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Quantitative research tools and methods provide emphasis on the objectives measurements as well as statistical analysis of data collected through questionnaires and surveys. Primarily, quantitative research is geared towards gathering numerical data and generalizing the collected information across a wide range of variables so as to explain a specific phenomenon (Brandimarte, 2013).
Quantitative research deals with numeric and unchanging data by providing a wide range of ideas concerning a particular research problem in a free-flowing manner (Information Resources Management Association, 2015). Therefore, this paper seeks to highlight specific quantitative research tools and methods used in collecting data, as well as how these tools can be used in the decision-making process. The paper will stretch further to evaluate the effectiveness of each tool with respect to certain areas of business and commerce and consequently highlight the future of quantitative research in general.
Quantitative research tools play an important role in helping managers and other corporate leaders of the various organization in improving the overall quality of decision making within the working environment. These tools are adopted mostly in the rational/logical decision model, but they can as well as be used in other models. Among the most common quantitative tools includes decision trees, simulations and payback analysis (Information Resources Management Association, 2015).
For instance, a decision tree enhances the decision making process within the organization by providing the manager with a complete pictures of different potential decisions allowing him/her graph alternative decision paths. This helps the manager to critically analyze different decisions that involves a progression of smaller decisions such as pricing of products, hiring of new employees, purchasing of new equipment, marketing etc. Due to its flexibility, a decision tree helps managers evaluate a resolution under uncertain conditions, coming up with the most appropriate decision (Brandimarte, 2013).
Additionally, payback analysis in another quantitative technique used by managers when trying to make a particular decision regarding the purchase of new equipment. For instance, if a manager wants to buy cars for a rental car company, in as much as the less expensive cars have a shorter payback period, some managers may choose to go for the more luxurious model.
So as to make the right decision concerning the type of cars to go for, payback analysis helps managers evaluate different factors such as insurance costs of the cars, their rental demands, the useful life of the cars among many other factors. Based on the information collected, a manager can choose the most suitable alternative on the basis of the cost of each car. As such, luxurious model cars are more appropriate because they have a longer useful life as well as customer demand. Ultimately, high priced cars have the quickest payback period (Information Resources Management Association, 2015).
Finally, network analysis is another essential quantitative tools used in decision making. This technique focuses on demonstrating the relationships events and tasks. Networks analysis presents all operations and activities of the project as a path, beginning from the onset of the project to the time of completion. In addition, this technique determines the amount of time spent on each and every activity of the project. An example of network analysis is a critical path analysis. Critical path analysis providing duration estimates of the total project (Information Resources Management Association, 2015).
Quantitative methods and tools used in gathering data quantify the already collected data into numerical forms after which, the generated data is mathematically converted and processed into useful information (Jupp & Sapsford, 2015). The results are later presented in form of meaningful statistics, hence useful. Contrary to qualitative methods, quantitative data collection methods make use of large sample sizes because there are measurable in nature making computations easier and doable. Therefore, the quantitative techniques used in gathering data includes quantitative surveys, interviews, quantitative observations and experiments (Jupp & Sapsford, 2015).
Contrary to open-ended questionnaires in a qualitative study, quantitative research poses closed questions, providing options for answers. This allows respondents to fill the questionnaire by only choosing their answers from the choices provided in the questionnaire. This method of collecting data is very effective especially when the survey involves a large sample size. Also, the researcher can be able to make generalizations out of the results due to the standardized nature of the questionnaires (Jupp & Sapsford, 2015).
Interviews can also be used in collecting quantitative data. However, when collecting quantitative data, there is the need to have a set of standard questions and your interviews are more structured. These interviews can take various forms such as face-to-face interviews, web-based interviews, and computer-assisted interviews. Using interviews in collecting data is also effective in various ways, for instance, face-to-face interviews allow the researcher to seek clarity on any answer given by the interviewee. On the other hand, computer-assisted interviews save a lot of time and other resources because of the direct input and processing of data. This takes places immediately after it has been obtained from the source and entered into the database.
Finally, a researcher may use quantitative observation to collect quantitative data for the study. Quantitative observation is the systematic way of collecting data based on a naturalistic approach which mostly involves the use of the senses and skill observation. This method of collecting data is simple and less expensive as compared to another method, and hence effective and most appropriate in a low budget quantitative study (Jupp & Sapsford, 2015).
Based on recent development in modern technologies and a shift towards social networking, the future of qualitative research on collecting data will not only longer be limited to surveys and interviews, but also extends further to communication as well as interaction (Nonnenmann, 2014). Researchers will be required to create and implement new platforms for collecting data. This will include new data collection software that will incorporate new technologies that support data collection such as geospatial information resources and wearable computers (Nonnenmann, 2014).
Quantitative research is gradually shifting away from collecting data using old, traditional methods and moving towards real-time and analytics, where collected data can be tracked by the use of advanced information systems. Therefore, given the new research endeavors and technology advances, there is an exciting future of quantitative research.
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