Data are usually collected in a raw format and thus the inherent information is difficult to understand. Therefore, raw data need to be summarized, processed, and analyzed. However, no matter how well manipulated, the information derived from the raw data should be presented in an effective format, otherwise, it would be a great loss for both authors and readers.
The techniques of data and information presentation in textual, tabular, and graphical forms are to be introduced. Text is the principal method for explaining findings, outlining trends, and providing contextual information. A table is best suited for representing individual information and represents both quantitative and qualitative information. A graph is a very effective visual tool as it displays data at a glance, facilitates comparison, and can reveal trends and relationships within the data such as changes over time, frequency distribution, and correlation or relative share of a whole.
Text, tables, and graphs for data and information presentation are very powerful communication tools. They can make an article easy to understand, attract and sustain the interest of readers, and efficiently present large amounts of complex information. Moreover, as journal editors and reviewers glance at these presentations before reading the whole article, their importance cannot be ignored.
Mathematical presentation: Measures of central tendency: Mean, mode, median and midrange. Measures of dispersion: Range, mean deviation, variance and standard deviation. Tabular presentation: Simple tables, Frequency distribution tables. Cumulative frequency distribution tables: Ascending & Descending.
Graphical presentation: Pictogram, stem and leaves chart, bar chart, pie chart, line diagram, histogram.
Bar chart: The data variables are represented by bars. It can be simple, multiple or component. The multiple and component bar charts require a key to refer to the different variables.
Pie chart: The chart is in the form of a circle which is divided into portions representing the data variables. It requires a key to refer to the data variables.
Histogram: The chart is in the form of blocks representing the data variables. Example is the histogram representing age intervals as ( 0 – 10 ), ( 10 – 20 ), ( 20 – 30 ), etc.