Dissertation writing is a multi-stepped process that asks researchers to make several critical decisions at different point of time. Among all these, the most important stage is to summarise the large data set into small manageable units. These small manageable, but meaningful units must state something about the questions under study. In this way, data reduction is a part of data analysis process, which can be done in a variety of ways depending upon the availability of data. For descriptive data, that is in the form of statements, qualities, and characteristics, data reduction strategies may include theming and coding followed by a brief description.
Descriptive Data Reduction- A Brief Introduction:
Data reduction is the process of cleaning or summarising the collected data in an effort to reach logical consequences. It is important for commercial as well as academic research as large or widely scattered data set is not only difficult to organise but also includes many outliers making data analysis difficult. Thereby, after collecting the data from varying sources, it is advisable for researchers to reduce the large data set for observing trends effectively.
Descriptive data is another name for qualitative data. In this regard, statements, observations, opinions, and experiences in any textual or verbal form of data will be considered descriptive data. Contrastingly, the data in the form of digits or numbers is another form that requires statistical techniques to reduce the large data set. However, statistical techniques cannot be sorted, organised, and reduces using any statistical method; rather, critical analysis, thematic analysis, or discourse analysis are devised for this purpose. More deeply, coding and theming are two eminent techniques for descriptive data reduction.
Top Descriptive Data Reduction Strategies:
As far as academic research is concerned, data analysis or reduction can be done by using any reasonable data analysis framework, including content analysis, thematic analysis, discourse analysis, phenomenological analysis, and narrative analysis. The selection of a suitable method of analysis for your dissertation depends on your area of interest. However, online Dissertation Writing Services can assist you in making an educated decision about the selection of the right data analysis method for your dissertation. In all these methods, the basic technique or strategy to reduce data is coding and theming. Let’s start discussing descriptive data reduction strategies by describing coding and theming followed by frequencies and graphical-theoretic data reduction technique:
Top strategy# 1: Theming
Theming is a method of identifying repeated patterns across a large data set. This is an effective method of reducing descriptive data. After making themes, you will have only a few themes or subjects reflecting the crux of the whole research.
Top strategy# 2: Coding
Coding is another method of searching and identifying concepts in the descriptive data available either in the form of photographs or paragraphs. Coding refers to assigning words or digits to certain repeated trends to be used later for analysis. It represents the relationship between the different variables involved in a study. Therefore, coding is a data reduction strategy that not only assigns labels to your data but it links the ideas with the aims to achieve so logical consequences can be drawn.
Top strategy# 3: Frequencies:
In the descriptive data reduction technique, measuring frequencies is an act of counting how many times a certain word or phrase is present in a statement or response. Measuring frequencies by critically reading the text is one of the easiest ways to start up the reduction process. This is because; it helps you to measure the prevalence of thematic responses across the qualitative collected data. Identifying simple keywords and searching them in the descriptive data can help you achieve the reduction aims by using frequency as a top strategy.
Top strategy # 4: Graphical-theoretic data reduction technique:
Though theming and coding are the traditional methods of reduction of descriptive data, sometimes they ends by giving various codes that still are difficult to manage. Thus, the graph-theoretical technique makes its way. In other terms, the graph-theoretical technique is also called semantic network analysis. It makes finding the relationship between a large number of codes and themes by using co-occurrence matrices as input and giving graphical outputs. Graphical representation of the relationship between the different codes or themes is easy to understand and often gives more logical results.
In a nutshell, data analysis is important to creating a scientific story to justify the thesis claim. However, to increase the generalisation of research, researchers have to collect large data sets, but analysis cannot be done directly of analysing the widely scattered or raw data; thus, data reduction strategies are important. Thereby, the article has provided a brief overview of the top four reduction strategies of descriptive data that you must follow to complete commercial as well as academic research.
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