In the first steps, definitions of the concept and operation come after the topic and the hypothetical approach has to be decided. The research hypothesis is in the form of a question and forms the central objective for the research. The hypothesis undergoes rigorous analysis and testing after the data is collected from reliable source documents or gathered after experiments are performed and after this, the last step is to conclude the findings. When test results do not conform with the hypothetical approach the research conclusions lead to acquisition of new knowledge after the accuracy of all data is reconfirmed (Cooper & Schindler, 2006).
This chapter discusses the various research methods and the technique employed in this study and justifies the reasons for employing these methods in this study.
This research study employs the quantitative research methodology.
The goal of a quantitative research methodology for studying phenomena is to develop and use mathematical models, postulates, and conjectures that align with algorithms’ and equations and are having measurable and interconnected processes so that a relationship can be established. Any data, which is numerical in nature, is a quantitative data like statistical data, data yielding percentages etc. Interpretation of data collected through questionnaires and interviews of participants is analyzed to yield a result, which is measurable, and a pattern can be found (Kumar & Kapil, 2009).
This approach becomes very useful in studies like establishing a pattern in illegal drug use in a targeted age group of participants. Numerical data when collected while studying a specific occurrence where a questionnaire can be designed which will enable measurable data collection. When data can be reduced to numerical quantities like for example while studying a percentage of teenage pregnancies in a population the quantitative approach is suitable. These data are amenable to extrapolation when making generalized theories for larger groups. Analysis is used to test postulates and give a greater meaning while examining patterns in numerical quantities (Saunders & Lewis, 2009).
Views regarding the role of measurement in quantitative research are somewhat divergent. Measurement of quantities plays a very important role when examining data patterns. Anomalies in data are important during interpretation of natural phenomena as they could prove or disprove a theory or even form refinements to existing lines of thinking. Anomalies are interesting if located during a data collection process as they can serve to redesign the strategy.