Ten keys to successful NCQA accreditation: a health plan perspective.
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Biomedical subjects
John P Hansen
Publications and source records attributed to John P Hansen.
CAN'T MISS--conquer any number task by making important statistics simple. Part 1. Types of variables, mean, median, variance, and standard deviation.
Healthcare quality improvement professionals need to understand and use inferential statistics to interpret sample data from their organizations. In quality improvement and healthcare research studies all the data from a population often are not available, so investigators take samples and make inferences about the population by using inferential statistics. This three-part series will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals for samples of data. This article, Part 1, presents basic information about data including a classification system that describes the four major types of variables: continuous quantitative variable, discrete quantitative variable, ordinal categorical variable (including the binomial variable), and nominal categorical variable. A histogram is a graph that displays the frequency distribution for a continuous variable. The article also demonstrates how to calculate the mean, median, standard deviation, and variance for a continuous variable.
Analysis of Variance↗
CAN'T MISS--conquer any number task by making important statistics simple. Part 2. Probability, populations, samples, and normal distributions.
Healthcare quality improvement professionals need to understand and use inferential statistics to interpret sample data from their organizations. In quality improvement and healthcare research studies all the data from a population often are not available, so investigators take samples and make inferences about the population by using inferential statistics. This three-part series will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals for samples of data. This article, Part 2, describes probability, populations, and samples. The uses of descriptive and inferential statistics are outlined. The article also discusses the properties and probability of normal distributions, including the standard normal distribution.
Data Interpretation, Statistical↗
CAN'T MISS--conquer any number task by making important statistics simple. Part 3. Standard error, estimation, and confidence intervals.
Healthcare quality improvement professionals need to understand and use inferential statistics to interpret sample data from their organizations. In quality improvement and healthcare research studies all the data from a population often are not available, so investigators take samples and make inferences about the population by using inferential statistics. This three-part series will give readers an understanding of the concepts of inferential statistics as well as the specific tools for calculating confidence intervals for samples of data. This article, Part 3, describes standard error and margin of error for a continuous variable and how they are calculated from the sample size and standard deviation of a sample. The article then demonstrates how the standard error and margin of error are used to calculate the confidence interval for estimating a population mean based on a sample mean.
Analysis of Variance↗
Can't miss: conquer any number task by making important statistics simple. Part 4. Confidence intervals with t distributions, standard error, and confidence intervals for proportions.
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.
Confidence Intervals↗
Can't miss: conquer any number task by making important statistics simple. Part 5. Comparing two confidence intervals, standard error of the difference between two means and between two proportions.
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.
Confidence Intervals↗
Can't miss: conquer any number task by making important statistics simple. Part 6. Tests of statistical significance (z test statistic, rejecting the null hypothesis, p value), t test, z test for proportions, statistical significance versus meaningful difference.
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.
Confidence Intervals↗
CAN'T MISS: conquer any number task by making important statistics simple. Part 7. Statistical process control: x-s control charts.
Statistical process control (SPC) can be thought of as the frequent monitoring of processes using inferential statistics. The feature that distinguishes SPC from the typical use of inferential statistics for analyzing populations is that in the former frequent samples are taken over time, whereas in inferential statistics a single sample is generaLLy taken before and after some intervention or treatment. An x-s control chart is used to monitor a continuous variable that reflects the output of a process. The x-s control chart is a graph that includes serial sample means (x) as the variables of interest, a centerline that represents the grand mean of the samples (x), and upper control limit (UCL) and lower control limit (LCL) that represent three standard errors (SEx) above and below the centerline. An x-s control chart is used to estimate with 99.7% confidence that the population mean of a continuous output variable was within the interval defined by the UCL and LCL during a period of baseline monitoring. It is further assumed that if the process remains stable, future population means wiLL remain between the control Limits for additional process outputs. Control charts allow the evaluation of both common- and special-cause variation. AnaLysis of the common-cause variation aLLows an assessment of the current process performance. Special-cause variation is identified when there is a sample mean that is beyond the UCL or LCL.
Confidence Intervals↗
CAN'T MISS: conquer any number task by making important statistics simple. Part 8. Statistical process control: n, np, c, u control charts.
A p control chart is used to monitor a proportion from a binomial variable that reflects the output of a process. The p control chart is a graph that includes serial sample proportions (p) as the variables of interest, a centerline that represents the overall proportion of the samples (p), and upper control limits (UCLs) and Lower control Limits (LCLs) that represent three standard errors (SEp) above and below the centerline. We use a p control chart to estimate with 99.7% confidence that the population proportion of an output variable was within the interval defined by the UCLs and LCLs during a period of baseline monitoring. We further assume that if the process remains stable, the values of future population proportions will remain between the established control limits. An np control chart simply plots the numerators of the sample proportions as the variables of interest. A u control chart is used to monitor ratios. In u control charts, ratios are quantities in which the numerator can have one or more occurrences in reference to the denominator. A c control chart is analogous to an np control chart in that it graphs the numerators from the ratios.
Confidence Intervals↗