Methods of Analysis for Business Operations - Essay Example

ASSESSMENT OF QUANTITATIVE RESEARCH METHODS The paper is aimed at the assessment of the quantitative research methods in reference to two different research papers. The report upon highlighting the aspects of the scale of measurements has developed the critical analysis in order to establish that using combination of scales have a greater chance to accommodate greater insight to the research. Also, the paper shed light on the role and impact of variation in the data as a result of different factors. Hence, each critical aspect reported have been provided with the recommendation for the improved results generation from the case studies assessed for the purpose. TABLE OF CONTENTS ASSESSMENT OF QUANTITATIVE RESEARCH METHODS 1 ABSTRACT 1 TABLE OF CONTENTS 2 INTRODUCTION 3 PAPERS UNDER DISCUSSION 4 TYPE OF DATA 4 SCALE OF DATA MEASUREMENT 5 VARIATIONS IN THE DATA 8 CONCLUSION 9 REFERENCES 10 INTRODUCTION Research methodology is one of the most critical parts of the research (Sekaran, 2006). It is important for the fact as the systematic, as well as scientific path used in the research methodology, enables the researcher to achieve the desired result. The systematic and scientific methodology is guided by the discipline of Statistics. Statistics is a set of different techniques and procedures using which the research can collect the data, organize it in the desired form, conduct an analysis, interpret the results from the analysis and finally present the outcome in the form of information in a numerical form (Black, 1999). As the definition implies the first step in the statistical body of study is the collection of data. Statistics provides a more comprehensive meaning to the system of data collection that is generally known as measurement. Statistically, the measurement is referred to as the scale of measurement that takes into account the approaches of defining and categorizing the collected variable and/ or numbers. Each research methodology is developed using procedures and techniques of the scale of measurement that has best fit with the research requirement as well as data availability (Jankowicz, 2005). This paper will be analyzing the data measurement system of the three research papers as a reference in order to explore the variations employed in the scale of measurement and its impact on the respective results. The paper also critically evaluates the selection of the scale of measurement and its impact on the overall results. Additionally, the paper also highlights the aspects to be added in the information collection system. PAPERS UNDER DISCUSSION Impact of recession on buying behavior of Indian Consumers is a research study put forward by Sharma (2011). The research study is aimed at assessing the change in the behavior of the consumer shopping pattern under the impact of the recession. The data has been collected from the 50 retailers with respect to different aspects of the study. Also, the data has been analyzed using statistical techniques correlation. Factors affecting future demand for electric vehicles: A model based study is a research paper developed by Shepherd et.al (2012) to assess the future of the electric vehicles in UK for the next 40 years. Using the coefficients developed in the previous research, the paper has developed model using statistical techniques to predict the future demand. Also, the paper has assessed the conditional and the sensitivity analysis to explore the impact of different factors. TYPE OF DATA In both papers referred above the scale of measurement implies that the type of data that has been used is quantitative in nature. The quantitative data is one that involves numbers. These can be measured and provides considerable value to the research work and to the researcher in generating meaningful results with the application of statistical techniques. Using quantitative data for the analysis provides the researcher with an opportunity to have an objective response. Being absolute in nature, the quantitative data is easier to assess as compared to the qualitative data as it enables to differentiate the large number of similar and related factors. The usage of quantitative data in the papers of assessment has a reason that both papers aimed to assess the relationship between the factors. For example, using quantitative data Sharma (2011) has managed to apply the statistical technical of correlation that in turn provides the answer to the research objective in an absolute numbers. Similarly using the quantitative data, Shepherd et.al (2012) have systematically and statistically attempted to analyze the growth of the electric vehicle adoption rate in UK over the next 40 years. However, the use of the qualitative data as well as complete reliance on it has posed certain limitations to research studies. For example, quantitative data lacks richness in the information. This lack of comprehensive or richness in the research results reduces as the data collected using quantitative system has to determine certain boundaries during the collection process. As a result, the critical information is feared to be missed. For instance, in the work of Sharma (2011) the paper has only managed to develop the numerical results. In the case of research that was conducted in the Indian context, the researcher could have collected additional data using a combination of quantitative and qualitative data by presenting some open ended questions. This system would have benefitted the data with the reasoning behind the impact explored. Moreover, such system had the potential to take into consideration the important information that could not be accounted numerically. SCALE OF DATA MEASUREMENT Broadly, the domain of statistics has developed four categories of scale of measurement. Each scale has different properties that play an important role in the selection of the scale for the data collection in a particular study. The four Scales of measurement are as follows (Saunders, Thornhill, & Lewis, 2009): Nominal: The nominal scale is used to provide the nomination to a particular category. Importantly, as the scale is used to nominate the categories; therefore, it becomes a mutually exclusive. The purpose of nominal scale is to nominate only; therefore, the numbers assigned to a category does not carry any significance on the basis of the numerical values assigned. For example, 1 refers Female and 2 refer males etc. Ordinal: This scale of measurement is used to rank the categories in a particular order. The ranking of the categories enables the researcher to rank things in particular order of preference. However, the exact difference between each category is not known. For example, rating particular thing on the scale of 0 to 10 with Zero being lowest and 10 being highest etc is an ordinal scale in which the exact status behind the ranking of 10 is not known. Interval: Interval scales are used to represent the quantity. The numerical values assigned with the interval scales have a numerical value, unlike ordinal scale. In the interval scale, the numerical values are measured on the basis of their absolute difference from zero on either side below and above. For example, Fahrenheit or the sea levels are the examples of the interval scale as these are measurable, and Zero in these cases does not refer to the lowest value. The mathematical functions can be applied of addition and subtraction can be applied on it. However, without true value of Zero such as Zero does not mean anything in interval scale. The interval scale based measurement makes it difficult to calculate the ratio. Ratio: The ratio scale based measurement is similar to the interval scale. It also has an additional feature of having zero with the absolute value. Ratio scale has an absolute zero which allows taking the ratios as well as other statistical and the inferential statistical techniques. For example, the age of the person being 58 mean he is elder while 28 means younger. Furthermore, the scale implies that the difference between 58 and 28 is based on 30 equal units. Also, 58 years refer to 58 equal units of distance from Zero. The study of Sharma (2011) has used the combination of two scales of measurement in the study. The paper has used a structured questionnaire for the collecting data, and so different scales have been used suiting to the need of each question. For example, the categories of retail stores were developed using the nominal scale. Number of footfalls has been measured by calculating the ratio scale. The measure developed using the ratio scale has enabled the researcher to present the results by assessing change in terms of percentages. The data collected using the ratio scale is then also used for the correlation techniques of statistics. As the study was aimed at exploring the impact of recessing then, it was advisable for the study to have taken into consideration the ordinal scale in few aspects. For instance, the range based scale would have provided more comprehensive information about the decline. The data collected with the ordinal scale of measurement had a chance to collect information with greater clarity. For example, the retailer could have notified that differentiated levels of the change in the footfalls in the stores. The research study by Shepherd et.al (2012) has used the ratio scale for the collection of the data. The data collected is centrally constituted of coefficients of variables that were used in different studies developed for the purpose of assessing the level of impact of factors on the adoption rate of electric vehicles in UK. These coefficients are numbers developed as a result of a statistical technique of regression. As the study was aimed at developing the trend using a statistical technique, the ratio scale measure was most suited for the purpose. VARIATIONS IN THE DATA The statistics in many statistical techniques assumes that data collected for the assessment has followed normal distribution. Normal distribution refers to the data that results in a bell shaped structured when depicted graphically. However, in the research studies the data collected may vary from the standard bell shape for the normal distribution (Maylor & Blackman, 2005). Differences in the data may often results in any of the following shapes of normal distribution: (Bernard, 2011) Therefore, in order to meet the requirement of the data to be according to the normal distribution, the researcher often eliminates or trims such entries. The referred trimming then result in the exclusion of the certain information from the data collected. In addition to this, other forms of variations are also possible in the data. For example, Townsend, Rutter, & Foster (2011) refer that data collected for different countries has potential to have variations due to the prevailing circumstances in different countries. Sharma (2011) has developed a research study with limited size of the 50 retailers in the particular region of the India. In addition to this, the research study was conducted with the four different types of stores. Simply the difference in the size of the store is expected to have implications on the data collected. However, the researcher has not mentioned any measure taken to overcome the data variation. Therefore, it was important for the study to have mentioned the presence of any outliers in the study. In the presence of such outliers in the data, the results of the correlation become questionable. On the other hand, the study of Shepherd et.al (2012) referred to an implication with respect to the implication of the stated preference in the data collected that can have implication in the form of overestimation of the data. The paper in the context has taken care of the issue by scaling the data in order to produce results that have more appropriate fit with greater reliability of estimates for the market shares in all. In addition to this factors that were considered and dealt accordingly, it was important that the study had developed its own coefficients. It was important for the reason the reason that the study from which the coefficients have been taken was conducted in 2008. Since then, things have changed considerably, as a result having impact on the consumer adoption rate on different factors. CONCLUSION Data collection is much valued activity of the research study and is required to be due attention in order to achieve the desired results. The report has concluded that impacts of the data collection system are considerably reflected on the research results. Therefore, assessment of all factors that might have implications is a much attention seeking course. The report has identified some of the factors with regard to the data collection in order to comprehensively achieve the set objectives for the research paper. REFERENCES Bernard, H. R. (2011). Research methods in anthropology. Rowman Altamira. Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics. Sage. Jankowicz, A. (2005). Business Research Projects, London: Thomson Learning. Maylor, H, & Blackman, K. (2005), Research Business & Management, Basingstoke, Uk: Palgrave Macmillan. Saunders, M, Thornhill, A, & Lewis, P. (2009). Research Method for Business Students. London: Financial Times Prentice Hall. Sekaran, U. (2006). Research Methods for Business, NJ: John Wiley & Sons, Inc. Sharma, K. (2011). Impact of recession on buying behavior of Indian Consumers. International Review of Business Research Papers, 7(1), 381-392. Shepherd, S., Bonsall, P., & Harrison, G. (2012). Factors affecting future demand for electric vehicles: A model based study. Transport Policy, 20, 62-74. Townsend, N., Rutter, H., & Foster, C. (2011). Variations in data collection can influence outcome measures of BMI measuring programmes. International Journal of Pediatric Obesity, 6(5‐6), 491-498. Read More