This is a process through which data is inspected, transformed and modelled in order to indentify the most useful information. Data analysis leads to suggestions that point to conclusions and establishment of effective decisions that supports the data. Michael Lewis (1995, p. 223) emphasizes that diverse techniques should be employed in the process of data analysis. Thus, various approaches are used depending on the nature of data being analysed. The field of study also determines the nature of data analysis. As a technique, data mining is a method that aims at changing and discovering the knowledge for predictive purposes, rather that analysing data in descriptive aims. Nevertheless, there are two major methods of analysing data, presented below.
Qualitative analysis of data
This type of analysis involves expression of all data collected from the participants in numerical form. For example, the data may involve the number of items collected, number of reactions, as well as the number of acts that were aggressive. Beynon (2009, p. 225) maintains that individuals, carrying this type of research, often use direct quotations based on their participant’s reactions. These quotations often add meaning or reveal something to the interpretation of the data collected.
In qualitative analysis, there are usually two types of measures that can be taken. Irrespective of whether we have a set of scores from the participants or not, the condition still applies. These measures include measures of central tendency, where the size of the average is provided. Secondly, the measure of dispersions indicates the extent to which the scores around the average are spread out. Gauch (2003, p. 96) stipulates that measures of central tendency are normally defined by the mean which is the average of the all the data collected divided by the number of participants. Medium is the most central number in a set of data. The final measure of central tendency is the mode which defines the most frequently occurred score in a set of data.
Quantitative Analysis of Data
In this type of analysis, data collected from the field is not expressed in numbers. Roger (1995, p. 160) states that the main focus is on the number of experiences that participants have gone through, as well as on the meanings that are attached to them and their environment. In quantitative analysis, relationships and theoretical statements are developed. These relations are based on the type of data collected. In this case, qualitative analysis is not influenced by biases and assumptions, like in quantitative analysis. Moreover, qualitative analysis provides an avenue, where prospects of understanding the participants in the study are ground in their social settings. On the contrary, quantitative analysis relies on a narrow aspect of individual’s behaviour. This implies that quantitative analysis does not put into consideration the entire observation of the an individual`s behaviour.
The major limitation with qualitative method of analysis data is that the reported findings are often unreliable and sometimes difficult to replicate. Thus, this kind of research is subjective and not very impressive (Tabachnick & Fidell, 2007 p. 26). In the same time, depending on the investigator, the categories in which information is put and the interpretations given to the data vary significantly. This leads to the lack of uniformity and to some differences when it comes to data analysis of the same phenomena that may require the use of the two major methods of data interpretations.
In order to show that qualitative research is reliable, the investigator should try to replicate the findings. This can be done by comparing the findings with those collected by a different method, e.g., interviews. In order to determine whether the findings are reliable two researches should be employed to carry out independent qualitative research on the same topic. At the end, their results and findings must be compared.