Phenomenological inquiry is a qualitative research approach that aims to explore and understand the lived experiences of people who share a common phenomenon. Data analysis in phenomenological inquiry can be done in different ways, depending on the theoretical perspective and the research question. One possible way to analyze data in phenomenological inquiry is descriptive analysis.

Descriptive analysis is a method of data analysis that follows the principles of descriptive phenomenology, also known as Husserlian or transcendental phenomenology (Giorgi, 1997). Descriptive analysis seeks to describe the essence of the phenomenon as it appears to the participants, without imposing any interpretation or explanation. According to Husserl (1913/1983), descriptive analysis involves four steps: bracketing, horizonalization, clustering, and textualization. Bracketing is the process of suspending one's own preconceptions and biases about the phenomenon. Horizonalization is the process of identifying significant statements or meaning units from the data that express the participants' experiences. Clustering is the process of grouping the meaning units into themes that reflect the essential aspects of the phenomenon. Textualization is the process of writing a description of the phenomenon based on the themes (Sundler et al., 2019). Descriptive analysis can be useful for capturing the richness and complexity of the participants' experiences, as well as highlighting their similarities and differences (Paley, 2017).

Descriptive analysis is a method of data analysis that follows the principles of descriptive phenomenology, also known as Husserlian or transcendental phenomenology (Giorgi, 1997). Descriptive analysis seeks to describe the essence of the phenomenon as it appears to the participants, without imposing any interpretation or explanation. According to Husserl (1913/1983), descriptive analysis involves four steps: bracketing, horizonalization, clustering, and textualization. Bracketing is the process of suspending one's own preconceptions and biases about the phenomenon. Horizonalization is the process of identifying significant statements or meaning units from the data that express the participants' experiences. Clustering is the process of grouping the meaning units into themes that reflect the essential aspects of the phenomenon. Textualization is the process of writing a description of the phenomenon based on the themes (Sundler et al., 2019). Descriptive analysis can be useful for capturing the richness and complexity of the participants' experiences, as well as highlighting their similarities and differences (Paley, 2017).Descriptive analysis is a method of data analysis that follows the principles of descriptive phenomenology, also known as Husserlian or transcendental phenomenology (Giorgi, 1997). Descriptive analysis seeks to describe the essence of the phenomenon as it appears to the participants, without imposing any interpretation or explanation. According to Husserl (1913/1983), descriptive analysis involves four steps: bracketing, horizonalization, clustering, and textualization. Bracketing is the process of suspending one's own preconceptions and biases about the phenomenon. Horizonalization is the process of identifying significant statements or meaning units from the data that express the participants' experiences. Clustering is the process of grouping the meaning units into themes that reflect the essential aspects of the phenomenon. Textualization is the process of writing a description of the phenomenon based on the themes (Sundler et al., 2019). Descriptive analysis can be useful for capturing the richness and complexity of the participants' experiences, as well as highlighting their similarities and differences (Paley, 2017).

Descriptive statistics gives a description of the data without any additional or predictive information. As for other data analysis methods, such as predictive, it is useful to predict and the possibility of generalizing the results.