The Implication of Using NVivo Software in Qualitative Data Analysis: Evidence-Based Reflections

For a long time, electronic data analysis has been associated with quantitative methods. However, Computer Assisted Qualitative Data Analysis Software (CAQDAS) are increasingly being developed. Although the CAQDAS has been there for decades, very few qualitative health researchers report using it. This may be due to the difficulties that one has to go through to master the software and the misconceptions that are associated with using CAQDAS. While the issue of mastering CAQDAS has received ample attention, little has been done to address the misconceptions associated with CAQDAS. In this paper, the author reflects on his experience of interacting with one of the popular CAQDAS (NVivo) in order to provide evidence-based implications of using the software. The key message is that unlike statistical software, the main function of CAQDAS is not to analyse data but rather to aid the analysis process, which the researcher must always remain in control of. In other words, researchers must equally know that no software can analyse qualitative data. CAQDAS are basically data management packages, which support the researcher during analysis.

Self-reported vaccination in the elderly : SABE Bogotá study, Colombia

Objectives: To determine the frequency of vaccination in older adults within the city of Bogotá and to estimate the association with sociodemographic and health factors. Methods: This is a secondary data analysis from the SABE-Bogotá Study, a cross-sectional population-based study that included a total of 2,000 persons aged 60 years. Weighted percentages for self-reported vaccination [influenza, pneumococcal, tetanus] were determined. The association between vaccination and covariates was evaluate by logistic regression models. Results: A total of 73.0% of respondents received influenza, 57.8% pneumococcal and 47.6% tetanus vaccine. Factors independently associated with vaccination included: 1- age (65-74 years had higher odds of receiving vaccinations, compared to 60-64 years; 2- socioeconomic status (SES) (higher SES had lower odds of having influenza and pneumococcal vaccines, compared to those with lower SES); 3- health insurance (those with contributive or subsidized health insurance had higher odds (between 3 and 5 times higher) of having vaccinations, compared to those with no insurance); 4- older adults with better functional status (greater Lawton scores) had increased odds for all vaccinations; 5- older adults with higher comorbidity had increased odds for influenza and pneumococcal vaccinations. Conclusion: Vaccination campaigns should be strengthened to increase vaccination coverage, especially in the group more reticent to vaccination or vulnerable to reach it such as the disable elder.