Non-Random Sampling

Duration: 6 min

This video lesson is available to enrolled students.

Return to /learn/DSSSB-TGT-COMPUTER-SCIENCE-PAPER-2/mathematics-i-ii-iii-iv/research-process-steps/sampling-fundamentals-types-probability-non-probability-sampling/asset-non-random-sampling after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. About Course: About the Course
  2. Programming, Data Structures, OOP & Java: C Fundamentals, Control Flow, Functions, Arrays & Pointers, Storage Classes, Structures & Enums, DMA, Macros, Scoping & File Handling, Introduction to DS, Array, Stack, Queue, Linked List, Tree, Graphs, Hashing, Java, OOPS with C++, Java Fundamentals, Array, String, IO & References, Language Design, Intro to Compilers, Visual Basics, Syntax Analysis: Bottom-Up, Object Oriented Technology
  3. Computer Networks, TCP/IP & Network Security: Introduction to CN, Data Communication, DLL: Flow Control, DLL: Access Control, DLL: Error Control, Data Link Layer - Ethernet, Net Layer: IPv4 & Proto, Net Layer: IP Addressing, Transport Layer Services, TL: Congestion & UDP, Application Layer, Hardware basics, Network Access Technologies, Network Security, Society, Law & Ethics, Cloud Computing, Mobile Computing, Net Layer:Routing Protocol, Cyber Safety and Security
  4. Teaching Methodology: वृद्धि और विकास / Growth and Development, Concept of Pedagogy, विकास के सिद्धांत / Theories of Development, समावेशी शिक्षा / Inclusive Education, Experiential Pedagogy, आकलन और मूल्यांकन / Assessment and Evaluation, समाजीकरण और जेंडर / Socialisation and Gender, National Education Policy (NEP) 2020, संज्ञानात्मक प्रक्रियाएँ / Cognitive Processes, शिक्षण योजना और शिक्षण कौशल / Teaching Planning and Teaching Skills, Personality Development, शिक्षण विधियाँ / Teaching Methods, अधिगम और व्यवहारवादी सिद्धांत / Learning and Behavioural Theories, Learners & Learning Process, Factors Affecting Teaching, शिक्षण विधियाँ एवं उपागम / Teaching Methods and Approaches, शिक्षण-अधिगम प्रक्रिया एवं कक्षा प्रबंधन / Teaching-Learning Process and Classroom Management, शिक्षण सहायक सामग्री एवं शैक्षिक तकनीकी / Teaching Aids and Educational Technology, सूक्ष्म शिक्षण एवं कक्षा संप्रेषण / Microteaching and Classroom Communication, आकलन एवं मूल्यांकन / Assessment and Evaluation, पाठ्यचर्या, कला एवं सह-शैक्षिक क्षेत्र / Curriculum, Arts and Co-curricular Areas, दिव्यांग बच्चों की शिक्षा / Education of Children with Disabilities, शिक्षक: गुण, व्यावसायिक नैतिकता एवं विकास / Teacher: Qualities, Professional Ethics and Development
  5. Digital Electronics / Boolean Logic: Digital Systems & Boolean Basics, Logic Gates & Hardware, Boolean Expression, Boolean Minimization, Combinational Circuit, Sequential Circuits, Number System, Number Representation, Analog-Digital Conversion
  6. Operating Systems & Linux: Introduction to OS, Process Management, CPU Scheduling, Process Synchronization, Threads & Process Creation, Deadlock, Memory Management, Virtual Memory, File Management, Disc Scheduling, Unix OS, Linux OS, Distributed Systems, Windows OS
  7. Data Base Management System: Basics of DBMS, ER Diagram, Relational Model & Functional Dependencies, Keys & Integrity Constraints, Normalization (1NF - BCNF), Decomposition Properties & 4NF, Relational Algebra, SQL, File Organization & Indexing, Transaction Management, Concurrency Control, Database Security & Authorization, Database Recovery, Data Warehousing & Mining
  8. Internet & Web Technologies: Website, Browser & Services, Internet Fundamentals, Web Design & Development, Protocols & Services, Web Publishing & Hosting, Server Side & Security, HTML Basics, CSS, Emerging Technologies, JavaScript-Basics, .Net Programming, Electronic Data Interchange and E-Commerce, XML
  9. Computer Fundamentals, IT Basics & Architecture: Basics of COA, Cache Memory Organization, Input Output Organisation, Register Transfer and Microoperations, Control Unit Design, Programming the Basic Computer, Terms & Abbreviations, MS Office Applications, Instr Formats & Modes, Floating Point Rep, कंप्यूटर के मूल सिद्धांत / Computer Fundamentals, साइबर सुरक्षा / Cyber Safety and Security, कंप्यूटर सॉफ़्टवेयर / Computer Software, ऑपरेटिंग सिस्टम / Operating System, वर्ड प्रोसेसिंग (MS Word) / Word Processing (MS Word), कंप्यूटर नेटवर्क / Computer Networks, प्रेज़ेंटेशन (MS PowerPoint) / Presentation Software (MS PowerPoint)
  10. Python Language: Introduction to Python, Tokens, Variables, Data Types, I/O, Operators & Expressions, Control Flow & Looping, Collections & Strings, Functions & Modules, File Handling & Data Analysis
  11. Software Engineering: Fundamentals of Software Engineering, Software Requirements and Quality Assurance, Software Design, Estimation and Metrics, Software Testing, Software Maintenance and Configuration Management, extras, Fundamentals of Systems and Information Systems
  12. Design & Analysis of Algorithms: Algorithm Analysis, Time Complexity Analysis, Sorting Algorithms, Greedy Algorithms, Dynamic Programming, Minimum Spanning Trees, Shortest Path Algos, Advanced Algorithms
  13. Mathematics - I, II, III, IV: Permutation and Combination, Logarithm, Linear Algebra, Algebra, Calculus, Trigonometry, Vectors, Set Theory, Functions, Relations, Propositional and Predicate Logic, Research Process Steps, Graph Theory, समुच्चय, संबंध, फलन और विविक्त गणित / Sets, Relations, Functions and Discrete Mathematics, संख्या पद्धति और संख्या सिद्धांत / Number Systems and Number Theory, यूक्लिडीय ज्यामिति और क्षेत्रमिति / Euclidean Geometry and Mensuration, सांख्यिकी और प्रायिकता / Statistics and Probability, निर्देशांक ज्यामिति, सदिश और त्रिविमीय ज्यामिति / Coordinate Geometry, Vectors and 3D Geometry, बहुपद, समीकरण और सम्मिश्र संख्याएँ / Polynomials, Equations and Complex Numbers, समतल क्षेत्रमिति / Plane Mensuration, प्रतिचयन / Sampling, प्रतिशत / Percentage, सरलीकरण, घातांक और करणी / Simplification, Indices and Surds, अनुपात और समानुपात / Ratio and Proportion
  14. Computer Graphics, Multimedia & Mobile Computing: Basics of Computer Graphics, 2-D Geometrical Transforms and Viewing, 3-D Object Representation, Geometric Transformations and Viewing, Multimedia
  15. Emerging Trends: Emerging Tech Trends, Knowledge Representation, Natural Language Processing, व्यापार, विपणन एवं उपभोक्ता संरक्षण / Trade, Marketing and Consumer Protection
  16. Statistical Techniques: Probability, Statistics
  17. Interpolation - DI: आँकड़ा निर्वचन / Data Interpretation
  18. Basis of Physics: वैज्ञानिक उपलब्धियाँ और अनुसंधान / Scientific Achievements and Research
  19. Previous Year Paper:
  20. Business Studies and Management: Business Communication, प्रबंध के सिद्धांत एवं कार्य / Principles and Functions of Management, लेखांकन के आधार एवं लेन-देनों का अभिलेखन / Accounting Fundamentals and Recording of Transactions, Business Economics, ह्रास, अंतिम खाते व कंप्यूटरीकृत लेखांकन / Depreciation, Final Accounts and Computerised Accounting, Knowledge Management & New Economy
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

This educational video provides a comprehensive overview of non-probability or non-random sampling methods, which are used in research when a structured random process is not feasible. The lecture begins by defining non-probability sampling as a method where participants are selected based on the researcher's convenience, leading to a higher risk of bias and error compared to probability sampling. The video then systematically introduces four distinct types of non-probability sampling. First, Convenience Sampling is explained as selecting participants who are easily accessible, such as students on a university campus, which can lead to overrepresentation of certain groups. Second, Judgement or Purposive Sampling is described as a method where the researcher deliberately chooses participants based on their expertise or specific characteristics, such as selecting individuals with video game addiction for a study. Third, Quota Sampling is presented as a technique where the population is divided into subgroups (e.g., by age or income), and a predetermined number of participants are selected from each group to ensure the sample reflects the population's demographics. Finally, Snowball Sampling is detailed as a method for studying hard-to-reach populations, where initial participants are asked to refer others who share similar characteristics, creating a network of interconnected individuals. The video uses clear examples for each method to illustrate its application and potential limitations.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video opens with a title slide, 'Non-Probability or Non-Random Sampling,' and a definition of the method. The text explains that this approach involves selecting items based on the researcher's convenience rather than a structured random process. It highlights that this method is quick and convenient, often used when time and resources are limited, but it has a significant drawback: not all members of the population have an equal chance of being selected. This can introduce bias and result in a higher margin of error compared to probability sampling. The slide concludes by transitioning to the next topic with the text, 'Let us now dive into the types of non-probability or non-random sampling.'

  2. 2:00 – 5:00 02:00-05:00

    The video presents the first type of non-probability sampling, '1. Convenience Sampling,' defined as selecting participants who are easy to reach. An example is given of a researcher surveying students on a university campus for a student satisfaction survey, which is convenient but may lead to bias as more active or opinionated students are overrepresented. The second type, '2. Judgement or Purposive Sampling,' is introduced as a method where the researcher specifically chooses participants based on their knowledge or judgment. The example provided is a study on video game addiction, where the researcher targets individuals with a history of addiction from online gaming communities to gain valuable insights. The third type, '3. Quota Sampling,' is explained as dividing the population into subgroups (e.g., by age, gender, or income) and selecting a specific number of participants from each category to ensure the sample accurately represents the population. An example of studying smartphone usage in a city using age-based quotas (18-25, 26-40, etc.) is given to illustrate how this method ensures demographic representation.

  3. 5:00 – 5:51 05:00-05:51

    The video introduces the fourth type of non-probability sampling, '4. Snowball Sampling,' also known as Chain Referral Sampling. The text defines it as a method where participants help identify and recruit other participants, creating a 'snowball effect.' This technique is particularly useful for studying hard-to-reach populations, such as individuals with a rare medical condition. The example provided is a researcher who starts by finding one person with the condition and then asks that person to recommend others who also have it. This process continues, building a network of interconnected individuals who share similar experiences. The video concludes by summarizing that this method is effective when traditional sampling methods are difficult to use.

The video provides a structured and logical progression through the topic of non-probability sampling. It begins with a foundational definition and a clear contrast with probability sampling, establishing the core concept of bias and error. The subsequent sections are organized as a numbered list, each introducing a distinct method with a clear definition and a concrete, real-world example. This pedagogical approach allows the viewer to systematically understand the different techniques, their specific applications, and their inherent limitations. The synthesis of the lesson is that while non-probability sampling is a practical and efficient tool for researchers, it is fundamentally different from probability sampling and requires careful consideration of its potential for bias to ensure the validity of research findings.

Loading lesson…