Researchers
May 25, 2020

The Intersection Between Qualitative Datasets and the Impact on Public Policy Reform

Federal public policies don’t always equate to standards that are accessible to all public schools. Qualitative datasets have shown, through analysis, that when it comes to such focuses as nutrition education and other inequalities, state-level policies need to include both qualitative and quantitative intersections.
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The integration of state and federal government policies on local schools from kindergarten to universities is becoming more intertwined. At such a distant level, though, policies for public reform that are meant to help often hinder instead. Those sitting in a committee on a board entrusted with creating the policies may not have the experience necessary to enact implementation processes, or the processes themselves may not be accessible, relevant, or possible for every school.

Many of these policies use quantitative measures to determine whether an institution is implementing the requirements effectively or not. It’s an all-or-nothing viewpoint that fails to take into perspectives such as the amount of funding tied to the execution of each policy or the demographics of a school and how the rollout of the new expectations would be perceived and accepted.

The Danger of Quantitative Measures on Academic Impact

Universities are frequently reprimanded for not meeting the quantitative measures expected by state and federal public reform policies. This creates a domino effect of consequences that are felt starting at the administrative level and barreling down to the final victims of the students and their academic success. The damage caused by a tunnel-vision focus on one narrowly viewed goal needs to be mitigated, and it can be by allowing an intersection between quantitative and qualitative datasets in public policy reform.

Quantitative analyses tend to be superficial. They look at the surface numbers to see if a public policy appears and meets the data standards that they are looking for rather than asking why those standards were not achieved. With qualitative analyses in play, though, the “why” is deeply examined so that the problem can be corrected and the school can move forward with the public reform policy target still in sight.

Moving into Quantitative and Qualitative Research

Policymakers in the field of education are in a current state of flux. The digital era has redefined how classrooms are operated, how teachers instruct, and how students absorb information. It’s the perfect time to designate research and reform measures to construct policies that rely on both quantitative and qualitative data to determine effectiveness.

To thoroughly design these types of reformative policies, each one must address factors such as the idea of intersectional studies, the impact of socioeconomic background and other demographics on the overall results, and the challenges that are faced by institutions in overcoming educational inequality.

Applying These Factors to State-Level Datasets

Because of the new trend leading scholars to explore data on intersectionality, there is a need for a framework to guide policymakers and educators as to what is reputable data and what is not. As that is being developed, policies can still be generated by using state-level datasets that currently exist, as long as the factors are integrated. 

Qualitative research should be used to examine the overall complexity of a source and how it impacts the factors that challenge academic equality. Quantitative research should be applied to measures that can be pinpointed at and then relegated to a specific category that will then be applied to a solution.

As a general rule, quantitative research depends on data analysis at a secondary level. Surveys, administrative data collections, demographics, and other factors that can be numbered offer insights into qualitative results. Quantitative is more subjective, but it serves a purpose when qualitative measures are not met. The qualitative has to be analyzed to determine how to solve the problem.

Through techniques such as sub-group differences combined with analysis of these differences that contextualize them, state-level datasets can be applied to determine where the need for reformation is. Context is a necessary variable in all data collection. Without looking at the results in the proper context, the numbers can skew the need for reform and create a focus target that can’t be fixed without a deeper analysis and solution. Intersection qualitative and quantitative initiatives solve this dilemma.

Making Your Point with Impactio

Both qualitative and quantitative data results, in order to make an impact on your audience, must be displayed clearly. You can spend hours creating pie charts and bar graphs, putting together a PowerPoint presentation, and printing your slides, but the end result is going to be a time-consuming chore.

Impactio combines the best of textual and visual worlds into one easy-to-use program. With simple few clicks, your citation and publication information is quickly compiled into professional-looking graphs and texts with no muss or fuss. Save and print as PDFs or display as a web document to impress your colleagues or organizations at your next presentation!

Tags Academic Impact Analysis Qualitative Research Quantitative Research
Jason Collins
Writer
Jason is a writer for many niche brands with experience “bringing stories to life” for both startups and corporate partners.

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