Quantitative research refers to the process in which observations are numerically represented and manipulated in order to describe and explain the phenomena reflected by the observations. This type of research is widely used in social sciences including sociology and psychology (Bryman & Bell, 2011). This paper describes specific quantitative methods and tools that could be used within the business field to gather data while evaluating their effectiveness in relation to the field. For further illustrations, organizational examples will be provided while finally the future of quantitative research both within the business field and in general will be provided.
In the field of business, quantitative methods and tools have a wide range of uses. To start with, there is the regression analysis which is a popular quantitative technique that can be used in business for the estimation of a number of factors known as independent variables or predictors on a dependent variable which is the outcome of interest (Welc & Esquerdo, 2017). For instance, in a company, the effect that experience and education have on the annual earnings of workers can be estimated using regression analysis. This can be achieved using the appropriate statistical equations and the independent factors, such as years of experience, to estimate the independent variable, such as worker`s annual earnings. Regression analysis can also be used in the estimation of the effect of advertising on the profits of a company. Data analysis using regression analysis leads to the estimation of whether there is any correlation between variables and whether the relationship has statistical significance (Welc & Esquerdo, 2017). With respect to business, regression analysis is effective in the prediction of an organizations future by giving key insights into future shifts in taxes, consumer spending as well as how these would affect the company. Thus, this method also effectively supports decisions by providing the necessary data on operations, customer purchases and finances.
Another quantitative method that can be used in the business field is linear programming. This technique uses mathematical methods to determine how an optimal outcome can be achieved subject to some constraints (Srinivasan, 2014). For example, in a company, linear programming can be used to determine how optimal outcomes such as the lowest operating costs or highest profits can be achieved with limited supplies and labor. According to Srinivasan (2014), linear programming is the most effective method when it comes to solving complex problems hence helps in the productive management of a firm by improving the quality of decisions made. The effectiveness of this quantitative technique in solving problems is further demonstrated by its flexible nature compared to other systems.
Factor analysis is another quantitative technique that can be used in business and is a combination of data reduction and analysis. Since this method is often used with survey data (Cleff, 2013), it can be used in business for market research. In factor analysis, the underlying factors are identified by exploring correlations in the available data which helps to explain existing relationships (Bryman & Bell, 2011). For instance, in an organization, factor analysis can be used in the analysis of data on the spending habits of consumers for the identification of factors that explain specific patterns such as a consumer preference for a given product. The information obtained by marketers is key to the decision-making process with regard to various areas such as promotions, stocking and delivery. As argued by Cleff, (2013), factor analysis is one of the most effective methods in the marketing area of a business because it is a reflection of the buyers perception of a given product. By changing variables, marketers are able to see the effect that a specific marketing variable has on the overall outcome hence helping the firm identify the marketing efforts that should be pursued and the efforts that need improvements.
There is also data mining as a quantitative research technique that can be used in business. Since this method combines computer programming and statistical skills, it can be very helpful in handling large quantities of statistical data. According to Sun and Li (2008), data mining refers to a set of methods used to analyze large sets of data as well as uncover correlations and patterns hidden within raw data masses. An example of a company that has applied data mining is the Amazon whereby the techniques have helped the organization to develop profiles of consumer buying habits (Sun & Li, 2008). The information obtained is crucial to making recommendations for certain products based on the customers purchase history. Therefore, data mining is effective when handling large amounts of information that require analysis from a number of angles, mostly in companies that strongly emphasize customer information such as buying habits.
Over the years, the demand for quantitative research has grown both in the business field and in general and is expected to follow the same trend in the future. Welc and Esquerdo (2017) argue that quantitative research has been a key new product development facilitator by turning business ideas into postulates and models that are workable and profitable to an organization. Based on this, it can be said that quantitative research will grow in terms of usage in business and in the general world as more advances are made in technologies. The technology advancements also imply that in the future, traditional methods of quantitative research will be substituted with more powerful methods that provide real-time data. The influence of qualitative research will continue to increase in both local and international markets and hence has a bright future that is characterized by endless possibilities.
In conclusion, as demonstrated herein, there are various quantitative methods and tools that could be used within the business field to gather data including regression analysis, linear programming, Factor analysis and data mining. Also, it has been evident that these methods play key roles in various areas of business including prediction of workers annual earnings, marketing and management by providing information that is crucial to the decision-making process. Further, it has been evident that each of the methods has unique features that make it more effective in a particular area of application. Additionally, based on the current trends of quantitative research application in business and in general, it can be predicted that in the future, this type of research will find even more applications with the continued advancements in technologies.
Bryman, A., & Bell, E. (2011). Business research methods. New York: Oxford University Press.
Cleff, T. (2013). Factor Analysis. Exploratory Data Analysis in Business and Economics, 183-195. doi:10.1007/978-3-319-01517-0_8
Network Data. (2013). Data Mining and Business Analytics with R, 272-292. doi:10.1002/9781118596289.ch20
Srinivasan, R. (2014). Introduction to Linear Programming. Strategic Business Decisions, 9-21. doi:10.1007/978-81-322-1901-9_2
Sun, J., & Li, H. (2008). Data mining method for listed companies financial distress prediction. Knowledge-Based Systems, 21(1), 1-5. doi:10.1016/j.knosys.2006.11.003
Welc, J., & Esquerdo, P. J. (2017). Common Pitfalls in Regression Analysis. Applied Regression Analysis for Business, 173-212. doi:10.1007/978-3-319-71156-0_6
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