Hello again, ANOVA: rethinking ANOVA in the context of confirmatory data analysis

Analysis of variance (ANOVA) is one of the most popular statistical methods employed for data analysis in psychology and other fields. Nevertheless, ANOVA is frequently used as an exploratory approach, even in confirmatory studies with explicit hypotheses. Such misapplication may invalidate ANOVA conventions, resulting in reduced statistical power, and even threatening the validity of conclusions. This paper evaluates the appropriateness of ANOVA conventions, discusses the potential motivations possibly misunderstood by researchers, and provides practical suggestions. Moreover, this paper proposes to control the Type I error rate with Hypothesis-based Type I Error Rate to consider both the number of tests and their logical relationships in rejecting the null hypothesis. Furthermore, this paper introduces the simple interaction analysis, which can employ the most straightforward interaction to test a hypothesis of interest. Finally, pre-registration is recommended to provide clarity for the selection of appropriate ANOVA tests in both confirmatory and exploratory studies.

Modelling the Paucity of COVID-19 Pandemic Spread Incidence across the India Nation

Background: The highly contagious Co rona vi rus d isease (COVID-19) pandemic affected nearly all nations across the world. It was emerged as most swiftly affected disease across the world and more than 2934 lakhs population suffered in four months of the time period as on date April 26, 2020. Its first epicenter was at Wuhan city of China during the month of December 2019. Currently, the most affected people and new epicenter of Coronavirus is at the United States of America (USA). Various nation’s administration including the India government called for the regional and local lockdown. We predicted the confirmed COVID-19 cases for next May-2020 month, map the magnitude of COVID-19 disease for Indian states and model the paucity of COVID-19 disease with statistical confirmatory data analysis model for declining rate for the cases represented for the Indian proportion of population. Method: The ARIMA model used to predict for next short-term cases, based moving average of past confirmed cases. The restriction of COVID-19 pandemic disease analyzed with predicted cases for month May 2020 data at 95 percent confidence is more than 2.5 lakh cases. Results: The confirmatory data analysis model for the time estimation for the paucity of cases it takes in between six to eighteen months of time frame. The Confirmatory model which considers recovery rate, social, economic and government policy. To complete recovery from the COVID-19 cases it takes on an average more than next ten months. Conclusion: The disease impacts also depend upon administrative and local people support for self-quarantine and other measures. The India nation Gross Domestic Product (GDP) based on more than 17% of its agriculture production, due to longer affect of the disease and extended lockdown period it will be severely affected. However, all the economic activities with full of its intensity takes-up after complete paucity of COVID-19 disease spread. Keywords: SARS-CoV-2; Lockdown; GDP; Nobel-Corona; Confirmatory data model

Modelling the Paucity of COVID-19 Pandemic Spread Incidence across the India Nation (Preprint)

BACKGROUND The highly contagious Coronavirus disease (COVID-19) pandemic affected nearly all nations across the world. It was emerged as most swiftly affected disease across the world and more than 2934 lakhs population suffered in four months of the time period as on date April 26, 2020. Its first epicenter was at Wuhan city of China during the month of December 2019. Currently, the most affected people and new epicenter of Coronavirus is at the United States of America (USA). It is identified as the most severe pandemic disease in human history during the past 100 years. Due to non-availability of specific medication, the World Health Organization (WHO) suggested various measures of precautions and social distance in between the people for the restricting the spread of the COVID-19 disease. Various nation’s administration including the India government called for the regional and local lockdown. OBJECTIVE We predicted the confirmed COVID-19 cases for next May-2020 month, map the magnitude of COVID-19 disease for Indian states and model the paucity of COVID-19 disease with statistical confirmatory data analysis model for declining rate for the cases represented for the Indian proportion of population. METHODS The ARIMA model used to predict for next short-term cases, based moving average of past confirmed cases. The restriction of COVID-19 pandemic disease analyzed with predicted cases for month May 2020 data at 95 percent confidence is more than 2.5 lakh cases. RESULTS The confirmatory data analysis model for the time estimation for the paucity of cases it takes in between six to eighteen months of time frame. The Confirmatory model which considers recovery rate, social, economic and government policy. To complete recovery from the COVID-19 cases it takes on an average more than next ten months. CONCLUSIONS The disease impacts also depend upon administrative and local people support for self-quarantine and other measures. The India nation Gross Domestic Product (GDP) based on more than 17% of its agriculture production, due to longer affect of the disease and extended lockdown period it will be severely affected. However, all the economic activities with full of its intensity takes-up after complete paucity of COVID-19 disease spread. CLINICALTRIAL wqew ere re

Novel Parameterized Utility Function on Dual Hesitant Fuzzy Rough Sets and Its Application in Pattern Recognition

Based on comparative studies on correlation coefficient theory and utility theory, a series of rules that utility functions on dual hesitant fuzzy rough sets (DHFRSs) should satisfy, and a kind of novel utility function on DHFRSs are proposed. The characteristic of the introduced utility function is a parameter, which is determined by decision-makers according to their experiences. By using the proposed utility function on DHFRSs, a novel dual hesitant fuzzy rough pattern recognition method is also proposed. Furthermore, this study also points out that the classical dual tool is suitable to cope with dynamic data in exploratory data analysis situations, while the newly proposed one is suitable to cope with static data in confirmatory data analysis situations. Finally, a medical diagnosis and a traffic engineering example are introduced to reveal the effectiveness of the newly proposed utility functions on DHFRSs.

CHALLENGE-STRESS AND HINDRANCE-STRESS IN THE SOUTHEAST ASIAN HOTEL SECTOR

The hospitality industry is an entity that is continuously determined by varying new demands and the needs of its customers. This ever-changing and complex working environment has caused and become a source of stress for the hospitality industries’ workforce. Workplace stress is increasing from year to year and has become a focus of research interest in recent years.Responding to the demands of management who require a more precise understanding of the issues of workplace stress, researchers have conducted studies on a total of 115 respondents from a 3 star-hotel and a 4 star-hotel. The personnel involved came from the food and beverage departments, room services and the front offices, whose daily routines involved direct face toface serving activities and fulfilling their customers’ demands. Using the Statistical Package for Social Science (SPSS) version 19.0 and AMOS version 18.0, the results of Exploratory Data Analysis (EFA) and Confirmatory Data Analysis (CFA) have confirmed that there are two stress factors, namely challenge stress and hindrance stress. Both of these stress factors have asignificantly negative relation to one another. Understanding these dimensions in detail can help the hospitality organizations to be well prepared for the task of motivating their employees.Keywords: challenge-stress, hindrance-stress, service, Southeast Asia, hotel

The logical and psychological structure of physical chemistry and its relevance to graduate students' opinions about the difficulties of the major areas of the subject

In a previous publication, Jensen's scheme for the logical structure of chemistry was employed to identify a logical structure for physical chemistry, which was further used as a tool for analyzing the organization of twenty physical chemistry textbooks. In addition, science education research was considered for the study of the psychological structure of physical chemistry. In this companion paper, the findings are presented of a semi-structured interview study with seventeen chemistry graduates, which aimed to find out their opinion about the difficulties of the various areas of physical chemistry, their disposition towards the subject, and their explanations for the difficulties identified, and in this way to study further the psychological structure of physical chemistry. A mixture of an intensive inductive and a confirmatory data analysis was carried out that revealed ideas and trends and allowed for a reliable portrait of learners to emerge by identifying similarities and differences in the data. Students unanimously found the phenomenological subjects (classical thermodynamics, electrochemistry, chemical kinetics) easier than the submicroscopic subjects of quantum chemistry and statistical thermodynamics. The reasons invoked included that the latter subjects deal with more difficult and abstract concepts, and also their highly mathematical nature. Many students found classical thermodynamics simpler than quantum chemistry, because it “has logic”, includes “tangible examples”, and they had encountered related topics before (especially in high school). The findings for electrochemistry and chemical kinetics were more or less similar to those for classical thermodynamics. Implications, generalizability, and limitations of the findings and prospects for further research are discussed.