What comes to your mind when you read or hear the words DATA PRESENTATION AND ANALYSIS?

What comes to your mind may include;

  • Graphs
  • Figures
  • Response rate
  • Demographic analysis

DEFINITION: The data presentation and analysis chapter presents and analyses data collected from a research. Some of the major issues discussed in this section include the response rate, the demographic profile of the respondents and the main research findings which are discussed as per objective. Findings can either be presented orally (viva) or as a written document (project/ dissertation / thesis) (Robindos Research Consultancy, 2020).

  • At Robindos Research Consultancy we assist our clients to present and analyse their data.

Data presentation and analysis chapter structure!!!

There is no fixed structure writing the data presentation and analysis chapter and usually universities and supervisors provide their preferred structure. However, the common structure at Robindos Research Consultancy is as follows;

  • Introduction
  • Background information of respondents
  • The other discussions should be in line with you research objectives (may be under sub headings related to your objectives)
  • Chapter summary

How to present research data?

The result section of an original research paper provides answer to this question “What was found?” The amount of findings generated in a typical research project is often much more than what can accommodate in one article. So, the first thing the author needs to do is to make a selection of what is worth presenting. Having decided that, he/she will need to convey the message effectively using a mixture of text, tables and graphics. The level of details required depends a great deal on the target audience of the paper.

Some general rules of data presentation!!!

  • Always use past tense in describing results
  • The main core of the result section consists of text, tables and graphics
  • Text provides narration and interpretation of the data presented
  • Leave the process of data collection to the methods section
  • Simple data with few categories is better presented in text form
  • Tables are useful in summarising large amounts of data systemically and graphics should be used to highlight evidence and trends in the data presented
  • The content of the data presented must match the research questions and objectives of the study in order to give meaning to the data presented
  • Keep the data and its statistical analyses as simple as possible to give the readers maximal clarity

Text, tables or graphics?

These complement each other in providing clear reporting of research findings. Do not repeat the same instrument in more than one structure. Select the best method to convey the message.

Text : Data, which often are numbers and figures, are better presented in tables and graphics, while the interpretation are better stated in text. By doing so, we do not need to repeat the values the text (which will be illustrated in tables or graphics), and we can interpret the data for the readers. However, if there are too few variables, the data can be easily described in a simple sentence including its interpretation. For example, the majority of diabetic patients enrolled in the study were male (80%) compared to female (20%). Using qualitative words to attract the readers’ attention is not helpful. Such words like “remarkably” decreased, “extremely” different and “obviously” higher are redundant. The exact values in the data will show just how remarkable, how extreme and how obvious the findings are. Avoid redundant words. Do not repeat the result within the text, tables and figures. Well-constructed tables and graphics should be self-explanatory, thus detailed explanation in the text is not required. Only important points and results need to be highlighted in the text.

Tables: Tables are useful to highlight precise numerical values; proportions or trends are better illustrated with charts or graphics. Tables summarise large amounts of related data clearly and allow comparison to be made among groups of variables. Generally, well-constructed tables should be self-explanatory with four main parts: title, columns, rows and footnotes.

  • Title;Keep it brief and relate clearly the content of the table. Words in the title should represent and summarise variables used in the columns and rows rather than repeating the columns and rows’ titles.
  • Columns and rows; Columns are vertically listed data, and rows are horizontally listed data.  Similar data ought to be presented in columns. Often these are dependant variables and allow clearer comparison among groups. A table with too many dependent variables would become too wide for a page.  There are two alternatives to this problem. We can list the dependant variables in the first left column and independent variables across the top. However, doing so should not compromise clarity of the message we want to get across. The second alternative is to cut down unnecessary columns, which, can be replaced by footnotes explaining their definition.
  • Footnotes; These add clarity to the data presented. They are listed at the bottom of tables. Their use is to define unconventional abbreviation, symbols, statistical analysis and acknowledgement (if the table is adapted from a published table).

Graphics: Graphics are particularly good for demonstrating a trend in the data that would not be apparent in tables. It provides visual emphasis and avoids lengthy text description. However, presenting numerical data in the form of graphs will lose details of its precise values which tables are able to provide. The authors have to decide the best structure of getting the intended message across. Is it for data precision or emphasis on a particular trend and pattern? Likewise, if the data is easily described in text, than text will be the preferred method, as it is more costly to print graphics than text.