Introduction
A quantitative research design is the process of explaining a situation under observation through collecting numerical data, which is statistically analyzed. Every research must explain a phenomenon (Wetcher-Hendricks, 2011). For example, the two studies selected in this case explain different phenomena. The study by Tesli, Egeland, Sonderby, Haukvik, Bettella, and many others investigates the influence of genetic risk variants such as enlarged ventricles and volumetric reductions on structural brain abnormalities in Bipolar Disorder (BD). The second study by Meesters, Schouws, Stek, Haan, Smit, Eikelenboom, Beekman and Comijs investigated the distinction between the cognitive impairment in late-life schizophrenia and bipolar I disorder. According to Wetcher-Hendricks (2011), quantitative research involved the collection of numerical data where the researcher asks the respondent specific questions that are best answered using quantitative methods. The following discussion identifies the application of quantitative research in the two studies and criticizes the appropriateness of the design used.
Application of quantitative research
In the first study, Tesli and other authors used large sample sizes to represent a large population. 517 participants took place in the research. The participants came from Norway and some from the ongoing Thematically Organized Psychosis (TOP) Study. Secondly, data collection took place using more structured research instruments. The researchers used image acquisition and post-processing techniques such as MRI scanning that diagnosed individuals for bipolar spectrum disorder or schizophrenia spectrum disorder. In addition, researchers had carefully designed most aspects of the study such as selection of the target group, literature of the previous studies and the data analysis processes. Finally, the data collected was in the form of numbers and statistics. The table of demographics and clinical data shows results in the form of numbers, and statistical calculations of mean, median, variance, and standard deviation calculated using the SPSS software (Tesli et al., 2013).
Meesters et al also utilized the quantitative research design in different forms in their study. Firstly, the target group represented a bigger percentage of the population. A total of 141 respondents took place in the study. Secondly, numerical data was collected using tests that acted as questionnaires in four cognitive domains. Statistical analysis tools compared the demographics and clinical variables obtained from participants through the analysis of variance (ANOVA), a statistical tool that analyses numerical data obtained from a quantitative research. Finally, Meesters et al could easily predict future results of the research through observing the characteristics of participants and relating them to the theory (Meesters et al., 2013).
Critics on the appropriateness of the design used
Researchers in both studies made an effort of using the quantitative research design in analyzing their phenomena, but some concepts did not fully demonstrate application of the research design. For instance, the researchers did not define research questions, which assist in achieving the objectives and making the reader generalize the phenomena analyzed. Secondly, the reports only talks of the actual results collected from the study and never reported on anticipated reports. A quantitative research should indicate the expected results and the researcher must explain how the collected data differed from the planned data. Finally, the researchers used tables in recording their findings with sample sizes, but these tables lacked columns representing value of test statistics and confidential intervals for each variable.
Conclusion
According to Rovai, Baker, & Ponton (2013), choosing an inappropriate design for a research study causes many effects to the actual outcomes of the research. An inappropriate research design leads to unreliable evaluation that results into unexpected results. Secondly, choosing a design that does not appropriately fit the research requirements leads to negative consequences in the research and the study does not meet its intended aims. Researchers should carefully choose the appropriate design for a research that meets all the requirements and leads to a likable evaluation.
References
Meesters, P. D. (2013). Cognitive impairment in late schizophrenia and bipolar I disorder.
International Journal of Geriatric Psychiatry, 28, 82-90.
Rovai, A. P., Baker, J. D., & Ponton, M. K. (2013). Social science research design and statistics:
A practitioner's guide to research methods and SPSS analysis. Chesapeake, VA: Watertree Press.
Telsi, M. et al. (2013). No evidence for association between bipolar disorder risk gene variants
and brain structural phenotypes. Journal of Affective Disorders, 151, 291–297
Wetcher-Hendricks, D. (2011). Analyzing quantitative data: An introduction for social
researchers. Hoboken, N.J: Wiley.
