Data

Data are the facts and figures that are collected, analyzed and summarised for presentation and interpretation.  Data are made up of symbols such as alphabets (A to Z), digits (0 to 9) and special characters (like +,-,etc.). From a business perspective, data is a group of observations or measurements which is to be used for analysis and decision making. Data may be classified as either quantitative or qualitative. 

 

Quantitative data measure either how much or how many of something, i.e. the observations are a measurement or a count.  The mean, median, mode, percentiles, range, variance and standard deviation are the most commonly used numerical measures for quantitative data.

 

Qualitative data provide labels, or names, for categories of like items.  It is generated by observing an attribute of the population and then classifying individuals of the population into different classes or categories based on that attribute.  The proportion, or percentage of data values in each category is the primary numerical measure for qualitative data.

 

Data can also be classified as  primary & secondary.   Primary data is that collected by the researcher himself.  Secondary data is data collected by others to be 're-used' by the researcher. 

 

Data may be from different sources at different times, primary data could be from sources like mail surveys returns, coded interview data, pretest of posttest data, observational data.  The most common source would be a questionnaire, the research scholar would then have to devise a questionnaire.  He needs to take extreme care and attention in designing a questionnaire and be 100% certain that he has covered all the relevant questions and contexts before he mails them to the respondents.  Questionnaires could be structured, non-structured, codified and unmodified.  Instead of mailing the questionnaires, he could also resort to the interview technique which has  number of advantages, however this could be expensive if the respondents are widely scattered.

 

Secondary data is collected from either qualitative sources like Biographies, Diaries, Letters, Newspapers, Literature, Handbooks, Policy Statements, Published statistics from Central or State Government sources, Market & Opinion Researches, Professional Bodies, Company Law Board, Chambers of Commerce, Academic & Research Institutes, Data Archives & International Sources on the Internet, etc.

 

Descriptive statistics are tabular, graphical and numerical summaries of data.  The purpose of descriptive statistics is to facilitate the presentation and interpretation of data.  Most of the statistical presentations appearing in newspapers and magazines are descriptive in nature.  Univariate methods of descriptive statistics use data to enhance the understand of a single variable, multivariate methods focus on using statistics to understand the relationships amongst two or more variables. 

 

Classification thus involves sorting and grouping of relevant items together to constitute classes.  Characteristics / Properties of items in one class differ from those of the items in any other class with respect to the basis of classification under consideration.  The basic purpose is to help comparison, to accommodate a large number of observations into a few classes, to highlight important features and to do away with unimportant points and to present data in a form from which further statistical treatment of data is feasible and easier.  In general data is classified into Geographical, Chronological and Characteristical attributes.   

 

Tabulation succeeds classification in the research and statistical processes.  It refers to the presentation of data in a tabular form where each table has a title and body and each row and column has a caption.  Row and column headings describe the characteristic together with sub-classifications, if any.  Data may also have groupings, sub-totals, totals and grand totals.  All classifications whether they are quantitative or qualitative, can be treated in this fashion.

 

The most commonly used tabular summary of data for a single variable is frequency distribution.  A frequency distribution shows the number of data values in each of the non-overlapping classes.  Another tabular summary, called a relative frequency distribution, shows the fraction, or percentage of data values in each class.  The most common tabular summary of data for two variables is a cross tabulation, a two-variable analogue of a frequency distribution.

 

For a qualitative variable, a frequency distribution shows the number of data values in each qualitative category.  Constructing a frequency distribution for a quantitative variable requires more care in defining the classes and the division points between adjacent classes.  A frequency distribution would show the number of data values in each of these classes, and a relative frequency distribution would show the fraction of data values in each. 

 

 

EXAMPLES

 

Geographical classification & tabulation:

 

 

 

 

Chronological Classification & tabulation:

 

 

 

 

Characteristical Classification & Tabulation: