자연주의적 탐구조사(Naturalistic Inquiry)

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Part I. Experimental Studies: 2. Experiment in psychology 3. Experiments on perceiving III Experiments on imaging 4-8. Experiments on remembering: (a) The method of description (b) The method of repeated reproduction (c) The method of picture writing (d) The method of serial reproduction (e) The method of serial reproduction picture material 9. Perceiving, recognizing, remembering 10. A theory of remembering 11. Images and their functions 12. Meaning Part II. Remembering as a Study in Social Psychology: 13. Social psychology 14. Social psychology and the matter of recall 15. Social psychology and the manner of recall 16. Conventionalism 17. The notion of a collective unconscious 18. The basis of social recall 19. A summary and some conclusions.

Remembering. A Study in Experimental and Social Psychology, Cambridge (University Press) 1964.

Reviewers of research reports frequently criticize the choice of statistical methods. While some of these criticisms are well-founded, frequently the use of various parametric methods such as analysis of variance, regression, correlation are faulted because: (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. In this paper, I dissect these arguments, and show that many studies, dating back to the 1930s consistently show that parametric statistics are robust with respect to violations of these assumptions. Hence, challenges like those above are unfounded, and parametric methods can be utilized without concern for “getting the wrong answer”.

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Likert scales, levels of measurement and the "laws" of statistics.

Infectious Diseases Society of America.

A recent article by Jamieson in Medical Education outlined some of the (alleged) abuses of "Likert scales" with suggestions about how researchers can overcome some of the (alleged) methodological pitfalls and limitations[1]. However, many of the ideas advanced in the Jamison article, as well as a great many of articles it cited, and similar recent articles in medical, health, psychology, and educational journals and books, are themselves common misunderstandings, misconceptions, conceptual errors, persistent myths and "urban legends" about "Likert scales" and their characteristics and qualities that have been propagated and perpetuated across six decades, for a variety of different reasons. This article identifies, analyses and traces many of these aforementioned problems and presents the arguments, counter arguments and empirical evidence that show these many persistent claims and myths about "Likert scales" to be factually incorrect and untrue. Many studies have shown that Likert Scales (as opposed to single Likert response format items) produce interval data and that the F-test is very robust to violations of the interval data assumption and moderate skewing and may be used to analyze "Likert data" (even if it is ordinal), but not on an item-by-item "shotgun" basis, which is simply a current research and analysis practice that must stop. After sixty years, it is more than time to dispel these particular research myths and urban legends as well as the various damage and problems they cause, and put them to bed and out of their misery once and for all.

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Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes

How Likert type measurement scales should be appropriately used and analysed has been debated for over 50 years, often to the great confusion of students, practitioners, allied health researchers and educators. Basically, there are two major competing views that have evolved somewhat independently of one another and of the associated empirical research literature on this ‘great debate’. Most recently in this journal, Jamieson outlined the view that ‘Likert scales’ are ordinal in character (i.e., produce rank order data) and that they, therefore, must be analysed using non-parametric statistics. Non-parametric statistics, however, are less sensitive and less powerful than parametric statistics and are, therefore, more likely to miss weaker or emerging findings.

Resolving the 50-year debate around using and misusing Likert scales.

Background Those working in healthcare today are challenged more than ever before to quickly and efficiently learn from data to improve their services and delivery of care. There is broad agreement that healthcare professionals working on the front lines benefit greatly from the visual display of data presented in time order.

Aim To describe the run chart—an analytical tool commonly used by professionals in quality improvement but underutilised in healthcare.

Methods A standard approach to the construction, use and interpretation of run charts for healthcare applications is developed based on the statistical process control literature.

Discussion Run charts allow us to understand objectively if the changes we make to a process or system over time lead to improvements and do so with minimal mathematical complexity. This method of analyzing and reporting data is of greater value to improvement projects and teams than traditional aggregate summary statistics that ignore time order. Because of its utility and simplicity, the run chart has wide potential application in healthcare for practitioners and decision-makers. Run charts also provide the foundation for more sophisticated methods of analysis and learning such as Shewhart (control) charts and planned experimentation.

https://qualitysafety.bmj.com/content/qhc/20/1/46.full.pdf

The run chart: a simple analytical tool for learning from variation in healthcare processes

Context The phrase "Science of Improvement" or "Improvement Science" is commonly used today by a range of people and professions to mean different things, creating confusion to those trying to learn about improvement. In this article, we briefly define the concepts of improvement and science, and review the history of the consideration of "improvement" as a science. Methods We trace key concepts and ideas in improvement to their philosophical and theoretical foundation with a focus on Deming's System of Profound Knowledge. We suggest that Deming's system has a firm association with many contemporary and historic philosophic and scientific debates and concepts. With reference to these debates and concepts, we identify 7 propositions that provide the scientific and philosophical foundation for the science of improvement. Findings A standard view of the science of improvement does not presently exist that is grounded in the philosophical and theoretical basis of the field. The 7 propositions outlined here demonstrate the value of examining the underpinnings of improvement. This is needed to both advance the field and minimize confusion about what the phrase "science of improvement" represents. We argue that advanced scientists of improvement are those who like Deming and Shewhart can integrate ideas, concepts, and models between scientific disciplines for the purpose of developing more robust improvement models, tools, and techniques with a focus on application and problem solving in real world contexts. Conclusions The epistemological foundations and theoretical basis of the science of improvement and its reasoning methods need to be critically examined to ensure its continued development and relevance. If improvement efforts and projects in health care are to be characterized under the canon of science, then health care professionals engaged in quality improvement work would benefit from a standard set of core principles, a standard lexicon, and an understanding of the evolution of the science of improvement.

Seven propositions of the science of improvement: exploring foundations

Context: Good health is the most important outcome of health care, and healthy life expectancy (HLE), an intuitive and meaningful summary measure combining the length and quality of life, has become a standard in the world for measuring population health. Methods: This article critically reviews the literature and practices around the world for measuring and improving HLE and synthesizes that information as a basis for recommendations for the adoption and adaptation of HLE as an outcome measure in the United States. Findings: This article makes the case for adoption of HLE as an outcome measure at the national, state, community, and health care system levels in the United States to compare the effectiveness of alternative practices, evaluate disparities, and guide resource allocation. Conclusions: HLE is a clear, consistent, and important population health outcome measure that can enable informed judgments about value for investments in health care.

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Rocco J. Perla

Papers

Ten Common Misunderstandings, Misconceptions, Persistent Myths and Urban Legends about Likert Scales and Likert Response Formats and their Antidotes

Resolving the 50-year debate around using and misusing Likert scales.

The run chart: a simple analytical tool for learning from variation in healthcare processes

Seven propositions of the science of improvement: exploring foundations

A healthy bottom line: healthy life expectancy as an outcome measure for health improvement efforts.