Conditions of Use
This is a very good high-level textbook; I am not sure what knowledge of Statistics is a prerequisite for the target public health students. I feel that there is a need for detail on say the characterization of Big Data, and difference with and to... read more
This is a very good high-level textbook; I am not sure what knowledge of Statistics is a prerequisite for the target public health students. I feel that there is a need for detail on say the characterization of Big Data, and difference with and to small data. There is a need for some literature on survey designs, types of biases, the challenge of confounding, study designs and how to address the limitations or their consequences on actionable decisions, data ethics and ethical reasoning are pivotal for data quality, timeliness, and analysis. How to address the aspects of coverage and response rate, in the cases where they are reported low and the need for representative data.
For a high-level Statistics student, this is ideal for applications to personal data sets. The acknowledgement of the non-representativeness of the datasets, may put a challenge on EDI and stereotype effects on learning. But if this can be shrugged, either by pointing to other smaller representative datasets for practical or homework.
I cherish the use of SAS, and the prospects for STATA, it would also be ideal to use R and acknowledge the active community updating it, with links for updates for software and the datasets.
Great clarity, though there is a need to clarify on the objectives, aims for the different datasets. In section 7.5 there is a need to explain how the weights are being determined.
one can easily flow from chapter to chapter, and the framework is seamless. Terminology needs to be consistent, exemplified by urban/rural then metro/non-metro. Some terms were used without elaboration such as causality, and Taylor-linear methods.
I cherish the consistency in modularity from chapter to chapter, it builds an anticipation of the work to be done.
This is a good practical manual interfaced with literature, way to go in our pursuit for active learning with real and relevant data.
Excellent grammar used here.
Text accommodates for EDI, the acknowledgement of lack of representativeness of the datasets and the efforts to address this through highlighting author's studies with minorities is a perfect salvage.
Covid-19 happened, an acknowledgement of it as a contextual/situational factor and how it may influence future investigations on such datasets may be a good plus, as students have or are already invested in Covid-19 research in public health.
Table of Contents
- About the Publisher
- Accessibility Statement
- About This Project
- 1. Introduction
- 2. Overview of National Health Surveys
- 3. Literature Review
- 4. Basic Data Analysis
- 5. Complex Survey Design Features
- 6. National Health Interview Survey
- 7. Medical Expenditure Panel Survey
- 8. Health Information National Trends Survey
- 9. Behavioral Risk Factor Surveillance System
- 10. National Health and Nutrition Examination Survey
- 11. Dissemination
- 12. Conclusions
- Links by Chapter
- Image Credits
- Errata and Versioning History
- Accessibility Rubric
About the Book
National data sets provide an avenue for students to practice data analytic skills while also answering meaningful research questions. This open education resource was developed to train future public health professionals how to conduct secondary data analysis of national health surveys using SAS statistical software. SAS software was selected because it is one of the most commonly used software programs used among public health departments and academia. The book includes details on how to analyze public-use data from five common national health surveys, including the National Health Interview Survey (NHIS), Medical Expenditure Panel Survey (MEPS), Health Information National Trends Survey (HINTS), Behavior Risk Factor Surveillance System (BRFSS) and National Health and Nutrition and Examination Survey (NHANES). All datasets and corresponding syntax files are available from the Open ICPSR Data Repository.
About the Contributors
Tiffany B. Kindratt, PhD, MPH, is an assistant professor in the Public Health Program, Department of Kinesiology, College of Nursing and Health Innovation at the University of Texas at Arlington. She is Director of the Health Survey Research Laboratory and conducts research focused on predisposing (e.g. race/ethnicity, specifically Arab/Middle Eastern and North African) and enabling (e.g. patient-provider communication, patient experiences) factors that influence individuals’ health behaviors, morbidity, mortality and use of health services across the life course using big data methodologies. She has an extensive background in epidemiologic and large database analysis, Arab/Middle Eastern and North African American health disparities, and training of medical learners. She has 13 years of experience analyzing large databases and complex surveys, including those included in this book. She currently has federal research funding from the National Institutes of Health (National Institute on Aging) and Health Resources and Services Administration and has over 50 manuscripts published in peer reviewed scientific journals.