[PDF] Advanced Statistics For Physical And Occupational Therapy eBook
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Advanced Statistics for Physical and Occupational Therapy explains the basis for statistical analyses that are commonly used to answer clinical research questions related to physical and occupational therapy. This textbook provides a resource to help students and faculty in physical and occupational therapy graduate programs understand the basis for common statistical analyses and be able to apply these techniques in their own research. This textbook provides readers with the basis for common statistical analyses, including t-tests, analysis of variance, regression, and nonparametric tests. Each chapter includes step-by-step tutorials with corresponding example data sets explaining how to conduct these statistical analyses using Statistical Package for the Social Sciences (SPSS) software and the Excel Analysis ToolPak, as well as how to identify and interpret relevant output and report results. Advanced Statistics for Physical and Occupational Therapy is key reading for students in physical therapy, occupational therapy, sport performance, and sport rehabilitation graduate programs as well as students in athletic training courses, applied statistics in sport, and research methods in sport modules. This new text will also be of interest to practicing clinicians who hope to better understand the research they are reading and/or are interested in starting to conduct their own clinical research.
Advanced Statistics for Physical and Occupational Therapy explains the basis for statistical analyses that are commonly used to answer clinical research questions related to physical and occupational therapy. This textbook provides a resource to help students and faculty in physical and occupational therapy graduate programs understand the basis for common statistical analyses and be able to apply these techniques in their own research. This textbook provides readers with the basis for common statistical analyses, including t-tests, analysis of variance, regression, and nonparametric tests. Each chapter includes step-by-step tutorials with corresponding example data sets explaining how to conduct these statistical analyses using Statistical Package for the Social Sciences (SPSS) software and the Excel Analysis ToolPak, as well as how to identify and interpret relevant output and report results. Advanced Statistics for Physical and Occupational Therapy is key reading for students in physical therapy, occupational therapy, sport performance, and sport rehabilitation graduate programs as well as students in athletic training courses, applied statistics in sport, and research methods in sport modules. This new text will also be of interest to practicing clinicians who hope to better understand the research they are reading and/or are interested in starting to conduct their own clinical research.
This book presents new and powerful advanced statistical methods that have been used in modern medicine, drug development, and epidemiology. Some of these methods were initially developed for tackling medical problems. All 29 chapters are self-contained. Each chapter represents the new development and future research topics for a medical or statistical branch. For the benefit of readers with different statistical background, each chapter follows a similar style: the explanation of medical challenges, statistical ideas and strategies, statistical methods and techniques, mathematical remarks and background and reference. All chapters are written by experts of the respective topics.
An easy-to-read book that deals with the more advanced concepts necessary for a good understanding of statistics. It makes the subject accessible to students while covering the subject at a level required for the practicing professional.
Author : National Center for Health Statistics (U.S.) Publisher : Page : 108 pages File Size : 24,22 MB Release : 1974 Category : Health surveys ISBN :
Physical (PT) and Occupational Therapists (OT) appear to be in over- or undersupply in proportion to the adults at the county and state levels in the US. The supply of PT/OT services can be more precisely optimized if the utilization of PT/OT could be predicted based on characteristics of an adult. Prior studies in prediction analyses for the utilization of PT/OT services are either outdated or limited by sampling. With publicly available survey data on national, yearly samples of US adults, there is an opportunity to address this gap in knowledge. This opportunity can be better leveraged with the emergent methods of data mining or machine learning that use computation in combination with statistics. Data mining can allow for future automation of forecasts for the utilization of PT/OT and consequently provide a dynamic support for business and policy decisions to optimize the supply of PT/OT services. Therefore, the aim of this dissertation is to build and validate machine learning models to predict the use of PT/OT services in the US adult population using publicly available survey data. Methods: Using the 2012 National Health Interview Survey (NHIS) data on US adults (n = 34,083), logistic regression, neural network, and decision tree were initially trained and compared for the prediction of whether a sampled adult used PT/OT services. Seeking further gains in generalizability of predictive modeling, averaged models based on ensemble theory were built and compared next. These models included decision tree variants that use bootstrapping (bagging, random forest) and gradient boosting. Stability of explanatory variables was examined across models and variables important for prediction were identified. Finally, the best of these models were empirically tested on NHIS samples of 2013 (n = 34,296) and 2014 (n = 36,359) for their predictive accuracy. Results: Models built on 2012 data showed promising Receiver Operator Characteristic Curve Indexes ranging from 0.722 to 0.823. The best model was the ensemble model that averaged logistic regression, neural network, and decision tree. This model performed consistently well when empirically tested for 2013 (misclassification rate = 9.32%) and 2014 (10.35%) data as well, though there were only small differences across models overall. Important input variables that were significant for their predictive association with the use of PT/OT across more than half of the models included having seen a medical specialist, higher numbers of office visits, being hospitalized, having health problem that requires special equipment, higher frequency of strength activity, surgery, joint pain/aching/stiffness, difficulty standing 2 hours without special equipment, difficulty pushing large objects without special equipment, and having low back pain. Conclusions: The data mining approach deploying multiple-models and model averaging to predict whether an adult will use PT/OT can potentially be translated to practice to support business and policy decisions toward optimizing the supply of PT/OT services to the needs of the population units in the US. Future research may explore local-level data sources like Electronic Health Records, consistent with privacy protection laws, to drive prediction analyses.
Abstract: Service-learning as a pedagogy has gained in popularity and utilization on college campuses from the late 1980's through the 1990's. Based upon principles advanced by Dewey and other learning theorists in the early 20th century, service- learning is a form of experiential learning. Service-learning experiences are a balance of both student learning and service to the community. Accreditation standards for both physical therapy (PT) and occupational therapy (OT) programs include requirements that these programs provide opportunities for students to develop cultural competence, appreciation for different styles of thinking and lifestyle choices, and the ability to accept and meet the needs of a changing society. Research into the effectiveness of service-learning is critical in the fields of PT and OT to determine if accreditation standards are being met and if graduating practitioners are equipped to meet the needs of our changing societies. The research conducted was a descriptive study utilizing existing data collected by the Service Learning Initiative Program at a large Midwestern university. Data regarding change in beliefs and opinions, as reported on a 7-point Likert Scale, of OT and PT graduate students following a service-learning experience was evaluated. Data analysis involved descriptive statistics, including: percentages, means, and standard deviations. Pre-course and post-course differences were evaluated using paired t-tests. Differences in responses of male and female, OT and PT students and students with and without previous service experiences were assessed with t-tests. As data from three different quarters of this course was utilized in the study, a 2-way ANOVA with repeated measures was performed to evaluate responses by quarter, verifying that there was no untoward effect of history. Statistically significant change was observed on questions regarding student comfort in working closely with people from different racial, ethnic or cultural groups, students' belief that they have a deep understanding of people with backgrounds different from their own, and student understanding of the needs of the community in which service was provided. The difference between OT and PT students on the question of students' belief that they have a deep understanding of people with backgrounds different from their own was statistically significant, with OT students rating themselves more positively than PT students. No statistical significance was found in comparing responses of students with previous service experience and those without previous experience, although the means were higher for students with previous experience on four of five of the research questions. In comparing responses of males and females, on the question of student comfort in working with different "others," the mean for males was .30 higher than that for females. While not statistically significant, this finding is in contrast to previous research. Students engaged in this service-learning experience reported greater comfort in working with persons different from themselves, felt they had a deeper understanding of the people with backgrounds different from their own, and reported a greater understanding of the needs of the community in which service was provided.