Data And Its Types PDF

# Data And Its Types ## 1. Meaning of Data - Data are values of qualitative or quantitative variables, belonging to a set of items. - Data is a plural of datum, which is originally a Latin noun meaning "something given." - Today, data is used in English both as a plural noun meaning "facts or pi...

# Data And Its Types ## 1. Meaning of Data - Data are values of qualitative or quantitative variables, belonging to a set of items. - Data is a plural of datum, which is originally a Latin noun meaning "something given." - Today, data is used in English both as a plural noun meaning "facts or pieces of information" and as a singular mass noun meaning "information." - These data represent the results of analyses. - Thus, data are assignments of values onto observations of events and objects. - Some examples of data are as follows: - 20 per cent of the people in the working age group are unemployed in India. - 50 per cent of total cattle population belongs to India. - In other words, statistical data is a sequence of observation, made on a set of objects included in the sample drawn from population. ### Definition - According to Webster's Third New International Dictionary, data refers to, "something given or admitted; facts or principles granted or presented; that upon which an inference or argument is based, or from which an ideal system of any sort is constructed." - The Oxford Encyclopedia English Dictionary describes data as, "known facts or things used as a basis for inference or reckoning." - UNESCO defines data as "facts, concepts or instructions in a formalised manner suitable for communication, interpretation or processing by human or automatic means." - The Dictionary of Modern Economics defines data as, "observations on the numerical magnitude of economic phenomena such as national income, unemployment, or the retail price." ## 2. Types of Data - Data can be classified in the following ways: - (i) Primary and Secondary Data - (ii) Univariate and Bivariate Data - (iii) Qualitative and Quantitative Data - (iv) Nominal and Ordinal Data - (v) Time Series and Cross Section Data ### I. Primary and Secondary Data #### 1. Primary Data - Data collected by the investigator for his own purpose, for the first time, from beginning to end, are called primary data. - These are collected from the source of origin. - In the words of Wessel "Data originally collected in the process of investigation are known as primary data." - Primary data are original. - The concerned investigator is the first person who collects these information. - The primary data are, therefore, a first-hand information. #### 2. Secondary Data - In the words of M.M. Blair, "Secondary data are those which are already in existence, and which have been collected, for some other purpose than the answering of the question in hand." - According to Wessel, "Data collected by other persons are called secondary data." - These data are, therefore, called second hand data. ### II. Univariate and Bivariate Data - Univariate, bivariate and multivariate are the various types of data that are based on the number of variables. - Variable means something that may or does vary. - In mathematics and statistics, it refers to a quantity or function that may assume any given value or set of values. #### 1. Univariate Data - Univariate refers to an expression, equation, function or polynomial of only one variable. - Univariate data is used for the simplest form of analysis. - It is the type of data in which analysis are made only based on one variable. - Univariate data does not deal with relationships between two things. #### 2. Bivariate Data - Bivariate data is data that has two variables. - It deals with relationships between these two variables. - The purpose of bivariate data is to analyse and explain this relationship. ### III. Qualitative and Quantitative Data #### 1. Qualitative Data - Qualitative data is a categorical measurement expressed not in terms of numbers, but rather by means of a natural language description. - In statistics, it is often used interchangeably with "categorical" data. - Examples might be gender, race, religion, or sport. - The qualitative data only tells us about something but does not tell the extent of something. #### 2. Quantitative Data - Quantitative data is a numerical measurement expressed in terms of numbers. - For example, the temperature of a city would be given in accurate measurement like 25 degrees C, the number of people in a town, or the number of deaths occurring due to a disease, etc. - Variables that can be quantified are: Length, height, area, volume, weight, speed, time, temperature, humidity, sound levels, cost, members, ages, etc. - Quantitative data can be represented visually in graphs, histograms, tables, and charts. ### IV. Time Series and Cross Section Data #### 1. Time Series data - Time series data consists of numerical values recorded at intervals of time. - Time series data occurs wherever the same measurements are recorded on a regular basis. #### 2. Cross-section Data - Cross-sectional studies look at only one time point. - Data is collected by analyzing different sets of data from different sources at a particular time. ## 3. Importance of Data in Research - Data plays a crucial role in research, serving as the foundation upon which numerous studies are built and conclusions are drawn. - Its importance can be highlighted through several key aspects: ### 1. Evidence-Based Findings - Data provides the empirical evidence needed to support or refute hypothesis. - Through systematic collection and analysis, researchers can substantiate their findings with objective evidence, ensuring that conclusions are grounded in reality rather than speculation. ### 2. Informed Decision-Making - Data-driven research enables more accurate and informed decision-making. - By analysing data, researchers can identify patterns, trends, and relationships, leading to well-supported recommendations and strategies in various fields, from public health to business. ### 3. Validation of Theories - Data is essential for testing and validating theories. - Researchers use data to confirm or challenge existing theories, refine theoretical models, and advance knowledge in their field. ### 4. Replication and Reliability - High-quality data allows for the replication of studies, which is crucial for verifying results and ensuring reliability. - Replication helps to establish the credibility of research findings and contributes to the robustness of scientific knowledge. ### 5. Innovation and Improvement - Data analysis often reveals new insights and opportunities for innovation. - In fields like technology and medicine, data-driven discoveries can lead to advancements and improvements in practices, products, and interventions. ### 6. Objective Measurement - Data provides a quantitative basis for measurement, reducing bias and subjectivity in research. - By relying on measurable and verifiable data, researchers can achieve more accurate and consistent results. ### 7. Policy and Planning - Data is instrumental in shaping policies and planning. - Governments, organizations, and institutions use data to inform public policies, allocate resources, and address societal issues effectively. ## Conclusion - In summary, data is crucial for advancing research, supporting evidence-based practices, and driving progress across various disciplines. - Its role in providing objective, reliable, and actionable insights underscores its significance in the research process. ## QUESTIONS ### I. Short Answer Type Questions 1. What do you understand by Data? 2. Write three differences between nominal and ordinal data. 3. Write a short note on primary data and secondary data with an example for each. 4. Define cross section data. 5. What is time series data? ### II. Long Answer Type Questions 1. Differentiate between nominal, ordinal, interval, and ratio data. 2. Explain how is data important in research among different fields? 3. Explain in detail Qualitative data and Quantitative data with examples. 4. Describe Univariate and Bivariate data.