Is Qualitative Research Non-Scientific?

Quantitative studies rely on objective measurements and numerical analysis of data collected via surveys questionnaires, and pools, wherein the researcher manipulates pre-existing data by use of computational means. In general, quantitative studies focus on the collection of data and generalization of findings across all groups of people who experience the same phenomenon in the same context. On the other hand, qualitative research depends on a broad methodological approach involving collection of non-numerical data and its interpretation. It is therefore exploratory in nature and is used to primarily understand underlying reasons, motivations, and opinions by providing insights into the studied problem or by hypothesizing and developing ideas for potential quantitative research. The mixed methods research approach integrates both methods in a single study with quantitative analysis as a base technique to uncover common patterns in data and qualitative exploration as a way to get deeper into the problem under study. Because of its exploratory nature, qualitative research has bed cited as “non-scientific” by many critics. This essay aims to compare a quantitative and a qualitative study to find out whether the claim is true.

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Viorela sought to evaluate the secular trends of overweight and obese children in Finland using a quantitative approach in order to focus resources to obesity prevention and achieve cost-effectiveness in the healthcare system. The main aims of the study were to analyze whether prevalence of obesity in children has changed in four decades, to analyze secular trends in BMI, as well as to evaluate how Finnish parents can assess weight classes for their children. The data used for the research was pre-collected in hospitals via auxological measurements of height and weight. The researcher began by providing a comprehensive review of literature concerning the definition of obesity, assessment of body composition, classification and origin of obesity epidemiology of obese and overweight children, consequences of the condition, and secular trends in maturation and growth.

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To make sense out of the data collected in the span of four decades, the study statistically analyzed the data by use of SPSS. The researchers checked for validity and reliability of the data prior to the evaluation by examining the completeness of the data and then presented it in form of charts, odds ratios, and their confidence intervals according to year. Descriptive statistics were also used to represent distribution. Although the researcher did not collect primary data by themselves, the use of quantitative methods allowed them to measure and analyze data using variables. This was beneficial because it allowed for objectivity about the findings. The technique was also relevant in hypothesis testing because of its ability to analyze data via statistics. Even so, the researcher ignored a larger part of the study’s context and had to use a large sample of data in order to achieve more accurate results.

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In a second study, Moyer and a group of researchers wanted to conduct a body-mass index screening by assessing the readability of the results letter and qualitatively analyzing parents’ responses to it. The methods used for the study involved calculation of readability, creation of focus groups of parents of guardians of 8-14 year-old obese children, and the use of a semi-structured interview guide for eliciting responses. The researchers conducted qualitative content analysis to recognize emergent themes (Creswell and Creswell). Readability levels indicated high levels than recommended and the analysis pointed to the various themes about obesity such as concerns for screening, usefulness of the BMI letter, impact of self-esteem, and failure to understand the letter. The ultimate conclusion was that the BMI letter had underachieved its intended purpose with select parents.

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By using a qualitative approach, Moyer et al. enjoyed the freedom to let the research unfold in a natural manner. Indeed, their study did not require a strict design, yet the researchers were able to gain detailed and rich data in the form of comprehensive descriptions. The main approach was to look at the context and social meaning of the BMI phenomenon in the studied population. However, the researchers were heavily involved in the research process and therefore may have given the study a degree of subjectivity (Neuman). This could skew the gathered data. Overall, the results gained from Viorela’s study were based on a scientific analysis by use a statistical computing tool, and are therefore scientifically relevant while those obtained from Moyer et al. were based on multiple realities which gave a holistic presentation of experiences in a particular setting, despite leaving out contextual sensitivities.

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In conclusion, this essay found out that both quantitative and qualitative approaches have their pros and cons. Quantitative research involves a large sample and do not require a substantive length of time for data collection but are not in-depth and may overlook testers’ or test-takers’ experiences as well as their meanings. Oppositely, qualitative research elicits deeper insights into the design administration, and interpretation of testing and assessment in addition to exploring participant behavior, feelings, understanding, and perception. Thus, qualitative research is not scientifically inferior as it focuses on the context behind data patterns. In fact, qualitative research method can be used to identify variables in preparation for quantitative research.

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