Difference Between Quantitative and Qualitative Analysis - Assignment Example

Question number Difference between quantitative and qualitative analysis In data analysis, there are two most common types of analysis used: quantitative analysis and qualitative analysis. The other analysis is not superior to the other but the combination of both will exactly make a much better analysis. There are many reasons to use quantitative analysis. The world is composed of numerical data and in order to understand and make use of them, quantitative analysis is a powerful tool that can present and interpret them. In its general and broad definition, quantitative analysis is simply a way of giving measurement to things (Investopedia, par. 3). With this definition, it is implied that in the field of business or financial for instance, quantitative analysis can be implemented for measurement, performance evaluation, valuation of financial instrument and can be used to predict future real world events (Investopedia, par. 2). In order to successfully implement all of these, the use of complex mathematical and statistical models, measurement research and giving numerical values to variables are what should a quantitative analyst needs to do in order to come up with mathematical replication of reality (Investopedia, par. 1). On the other hand, not all information or data can be quantified. The presence of nonquantifiable information like management expertise, industry cycles, strength of research and development and labor relations are the bases of subjective judgment which fall under qualitative analysis (Investopedia, par. 1). In its general term, qualitative analysis is about analysing things without the presence of numbers. It can often be subjective. In business, qualitative analysis often gives many insights about the management that will not often show up in the quantitative analysis (Investopedia, par. 2). It is clear that the major difference between quantitative analysis and qualitative analysis is the consideration of numbers. With quantitative analysis, there is quantifiable information involved. On the other hand, there is no quantifiable information present in qualitative analysis, but everything falls under subjective judgment. In business, it is important to combine these two in order to come up with a better view of the company’s performance or employees’ performance evaluation for instance. In other fields such as the academe, both qualitative analysis and quantitative analysis are very important. In physics, chemistry, biology just to name a few, both quantitative analysis and qualitative analysis are important in order to come up with the best information that can be added to the body of knowledge. In chemistry, quantitative analysis is concerned with finding the quantity of a particular substance in a sample while qualitative analysis deals with the identification of substances (Day and Underwood, 3). Qualitative and quantitative analysis are very evident in an organisation. Each organisation has different segments and they can eventually use either quantitative analysis, qualitative analysis or both. For example in accounting department, forecasting system can be used to predict cost or profit estimates. In finance department, cash flow and funding can be elaborated further through series of analyses. In human resources, forecasting ca be applied on hiring, recruiting and training. The marketing concerns are about pricing, promotion and strategy. The management information system deals with information technology, information system and services. The operations take charge on schedules, MRP and workloads. Then the product or service design is responsible for creation of new products and services. Understanding the functions of each of this segment in the organisation is the key to understanding which part the quantitative and qualitative analysis should take place. Why organizations use scientific approach? In order to answer the above question, it is important to look in detail how each organisation performs or functions in order to operate. In fact, in every organisation there is a need to investigate and acquire new knowledge so as to have continuity in the operation and even to stand out above any other. With scientific approach, the method of investigating and acquiring of information must contain gathering of evidences that can be measured and observed and even empirical. The next point is clear that every organisation needs to collect data. This can only be properly implemented through an observation, formulation and testing hypotheses and experiment. In fact, all organisation needs to come up with a scientific approach because it is a reliable process that will be able to give sound solution and even understanding of phenomena that cannot be exactly determined by simple observation and explanation. In fact, the implementation of quantitative analysis and qualitative analysis in every organisation is a clear indication of implementing scientific approach. As discussed earlier, these two analyses are significantly used in order to obtain reliable information that can be added to the body of knowledge. This only shows that with scientific approach, every organisation is ensured to be able to come up with systematic approach in gaining information. Quantitative analysis process Anderson et al. (6-12) discussed about quantitative analysis. According to them, the process must significantly start with structuring the problem. After this, the following steps will take place: model development, data preparation, model solution and report generation. According to them the essence of qualitative analysis process lies on developing a model and solution procedures. In business, predicting sales is a very important approach and the generation of this information usually takes quantitative analysis process. The creation of model like linear trend model is usually based on the previous sales. In engineering, models are very important in order to come up with reliable basis on future decisions. To come up with reliable models, it is important to consider reliable data and the right solution process. In the academe, in order to contribute to the body of knowledge it is important to consider enough bases. In most of the time, claims are justified further with quantitative analysis since it is easy to explain things through formulated models, figures, graphs and tables with quantitative information. What is the breakeven point? In business, the breakeven point is considered the point in which gains equal losses (Investopedia, par. 1). Analysing on this further, it can be clearly understood that it is the point which can be found at the center. For example, in mathematics or statistics, it can be considered the average in a certain distribution. Simply, it can be the average of numbers. In chemistry or physics it can be considered the equilibrium. In art, it is the point where there is balance and harmony of colors for instance. These are just some of the common interpretation of the concept breakeven point and it can become qualitative or quantitative information depending on the area of discipline it is used. Question number 2. Steps used to develop a forecasting system On the other hand, even if there are different types of forecasting techniques that can be applied in each segment of an organisation, the bottom line is that there is a general process that needs to be followed in order to come up with reliable results. The following are steps used to develop a forecasting system according to Stevenson (3.7). The most important part prior to the development of a forecasting system is to determine the purpose of forecast. With this, one will be able to understand the capacity of a specific forecasting system since it can be aligned to what purpose it is made for. The second important step is the establishment of time horizon. It is important to know what specific time needs to be used in the forecast. Some forecasting systems are dependent on the information or data obtained from the past and they can be reliable in predicting or forecasting future trends. Thus, there is a great need to consider establishing time horizon in the creation of forecasting system. The third step is selecting a forecasting technique. Fortunately, there are different forecasting techniques to choose from. They can all be tested and in the end those with less error are considered the best forecasting techniques. The fourth step is gathering and analysing data. This is one of the most important steps since through this, information can be formulated. Preparation of the forecast is the next step. It is important that everything about the forecast is prepared. This is to ensure that the forecast must be reliable enough to predict the future or present reliable trend. Monitoring of the forecast is the final step in developing a forecasting system. Everything about the forecast must be monitored. This is to find or ensure that the forecast is still a representation of reality and with that it can still be a reliable tool for predicting future trends and future decision-making process. What is time series model? Time series model is part of quantitative analysis. This model is one of the techniques used in forecasting. It is a common technique used in forecasting and there are significant numbers of organizations which try to make use of this as tool for decision-making purposes. In time series model historical data are used and the future is assumed to be like the past (Stevenson, 3.8). With this, it is clear that the decision in the future is primarily based on what happened in the past. Thus, it is important to come up with many data sets as much as possible in order to recognise significant pattern or trend. Using this model requires more stored quality data that will help build significant and reliable tool for the analysis. Difference between casual model and time series model In casual model there is a great consideration on the cause and effect of things. This model tries to follow logical reasoning when it comes to describing cause and effect behavior of a system. At some point casual model can be considered an abstract model since the relationship or behavior of a system is directly defined by cause and effect. Thus, there can be no relationship defined in the model without cause and effect. Example of this model is a simple linear regression model. This model is composed of dependent variable and independent variable. The dependent variable is affected by the changes within the dependent variable. Thus, for example increasing the values for independent variable may give significant changes to the dependent variable. Unlike the casual model, time series model is not looking for cause and effect relationship. Most of the time, the time series model is looking for the significant trend in the past in order to apply it in the future. What is a qualitative forecasting model, and when it is appropriate? This is most probably a forecast based on opinions. The responses from respondents need not be precise. Most often this type of forecast is effective for short-term in projecting the future. This model works best when scope is on limitation and experts on a specific field are asked to agree on the forecast. Describe briefly Delphi techniques Delphi technique is part of qualitative forecasting model. Experts are asked what they believe will happen in the future relative to considered subject matter. For example, experts in automobile industry might most likely be asked about future innovation on mobile phones 10 years from now. What they have to give are general opinions on the subject matter and they need not be precise in a sense. Bibliography Anderson, R. David, Sweryney, D. J., Williams, T. A., Camm, J. D. and Martin, Kipp. Quantitative Methods for Business. 11th ed. Ohio: South-Western Cengage Learning, 2009 Day, R. A. Jr. and Underwood, A. L. Qualitative Analysis. 3rd ed. New Jersey: Prentice Hall, 1974. Ivestopedia. “Qualitative Analysis.” 2010. 24 July 2010 . Investopedia. “Quantitative Analysis.” 2010. 24 July 2010 . Stevenson, William J. Operations Management. 8th edition. Read More