What is the focus of non-parametric approaches in quantitative research?

Non-parametric approaches in quantitative research primarily focus on analyzing nominal and ordinal data rather than making assumptions about the parameters of population distributions, such as means or variances. These methods are particularly useful when dealing with data that do not meet the assumptions necessary for parametric tests, such as the normality of distribution.

In the context of nominal data, non-parametric methods allow for the analysis of categorical variables without requiring them to be converted into numerical form. This capability is essential for analyzing survey responses where categories rather than quantities are reported, such as preferences, yes/no responses, or classifications without inherent numerical order. Non-parametric techniques apply ranking or frequency-based methods to draw meaningful conclusions from this type of data.

While approaches such as regression analysis and summarizing qualitative data are common in various research contexts, they generally do not fall within the scope of non-parametric methods when the focus is strictly on nominal data analysis. Additionally, the estimation of means is fundamentally a characteristic of parametric approaches, which rely on the characteristics of data distribution that often isn't applicable under non-parametric circumstances.