Step - 7 Collecting Data

Duration: 4 min

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Module outline

  1. Paper - 1 | Unit - 1 | Teaching Aptitude: Nature, Objectives & Characteristics of Teaching, Learners & Learning Process, Factors Affecting Teaching, Methods of Teaching, Teaching-Learning Aids & ICT Integration, Evaluation, Assessment & Measurement
  2. Paper - 1 | Unit - 2 | Research Aptitude: Introduction to Research, Validity & Reliability, Research Paradigms & Types of Research, Research Process Steps, Research Ethics, Writing & Publication
  3. Paper - 1 | Unit - 3 | Comprehension: Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions)
  4. Paper - 1 | Unit - 4 | Communication: Communication Basics, Language and Semiotics, Types of Communication, Communication Models, Mass Communication, Mass Media, Journalism, General Knowledge and General Studies related to Communication
  5. Paper - 1 | Unit - 5 | Mathematical Reasoning and Aptitude: Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Number System, Percentage, Ratio and Proportion (Ratios), Simple Interest and Compound Interest, Speed Time and Distance, Powers and Exponents (Surds and Indices), Profit and Loss, Average, Blood Relations, Directions (Direction Test), Analytical Reasoning (Counting Figures Reasoning), Verbal Analogy (Word Based Analogy), Divisibility Rules, Calendar, Miscellaneous, Time and Work, Algebra, Permutation and Combination, Probability
  6. Paper - 1 | Unit - 6 | Logical Reasoning: Syllogisms, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Venn Diagram, School Of Thoughts, Fallacy, Western Logic
  7. Paper - 1 | Unit - 7 | Data Interpretation: Data Interpretation
  8. Paper - 1 | Unit - 8 | Information and Communication Technology: MS Office Applications, Cyber Threats and Malware Attacks, Role of Internet and Web Services, Electronic Data Interchange and E-Commerce, Internet Fundamentals, Web Design & Development, Web Publishing & Hosting, Emerging Technologies, Terms & Abbreviations, Artificial Intelligence, Basics of COA, Introduction to OS, Introduction to CN, Data Communication, Net Layer: IP Addressing, Network Security, Number System, Number Representation, Society, Law & Ethics, Keyboard Shortcuts, Website, Browser & Services
  9. Paper - 1 | Unit - 9 | People Development and Environment: Ecosystem, Biomes & Environmental Issues, MDG & SDG, Air Pollution, Water Pollution, Soil, Noise Pollution and Waste Management, Natural Resources, Energy & Disaster Management, Global Environmental Conventions – COP, Protocols & ISA
  10. Paper - 1 | Unit - 10 | Higher Education System: Vedic Education, Jainism & Buddhism, Ancient Universities, Pre-Independence Commissions, Post-Independence Policy, Higher Education Structure & Accreditation, Universities & Learning Programmes, Types of Education & NEP 2020
  11. Live Classes: NTA UGC NET 2025 Live Class
  12. Full Mock Tests:
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AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This educational video presents a lecture on the two main types of data used in research: primary and secondary data. The first part of the video, under the heading 'Step 7 Collecting Data', defines primary data as information collected directly by researchers from original sources for a specific study. It provides an example of researchers studying the impact of exercise on mental health by designing and distributing a survey to college students. The second part, titled 'Secondary Data', defines this type of data as information that has already been collected and published by others. It is described as more cost-effective and time-efficient because it is readily accessible, but it may not perfectly align with the current research's needs and can be outdated or less reliable. The video uses a clear, structured format with on-screen text to explain these concepts.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video begins with a slide titled 'Step 7 Collecting Data'. It explains that data collection involves gathering information from sources like surveys, interviews, and experiments. The first topic, 'Primary Data', is introduced. The text defines primary data as information collected directly by researchers from original sources for a specific research study. It states that this data has not been previously collected or analyzed by others. The slide lists methods for gathering primary data, including surveys, interviews, observations, experiments, and focus groups. An example is provided: researchers studying the impact of exercise on mental health among college students design a questionnaire to collect data on exercise habits and mental health status, which is considered primary data because it is collected firsthand for their study.

  2. 2:00 – 3:39 02:00-03:39

    The video transitions to a new slide titled 'Secondary Data'. The text defines secondary data as information that has already been collected and published by someone else for a different purpose. It uses the analogy of using a recipe from a cookbook instead of creating one from scratch. The slide lists external sources for secondary data, such as television, radio, magazines, books, and newspapers. It also mentions that researchers can gather secondary data from online surveys conducted by market research firms or social media analytics reports. The text highlights that secondary data is more cost-effective and time-efficient because it is readily accessible and does not require the researcher to undertake the data collection process. However, it also notes a key disadvantage: secondary data may not perfectly align with the specific needs of the current research and can sometimes be outdated or less reliable, as the researcher has no control over the original data collection process.

The video provides a clear and structured comparison between primary and secondary data. It establishes that primary data is original, collected firsthand for a specific research question, and is often resource-intensive. In contrast, secondary data is pre-existing, readily available, and more efficient to use, but it comes with the trade-off of potential lack of relevance and reliability. The lecture progresses logically from defining the first concept to the second, using a consistent example and clear on-screen text to reinforce the key differences.

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