A Step to Bridge the Gap between Qualitative and Quantitative Research by IAR score: A Mathematical Equation

Phenomenological studies in qualitative research are gaining increasing attention. However, they often face challenges in establishing robust statistical inferences, as they depend on the honesty of respondents and are affected by biases and measurement variability between different types of responses within a given domain. This study examines the challenges of using closed-ended responses in qualitative research and highlights strategies for mittigating bias and measurement variations in these responses. To capture the qualitative degrees of participants' experiences in response to a range of types of questions/items within a specific domain, a standardized approach assigning integer values to responses for balancing and equal weights in the form of ratio of a response to its highest value of the item within a domain has been used to mitigate bias and measurement variation. The average of all ratios within the domain represents the individual response for its latent variable.  Domain index of the latent variable for an individual response is calculated using mathematical formula Individual average of Ratio(s) (IAR) score. Systematic review with a pooled sample size of 6697 individuals to assess knowledge about organ donation, revealed rate of 69% (95% C.I.: 64.5% - 73.5%)  within Gulf  countries. This finding notably contrasts from a household survey using two stage systematic random sampling method for data collection from the same region, where the knowledge rate was 46%, (95% CI: 45% - 47%) calculated by RS score indicating 23% bias error in the estimate of the systematic review attributable to the coding structure. Appropriate sample size, assigning integer values to responses and IAR score empower researchers to leverage qualitative responses effectively, broadening the applicability of advanced statistical tools in qualitative research paradigms.