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      Understanding the nexus between public risk perception of COVID-19 and evacuation behavior during cyclone Amphan in Bangladesh

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          Abstract

          In May 2020, when Bangladesh was struggling with community transmission of COVID-19, the country had to face the strongest tropical storm- Cyclone Amphan -which puts the evacuation process in jeopardy. Thus, it is crucial to measure the public risk perception about COVID-19 and its influence on the evacuation decision. This study explores the nexus between COVID-19 risk perception and coastal peoples’ evacuation decisions during cyclone Amphan. With an analysis of 378 sample households survey data of the Satkhira district, this study developed the COVID-19 risk perception index using Principal Component Analysis (PCA) and categorized the respondents based on the score. The result shows that 1.85 %, 21.43 %, 45.77 %, 25.13 %, and 5.82 % have very low, low, moderate, high, and very high-risk perceptions, respectively. The analysis also reveals that 96.6 % of the respondents received an evacuation order during Amphan, but only 42 % complied with the order. The t-test analysis and common language effect size test of the survey data reveal that the respondents with a high perception score are 65 % less likely to evacuate than the respondents with low perception scores. This study has important implications in guiding concerned authorities to combat natural disasters during COVID-19 and other similar public health emergencies in the future.

          Abstract

          COVID-19, Risk perception, Cyclone Amphan, Evacuation behavior, Principal component analysis.

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          Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

          Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.
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            Principal component analysis: a review and recent developments.

            Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
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              User's guide to correlation coefficients

              When writing a manuscript, we often use words such as perfect, strong, good or weak to name the strength of the relationship between variables. However, it is unclear where a good relationship turns into a strong one. The same strength of r is named differently by several researchers. Therefore, there is an absolute necessity to explicitly report the strength and direction of r while reporting correlation coefficients in manuscripts. This article aims to familiarize medical readers with several different correlation coefficients reported in medical manuscripts, clarify confounding aspects and summarize the naming practices for the strength of correlation coefficients.
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                Author and article information

                Journal
                Heliyon
                Heliyon
                Heliyon
                Published by Elsevier Ltd.
                2405-8440
                22 July 2021
                July 2021
                22 July 2021
                : 7
                : 7
                : e07655
                Affiliations
                [a ]Asian Disaster Preparedness Center, Bangladesh Country Office, Rajshahi-6202, Bangladesh
                [b ]Urban and Rural Planning Discipline, Khulna University, Khulna-9208, Bangladesh
                Author notes
                []Corresponding author.
                Article
                S2405-8440(21)01758-8 e07655
                10.1016/j.heliyon.2021.e07655
                8295048
                34316522
                3eb5a129-7bb3-49b5-98ca-8ae61c9443b3
                © 2021 Published by Elsevier Ltd.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 17 February 2021
                : 19 May 2021
                : 20 July 2021
                Categories
                Research Article

                covid-19,risk perception,cyclone amphan,evacuation behavior,principal component analysis

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