The official journal of the National Association for Healthcare Quality
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July/August 2004 Table of Contents
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Guest Editorial: A Value Proposition: The Marriage of Research and Healthcare Quality
Christy L. Beaudin, PhD LCSW CPHQ; Jacqueline Fowler Byers, PhD RN CNAA CPHQ
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FEATURE ARTICLES
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 Validating a Patient Satisfaction Survey Translated into Spanish
Penny J. Miceli, PhD
Abstract: A measure of patient satisfaction with the inpatient care experience, which was originally developed in English was validated in Spanish, and differences in satisfaction between English-language and Spanish-language respondents were assessed.
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Improving the Quality of Diabetes Care in Primary Care Practice
Janice C. Zgibor, PhD; Harsha Rao, MD; Jacqueline Wesche-Thobaben, BSN RN CDE; Nancie Gallagher, BSN RN CDE; Janis McWilliams, MSN RN CDE; Mary T. Korytkowski, MD
Abstract: This quality improvement project examined the effect of a Diabetes Disease Management Program on compliance with recommended process measures of care.
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Interview with a Quality Leader: Carolyn M. Clancy on the Agency for Healthcare Research and Quality
Peter Lanser, MS CHE CPHQ
Abstract: Carolyn M. Clancy, MD, serves as Director of the Agency for Healthcare Research and Quality in the U.S. Department of Health and Human Services.
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Quality Research Toolbox: CAN’T MISS: Part 4. Confidence Intervals
John P. Hansen, MD MPH
Abstract: Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies, all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics, as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data. The statistical principles are equally applicable to quality improvement and healthcare research studies. This article, Part 4, starts with a review of the information contained in Parts 1, 2, and 3, which appeared in the July/August 2003 issue of the Journal for Healthcare Quality. This article describes t distributions and how these are used to calculate confidence intervals for estimating a population mean based on a sample mean of a continuous variable. Part 4 concludes with
a discussion of standard error, margin of error, and confidence intervals for estimating a population proportion based on a sample proportion from a binomial variable.
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Quality Research Toolbox: CAN’T MISS: Part 5. Comparing Two Confidence Intervals
John P. Hansen, MD MPH
Abstract: Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data. This article, Part 5, demonstrates the comparison of two confidence intervals as a method for estimating the difference between two population means. The concept of the standard error of the difference between two sample means is presented along with the confidence interval for estimating the difference between two population means. The article concludes with the standard error and confidence interval for estimating the difference between two population proportions from a binomial variable.
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Quality Research Toolbox: CAN’T MISS: Part 6. Tests of Statistical Significance
John P. Hansen, MD MPH
Abstract: Healthcare quality professionals need to understand and use inferential statistics to interpret sample data from their organizations. Since in quality improvement and healthcare research studies all the data from a population often are not available, investigators take samples and make inferences about that population using inferential statistics. This series of six articles will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals and tests of statistical significance for samples of data. This article, Part 6, merges the four concepts of the (1) standard error of the difference between sample means, (2) the z test statistic, (3) rejecting the null hypothesis, and (4) the p value to provide a comprehensive view of tests of statistical significance. This is followed by a description of t tests, statistical tests for comparing two sample proportions, and Type I and Type II errors. The series of articles concludes with a description of statistical significance versus meaningful difference.
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Back to 2004 JHQ Table of Contents
Web Exclusives Articles
A Note From the Editor-In-Chief: Welcome!
Variation in Breast Cancer Management in Hawaii: A Survey of Physician Practice
Rebecca P. Gelber, MD; Kenneth N. Sumida, MD; Todd B. Seto, MD MPH
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