The document discusses different research methods such as quantitative, qualitative, surveys, interviews, and case studies that can be used to gather data. It explains the difference between methods, which are techniques for gathering evidence, and methodologies, which are the underlying theories of how research should proceed. The purpose of understanding these methods is to allow individuals to intelligently participate in and understand research projects and studies.
Magnum Clothing Pvt Ltd is a sewing unit located in Oragadam, India that exports garments to leading UK retailers. It produces around 2,500 pieces per day using high quality woven fabric from cotton, polyester or Lurex that costs a minimum of 2.5k. The manufacturing process involves fabric relaxation, laying, cutting, bundling, ticketing, fusing, sewing, reinforcements, bar tacking, assembling, inspecting, packaging and exporting. Key steps include fabric relaxation for 10-12 hours, cutting using straight knife machines or band neck machines, fusing to add interlining, bundling cut pieces, ticketing for identification, sewing following an operation bulletin,
Management Information Systems and Decision-Making: An Overview discusses different types of information systems that support decision-making at various management levels. It describes strategic, tactical, and operational management and the kind of structured, semi-structured, and unstructured decisions made at each level. The document also outlines different types of information systems like Management Information Systems, Decision Support Systems, Executive Information Systems, and Specialized Processing Systems that provide information to managers and professionals. Finally, it notes that real-world information systems typically integrate various types of systems to support multiple management levels and business functions.
Quality may define as the level of acceptance of goods or services. It is a relative term. It completely depends on customer satisfaction. Actually product quality is based on product attribute. In textile and apparel industry, quality is calculated in terms of quality and standard of fibres, yarns, fabric construction, colour fastness, designs and the final finished garments.
Vendor Compliance PPT Infographic Template Graphics Example Compliance Perfor...SlideTeam
Our professionally designed PowerPoint presentation is sure to impress executives, inspire team members and other audience. With a complete set of thirteen slides, this PPT is the most comprehensive summary of Vendor Compliance Ppt Infographic Template Graphics Example Compliance Performance you could have asked for. The content is extensively researched and designs are professional. Our PPT designers have worked tirelessly to craft this deck using beautiful PowerPoint templates, graphics, diagrams and icons. On top of that, the deck is 100 percent editable in PowerPoint so that you can enter your text in the placeholders, change colors if you wish to, and present in the shortest time possible.
Use of Biostatics in Dentistry /certified fixed orthodontic courses by Indian...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
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The document discusses cluster sampling and multistage sampling methods. Cluster sampling involves splitting the population into clusters, randomly selecting some clusters, and sampling every unit within those clusters. Multistage sampling combines multiple sampling methods, such as stratified and cluster sampling. It is commonly used in surveys conducted by polling organizations. Some advantages of cluster and multistage sampling are that they are simpler and less costly than simple random sampling, while still allowing estimates of population characteristics.
This document outlines different types of sampling methods used in quantitative and qualitative research. It discusses probabilistic sampling techniques like simple random sampling, systematic sampling, stratified sampling, and cluster sampling that are used in quantitative studies to select representative samples from a population. It also covers non-probabilistic sampling methods like convenience sampling and snowball sampling. For qualitative research, it describes purposeful sampling techniques such as maximal variation sampling, typical case sampling, theory-based sampling, and opportunistic sampling that target information-rich cases for in-depth study.
Magnum Clothing Pvt Ltd is a sewing unit located in Oragadam, India that exports garments to leading UK retailers. It produces around 2,500 pieces per day using high quality woven fabric from cotton, polyester or Lurex that costs a minimum of 2.5k. The manufacturing process involves fabric relaxation, laying, cutting, bundling, ticketing, fusing, sewing, reinforcements, bar tacking, assembling, inspecting, packaging and exporting. Key steps include fabric relaxation for 10-12 hours, cutting using straight knife machines or band neck machines, fusing to add interlining, bundling cut pieces, ticketing for identification, sewing following an operation bulletin,
Management Information Systems and Decision-Making: An Overview discusses different types of information systems that support decision-making at various management levels. It describes strategic, tactical, and operational management and the kind of structured, semi-structured, and unstructured decisions made at each level. The document also outlines different types of information systems like Management Information Systems, Decision Support Systems, Executive Information Systems, and Specialized Processing Systems that provide information to managers and professionals. Finally, it notes that real-world information systems typically integrate various types of systems to support multiple management levels and business functions.
Quality may define as the level of acceptance of goods or services. It is a relative term. It completely depends on customer satisfaction. Actually product quality is based on product attribute. In textile and apparel industry, quality is calculated in terms of quality and standard of fibres, yarns, fabric construction, colour fastness, designs and the final finished garments.
Vendor Compliance PPT Infographic Template Graphics Example Compliance Perfor...SlideTeam
Our professionally designed PowerPoint presentation is sure to impress executives, inspire team members and other audience. With a complete set of thirteen slides, this PPT is the most comprehensive summary of Vendor Compliance Ppt Infographic Template Graphics Example Compliance Performance you could have asked for. The content is extensively researched and designs are professional. Our PPT designers have worked tirelessly to craft this deck using beautiful PowerPoint templates, graphics, diagrams and icons. On top of that, the deck is 100 percent editable in PowerPoint so that you can enter your text in the placeholders, change colors if you wish to, and present in the shortest time possible.
Use of Biostatics in Dentistry /certified fixed orthodontic courses by Indian...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
The document discusses cluster sampling and multistage sampling methods. Cluster sampling involves splitting the population into clusters, randomly selecting some clusters, and sampling every unit within those clusters. Multistage sampling combines multiple sampling methods, such as stratified and cluster sampling. It is commonly used in surveys conducted by polling organizations. Some advantages of cluster and multistage sampling are that they are simpler and less costly than simple random sampling, while still allowing estimates of population characteristics.
This document outlines different types of sampling methods used in quantitative and qualitative research. It discusses probabilistic sampling techniques like simple random sampling, systematic sampling, stratified sampling, and cluster sampling that are used in quantitative studies to select representative samples from a population. It also covers non-probabilistic sampling methods like convenience sampling and snowball sampling. For qualitative research, it describes purposeful sampling techniques such as maximal variation sampling, typical case sampling, theory-based sampling, and opportunistic sampling that target information-rich cases for in-depth study.
This document defines sampling and discusses key concepts for selecting samples. It defines sampling as selecting some members of a population to represent the whole. Probability and non-probability sampling methods are covered, including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. The importance of representativeness is emphasized. Sample size calculations and factors like design effect are also addressed. The goal of sampling is to obtain information about large populations with minimal cost, maximum speed and precision.
The document discusses various sampling techniques used in research methodology, including probability and non-probability sampling. It describes in detail random sampling, systematic sampling, stratified sampling, convenience sampling, snowball sampling, and quota sampling. For each technique, it covers when and how it is used, pros, cons, and examples. Probability sampling techniques like random sampling, systematic sampling, and stratified sampling aim for representativeness but can be costly, while non-probability methods like convenience sampling and snowball sampling are cheaper but less robust.
This document provides an overview of survey sampling techniques. It discusses why samples are used instead of censuses, defines key terms like population and sample, and describes different probability and non-probability sampling methods. The central limit theorem states that as sample size increases, the sample mean will converge on the population mean. There are sampling errors from using a sample instead of a census, as well as non-sampling errors from things like measurement error. Steps in sampling include defining the population, finding a sampling frame, drawing a random sample, and ensuring it is representative and unbiased.
This document discusses sampling design and various sampling methods used in research. It defines key concepts like population, sampling frame, and sampling unit. It also describes different types of probability sampling designs including simple random sampling, systematic random sampling, and stratified random sampling. Non-probability sampling methods like convenience sampling are also briefly covered. The aims and advantages of sampling are to obtain representative results in a timely and cost-effective manner while minimizing bias.
This document discusses various sampling methods used in research. It defines sampling as selecting a subset of individuals from a larger population to gather information about that population. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to provide an unbiased representation of the population. Non-probability methods like purposive sampling and snowball sampling are used when random selection is not feasible. Key factors that influence sampling like sample size, bias, and population characteristics are also reviewed. The document provides examples and compares advantages and disadvantages of different sampling techniques.
This document discusses sample design and the steps involved in determining an appropriate sample. It defines key terms like population, sample, sampling frame, and outlines different sampling techniques. It emphasizes the importance of sample size and how to calculate it using confidence intervals in order to achieve the desired level of accuracy and confidence in results. Sources of error like sampling error and non-sampling error are also explained.
This document discusses various methods for sampling populations and collecting data, including:
- Probability and non-probability sampling techniques like simple random sampling, stratified sampling, and cluster sampling.
- Data collection methods like questionnaires, literature reviews, observation, and interviews. It provides details on constructing questionnaires, conducting observations, and potential sources of error.
The document provides an overview of sampling and the sampling process. It defines key terms like population, target population, accessible population, and sample. It discusses different types of sampling methods including probability sampling methods like simple random sampling, stratified random sampling, cluster random sampling, and systematic random sampling. It also covers non-probability sampling methods like convenience sampling, purposive sampling, and quota sampling. The document explains how to select samples and highlights advantages and disadvantages of different sampling techniques.
The process of obtaining information from a subset (sample) of
a larger group (population)
The results for the sample are then used to make estimates of
the larger group
Faster and cheaper than asking the entire population
This document discusses key concepts in sampling, including different types of sampling methods. It defines population, sample, sample unit, and sampling frame. It also distinguishes between probability and non-probability sampling. Four main types of probability sampling are covered: simple random sampling, systematic sampling, cluster sampling, and stratified sampling. The document also discusses four non-probability sampling methods: convenience sampling, purposive sampling, referral sampling, and quota sampling. It provides examples of how to calculate probabilities of selection for simple random sampling and how to determine a skip interval for systematic sampling.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
The document discusses key concepts in research methodology including sampling, population, and bias. It defines population as the entire group of entities being studied and sampling as selecting a subset of units from the population. Probability and non-probability sampling methods are described. Probability methods like random, stratified, and cluster sampling aim for representativeness while non-probability methods like convenience sampling do not. Sources of error like non-sampling bias and sampling bias are also outlined.
Sampling is the process of selecting a representative subset of a population for research purposes. There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling uses random selection to give every member of the population an equal chance of being selected, reducing bias. Common probability sampling techniques include simple random sampling, stratified random sampling, and cluster sampling. Non-probability sampling does not use random selection and cannot accurately represent the entire population. Common non-probability techniques include convenience sampling, judgement sampling, quota sampling, and snowball sampling. The choice of sampling technique depends on factors like the size and nature of the population.
This document discusses key components and concepts of research methods. It covers:
1) Main components of research methods including study design, population, sampling, variables, data collection and analysis.
2) Probability and non-probability sampling techniques such as simple random sampling, stratified sampling, and cluster sampling.
3) Key terms related to sampling such as target population, study population, sampling unit, and sampling frame.
This document discusses sampling methods used in research. It defines key terms like population, sample, and sampling. There are two main types of sampling - probability sampling and non-probability sampling. Probability sampling uses random selection to ensure each member of the population has an equal chance of being selected, allowing for generalization of results. Common probability methods are simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Non-probability sampling relies on personal judgment and does not allow for generalization beyond the sample. Common non-probability methods are convenience sampling, purposive sampling, snowball sampling, and quota sampling. The document outlines the process, advantages, and disadvantages of different sampling techniques.
The document provides an overview of research process module 2, which covers topics related to sampling design and methods. It defines key terms like population, sample, sampling, random and non-random sampling. It then describes various probability sampling techniques like simple random sampling, stratified random sampling, cluster sampling, systematic sampling, and multi-stage sampling. It also discusses non-probability sampling techniques like convenience sampling and quota sampling. The document provides details on when and how to apply these various sampling methods.
This document defines sampling and discusses key concepts for selecting samples. It defines sampling as selecting some members of a population to represent the whole. Probability and non-probability sampling methods are covered, including simple random sampling, systematic sampling, stratified sampling, cluster sampling, and multistage sampling. The importance of representativeness is emphasized. Sample size calculations and factors like design effect are also addressed. The goal of sampling is to obtain information about large populations with minimal cost, maximum speed and precision.
The document discusses various sampling techniques used in research methodology, including probability and non-probability sampling. It describes in detail random sampling, systematic sampling, stratified sampling, convenience sampling, snowball sampling, and quota sampling. For each technique, it covers when and how it is used, pros, cons, and examples. Probability sampling techniques like random sampling, systematic sampling, and stratified sampling aim for representativeness but can be costly, while non-probability methods like convenience sampling and snowball sampling are cheaper but less robust.
This document provides an overview of survey sampling techniques. It discusses why samples are used instead of censuses, defines key terms like population and sample, and describes different probability and non-probability sampling methods. The central limit theorem states that as sample size increases, the sample mean will converge on the population mean. There are sampling errors from using a sample instead of a census, as well as non-sampling errors from things like measurement error. Steps in sampling include defining the population, finding a sampling frame, drawing a random sample, and ensuring it is representative and unbiased.
This document discusses sampling design and various sampling methods used in research. It defines key concepts like population, sampling frame, and sampling unit. It also describes different types of probability sampling designs including simple random sampling, systematic random sampling, and stratified random sampling. Non-probability sampling methods like convenience sampling are also briefly covered. The aims and advantages of sampling are to obtain representative results in a timely and cost-effective manner while minimizing bias.
This document discusses various sampling methods used in research. It defines sampling as selecting a subset of individuals from a larger population to gather information about that population. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to provide an unbiased representation of the population. Non-probability methods like purposive sampling and snowball sampling are used when random selection is not feasible. Key factors that influence sampling like sample size, bias, and population characteristics are also reviewed. The document provides examples and compares advantages and disadvantages of different sampling techniques.
This document discusses sample design and the steps involved in determining an appropriate sample. It defines key terms like population, sample, sampling frame, and outlines different sampling techniques. It emphasizes the importance of sample size and how to calculate it using confidence intervals in order to achieve the desired level of accuracy and confidence in results. Sources of error like sampling error and non-sampling error are also explained.
This document discusses various methods for sampling populations and collecting data, including:
- Probability and non-probability sampling techniques like simple random sampling, stratified sampling, and cluster sampling.
- Data collection methods like questionnaires, literature reviews, observation, and interviews. It provides details on constructing questionnaires, conducting observations, and potential sources of error.
The document provides an overview of sampling and the sampling process. It defines key terms like population, target population, accessible population, and sample. It discusses different types of sampling methods including probability sampling methods like simple random sampling, stratified random sampling, cluster random sampling, and systematic random sampling. It also covers non-probability sampling methods like convenience sampling, purposive sampling, and quota sampling. The document explains how to select samples and highlights advantages and disadvantages of different sampling techniques.
The process of obtaining information from a subset (sample) of
a larger group (population)
The results for the sample are then used to make estimates of
the larger group
Faster and cheaper than asking the entire population
This document discusses key concepts in sampling, including different types of sampling methods. It defines population, sample, sample unit, and sampling frame. It also distinguishes between probability and non-probability sampling. Four main types of probability sampling are covered: simple random sampling, systematic sampling, cluster sampling, and stratified sampling. The document also discusses four non-probability sampling methods: convenience sampling, purposive sampling, referral sampling, and quota sampling. It provides examples of how to calculate probabilities of selection for simple random sampling and how to determine a skip interval for systematic sampling.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
The document discusses key concepts in research methodology including sampling, population, and bias. It defines population as the entire group of entities being studied and sampling as selecting a subset of units from the population. Probability and non-probability sampling methods are described. Probability methods like random, stratified, and cluster sampling aim for representativeness while non-probability methods like convenience sampling do not. Sources of error like non-sampling bias and sampling bias are also outlined.
Sampling is the process of selecting a representative subset of a population for research purposes. There are two main types of sampling: probability sampling and non-probability sampling. Probability sampling uses random selection to give every member of the population an equal chance of being selected, reducing bias. Common probability sampling techniques include simple random sampling, stratified random sampling, and cluster sampling. Non-probability sampling does not use random selection and cannot accurately represent the entire population. Common non-probability techniques include convenience sampling, judgement sampling, quota sampling, and snowball sampling. The choice of sampling technique depends on factors like the size and nature of the population.
This document discusses key components and concepts of research methods. It covers:
1) Main components of research methods including study design, population, sampling, variables, data collection and analysis.
2) Probability and non-probability sampling techniques such as simple random sampling, stratified sampling, and cluster sampling.
3) Key terms related to sampling such as target population, study population, sampling unit, and sampling frame.
This document discusses sampling methods used in research. It defines key terms like population, sample, and sampling. There are two main types of sampling - probability sampling and non-probability sampling. Probability sampling uses random selection to ensure each member of the population has an equal chance of being selected, allowing for generalization of results. Common probability methods are simple random sampling, systematic sampling, stratified sampling, and cluster sampling. Non-probability sampling relies on personal judgment and does not allow for generalization beyond the sample. Common non-probability methods are convenience sampling, purposive sampling, snowball sampling, and quota sampling. The document outlines the process, advantages, and disadvantages of different sampling techniques.
The document provides an overview of research process module 2, which covers topics related to sampling design and methods. It defines key terms like population, sample, sampling, random and non-random sampling. It then describes various probability sampling techniques like simple random sampling, stratified random sampling, cluster sampling, systematic sampling, and multi-stage sampling. It also discusses non-probability sampling techniques like convenience sampling and quota sampling. The document provides details on when and how to apply these various sampling methods.
Conferences like DigiMarCon provide ample opportunities to improve our own marketing programs by learning from others. But just because everyone is jumping on board with the latest idea/tool/metric doesn’t mean it works – or does it? This session will examine the value of today’s hottest digital marketing topics – including AI, paid ads, and social metrics – and the truth about what these shiny objects might be distracting you from.
Key Takeaways:
- How NOT to shoot your digital program in the foot by using flashy but ineffective resources
- The best ways to think about AI in connection with digital marketing
- How to cut through self-serving marketing advice and engage in channels that truly grow your business
Are you struggling to differentiate yourself in a saturated market? Do you find it challenging to attract and retain buyers? Learn how to effectively communicate your expertise using a Free Book Funnel designed to address these challenges and attract premium clients. This session will explore how a well-crafted book can be your most effective marketing tool, enhancing your credibility while significantly increasing your leads and sales while decreasing overall lead cost. Unpacking practical steps to create a magnetic book funnel that not only draws in your ideal customers, but also keeps them engaged. Break through the noise in the marketing world and leave with a blueprint that will transform your sales strategy.
Unlock the secrets to enhancing your digital presence with our masterclass on mastering online visibility. Learn actionable strategies to boost your brand, optimize your social media, and leverage SEO. Transform your online footprint into a powerful tool for growth and engagement.
Key Takeaways:
1. Effective techniques to increase your brand's visibility across various online platforms.
2. Strategies for optimizing social media profiles and content to maximize reach and engagement.
3. Insights into leveraging SEO best practices to improve search engine rankings and drive organic traffic.
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
How to Use AI to Write a High-Quality Article that Ranksminatamang0021
In the world of content creation, many AI bloggers have drifted away from their original vision, resulting in low-quality articles that search engines overlook. Don't let that happen to you! Join us to discover how to leverage AI tools effectively to craft high-quality content that not only captures your audience's attention but also ranks well on search engines.
Disclaimer: Some of the prompts mentioned here are the examples of Matt Diggity. Please use it as reference and make your own custom prompts.
The Secret to Engaging Modern Consumers: Journey Mapping and Personalization
In today's digital landscape, understanding the customer's journey and delivering personalized experiences are paramount. This masterclass delves into the art of consumer journey mapping, a powerful technique that visualizes the entire customer experience across touchpoints. Attendees will learn how to create detailed journey maps, identify pain points, and uncover opportunities for optimization. The presentation also explores personalization strategies that leverage data and technology to tailor content, products, and experiences to individual customers. From real-time personalization to predictive analytics, attendees will gain insights into cutting-edge approaches that drive engagement and loyalty.
Key Takeaways:
Current consumer landscape; Steps to mapping an effective consumer journey; Understanding the value of personalization; Integrating mapping and personalization for success; Brands that are getting It right!; Best Practices; Future Trends
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
Google Ads Vs Social Media Ads-A comparative analysisakashrawdot
Explore the differences, advantages, and strategies of using Google Ads vs Social Media Ads for online advertising. This presentation will provide insights into how each platform operates, their unique features, and how they can be leveraged to achieve marketing goals.
In this humorous and data-heavy Master Class, join us in a joyous celebration of life honoring the long list of SEO tactics and concepts we lost this year. Remember fondly the beautiful time you shared with defunct ideas like link building, keyword cannibalization, search volume as a value indicator, and even our most cherished of friends: the funnel. Make peace with their loss as you embrace a new paradigm for organic content: Pillar-Based Marketing. Along the way, discover that the results that old SEO and all its trappings brought you weren’t really very good at all, actually.
In this respectful and life-affirming service—erm, session—join Ryan Brock (Chief Solution Officer at DemandJump and author of Pillar-Based Marketing: A Data-Driven Methodology for SEO and Content that Actually Works) and leave with:
• Clear and compelling evidence that most legacy SEO metrics and tactics have slim to no impact on SEO outcomes
• A major mindset shift that eliminates most of the metrics and tactics associated with SEO in favor of a single metric that defines and drives organic ranking success
• Practical, step-by-step methodology for choosing SEO pillar topics and publishing content quickly that ranks fast
In the digital age, businesses are inundated with tools promising to streamline operations, enhance creativity, and boost productivity. Yet, the true key to digital transformation lies not in the accumulation of tools but in strategically integrating the right AI solutions to revolutionize workflows. Join Jordache, an experienced entrepreneur, tech strategist and AI consultant, as he explores essential AI tools across three critical categories—Ideation, Creation, and Operations—that can reshape the way your business creates, operates, and scales.This talk will guide you through the practicalities of selecting and effectively using AI tools that go beyond the basics of today’s popular tools like ChatGPT, Claude, Gemini, Midjourney, or Dall-E. For each category of tools, Jordache will address three crucial questions: What is each tool? Why is each one valuable to you as a business leader? How can you start using it in your workflow? This approach will not only clarify the role of these tools but also highlight their strategic value, making it perfect for business leaders ready to make informed decisions about integrating AI into their workflows.
Key Takeaways:
>> Strategic Selection and Integration: Understand how to select AI tools that align with your business goals and how to conceptually integrate them into your workflows to enhance efficiency and innovation.
>> Understanding AI Tool Categories: Gain a deeper understanding of how AI tools can be leveraged in the areas of ideation, creation, and operation—transforming each aspect of your business.
>> Practical Starting Points: Learn how you can start using these tools in your business with practical tips on initial steps and integration ideas.
>> Future-Proofing Your Business: Discover how staying informed about and utilizing the latest AI tools and strategies can keep your business competitive in a rapidly evolving digital landscape.
In this humorous and data-heavy session, join us in a joyous celebration of life honoring the long list of SEO tactics and concepts we lost this year. Remember fondly the beautiful time you shared with defunct ideas like link building, keyword cannibalization, search volume as a value indicator, and even our most cherished of friends: the funnel. Make peace with their loss as you embrace a new paradigm for organic content: Pillar-Based Marketing. Along the way, discover that the results that old SEO and all its trappings brought you weren’t really very good at all, actually.
In this respectful and life-affirming service—erm, session—join Ryan Brock (Chief Solution Officer at DemandJump and author of Pillar-Based Marketing: A Data-Driven Methodology for SEO and Content that Actually Works) and leave with:
• Clear and compelling evidence that most legacy SEO metrics and tactics have slim to no impact on SEO outcomes
• A major mindset shift that eliminates most of the metrics and tactics associated with SEO in favor of a single metric that defines and drives organic ranking success
• Practical, step-by-step methodology for choosing SEO pillar topics and publishing content quickly that ranks fast
What’s “In” and “Out” for ABM in 2024: Plays That Help You Grow and Ones to L...Demandbase
Delve into essential ABM ‘plays' that propel success while identifying and leaving behind tactics that no longer yield results. Led by ABM Experts, Jon Barcellos, Head of Solutions at Postal and Tom Keefe, Principal GTM Expert at Demandbase.
Build marketing products across the customer journey to grow your business and build a relationship with your customer. For example you can build graders, calculators, quizzes, recommendations, chatbots or AR apps. Things like Hubspot's free marketing grader, Moz's site analyzer, VenturePact's mobile app cost calculator, new york times's dialect quiz, Ikea's AR app, L'Oreal's AR app and Nike's fitness apps. All of these examples are free tools that help drive engagement with your brand, build an audience and generate leads for your core business by adding value to a customer during a micro-moment.
Key Takeaways:
Learn how to use specific GPTs to help you Learn how to build your own marketing tools
Generate marketing ideas for your business How to think through and use AI in marketing
How AI changes the marketing game
Mastering Local SEO for Service Businesses in the AI Era"" is tailored specifically for local service providers like plumbers, dentists, and others seeking to dominate their local search landscape. This session delves into leveraging AI advancements to enhance your online visibility and search rankings through the Content Factory model, designed for creating high-impact, SEO-driven content. Discover the Dollar-a-Day advertising strategy, a cost-effective approach to boost your local SEO efforts and attract more customers with minimal investment. Gain practical insights on optimizing your online presence to meet the specific needs of local service seekers, ensuring your business not only appears but stands out in local searches. This concise, action-oriented workshop is your roadmap to navigating the complexities of digital marketing in the AI age, driving more leads, conversions, and ultimately, success for your local service business.
Key Takeaways:
Embrace AI for Local SEO: Learn to harness the power of AI technologies to optimize your website and content for local search. Understand the pivotal role AI plays in analyzing search trends and consumer behavior, enabling you to tailor your SEO strategies to meet the specific demands of your target local audience. Leverage the Content Factory Model: Discover the step-by-step process of creating SEO-optimized content at scale. This approach ensures a steady stream of high-quality content that engages local customers and boosts your search rankings. Get an action guide on implementing this model, complete with templates and scheduling strategies to maintain a consistent online presence. Maximize ROI with Dollar-a-Day Advertising: Dive into the cost-effective Dollar-a-Day advertising strategy that amplifies your visibility in local searches without breaking the bank. Learn how to strategically allocate your budget across platforms to target potential local customers effectively. The session includes an action guide on setting up, monitoring, and optimizing your ad campaigns to ensure maximum impact with minimal investment.
As the call for for skilled experts continues to develop, investing in quality education and education from a reputable https://www.safalta.com/online-digital-marketing/best-digital-marketing-institute-in-noida Digital advertising institute in Noida can lead to a a success career on this eve
The Strategic Impact of Storytelling in the Age of AI
In the grand tapestry of marketing, where algorithms analyze data and artificial intelligence predicts trends, one essential thread remains constant — the timeless art of storytelling. As we stand on the precipice of a new era driven by AI, join me in unraveling the narrative alchemy that transforms brands from mere entities into captivating tales that resonate across the digital landscape. In this exploration, we will discover how, in the face of advancing technology, the human touch of a well-crafted story becomes not just a marketing tool but the very essence that breathes life into brands and forges lasting connections with our audience.
1. Why Do I Need to Know About Different
Methods?
As a future practitioner…
To be able to intelligently participate in
research projects, evaluations, and
studies undertaken by your institution.
As an educated citizen ...
To understand the difference between
scientifically acquired knowledge and
other kinds of information.
1
2. What’s the Difference Between “Method” and
“Methodology”?
Method Methodology
•Techniques for
gathering evidence
•The various ways of
proceeding in gathering
information
•The underlying theory
and analysis of how
research does or
should proceed, often
influenced by discipline
2
3. What is research?
• We ask questions all the time
• Research is a formal way of going about
asking questions
• Uses methodologies
• Many different kinds (e.g. market
research, media research and social
research)
• Basic research methods can be learned
easily
3
4. Basic Research Methods
• Quantitative research (e.g. survey)
• Qualitative research (e.g. face-to-
face interviews; focus groups; site
visits)
• Case studies
• Participatory research
4
5. Surveys
• Think clearly about questions (need
to constrain answers as much as
possible)
• Make sure results will answer your
research question
• Can use Internet for conducting
surveys if need to cover wide
geographic reach
5
6. Quantitative Research
• Involves information or data in the form
of numbers
• Allows us to measure or to quantify
things
• Respondents don’t necessarily give
numbers as answers - answers are
analysed as numbers
• Good example of quantitative research is
the survey 6
7. Qualitative Research
• Helps us flesh out the story and develop a
deeper understanding of a topic
• Often contrasted to quantitative research
• Together they give us the ‘bigger picture’
• Good examples of qualitative research are
face-to-face interviews, focus groups and
site visits
7
8. Face-to-face interviews
• Must prepare questions
• Good idea to record your interviews
• Interviews take up time, so plan for an
hour or less (roughly 10 questions)
• Stick to your questions, but be flexible if
relevant or interesting issues arise during
the interview
8
9. Focus groups
• Take time to arrange, so prepare in
advance (use an intermediary to help
you if you can)
• Who will be in your focus group? (e.g.
age, gender)
• Size of focus group (8-10 is typical)
• Consider whether or not to have
separate focus groups for different ages
or genders (e.g. discussing sex and
sexuality) 9
10. Site visits and observation
• Site visits involve visiting an organization,
community project etc
• Consider using a guide
• Observation is when you visit a location
and observe what is going on, drawing
your own conclusions
• Both facilitate making your research
more relevant and concrete
10
11. Case Studies
• Method of capturing and presenting
concrete details of real or fictional
situations in a structured way
• Good for comparative analysis
11
12. Participatory Research
• Allows participation of community being
researched in research process (e.g.
developing research question; choosing
methodology; analysing results)
• Good way to ensure research does not
simply reinforce prejudices and
presumptions of researcher
• Good for raising awareness in
community and developing appropriate
action plans 12
13. Planning your research:
Key questions
• What do you want to know?
• How do you find out what you want to
know?
• Where can you get the information?
• Who do you need to ask?
• When does your research need to be
done?
• Why? (Getting the answer)
13
14. Objectives of presentation
• Definition of sampling
• Why do we use samples?
• Concept of representativeness
• Main methods of sampling
• Sampling error
• Sample size calculation
14
15. Definition of sampling
Procedure by which some members
of a given population are selected as
representatives of the entire population
15
16. Definition of sampling terms
• Sampling unit
– Subject under observation on which information
is collected
• Sampling fraction
– Ratio between the sample size and the population
size
• Sampling frame
– Any list of all the sampling units in the population
• Sampling scheme
– Method of selecting sampling units from sampling
frame
16
17. Why do we use samples ?
Get information from large populations
–At minimal cost
–At maximum speed
–At increased accuracy
–Using enhanced tools
17
19. What we need to know
• Concepts
– Representativeness
– Sampling methods
– Choice of the right design
• Calculations
– Sampling error
– Design effect
– Sample size
19
21. Representativeness
• Person
• Demographic characteristics (age, sex…)
• Exposure/susceptibility
• Place (ex : urban vs. rural)
• Time
• Seasonality
• Day of the week
• Time of the day
Ensure representativeness before starting,
confirm once completed !!!!!!
21
23. Non probability samples
• Quotas
• Sample reflects population structure
• Time/resources constraints
• Convenience samples (purposive units)
• Biased
• Best or worst scenario
Probability of being chosen : unknown
23
24. Probability samples
• Random sampling
• Each subject has a known probability of
being chosen
• Reduces possibility of selection bias
• Allows application of statistical theory to
results
24
25. Sampling error
• No sample is the exact mirror image of
the population
• Magnitude of error can be measured in
probability samples
• Expressed by standard error
– of mean, proportion, differences, etc
• Function of
– amount of variability in measuring factor of
interest
– sample size
25
26. Methods used in probability samples
• Simple random sampling
• Systematic sampling
• Stratified sampling
• Multistage sampling
• Cluster sampling
26
27. Quality of an estimate
Precision &
validity
No precision
Random error !
Precision but
no validity
Systematic
error (Bias) !
27
28. Simple Random Sampling
• Principle
–Equal chance of drawing each unit
• Procedure
–Number all units
–Randomly draw units
28
29. Simple random sampling
• Advantages
–Simple
–Sampling error easily measured
• Disadvantages
–Need complete list of units
–Does not always achieve best
representativeness
–Units may be scattered
29
30. Example: evaluate the prevalence of tooth
decay among the 1200 children attending a
school
• List of children attending the school
• Children numerated from 1 to 1200
• Sample size = 100 children
• Random sampling of 100 numbers between 1
and 1200
How to randomly select?
Simple random sampling
30
33. Systematic sampling
• N = 1200, and n = 60
⇒ sampling fraction = 1200/60 = 20
• List persons from 1 to 1200
• Randomly select a number between 1 and
20 (ex : 8)
⇒ 1st
person selected = the 8th
on the
list
⇒ 2nd
person = 8 + 20 = the 28th
etc .....
33
37. Stratified Sampling
• Principle :
–Classify population into internally
homogeneous subgroups (strata)
–Draw sample in each strata
–Combine results of all strata
37
38. Stratified Sampling
• Advantages
– More precise if variable associated with
strata
– All subgroups represented, allowing
separate conclusions about each of
them
• Disadvantages
– Sampling error difficult to measure
– Loss of precision if very small numbers
sampled in individual strata
38
39. Example: Stratified sampling
• Determine vaccination coverage in a
country
• One sample drawn in each region
• Estimates calculated for each stratum
• Each stratum weighted to obtain
estimate for country (average)
39
40. Multiple Stage Sampling
Principle
• = consecutive samplings
• example :
sampling unit = household
– 1rst
stage : drawing areas or blocks
– 2nd
stage : drawing buildings, houses
– 3rd
stage : drawing households
40
41. Cluster Sampling
• Principle
–Random sample of groups
(“clusters”) of units
–In selected clusters, all units or
proportion (sample) of units included
41
43. Cluster Sampling
• Advantages
– Simple as complete list of sampling units
within population not required
– Less travel/resources required
• Disadvantages
– Imprecise if clusters homogeneous and
therefore sample variation greater than
population variation (large design effect)
– Sampling error difficult to measure
43
44. Cluster Sampling
To evaluate vaccination coverage:
• Without list of persons
• Total population of villages
• Randomly choose 30 clusters
• 30 cluster of 7 children each= 210 children
44
45. Drawing the Clusters
You need :
– Map of the region
– Distribution of population (by villages or area)
– Age distribution (population 12-23 m :3%)
1600
220
3200
400
800
200
1200
200
1600
400
53000
7300
106000
13000
26500
6600
40000
6600
53000
13200
A
B
C
D
E
F
G
H
I
J
12-23Pop.Village
45
46. Distribution of the clusters
A
B
C
D
E
F
G
H
I
J
1600
220
3200
400
800
200
1200
200
1600
400
1600
1820
5020
5420
6220
6420
7620
7820
9420
9820
Total population = 9820
Compute cumulated population
46
47. Distribution of the clusters
Then compute sampling fraction :
K = = 327
Draw a random number (between 1
and 327)
Example: 62
Start from the village including “62”
and draw the clusters adding the
sampling fraction
9820
30
A
B
C
D
E
F
G
H
I
J
1600
1820
5020
5420
6220
6420
7620
7820
9420
9820
I I I I
I
I I I I I I I I I I
I
I I
I
I I I I
I
I I I I I
I
47
48. Drawing households and children
On the spot
Go to the center of the village , choose direction
(random)
Number the houses in this direction
Ex: 21
Draw random number (between 1 and 21) to
identify the first house to visit
From this house progress until finding the 7
children ( itinerary rules fixed beforehand)
48
49. Selecting a sampling method
• Population to be studied
– Size/geographical distribution
– Heterogeneity with respect to variable
• Level of precision required
• Resources available
• Importance of having a precise estimate
of the sampling error
49
50. Steps in estimating sample size
• Identify major study variable
• Determine type of estimate (%, mean, ratio,...)
• Indicate expected frequency of factor of interest
• Decide on desired precision of the estimate
• Decide on acceptable risk that estimate will fall
outside its real population value
• Adjust for estimated design effect
• Adjust for expected response rate
• (Adjust for population size? In case of small size
population only)
50
51. Place of sampling
in descriptive surveys
• Define objectives
• Define resources available
• Identify study population
• Identify variables to study
• Define precision required
• Establish plan of analysis (questionnaire)
• Create sampling frame
• Select sample
• Pilot data collection
• Collect data
• Analyse data
• Communicate results
• Use results
51
53. • If in doubt…
Call a statistician !!!!
Thank You
53
Editor's Notes
précision = si on répète les mesures, on obtient des estimations proches (mesurée par la variance de l’échantillon)
validité = capacité à estimer la vraie valeur du paramètre dans la population
pour chaque individu,
probabilité égale
d'être désigné dans l'échantillon
il y a 1200 individus
donc tout chiffre entre 1 et 1200 doit pouvoir être tiré
on décide de prendre les 4 derniers chiffres de chaque série
à partir d'un chiffre pris au hasard, on descend jusqu'à rencontrer un nombre (à 4 chiffres) compris entre 1 et 1200 : ce nombre est alors retenu pour l'échantillon
on continue jusqu'à obtenir 60 enfants.