptions of teachers, administrators, and instructional coaches on personalized professional development (PPD) practices in the Metropolitan School District (MSD) of Warren Township. MSD of Warren Township is a large, mostly urban school district located in central Indiana. After reviewing the literature, the need for this specific research became evident as there were limited quantitative findings available regarding PPD at the national, state, or district levels. Therefore, this study sought to provide research to inform current practice in the district of the study as well as other districts looking to implement PPD. Data for this study were collected using the second version of Learning Forward’s Standards Assessment Inventory (SAI-2). The SAI-2 is an online, anonymous Likert-scale survey tool that was developed based on the seven Learning Forward Professional Learning Standards. The standards are: communities, leadership, resources, data, learning designs, implementation, and outcomes. The results of this study suggested that in most cases, teachers, administrators, and instructional coaches were in agreement regarding the quality of the PPD being delivered in MSD of Warren Township. Although no statistically significant differences in perceptions about PPD were revealed between the groups, the results still provided important information for those in MSD Warren charged with creating high quality, effective, PPD. Implications for practice included recommendations for planning and improving PPD programs at the district and school level.Thesis (D. Ed.)Department of Educational Leadershi

DPpackage: Bayesian Semi- and Nonparametric Modeling in R

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.

spBayesSurv: Fitting Bayesian Spatial Survival Models Using R

Spatial survival analysis has received a great deal of attention over the last 20 years due to the important role that geographical information can play in predicting survival. This paper provides an introduction to a set of programs for implementing some Bayesian spatial survival models in R using the package spBayesSurv. The function survregbayes includes the three most commonly-used semiparametric models: proportional hazards, proportional odds, and accelerated failure time. All manner of censored survival times are simultaneously accommodated including uncensored, interval censored, current-status, left and right censored, and mixtures of these. Left-truncated data are also accommodated. Time-dependent covariates are allowed under the piecewise constant assumption. Both georeferenced and areally observed spatial locations are handled via frailties. Model fit is assessed with conditional Cox-Snell residual plots, and model choice is carried out via the log pseudo marginal likelihood, the deviance information criterion and the WatanabeAkaike information criterion. The accelerated failure time frailty model with a covariatedependent baseline is included in the function frailtyGAFT. In addition, the package also provides two marginal survival models: proportional hazards and linear dependent Dirichlet process mixtures, where the spatial dependence is modeled via spatial copulas. Note that the package can also handle non-spatial data using non-spatial versions of the aforementioned models

Multimodality during fixation – Part II: Evidence for multimodality in spatial precision-related distributions and impact on precision estimates

This paper is a follow-on to our earlier paper (Friedman, Lohr, Hanson, & Komogortsev, 2021), which focused on the multimodality of angular offsets.  This paper applies the same analysis to the measurement of spatial precision.  Following the literature, we refer these measurements as estimates of device precision, but, in fact, subject characteristics clearly affect the measurements.  One typical measure of the spatial precision of an eye-tracking device is the standard deviation (SD) of the position signals (horizontal and vertical) during a fixation.  The SD is a highly interpretable measure of spread if the underlying error distribution is unimodal and normal. However, in the context of an underlying multimodal distribution, the SD is less interpretable. We will present evidence that the majority of such distributions are multimodal (68-70% strongly multimodal).  Only 21-23% of position distributions were unimodal. We present an alternative method for measuring precision that is appropriate for both unimodal and multimodal distributions.  This alternative method produces precision estimates that are substantially smaller than classic measures.  We present illustrations of both unimodality and multimodality with either drift or a microsaccade present during fixation.  At present, these observations apply only to the EyeLink 1000, and the subjects evaluated herein