Shape Measures for the Distribution of a Qualitative Variable

There are several shape measures for quantitative variables, some of which can also be applied to ordinal variables. In quantitative variables, symmetry, peakedness, and kurtosis are essential properties to evaluate the deviation from assumptions, particularly normality. They aid in selecting the most appropriate method for estimating parameters and testing hypotheses. Initially, these properties serve a descriptive role in qualitative variables. Once defined, they can be considered to check for non-compliance with assumptions and to propose modifications for testing procedures. The objective of this article is to present three measures of the shape of the distribution of a qualitative variable. The concepts of qualitative asymmetry and peakedness are defined. The measurement of the first concept involves calculating the average frequency difference between qualitative categories matched by frequency homogeneity or proximity. For the second concept, the peak-to-shoulder difference and the qualitative percentile kurtosis are taken into consideration. This last measurement is a less effective option than the peak-to-shoulder difference to measure peakedness. A simulated example of the application of these three measures is given and the paper closes with some conclusions and suggestions.

Shape Measures for the Distribution of a Qualitative Variable

There are several shape measures for quantitative variables, some of which can also be applied to ordinal variables. In quantitative variables, symmetry, peakedness, and kurtosis are essential properties to evaluate the deviation from assumptions, particularly normality. They aid in selecting the most appropriate method for estimating parameters and testing hypotheses. Initially, these properties serve a descriptive role in qualitative variables. Once defined, they can be considered to check for non-compliance with assumptions and to propose modifications for testing procedures. The objective of this article is to present three measures of the shape of the distribution of a qualitative variable. The concepts of qualitative asymmetry and peakedness are defined. The measurement of the first concept involves calculating the average frequency difference between qualitative categories matched by frequency homogeneity or proximity. For the second concept, the peak-to-shoulder difference and the qualitative percentile kurtosis are taken into consideration. This last measurement is a less effective option than the peak-to-shoulder difference to measure peakedness. A simulated example of the application of these three measures is given and the paper closes with some conclusions and suggestions.

Everything You Wanted to Know but Could Never Find from the Cochran-Mantel-Haenszel Test

The Cochran-Mantel-Haenszel (CMH) test, developed in the 1950s, is a classic in health research, especially in epidemiology and other fields in which dichotomous and polytomous variables are frequent. This nonparametric test makes it possible to measure and check the effect of an antecedent variable X on a health outcome Y, statistically controlling the effect of a third variable Z that acts as a confounding variable in the relationship between X and Y. Both X and Y are measured on a dichotomous qualitative scale and Z on a polytomous-qualitative or ordinal scale. It is assumed that the effect of X on Y is homogeneous between the k strata of Z, which is usually tested by the Breslow-Day test with the Tarone’s correction or the Woolf’s test. The main statistical programs have the CMH test together with a test to verify the assumption of a homogeneous effect across the strata, so that it is easy to apply. However, its fundamentals and details of calculations are a mystery to most researchers, and even difficult to find or understand. The aim of this article is to present these details in a clear and concise way, including the assumptions and alternatives to non-compliance. This technical knowledge is applied to a simulated realistic example of the area of epidemiology in health and, finally, an interpretive synthesis of the analyses is given. In addition, some suggestions for the test report are made.

The Influence of Allergic Rhinitis Treatment on Asthma

Background: Rhinopathy, a dysfunction or inflammation of the nasal mucosal lining, presents with symptoms of nasal obstruction, posterior and anterior rhinorrhea, sneezing, nasal itching, and hyposmia, with variations in symptom intensity in each subtype. Asthma originates from a combination of genetic and environmental factors. Objective: This study aimed to treat allergic rhinitis in patients with controlled asthma and to verify the behavior of the variables. Methods: In this prospective study, quantitative and qualitative assessment of rhinopathy in asthma was performed. Patients with symptoms of rhinopathy and controlled asthma, who were controlled with treatment at the pulmonology outpatient clinic of the Center for Medical Specialties at [hospital], were included. Patients were treated for 2 months according to the IV Rhinopathy Consensus. They underwent a pulmonary function test and completed a questionnaire before and after treatment for rhinopathy. Results: In total, 47 patients aged 7 - 12 years (9.30 ± 1.70 years;median 9 years) were evaluated, including 29 (61.7%) males and 18 (38.3%) females. Patients were evaluated at two timepoints, with an interval of 12 days to 14 months (3.81 ± 3.21 months;median 3 months), and were evaluated regarding the various characteristics of their allergy. Conclusion: The treatment of allergic rhinitis in patients with asthma resulted in an improvement in variables related to nasal congestion, rhinorrhea, cough, dyspnea, wheezing, and dyspnea on exertion, and maintaining physical activities without dyspnea.

A Dynamic Programming Model of Qualitative Mixture System for Canal Engineering

This paper presents a dynamic programming model and solving method of qualitative mixture system for canal engineering. This model may solve the optimal design that computer can not solve or can solve very difficultly with the current methods, so that the engineering investment can be saved.