Quantitative and Qualitative Methods of Research - Assignment Example

 QUANTITATIVE AND QUALITATIVE METHODS Keywords: Central tendency, Median Why measurement is difficult when we are dealing with people and society, compared with the natural sciences The measurement of people and society is very important to researchers who deal with issues that affect or have impact on people and the entire society. These researchers need to analyse the human population the same way scientists analyse their data after carrying experiments (Sharma, 2009). A big margin separates the type of measurement carried out when dealing with people and society and that carried out in natural sciences such as chemistry. The measurements for people are difficult since it uses estimations to in analysing the data while that of natural sciences uses accurate figures from the conduction of experiments. Measurements of data in a big population are carried out through random sampling. Random sampling does not give an accurate measurement in people since people are different from each other as compared to elements tested in natural sciences that are always identical (Graham, 2008). The best approach to measurement that should be used in the measurement of people and society is the use of a computerized sampling method that is programmed to practice simulation. Use of computerized sampling will ensure that the whole population is well represented (Davies, 2007). Concept of reliability and validity in quantitative and qualitative research Both qualitative and quantitative researches need to be valid and reliable to enhance their use by analysts. The validity of these research methods ensure the connection of the research carried out and the theoretical ideas of the researcher (Chikkodi and Satyaprasad, 2010). Much of it refers to the generalizability of the findings in both the quantitative and qualitative research. In a bid to assess, the validity of these two types of validity in research, their credibility and transferability aspect is very important (Connaway and Powell, 2010). The credibility aspect is used to determine whether the findings in the research seem believable while the transferability aspect determines whether the findings of the research are applicable in other context. Reliability of these types of research refers to the situation in which more than one observer of the study agrees with the findings (Katsirikou and Skiadas, 2012). The validity and reliability of qualitative research will mostly depend on the skill of the researcher. A good is example is how the researcher uses their good statistical skills to formulate, represent and analyse data. The validity and reliability of a quantitative research heavily depends on the measurement of an instrument. A good example is how an efficient apparatus in laboratory gives accurate data (Creswell and Plano Clark, 2007). Descriptive statistics In Business and Management, descriptive statistics are very essential as they show the analysis the data presented to them to help managers and business owners to make appropriate decisions. Examples of the data analysed by the descriptive statistics include sales revenue, profit margin and dividends. Some of the most common descriptive statistics used in this field are the measures of central tendency and the measures of dispersion (Neubauer, 2010). The measures of central tendency include the mean, the median and the mode. The mean is simply the average of the data presented and is calculated by finding the sum of all the data presented and dividing by the number of the data presented (Govaert, 2009). The mode is the most popular number in the data set. Lastly, the median is the middle value in a given data set arranged in an ascending order. The measures of central tendency are essential in estimating the value in which time may have, considering the research conducted. Measures of dispersion include the variance and the standard deviation. These descriptive statistics show the extent in which the data can vary from the mean (Sarantakos, 2007). Concept of statistical significance Statistical significance is a method that expresses the likelihood that a given result or outcome of an experiment could have occurred by mere chance (Neubauer, 2010). Statistical significance goes by the assumption that the sample of the data they are assigning probabilities to come from a population whose null hypothesis is true. Statistical significance is used to indicate that the static used is highly reliable (Foster, Barkus and Yavorsky, 2006). The findings found from it are of no importance towards decision making. A good example of in explaining the concept of statistical significance is the conducting of a mathematic test to a population of a thousand people that is comprised of both men and women. The null hypothesis is to find whether there is a difference in scores between men and women. The mean score for men is 100 while that for women is 98. If an independent group t-test method is used the difference found will have a 0.00l significance level. There is no importance in this significance level since the difference between the two mean scores is negligible (Poonia, et al., 2011). References Chikkodi, C. and Satyaprasad, B. 2010. Business statistics. Mumbai: Himalaya Pub. House. Connaway, L. and Powell, R. 2010. Basic research methods for librarians. Santa Barbara, Calif.: Libraries Unlimited. Creswell, J. and Plano Clark, V. 2007. Designing and conducting mixed methods research. Thousand Oaks, Calif.: SAGE Publications. Davies, M. 2007. Doing a successful research project. Basingstoke [England]: Palgrave Macmillan. Foster, J., Barkus, E. and Yavorsky, C. 2006. Understanding and using advanced statistics. London: SAGE Publications. Govaert, G. 2009. Data analysis. London: ISTE. Graham, A. 2008. Statistics. Blacklick, OH: McGraw-Hill. Katsirikou, A. and Skiadas, C. 2012. New trends in qualitative and quantitative methods in libraries. Singapore: World Scientific. Neubauer, D. 2010. Manual on presentation of data and control chart analysis. West Conshohocken, PA: ASTM International. Poonia, V., Poonia, V. and Poonia, M. 2011. Advanced statistics. New Delhi: Vishvabharti Publications. Sarantakos, S. 2007. Data analysis. Los Angeles: SAGE. Sharma, A. 2009. Statistics for management. Mumbai: Himalaya Pub. House. Read More