Research Data Presentation

• Data


  Facts or figures, which are numerical or otherwise, collected with a definite purpose
• Examples of data


  • Cricket batting or bowling averages
  • Profits of a company
  • Temperatures of cities
  • Expenditures in various sectors of a five year plan
  • Polling results
• Primary Data


  Data that is collected for the first time through personal experiences or evidence, particularly for research
• Primary Data


  • It is also described as raw data or first-hand information
  • The mode of assembling the information is costly
  • It is mostly collected through observations, physical testing, mailed questionnaires, surveys, personal interviews, telephonic interviews, case studies, and focus groups, etc.
• Secondary Data


  Second-hand data that is already collected and recorded by some researchers for their purpose, and not for the current research problem
• Secondary Data


  • It is accessible in the form of data collected from different sources such as government publications, censuses, internal records of the organisation, books, journal articles, websites and reports, etc.
  • This method of gathering data is affordable, readily available, and saves cost and time
  • The information assembled is for some other purpose and may not meet the present research purpose or may not be accurate
• Types of data classification

  • Geographical classification
  • Chronological classification
  • Qualitative classification
  • Quantitative classification
• Geographical classification


  Classification of data on the basis of location or areas
• Chronological classification


  Classification of data on the basis of time, like months, years etc.
• Qualitative classification


  Classification of data on the basis of some attributes or quality such as gender, colour of hair, literacy and religion. The attribute under study cannot be measured, it can only be found out whether it is present or absent in the units of study.
• Quantitative classification


  Classification of data according to some characteristics, which can be measured such as height, weight, income, profits etc.
• Nominal data is one of the types of qualitative information which helps to label the variables without providing the numerical value. Nominal data is also called the nominal scale. It cannot be ordered and measured. But sometimes, the data can be qualitative and quantitative. Examples of nominal data are letters, symbols, words, gender etc.
• Nominal data


  Qualitative information which helps to label the variables without providing the numerical value. It cannot be ordered and measured.
• Ordinal data/variable


  A type of data that follows a natural order. The significant feature of the ordinal data is that the difference between the data values is not determined.
• Discrete data


  Information that can only take certain values. These values don't have to be whole numbers but they are fixed values. Includes discrete variables that are finite, numeric, countable, and non-negative integers (5, 10, 15, and so on).
• Continuous data


  Data that can take any value. Height, weight, temperature and length are all examples of continuous data. Changes over time and can have different values at different time intervals like weight of a person.
• Quantitative data collection methods

  • Surveys
  • Polls
  • Experiments
• Surveys


  One of the most common research methods with quantitative data that involves questioning a large group of people. Questions are usually closed-ended and are the same for all participants.
• Polls


  Similar to surveys, yield quantitative data. You poll a number of people and apply a numeric value to how many people responded with each answer.
• Experiments


  A common method that usually involves a control group and an experimental group. The experiment is controlled and the conditions can be manipulated accordingly.
• Using both qualitative and quantitative methods is helpful because they collect rich and reliable data, which can be further tested and replicated.
• Examples of quantitative experiments

  • Controlled experiments
  • A/B tests
  • Blind experiments
• Quantitative data


  Numbers-based, countable, or measurable
• Qualitative data


  Interpretation-based, descriptive, and relating to language
• Quantitative data


  Tells us how many, how much, or how often in calculations
• Qualitative data


  Can help us to understand why, how, or what happened behind certain behaviors
• Quantitative data


  Fixed and universal
• Qualitative data


  Subjective and unique
• Quantitative research methods


  Measuring and counting
• Qualitative research methods


  Interviewing and observing
• Quantitative data


  Analyzed using statistical analysis
• Qualitative data


  Analyzed by grouping the data into categories and themes
• Methods of presenting data

  • Tabulation/Tabular Presentation
  • Drawing/Graphical Presentation
• Graphical Presentation


  • Line Graph
  • Scatter diagram
• Graphical Presentation


  We look for the overall pattern and for striking deviations from that pattern. Overall pattern usually described by shape, center, and spread of the data. An individual value that falls outside the overall pattern is called an outlier.
• Graphs for categorical variables

  • Bar diagram
  • Pie charts
• Graphs for numerical variables

  • Histogram
  • Stem and leaf
  • Box-plot
• Simple Tabulation


  Data are tabulated to one characteristic
• Complex tabulation


  Data are tabulated consistently with many characteristics
• Histogram


  A graphical display of data using bars of various heights. Each bar groups numbers into ranges. Taller bars show that more data falls in this range. Displays the form/shape and spread of continuous sample data.