Control Charts in Statistical Research

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Table of Contents

Introduction
Types of Control Charts
Types of Data
Quality and Performance Improvement
Conclusion
References

Introduction

Control charts are primarily applied in various statistical research for institutions to evaluate whether a particular process is in a steady state. The charts help determine how an experiment changes with time. Plotting data in control graphs is done promptly and systematically, with the average middle line separating the lower and upper limits. In addition to the process changes check, the information obtained can be used in making future predictions about performances. When the data from the control graphs depict that the process is under control, it reveals doubt that it is in a stable condition using variations that originate from other sources conversant with the method. In this case, no corrections or changes need to be made to the process control parameters. To develop a chart, it is not necessarily essential to know the name of any graph.

Types of Control Charts

Variable Control Graphs

The variable control charts can be categorized based on the subgroup data summary intrigued by the graph. In this case, the graphs can be R, S, or X- charts depending on the information they describe (Mizuno, 2020). For instance, the R graph gives the subgroup limits, while the S chart contains the standard deviations of each subcategory. Moreover, the X-diagram is applied when details regarding the subject means are needed. Depending on what is tested, the three types of charts give apparent details on the state of the analyzed process.

Levey-Jennings Graphs

Depending on the existing long-term sigma, Levey charts display an average process with proper control limits. The placement of borders is done to ensure the space between the central line and the upper or lower boundaries is 3s (Mizuno, 2020). In these charts, the deviation values’ are mostly determined using a similar method applied in most distribution platforms. The process data can occur in the middle, above, or below the set line.

Types of Data

The type of control chart used in various situations depends on the available data to be analyzed. The data can either be attribute or variable in nature, which influences the choice of the graph. Variable data refers to the aspects that can be evaluated using a continuous scale like tape rule or thermometer (Mizuno, 2020). On the other hand, attributes involve details that can be counted as bad or good, having or lacking specific features. However, most researchers prefer variable data because it gives precise information about the process and requires fewer samples to arrive at meaningful conclusions.

Quality and Performance Improvement

Quality improvement, particularly in healthcare, requires several methods and strategies. Advancement in quality involves continuous and concentrated efforts to ensure desirable patient outcomes, expertise development, and system performance (Mizuno, 2020). With the existing complex systems due to technological advancements, using control charts has become increasingly essential in evaluating whether the available changes will lead to better patient outcomes. In most healthcare leadership roles, the issue of proper process control is one of the most perplexing tasks. However, control charts have provided the necessary quality monitoring and processes solution.

Conclusion

Learning about a particular variation and its cause, using control graphs will help control and ensure improvement in process performance. A control chart gives a clear distinction between standard and notable variation causes (Newhart et al., 2019). Such differences can guide in improving the process to make it more predictable and consistent. Control charts help to determine when the process is moving on the wrong side, making it necessary to institute corrective measures. They enable understanding specific patterns when using certain information, enabling leaders to make future predictions.

References

Mizuno, S. (2020). Management for quality improvement: The seven new QC tools. Productivity Press.

Newhart, K. B., Holloway, R. W., Hering, A. S., & Cath, T. Y. (2019). Data-driven performance analyses of wastewater treatment plants: A review. Water research, 157, 498-513.

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