Please reply to post below using RISE positive feedback tool

In quantitative research design, the researcher should remember that it is not straightforward to formulate a practical research question because the journey towards stating the relationship between variables must be done with a careful step-by-step procedure of describing each variable. In the methodological literature of the recent decade, particularly Creswell and Creswell (2018), there is a logical trend that combines descriptive and inferential views, and, most recently, in works by Bazeley (2021) and Akerblad et al. (2021), we can observe a call for qualitative and quantitative methods to be united in the formulation and answers to such questions.

Any practical study is based on descriptive research questions. These questions help explain the independent and dependent variables and outline their main characteristics in the study situation. For example, when researching the outcome of a mindfulness-based intervention on college students to study stress, the independent variable is defined by the structure, frequency, and mode of delivery of the intervention. On the other hand, the indicators of the dependent variable could include cortisol levels, physiological indices, and psychometric stress scales. Research questions can, therefore, be descriptive, such as, How is the mindfulness intervention planned and implemented concerning the frequency of the sessions, session content, and mode? What tools and metrics will be used to assess the intervention's effect on students' stress levels? The answers to these questions determine the clarity of measurement and the context boundaries with which further analysis will be put forth (Creswell & Creswell, 2018).

After these variables are defined effectively, the researcher moves on to the inferential or comparative questions, which address how the independent variables connect with, or bring about transformations in, the dependent variables. Recent mixed-methods research showed that these comparative questions, in addition to indicating statistical differences, are based on an integrative logic described by Bazeley (2021) and Akerblad et al. (2021) in the sense that, in many cases, resolute conclusions are drawn when multiple forms of data are interlaced and a variety of approaches to analysis are connected. As an illustration, one can ask: What is the stress level after the intervention compared to the students not involved in the mindfulness program and placed in the waiting list condition or another condition? Or is there a difference in how the intervention is performed based on differences in demographic indicators, like gender, socioeconomic status, or past experiences with meditation? Bazeley (2021) interprets that integrative approaches enhance the inference process by choosing to draw links between quantitative statistical tests, qualitative backgrounds, and interpretative contexts, thus creating fuller findings with more actionable information.

Current research also presents an argument favoring a script-based combination of descriptive and inferential questions based on a comprehensive interpretation of the nature of the research phenomenon. It is possible to combine descriptive statistics and the use of comparative inferential testing in a single analytic line, such as in the script: What are the mean pre- and post-intervention stress scores for each group, and do they differ significantly as a function of the intervention, initially controlling for stress levels? kerblad et al. (2021) take this supposition to another level, suggesting that carefully constructed mixed-methods studies must initially plot out how descriptive findings, for example, frequency and distribution of stress outcomes, will inform and be integrated into cross-relational and comparative studies.

In summary, descriptive to comparative research questions also align with sound global principles of experimental and mixed-methods research works. Comparative questions allow us to understand the relationship or distinction between variables or to have experimental precision and replicability, particularly when that question is created in such a way that it can take advantage of combined and multidimensional evidence.