Objectives.: Motivation for the study. Risk perception of COVID-19 is a construct that varies according to the characteristics of the population in each geographic area; however, there is no validated scale to measure this construct in the Peruvian population. Main findings. A COVID-19 risk perception scale composed of two dimensions (cognitive and emotional) was designed and validated using qualitative and quantitative techniques. Implications. Having a valid and reliable instrument will help identify the variation of risk perception of COVID-19 according to contextual and psychological factors in the Peruvian population. . To develop and validate a risk perception scale for COVID-19 (PR-COVID-19-PE) in the Peruvian population.
Materials and methods.: Psychometric cross-sectional study conducted in 2022. In phase 1, in order to design the scale, we carried out a theoretical review and a documentary review of scales, we also used focus groups as well as an expert panel. Phase 2 included expert judgment and a pilot test. A virtual survey was conducted among 678 Peruvian adults during phase 3. A confirmatory factor analysis was carried out as well. We used a correlational analysis (Pearson's r) with a valid risk perception scale and the COVID-19 fear scale to determine criterion validity.
Results.: The PR-COVID-19-PE has two dimensions (cognitive and emotional) and showed good fit during construct validity (x2/gl=2.34, Comparative Fit Index=0.96, Tucker-Lewis Index=0.96, Root Mean Square Error of Approximation= 0.05 and Standardized Root Mean-Square=0.07) and optimal internal consistency (ώ=0.88). Likewise, the PR-COVID-19-PE showed correlation with another COVID-19 risk perception scale (r=0.70, p< 0.001) and a fear of COVID-19 scale (r=0.41, p<0.001). In addition, it presents metric and scalar invariance by both sex and educational level.
Conclusions.: The PR-COVID-19-PE scale showed adequate reliability and content, construct and criterion validity. It is an instrument that can measure COVID-19 risk perception in similar populations. However, further studies are required for different populations.
Objectives.: This article introduces randomized clinical trials and basic concepts of statistical inference. We present methods for calculating the sample size by outcome type and the hypothesis to be tested, together with the code in the R programming language. We describe four methods for adjusting the original sample size for interim analyses. We sought to introduce these topics in a simple and concrete way, considering the mathematical expressions that support the results and their implementation in available statistical programs; therefore, bringing health students closer to statistics and the use of statistical programs, which are aspects that are rarely considered during their training.