Public Policy Quantitative v Qualitative Research


Qualitative and quantitative research designs have traditionally dominated public policy research (Morçöl 192). However, they have some specific applications in research because of their differences in style and technique (Islam 23). Based on these differences, researchers have held contradictory opinions regarding their use in public policy studies (Morçöl 192). For example, proponents of the quantitative approach argue that the quantitative research design is the most appropriate methodology for use in public policy because it allows for the use of statistical techniques, which is at the center of policy implementation (Elmor 69-83).

A different set of researchers opposes this view based on the insights they have gathered from their qualitative findings (Islam 23-25). In this regard, proponents of both sets of methodologies appear to “be at war” regarding the supremacy of their research approaches. Transcending the academic field, the “qualitative vs. quantitative” debate has aroused the interest of many people, for varied reasons (Dunn 264). For example, a keen observer could notice this debate in the formulation of public policies for health reform and in the financial bailout of the automobile industry, in the aftermath of the 2007/2008 economic crisis. This paper addresses this conflict by evaluating the strengths and weaknesses of each approach and by highlighting the kinds of problems that each methodology could address in public policy research.

Quantitative Assessment

Advantages of Quantitative Research in Public Policy

It is easy to analyze quantitative data by expressing them in numbers. Since numerical data are part of quantitative assessments, researchers could simply apply statistical analytical tools in such analyses (Elmor 69-83). For example, we could use descriptive statistics like the mean, median, and standard deviation to analyze data in such assessments (Matsuo 75). Similarly, researchers could use inferential statistics, like t-tests and the analysis of variance (ANOVA), to analyze quantitative data (Islam 23). From the analysis of numbers, researchers could easily understand public policy trends and significant differences in policy formulation processes that could apply to different population groups (Dunn 264).

The use of multivariate statistical tools could help to further dissect such information and identify which preferential data to use in unique population groups or demographics. Quantitative data could also help public policy researchers to collect data using automated processes, which are fast and efficient in collecting data for a large population group (Elmor 69-83). Nonetheless, the greatest motivation for using quantitative data, in public policy studies, is its descriptive nature. Through this ability, public policy researchers could get a snapshot of a target population (Islam 23). However, some research studies have shown that interpretation challenges could arise during the data analysis process. In this regard, many researchers propose that qualitative information is useful in interpreting such information.

Disadvantages of Quantitative Research in Public Policy

Although the quantitative research method may use statistical techniques for data analysis, the calibration methods used in these statistics may not reflect the context of a study. Similarly, the theories applicable in the quantitative assessment may fail to reflect the gist of the paper (Islam 23). The intense focus on hypotheses development and the theoretical contributions, which are synonymous with quantitative assessments, may also distract the researcher from understanding the phenomenon of study. Lastly, the focus on numbers in quantitative analyses may provide abstract findings for general applications of research concepts that may make it difficult for policy experts to apply them in specific research situations.

Qualitative Assessment

Advantages of Qualitative Research in Public Policy

The qualitative analysis is important in public policy analysis because it provides information regarding subjective factors in the field (Dunn 264). Stated differently, it provides information regarding the data that policy experts cannot reduce to numbers. For example, it could provide them with information regarding human emotions and personality characteristics, as some subjective attributes in public policy formulation and implementation (Morçöl 192). Quantitative analysis is incapable of matching these data.

While quantitative research requires the standardization of research data, the qualitative analysis requires flexibility from researchers, especially during data collection (Babbie 312). Therefore, many qualitative assessments are products of naturalistic observations. Ethnography or structured interviews are common forms of study that adopt this design (Elmor 69-83). Using such data, researchers document important bits of information they encounter in their assessments, despite the uncertainty regarding whether they would be important in their analysis, or not (Dunn 264). Overall, this section of the paper shows that the qualitative assessment is important in public policy research because it gathers subjective information in the study field.

Disadvantages of Qualitative Research in Public Policy

A major disadvantage of the qualitative research design is its limited credibility in data analysis. While it is possible to use statistical analyses to evaluate the credibility of quantitative data, it is difficult to use the same tools in qualitative assessments. This is why experts often suggest the need to exercise caution when making conclusions from qualitative data (Babbie 312). In this regard, there is a need for data verification (in qualitative assessments) through a continuous qualitative analysis program.

Another limitation of qualitative analysis in public policy research is the difficulty associated with employing automation processes. This inability makes qualitative assessments an expensive and time-consuming methodology. In this regard, the qualitative research design is only suitable when analyzing information from a small sample (such as six or 12 people) (Islam 23). Comparatively, researchers could employ the quantitative research design to gather the views of hundreds (or thousands) of people.


The qualitative approach is useful to public policy research when using the positivist approach (Dunn 266). Some researchers also point out that the quantitative approach is useful when implementing policy goals (Dunn 266). For example, quantitative research has been useful in the recent public health care reform debate in the United States (US). Indeed, it helped to demystify the arguments regarding the impact of the public health reform agenda on state and federal funds (Islam 23). Democrats’ view on public policy issues is also a product of a post-positivist view, which aligns with the employment of the quantitative research analysis (Islam 23). The use of the post-positivist view aligns with the use of the quantitative research approach because it rejects the use of the natural science approach, which objects the use of the natural scientific approach to understanding human behavior and societal issues.

The qualitative research method is important in portraying the realities of public policy values. Furthermore, it is difficult to analyze the effectiveness of public policy initiatives by merely employing quantitative analyses. Indeed, as Dunn (264) says, public policy issues are not only products of quantifiable measures, but qualitative assessments as well. Relative to this view, Elmor says, “Numbers are key ingredients in the policy process and the public administration decision process, but ― a high proportion of activities in which public managers engage are not amenable to the application of analytical techniques; a small proportion is” (Elmor 69-83). To illustrate this analysis, public policy should respond to societal issues within the confines of social equity and social justice systems.

Researchers cannot assess these issues using quantitative factors only. For example, when economies experience periods of significant economic downturn, characterized by job losses, low spending, credit defaults, and suchlike factors, it is difficult to use cost-benefit analyses or other quantitative assessments as the only tools for making public policy decisions (Islam 23). Instead, public policy stakeholders need to employ qualitative measures to develop new healthcare reforms that address the needs of the victims. In this regard, there is a need to employ both qualitative and quantitative assessments in public policy.


Both qualitative and quantitative research approaches play a pivotal role in public policy. For example, quantitative data could inform decision-making processes in public policy, while qualitative data could improve the same decision-making processes by improving the design of public policies. To meet this goal, both research designs could provide decision-makers with valuable information about user needs, behavior patterns, and such attributes about a population. Based on this assessment alone, correctly, we could see that each approach has its unique strengths and weaknesses. Therefore, public policy stakeholders could benefit from combining the two approaches. Based on this analysis, this paper reveals that public policy research should not only focus on using quantifiable data, or statistically generated data, because it could be less objective than the qualitative research design when addressing public policy issues. In this regard, there is a need to merge both the qualitative and quantitative assessments of public policy issues.

Works Cited

Babbie, Earl. The Practice of Social Research, London, UK: Cengage Learning, 2007. Print.

Dunn, William. Public Policy Analysis, London, UK: Routledge, 2015. Print.

Elmore, Richard. ‘Graduate education in public management: Working the seams of government.” Journal of Policy Analysis & Management 6.1 (1986): 69-83. Print.

Islam, Khandaker. “Quantitative and Qualitative Research Controversy in Public Administration: An Analysis of Statistical Data Presented on United Kingdom People Category in the CIA World Factbook.” The Hilltop Review 4.2 (2011): 23-31. Print.

Matsuo, Makoto. The Role of Internal Competition in Knowledge Creation: An Empirical Study in Japanese Firms, New York, NY: Peter Lang, 2005. Print.

Morçöl, Göktuğ. A Complexity Theory for Public Policy, London, UK: Routledge, 2013. Print.