Quantitative data refers to that type of information that can be counted or expressed in numeric form. Accordingly, Quantitative data analysis defines a method to statistically analyze these sorts of data collected in experiments and manipulate them so that they provide a meaningful sense of interpretation. The analyses are visually represented in histograms, graphs, tables and charts. Quantitative research is mainly based on hypothesis, causality, generality and reliability.
Quantitative data analysis involves a number of steps starting from the organization of the data collected with the working out of calculations and finally interpreting the information and also detailing the limitations. Organization of data basically refers to the gathering of data (forms or questionnaires) in one unit and then checking for competencies and accuracy, ignoring or removing the one’s that are incomplete and don’t make any sense. During organization, it is very important to keep a tack on the decision made and also to assign a unique identifier to each form or questionnaire. Once the data is organized, they are recorded into Microsoft Excel (Spreadsheet), Microsoft Access (Database Management) and finally assessed with Quantitative Analysis SPSS (Statistical Software). The calculations are done based on the user’s requirement. It involves various mathematical means such as the count of frequencies, percentage, mean, median, mode, range, variance, ranking, and standard deviation and cross tabulation.
Finally after the completion of the calculations, interpretation comes into action. Interpretation of data or information is a process to provide meaningful sense to the calculated organized data. The process calls for fair and careful judgments. It defines the involvement of different people interpreting the same information in different ways. One of the common examples of the interpretation process would be, a meeting with the board of directors in an office asking their opinion on a particular issue or a project. The detailing of limitations is submitted either in written reports or in oral reports. It is very important to be honest about the limitations, be prepared to discuss them and also to know about the claims that one cannot make.
One of the advantages of Quantitative data analysis is that it involves the broader study of different subjects and allows the generalization of the out coming results. It facilitates comparisons over different categories and helps to avoid personal bias for the researchers keeping a gap from the known subjects and employing unknown subjects to them. But along the advantages, there are also some disadvantages involved the Quantitative data analysis. At times, the data collected can be a bit superficial and even the results can be limited as they define more numerical descriptions in comparison to the detailed narrative and thereby not reflecting the actual feeling on a particular subject.
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