Data presentation approach for EE
Data are scientific observations and measurement that, are analyzed and interpreted, to develop evidences for answering the research question question. The collected data need to be organized before analyzing. Organization of data is considered as self management skill, a child grasp at the age of 3 and enhance in it with practice.
A good data collection requires careful observation and appropriate methodology, but the more important task is to organize the collected data in a systematic manner, which is easy to interpret. The data that is gathered with observation is categorized as qualitative data and the data represented using numbers is categorized as quantitative data. There are different strategies to deal, present and analyze both types of data.
Qualitative Data:
It is the data gathered making observations like smell, color, sound, sensation (Hot/Cold) etc. This type of data also includes interviews, videos recordings and notes (more common in group 3 and group 2 cat.1 and 2 essays). Analyzing this data requires identification, of patterns or themes/ variables and most important deviations. This will help to find frequencies, magnitude, process, cause and consequences. Such data is well represented using diagrams or models explaining the relationship. If the data is in the form of interview / video / notes then narrative analysis will be a good way of analyzing it.
The best way to deal with such data to apply either deductive reasoning in which the research question is clear and focused. In this, method the data is categorized into groups (Subjective strands) keeping research question in mind and then each group is analyzed using theories. This work well when the data is gathered with survey.
Qualitative data if represented in categories it is considered as
Categorical (placed onto countable groups but no natural order): For example data from a survey representing percentage of people under different categories of food choice.)
Binary (placed into two groups) like number of male/ female or number of people with Yes/ No.
Both the data are better represented using pie chart but if the data is
Ordinal (placed into at least three groups with natural order) than bar graph representation is better.
Quantitative Data:
It is the data gathered in the form of numbers or values. Quantitative data is very important in subjects like science and mathematics. It gives a more realistic and provides stronger evidence to research. However in better analyzes of this data, its organizations plays the key role. Such data are generally in the form of numbers:
Numerical Data: In the form of numbers like height, weight, temperature, IQ etc. These values can be as:
Discrete: Which cannot be further divided. Example number of students. Such data is better represented with bar graphs and calculation of mean or standard deviation is carried out.
Continues: Which can be in a range of values like height of students or temperature. With continuous range of data, one can calculate mean, average, standard deviation and range. These data are better represented with line graph or histograms.
In Representation of quantitative data always, the X axis of graph is the independent variable and Y axis is the dependent variable.
Which ever method is used to organize or represent data the graphs always have labelled axis and are interpreted using following steps:
Step 1: Reading basics
Read the labels and the legend of the diagram
Step 2: Reading important numbers
Read the most important points. Important points are peaks, lows, turning points and intersection points.
Step 3: Define Trends
Find the trend in the graph
Step 4: Compare Trends
Find how and why the trend is changing?
Step 5: Analyze trends
What would be the reasons of this trend?
Step 6: Make predictions
State the reason of trend and deviations. Explain the information the graph is providing