Researchers know we're supposed to sample people in the proper proportions but how do we know what those proportions are? In this webinar, I will demonstrate how to use census data to determine what your sample should really look like in terms of variables like age, gender, region and .education
How to Sample the Right Percentages of People in your Study or How to Creat...Peanut Labs
How to Sample the Right Percentages of People in your Study
or
How to Create US Census Sampling Targets for Free Using Data Ferret
By Annie Pettit, Chief Research Offer
Peanut Labs
Karaoke is one of popular entertainment style for Saigonese with more than 365 places in the city. How is Saigonese' karaoke style and how is it different from other countries? Q&Me will help you to figure it out.
This document discusses sampling and different sampling techniques. Sampling involves selecting a subset of units from a population to study and make generalizations about the larger population. The benefits of sampling include reduced costs, faster data collection, and greater flexibility in the types of information that can be obtained compared to a complete census. There are two main types of sampling techniques: probability sampling, where each unit has an equal chance of selection, and non-probability sampling, where units are selected non-randomly based on accessibility or the researcher's judgment.
This document discusses sampling methods used in research. It defines key sampling concepts like population, sample, sampling unit and frame. It also describes different probability sampling methods like simple random, stratified, systematic and cluster sampling as well as non-probability methods like convenience, judgment and snowball sampling. The document provides guidance on developing a sampling plan including defining the population, identifying the sampling method and frame, determining sample size, executing the sample, and validating the sample. It emphasizes that a well-designed sampling plan clearly defines what will be learned, how long it will take and how much it will cost.
The document discusses different sampling and surveying techniques for collecting data from populations. It describes random, stratified, and systematic sampling methods. Random sampling involves selecting participants randomly without criteria. Stratified sampling divides the population into subsets based on attributes and samples within those subsets. Systematic sampling involves selecting every nth participant. The document also discusses considerations for survey timing, location, and duration to obtain a representative sample.
This document discusses key concepts and terminology related to probability sampling. It defines sampling as selecting observations from a population to make inferences about the entire population without bias. There are two main types of sampling: probability and non-probability. Probability sampling involves giving every member of the population an equal chance of being selected, which allows for more accurate inferences. Key concepts discussed include population, sample, parameter, estimate, sampling error, and standard error. The document also covers different probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It explains how and when each method should be used.
Aron chpt 4 sample and probability f2011Sandra Nicks
The document discusses key concepts related to sampling and making statistical inferences about populations based on samples. It defines population and sample, explains why samples are used instead of entire populations, and describes different sampling methods like random sampling and convenience sampling. It also discusses important sampling terminology, factors that influence sampling error like sample size, and concepts like probability, p-values, and how the normal distribution relates to probability.
Sampling is the process of selecting a subset of individuals from within a population to estimate characteristics of the whole population. There are several sampling techniques including simple random sampling, stratified sampling, cluster sampling, systematic sampling, and non-probability sampling. Each technique has advantages and disadvantages related to accuracy, cost, and generalizability. Proper sampling helps reduce sampling errors and increase the reliability of making inferences about the population from a sample.
How to Sample the Right Percentages of People in your Study or How to Creat...Peanut Labs
How to Sample the Right Percentages of People in your Study
or
How to Create US Census Sampling Targets for Free Using Data Ferret
By Annie Pettit, Chief Research Offer
Peanut Labs
Karaoke is one of popular entertainment style for Saigonese with more than 365 places in the city. How is Saigonese' karaoke style and how is it different from other countries? Q&Me will help you to figure it out.
This document discusses sampling and different sampling techniques. Sampling involves selecting a subset of units from a population to study and make generalizations about the larger population. The benefits of sampling include reduced costs, faster data collection, and greater flexibility in the types of information that can be obtained compared to a complete census. There are two main types of sampling techniques: probability sampling, where each unit has an equal chance of selection, and non-probability sampling, where units are selected non-randomly based on accessibility or the researcher's judgment.
This document discusses sampling methods used in research. It defines key sampling concepts like population, sample, sampling unit and frame. It also describes different probability sampling methods like simple random, stratified, systematic and cluster sampling as well as non-probability methods like convenience, judgment and snowball sampling. The document provides guidance on developing a sampling plan including defining the population, identifying the sampling method and frame, determining sample size, executing the sample, and validating the sample. It emphasizes that a well-designed sampling plan clearly defines what will be learned, how long it will take and how much it will cost.
The document discusses different sampling and surveying techniques for collecting data from populations. It describes random, stratified, and systematic sampling methods. Random sampling involves selecting participants randomly without criteria. Stratified sampling divides the population into subsets based on attributes and samples within those subsets. Systematic sampling involves selecting every nth participant. The document also discusses considerations for survey timing, location, and duration to obtain a representative sample.
This document discusses key concepts and terminology related to probability sampling. It defines sampling as selecting observations from a population to make inferences about the entire population without bias. There are two main types of sampling: probability and non-probability. Probability sampling involves giving every member of the population an equal chance of being selected, which allows for more accurate inferences. Key concepts discussed include population, sample, parameter, estimate, sampling error, and standard error. The document also covers different probability sampling methods like simple random sampling, systematic sampling, stratified sampling, and cluster sampling. It explains how and when each method should be used.
Aron chpt 4 sample and probability f2011Sandra Nicks
The document discusses key concepts related to sampling and making statistical inferences about populations based on samples. It defines population and sample, explains why samples are used instead of entire populations, and describes different sampling methods like random sampling and convenience sampling. It also discusses important sampling terminology, factors that influence sampling error like sample size, and concepts like probability, p-values, and how the normal distribution relates to probability.
Sampling is the process of selecting a subset of individuals from within a population to estimate characteristics of the whole population. There are several sampling techniques including simple random sampling, stratified sampling, cluster sampling, systematic sampling, and non-probability sampling. Each technique has advantages and disadvantages related to accuracy, cost, and generalizability. Proper sampling helps reduce sampling errors and increase the reliability of making inferences about the population from a sample.
Fairfax County Youth Survey School Year 2014-2015: Bullying and CyberbullyingFairfax County
The document summarizes key findings from the 2014 Fairfax County Youth Survey regarding bullying, cyberbullying, and related risk behaviors. Some key findings include:
- About 50% of students reported being bullied in the past year, while 35% reported bullying others.
- Chronic bullying, defined as 20 or more incidents in the past year, was reported by 10% of students as a victim and 5% as an aggressor.
- Students who experienced chronic bullying reported higher rates of other risk behaviors like substance use, violence, depression, and school problems.
SY2014-2015 Fairfax County Youth Survey HighlightsFairfax County
The document summarizes key findings from the 2014 Fairfax County Youth Survey, which assessed the behaviors, experiences, and risk factors of over 46,000 county students in grades 6, 8, 10, and 12. Some major findings included:
- 19.2% of students reported drinking alcohol in the past 30 days, while 11.3% used marijuana and 4.1% smoked cigarettes. Rates varied by grade and demographics.
- 31.7% of students reported experiencing depressive symptoms in the past year, with higher rates in older grades and among females. Factors like substance use and bullying victimization were associated with greater depression.
- Over 50% of students reported being victims of bullying
Fairfax County Youth Survey School Year 2014-2015: Alcohol, Tobacco and Other...Fairfax County
The document summarizes key findings from the 2014 Fairfax County Youth Survey. It provides data on substance use among county youth to help organizations assess needs, develop programs, monitor trends, and guide prevention efforts. Some key findings presented include that 19.2% of students reported drinking alcohol in the past 30 days, 8.8% reported binge drinking in the past 2 weeks, 4.1% reported smoking cigarettes in the past 30 days, and 11.3% reported using marijuana in the past 30 days. The survey also examines correlations between substance use and various risk factors and protective factors.
Perceptions on Gender Equality, Lived Poverty from the Citizens of NamibiaAfrobarometer
The document summarizes findings from an Afrobarometer survey conducted in Namibia. Key findings include:
1) Most Namibians support gender equality and women in political leadership. However, women express less interest in politics than men.
2) Namibians feel that alcohol abuse is the largest contributor to gender-based violence in the country, followed by unemployment and poverty. Culture is not seen as a major factor.
3) Reported experiences of food shortages and lack of cash income ("lived poverty") have declined over time, tracking official statistics showing reduced poverty.
4) Namibians feel they have high levels of personal freedoms like freedom of speech, voting preferences, and
Jake Duggan conducted a survey to learn about his target audience for HOOKED magazine. Originally focused on teenage girls interested in rock/indie music, the survey of 50 people showed most respondents were males aged 13-17 interested in post-hardcore music. Over two-thirds of respondents were in high school and spent over 26 hours per week online, suggesting web content would engage readers. Open-ended questions revealed a preference for pop punk bands and romantic comedy films and TV shows with witty humor, informing the magazine's content focus.
3.4 Effectively Collecting, Coordinating, and Using Youth Data
Speaker: Peter Connery
Data is essential to create effective evidence-based strategies to prevent and end homelessness. This workshop will examine methodologies of point-in-time counts and other surveys, discuss coordinating HMIS with mainstream data systems and explore ways to use these data to inform policy decisions and interventions.
Young People in Scotland Volunteering Survey. 45% of young people have formally volunteered, with the majority volunteering in their spare time. 9% of young people volunteer in both their spare time and in school time .
16-18 year olds are twice as likely to volunteer in school time than younger age groups
11-15 year olds are more likely to consider volunteering in the future than 16-18 year olds
More girls volunteer in their spare time than boys and girls are more likely to consider volunteering in the future
19% of boys expressed no interest in volunteering compared to only 9% of girls
Fairfax County Youth Survey School Year 2013-2014: Alcohol, Tobacco and Other...Fairfax County
The document summarizes key findings from the 2013 Fairfax County Youth Survey on substance use among youth. Some key points:
- The survey assessed behaviors, experiences, and risk/protective factors of over 47,000 FCPS students in grades 6, 8, 10, and 12.
- It found that having at least three protective "assets" dramatically reduces youth risk behaviors like substance use and violence.
- Rates of past 30-day alcohol, binge drinking, cigarette, and marijuana use were reported for different grades and demographics.
- Correlations were shown between substance use and factors like depression, risky sexual behaviors, poor school performance, and experiencing/perpetrating violence.
Fairfax County Youth Survey School Year 2013-2014: Bullying and CyberbullyingFairfax County
The document summarizes key findings from the 2013 Fairfax County Youth Survey on bullying and cyberbullying. It provides data on the survey's purpose and methodology, including that it surveyed over 47,000 students in grades 6, 8, 10, and 12 on their behaviors, experiences, and risk/protective factors. Key findings include that about half of students reported being bullied in the past year, with higher rates for females and lower rates for Asians. About 45% reported bullying others. Chronic bullying, defined as 20 or more incidents, affected 9.4% as victims and 6.6% as aggressors. The survey also examined correlations between bullying and other risky behaviors.
Fairfax County Youth Survey School Year 2013-2014Fairfax County
The document summarizes key findings from the 2013 Fairfax County Youth Survey. The survey assessed the behaviors, experiences, and risk/protective factors of over 47,000 county students in grades 6, 8, 10, and 12. It found that while alcohol, tobacco, and drug use decreased from previous years, depressive symptoms and unhealthy weight control behaviors remained concerns. The survey also examined nutrition, physical activity, and sleep patterns of youth.
2012-2013 Fairfax County Youth Survey HighlightsFairfax County
The document summarizes key findings from the 2012 Fairfax County Youth Survey, which assessed the behaviors, experiences, and risk factors of over 46,000 county students in grades 6, 8, 10, and 12. Some major topics covered include substance use, mental health, bullying, nutrition/physical activity, and sexual health. The survey is intended to help organizations develop programs, monitor trends, and guide prevention planning.
This document provides an outline for a research study investigating the causes and effects of ineffective parenting in the community of Canaan Heights. It includes sections on the topic, research questions, data collection methods, instruments used, procedures for collecting data from questionnaires distributed to community members, and plans for presenting and analyzing the data. The goal is to identify the main causes of ineffective parenting in the community and how it affects children, as well as recommendations for addressing the problem.
Peanut Labs is a market research firm that provides access to a global online sample of over 15 million diverse panelists. The document discusses key concepts in sampling, including how generalizing from a sample to the overall population. It provides examples of common sample types like census representative and internet representative. The document also discusses what makes a good sample, noting it should come from diverse sources, filter poor quality data, refresh regularly, and reward panelists with their preferred incentives. It recommends a minimum sample size of 400 to achieve a 5% error rate when generalizing.
New Generations of Donor Engagement | Kim ParkerOPERA America
Millennials, born between 1980 and 2000, are now the largest generation in the United States, surpassing Baby Boomers. They are more liberal and progressive than older generations on social issues like gay marriage and marijuana legalization. Many Millennials came of age during the Great Recession, which negatively impacted their employment and economic opportunities. Compared to previous generations at a similar age, Millennials are more likely to live in poverty and have lower wages. They are also less likely to be married and more likely to live with their parents.
A presentation by David Lam, Department of Economics and Population Studies Center, University of Michigan, as part of Impacts of Inequality on Children's Well-being panel discussion at the International Symposium on Cohort and Longitudinal Studies in Developing Contexts, UNICEF Office of Research - Innocenti, Florence, Italy 13-15 October 2014
The Perils of Perception in 2016: Ipsos MORIIpsos UK
Ipsos MORI have compared perceptions of the likes of portion of Muslim population, perceptions of happiness, homosexuality, sex before marriage, abortion, wealth, health spending, current and future population and whether Donald Trump would become US President with the actual figures across forty countries.
How do people in your country fare? How would you have fared with our questions? Take the quiz for your contry: https://perils.ipsos.com
Annie Pettit's AI presentation at the 2018 annual Travel and Tourism Research Association (TTRA) conference in Miami. Sharing results from a Sklar Wilton white paper on Canadian perceptions of AI, plus applications of AI in marketing research.
Links to videos I showed:
@SklarWilton #AI white paper on what Canadians think about #AI, #VoiceAssistants, and #Chatbots.
https://www.sklarwilton.com/wp-content/uploads/2017/12/Sklar-Wilton-Canadian-Artificial-Intelligence-Paper-2017.pdf
Joy Buolamwini of M.I.T.’s Media Lab shows how facial recognition technology has trouble recognizing dark faces.
https://www.youtube.com/watch?v=TWWsW1w-BVo
Google can now make #AI phone calls that are virtually indistinguishable from human beings.
https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html
#AI can write newspaper articles about anything.
http://articlecreator.fullcontentrss.com/index.php
#AI can create humour that people actually laugh at.
https://www.youtube.com/watch?v=Vhe-JOP7PCs
#AI can read your mind.
https://www.youtube.com/watch?v=RuUSc53Xpeg
mindread
#AI, #Chatbots, and #VoiceAssistants are already running #questionnaires.
https://www.messenger.com/t/202671200242510/?messaging_source=source%3Apages%3Amessage_shortlink
Examples of ten cognitive biases in the marketing world that you can apply to your own work. Presented at the AMA Houston conference in September 2016.
More Related Content
Similar to How to Create Census Sampling Targets for Free Using Data Ferret
Fairfax County Youth Survey School Year 2014-2015: Bullying and CyberbullyingFairfax County
The document summarizes key findings from the 2014 Fairfax County Youth Survey regarding bullying, cyberbullying, and related risk behaviors. Some key findings include:
- About 50% of students reported being bullied in the past year, while 35% reported bullying others.
- Chronic bullying, defined as 20 or more incidents in the past year, was reported by 10% of students as a victim and 5% as an aggressor.
- Students who experienced chronic bullying reported higher rates of other risk behaviors like substance use, violence, depression, and school problems.
SY2014-2015 Fairfax County Youth Survey HighlightsFairfax County
The document summarizes key findings from the 2014 Fairfax County Youth Survey, which assessed the behaviors, experiences, and risk factors of over 46,000 county students in grades 6, 8, 10, and 12. Some major findings included:
- 19.2% of students reported drinking alcohol in the past 30 days, while 11.3% used marijuana and 4.1% smoked cigarettes. Rates varied by grade and demographics.
- 31.7% of students reported experiencing depressive symptoms in the past year, with higher rates in older grades and among females. Factors like substance use and bullying victimization were associated with greater depression.
- Over 50% of students reported being victims of bullying
Fairfax County Youth Survey School Year 2014-2015: Alcohol, Tobacco and Other...Fairfax County
The document summarizes key findings from the 2014 Fairfax County Youth Survey. It provides data on substance use among county youth to help organizations assess needs, develop programs, monitor trends, and guide prevention efforts. Some key findings presented include that 19.2% of students reported drinking alcohol in the past 30 days, 8.8% reported binge drinking in the past 2 weeks, 4.1% reported smoking cigarettes in the past 30 days, and 11.3% reported using marijuana in the past 30 days. The survey also examines correlations between substance use and various risk factors and protective factors.
Perceptions on Gender Equality, Lived Poverty from the Citizens of NamibiaAfrobarometer
The document summarizes findings from an Afrobarometer survey conducted in Namibia. Key findings include:
1) Most Namibians support gender equality and women in political leadership. However, women express less interest in politics than men.
2) Namibians feel that alcohol abuse is the largest contributor to gender-based violence in the country, followed by unemployment and poverty. Culture is not seen as a major factor.
3) Reported experiences of food shortages and lack of cash income ("lived poverty") have declined over time, tracking official statistics showing reduced poverty.
4) Namibians feel they have high levels of personal freedoms like freedom of speech, voting preferences, and
Jake Duggan conducted a survey to learn about his target audience for HOOKED magazine. Originally focused on teenage girls interested in rock/indie music, the survey of 50 people showed most respondents were males aged 13-17 interested in post-hardcore music. Over two-thirds of respondents were in high school and spent over 26 hours per week online, suggesting web content would engage readers. Open-ended questions revealed a preference for pop punk bands and romantic comedy films and TV shows with witty humor, informing the magazine's content focus.
3.4 Effectively Collecting, Coordinating, and Using Youth Data
Speaker: Peter Connery
Data is essential to create effective evidence-based strategies to prevent and end homelessness. This workshop will examine methodologies of point-in-time counts and other surveys, discuss coordinating HMIS with mainstream data systems and explore ways to use these data to inform policy decisions and interventions.
Young People in Scotland Volunteering Survey. 45% of young people have formally volunteered, with the majority volunteering in their spare time. 9% of young people volunteer in both their spare time and in school time .
16-18 year olds are twice as likely to volunteer in school time than younger age groups
11-15 year olds are more likely to consider volunteering in the future than 16-18 year olds
More girls volunteer in their spare time than boys and girls are more likely to consider volunteering in the future
19% of boys expressed no interest in volunteering compared to only 9% of girls
Fairfax County Youth Survey School Year 2013-2014: Alcohol, Tobacco and Other...Fairfax County
The document summarizes key findings from the 2013 Fairfax County Youth Survey on substance use among youth. Some key points:
- The survey assessed behaviors, experiences, and risk/protective factors of over 47,000 FCPS students in grades 6, 8, 10, and 12.
- It found that having at least three protective "assets" dramatically reduces youth risk behaviors like substance use and violence.
- Rates of past 30-day alcohol, binge drinking, cigarette, and marijuana use were reported for different grades and demographics.
- Correlations were shown between substance use and factors like depression, risky sexual behaviors, poor school performance, and experiencing/perpetrating violence.
Fairfax County Youth Survey School Year 2013-2014: Bullying and CyberbullyingFairfax County
The document summarizes key findings from the 2013 Fairfax County Youth Survey on bullying and cyberbullying. It provides data on the survey's purpose and methodology, including that it surveyed over 47,000 students in grades 6, 8, 10, and 12 on their behaviors, experiences, and risk/protective factors. Key findings include that about half of students reported being bullied in the past year, with higher rates for females and lower rates for Asians. About 45% reported bullying others. Chronic bullying, defined as 20 or more incidents, affected 9.4% as victims and 6.6% as aggressors. The survey also examined correlations between bullying and other risky behaviors.
Fairfax County Youth Survey School Year 2013-2014Fairfax County
The document summarizes key findings from the 2013 Fairfax County Youth Survey. The survey assessed the behaviors, experiences, and risk/protective factors of over 47,000 county students in grades 6, 8, 10, and 12. It found that while alcohol, tobacco, and drug use decreased from previous years, depressive symptoms and unhealthy weight control behaviors remained concerns. The survey also examined nutrition, physical activity, and sleep patterns of youth.
2012-2013 Fairfax County Youth Survey HighlightsFairfax County
The document summarizes key findings from the 2012 Fairfax County Youth Survey, which assessed the behaviors, experiences, and risk factors of over 46,000 county students in grades 6, 8, 10, and 12. Some major topics covered include substance use, mental health, bullying, nutrition/physical activity, and sexual health. The survey is intended to help organizations develop programs, monitor trends, and guide prevention planning.
This document provides an outline for a research study investigating the causes and effects of ineffective parenting in the community of Canaan Heights. It includes sections on the topic, research questions, data collection methods, instruments used, procedures for collecting data from questionnaires distributed to community members, and plans for presenting and analyzing the data. The goal is to identify the main causes of ineffective parenting in the community and how it affects children, as well as recommendations for addressing the problem.
Peanut Labs is a market research firm that provides access to a global online sample of over 15 million diverse panelists. The document discusses key concepts in sampling, including how generalizing from a sample to the overall population. It provides examples of common sample types like census representative and internet representative. The document also discusses what makes a good sample, noting it should come from diverse sources, filter poor quality data, refresh regularly, and reward panelists with their preferred incentives. It recommends a minimum sample size of 400 to achieve a 5% error rate when generalizing.
New Generations of Donor Engagement | Kim ParkerOPERA America
Millennials, born between 1980 and 2000, are now the largest generation in the United States, surpassing Baby Boomers. They are more liberal and progressive than older generations on social issues like gay marriage and marijuana legalization. Many Millennials came of age during the Great Recession, which negatively impacted their employment and economic opportunities. Compared to previous generations at a similar age, Millennials are more likely to live in poverty and have lower wages. They are also less likely to be married and more likely to live with their parents.
A presentation by David Lam, Department of Economics and Population Studies Center, University of Michigan, as part of Impacts of Inequality on Children's Well-being panel discussion at the International Symposium on Cohort and Longitudinal Studies in Developing Contexts, UNICEF Office of Research - Innocenti, Florence, Italy 13-15 October 2014
The Perils of Perception in 2016: Ipsos MORIIpsos UK
Ipsos MORI have compared perceptions of the likes of portion of Muslim population, perceptions of happiness, homosexuality, sex before marriage, abortion, wealth, health spending, current and future population and whether Donald Trump would become US President with the actual figures across forty countries.
How do people in your country fare? How would you have fared with our questions? Take the quiz for your contry: https://perils.ipsos.com
Similar to How to Create Census Sampling Targets for Free Using Data Ferret (20)
Annie Pettit's AI presentation at the 2018 annual Travel and Tourism Research Association (TTRA) conference in Miami. Sharing results from a Sklar Wilton white paper on Canadian perceptions of AI, plus applications of AI in marketing research.
Links to videos I showed:
@SklarWilton #AI white paper on what Canadians think about #AI, #VoiceAssistants, and #Chatbots.
https://www.sklarwilton.com/wp-content/uploads/2017/12/Sklar-Wilton-Canadian-Artificial-Intelligence-Paper-2017.pdf
Joy Buolamwini of M.I.T.’s Media Lab shows how facial recognition technology has trouble recognizing dark faces.
https://www.youtube.com/watch?v=TWWsW1w-BVo
Google can now make #AI phone calls that are virtually indistinguishable from human beings.
https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html
#AI can write newspaper articles about anything.
http://articlecreator.fullcontentrss.com/index.php
#AI can create humour that people actually laugh at.
https://www.youtube.com/watch?v=Vhe-JOP7PCs
#AI can read your mind.
https://www.youtube.com/watch?v=RuUSc53Xpeg
mindread
#AI, #Chatbots, and #VoiceAssistants are already running #questionnaires.
https://www.messenger.com/t/202671200242510/?messaging_source=source%3Apages%3Amessage_shortlink
Examples of ten cognitive biases in the marketing world that you can apply to your own work. Presented at the AMA Houston conference in September 2016.
In the first of a two-part session, four research professionals throw caution to the wind and fight for their passionately held beliefs about the way the insight world works. This is the session for those who want to hear the uncensored, unshackled and revolutionary voice of research.
I wish I had kept track of every time a conference speaker said they didn't understand the statistics they were referring to but if anyone had a question, they could find someone to answer it.
I wish I had kept track of every presenter whose 20 slides consisted of 20 pictures.
I wish it was even possible to count the number of infographics floating around the interweebs spewing countless unsubstantiated and out-of-context percentages with multiple decimal places.
In this presentation, I will plead with the audience to reconsider how they communicate to their clients about research, and how they present that research to various audiences.
Of all the datasets that could be delivered to your desk, the most difficult one to work with might be that big dataset. Besides its massive size, it’s exponential growth even as you work on it, and the variety of data types present, big data presents many issues that make it difficult to turn data into action. In this presentation, you will learn how to take thousands of variables and billions of records and turn them into useable and actionable results, just as you would with any traditional research dataset.
People are sometimes intimidated by big data because it seems overwhelming and they’re much more familiar with using statistics on survey data or analyzing opinions from focus group data. But here are nine examples from companies like Netflix, Ceasars Entertainment, Walmart, eBay, and UPS, that could have conducted survey or focus group research have instead used big data to accomplish big things.
Previous research has suggested that people who are willing to provide their telephone numbers may be more likely to provide good quality data. This study examined whether asking people for their phone number 1) at the beginning of a survey or or 2) at the end of a survey affects the results. And yes, it matters.
Theory is nice but data is heaven. Most market researchers have heard a lot of theory about big data, but few have seen the data and worked with it themselves. And we all know that the best way to truly understand and internalise something is to see the raw data for yourself. In this presentation, we'll blast ten big data myths using stories that many researchers can actually relate to - survey panel data. With millions of panellists, millions of profiles, millions of survey clicks, and millions of incentives, market researchers have been sitting on pretty big data for nearly 20 years. See how easy it is for trained scientists like yourselves to learn some SAS, R, or SQL, and dig into that big data on your own.
We like to think that everyone answering our surveys is perfectly fluent in English but let's be realistic. About 10% of Americans have difficulty reading/writing in English because it is not their native language. And when we apply standard techniques to identify which survey takers provide good data and which are simply giving random answers, we are often making the mistake of applying measurements that all require high level language skills.
Jeffrey Henning of Researchscape International presented on survey analysis and best practices for analyzing survey data. He discussed the importance of considering analysis before designing and administering a survey to ensure the right questions are asked to answer the main research question. He also emphasized reviewing each question individually and looking at relationships between questions before analyzing responses. Finally, Henning stressed telling stories with the data rather than just presenting analysis, and leaving the data behind to focus on the insights and meaning.
The document is the June 2015 issue of Vue Magazine, which is published by the Marketing Research and Intelligence Association. It includes articles on topics like big data, data mining, competitive intelligence, and social media. It also provides information on the MRIA 2015 conference, new CMRPs, and chapters.
This document is the January/February 2015 issue of Vue Magazine, which is published by the Marketing Research and Intelligence Association (MRIA) 10 times per year. It contains articles, columns, and other content related to marketing research. The issue includes a special feature on staying ahead of industry trends, articles on qualitative research experiences in France and the relationship between lobotomies and surveys, as well as industry news, book reviews, columns, and information on upcoming MRIA events and courses.
This document is the May 2013 issue of Vue Magazine, which is published by the Marketing Research and Intelligence Association (MRIA) 10 times per year. The issue features articles on ethics in research, structural collaboration with consumers, and a summary of the 2013 MRIA national conference from the perspective of client-side researchers. It also includes industry news, columns, and information about upcoming MRIA events. The conference issue serves to inform MRIA members about current topics and issues in marketing research.
For the most part, people who answer marketing research surveys want and try to do a good job. However, sometimes respondents want to get through a survey as quick as possible in order to earn the incentive and move to the next task.
- Learn the various types of data quality questions you can use, beyond speeding and straightlining.
- How to fit them into your questionnaire with minimal impact on responders.
- And most importantly, how to use the data quality questions effectively so that you don't accidentally exclude data from honest respondents.
Just a few years ago, social media research was hailed as the panacea of all marketing research. The ridiculous quantities of brand opinions and opinionators available in social media would mean that focus groups would die, surveys would die, and all research questions would have instant answers. Fast forward to today and surveys continue to thrive. Learn why social media research didn’t hold up to expectations and why it’s finally breaking through. Presented at #IIEXap14 #IIEX
Surveys have a lot of tradition and norms behind them. And a lot of templates. Many of these templates were written many years ago when formal language made a lot more sense. Today, people expect casual and friendly language everywhere including from brands and companies. This presentation shows what happens to data quality and survey results when real language, not Charles Dickens language, is used.
The document discusses how people perceive surveys and provides suggestions for improving surveys. It finds that respondents feel surveys are too long, distrust incentives and data collection, and feel screened out too late. It recommends being transparent about data use, explaining screening reasons, using casual language to avoid boredom, and addressing respondents' concerns to improve perceptions of surveys.
The document summarizes research into the effects of splitting long surveys into two shorter surveys. It finds that data quality is maintained or improved with shorter surveys, as respondents make fewer errors and provide more detailed open-ended responses. Results from different question sets are largely equivalent between survey lengths. Respondent satisfaction may also be higher for shorter surveys, as indicated by more positive survey comments. While not a definitive solution, splitting long surveys appears to address issues like respondent fatigue without harming data quality or equivalence.
1) The document discusses using social media for new product development by collecting ideas and suggestions from online posts.
2) It outlines a process of iteratively searching social media to find posts about new product ideas, wishes, and things people want made or improved.
3) The sources of data have become more diverse over time but may still underrepresent certain groups, and the suggestions tend to focus on generic consumer needs, electronics, and popular brands.
Perhaps we are all speaking English but men and women do it just a little bit differently. Find out what words are used more often by men or women and see how you fit the stereotype.
More from Annie Pettit, Research Methodologist (20)
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.
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How to Create Census Sampling Targets for Free Using Data Ferret
1. November 20, 2014
How to Sample the Right
Percentages of People in
your Study
or
How to Create US Census Sampling Targets
for Free Using Data Ferret
180 Montgomery Street, Suite 1700 - San Francisco, CA 94104
2. What is sampling?
• Choosing people to participate in a study
• Types (in very basic terms)
– Random sampling: You have a list of every single person
who is relevant to the study
– Stratified sampling: Organizing people into groups so you
can select from those groups
– Convenience sampling: You have access to people who are
relevant to the study
3. What is a sampling plan?
• Plan for selecting who will be invited to participate in the
research
• Moms: Study about diapers
• Teenagers: Study about learning to drive
• Young adults: Study about job hunting for the first time
4. What is a sampling matrix
• Specific description
of who you will
sample
• Young adults: Study
about job hunting for
the first time
Percent
Gender Male 50%
Female 50%
Age 16 to 19 33%
20 to 24 33%
25 to 29 33%
Region Northeast 25%
Midwest 25%
South 25%
West 25%
11/20/14 4
5. But what do young adults REALLY look like
Percent
Gender Male 50%
Female 50%
Age 16 to 19 33%
20 to 24 33%
25 to 29 33%
Region Northeast 25%
Midwest 25%
South 25%
West 25%
Percent
Gender Male 50%
Female 50%
Age 16 to 19 25%
20 to 24 35%
25 to 29 40%
Region Northeast 20%
Midwest 20%
South 40%
West 20%
11/20/14 5
25. But what do young adults REALLY look like
“Fair” Guessing Reality
Gender Male 50% 50% 50.2%
Female 50% 50% 49.8%
Age 16 to 19 33% 25% 27.7%
20 to 24 33% 35% 36.8%
25 to 29 33% 40% 35.5%
Region Northeast 25% 20% 17.9%
Midwest 25% 20% 21.2%
South 25% 40% 37.0%
West 25% 20% 23.9%
11/20/14 25
26. Weighting
Census
Targets Returns Weights
Gender Male 50.2% 52% 104%
Female 49.8% 48% 96%
Age 16 to 19 27.7% 29% 105%
20 to 24 36.8% 35% 95%
25 to 29 35.5% 36% 101%
Region Northeast 17.9% 21% 117%
Midwest 21.2% 19% 90%
South 37.0% 36% 97%
West 23.9% 24% 100%
11/20/14 26
27. 11/20/14
27
Thank you!
Annie Pettit
Chief Research Officer
Peanut Labs
annie@peanutlabs.com
jonathan.cheriff@peanutlabs.com
Director of Sales and Marketing
: )
Hi everyone and thank you for finding the time to join me for the next 30 minutes or so. My name is annie pettit and I am the chief research officer at peanutlabs, a company that specializes in DIY sampling, survey programming, and polling. You can find recordings of webinars we’ve done in the past on the peanut labs website under the resources section. Todays webinar is about a very important topic in sampling. It focuses on how to figure out the right demographics to sample in your study. If you’re never used or never heard of data ferret, then you are in for a real treat. If you have any questions along the way, do feel free to type your questions in the question bar. I’ll answer as many of them as possible at the end.
First, let me spend a couple of minutes talking about various types of sampling. There are many different types but I’ll just focus on a few concepts here.
First, there is random sampling. In many cases, this is the absolute best type of sampling you can do. The basic premise here is that you have a list of contact information for every single person who is relevant to you study. For instance, you have email addresses for every single adult in the US. Or you have the telephone number for every single woman who has a child aged 3 to 9. Once we have this complete list, then we can simply start picking names out of a hat to determine who is part of our sample. Clearly, most of us will never be able to conduct a true random sample like this.
Stratified sampling is another type of sampling. The basic premise in this case is that people or things are divided into groups before you sample from them. So, you might create a group of people who live in the city and a group of people who live in the country, and then sample from each of those two groups.
The last type of sampling I’ll mention is called convenience sampling. Most of our work in market research is done with convenience samples. In other words, people who are easy to access. They might be easy to access because these people live closest to our office building, or they have a telephone, or they chose to join a survey panel.
Most of us in market research use stratified convenience samples. We sample from conveniently available people and we bucket them into groups for sampling purposes.
A sampling plan is important for every research project. You need to decide what kinds of people are relevant and important to your study. For instance, you wouldn’t want to invite men to participate in research about feminine hygiene products and you wouldn’t want teenagers participating in a study about retirement homes. When that happens, it’s a waste of everyone’s time and energy.
But, if think ahead of time about who we NEED to take our surveys, then we’ll have much better data in the end. For instance, if we’re going to run a study about diapers, you’ll probably want to talk to moms who have a baby. You might also want to talk to new dads since times are slowly changing and dads are getting in on those kinds of decisions too.
Or, if you’re doing a study about learning to drive, you’ll probably want to focus on younger people, people aged 15 to 20 or so.
Let’s take a specific example. Perhaps people who are job hunting for their first real job. First, we can make some hypotheses about who we want to listen to. We’ve decided that we want to listen to men and women. So let’s say that half of our sample should be men and half should be women. Let’s also say that we want to focus on young adults, perhaps people aged 16 to 29. We can easily divide that age range into three nice groups and then try to put of third of people into each group. Lastly, we might decide that we want to listen to people from all over the USA. We can divide the USA into four groups and then try to put a quarter of all people into each of those groups.
This is a sampling matrix. It makes a lot of sense. We won’t have to worry that everyone in the study is 25 to 29 or that everyone is from the Northeast. This sampling matrix will ensure that we’ve listened to a good range of people.
But is it as good as we think?
The most obvious problem is the first table you see here, the sampling matrix that we just built, is the region part. We know that when most people divide the USA into four regions, there aren’t equal numbers of people in each region. In fact, we know that the most commonly used regions end up putting more people in the south region. This means that the sampling matrix we guessed at isn’t listening to enough people from the south.
What we could do is put a little more care and think about what young people aged 16 to 29 really look like. If we think really carefully, we might come up with a sampling matrix that looks closer to the one on the right. We’ve put more people into the south and we’ve put more people into the older age group.
I still don’t know if this is what the USA really looks like but it’s the best I can do with what I know.
But is it really?
If you’re not already familiar with data ferret, it is an awesome tool put together by the census group in the US government. It’s completely free to use and it lets you look at and analyze a lot of the data that the census department collects from its surveys. For instance the American community survey, the American housing survey, the current population survey as well as population estimates and projections.
We’re going to use data ferret to figure out EXACTLY what our sampling matrix should look like. We don’t need to guess because the US census department conducts regular surveys to learn many many thing about its population including their age, gender, education, income, family status, and much more. The ferret will fill in that sampling matrix for us.
To start I’m going to click on launch data ferret. It is a little bit picky about software so you might find yourself needing to use a different browser, upgrading javascript, or allowing pop-ups. It’s totally fine to allow pop ups on this website.
You’ll be asked to fill out your email address here. It’s fine to do that. They never send spam. At least not yet.
Then you’ll come to this screen. All you really need to do here is choose Step 1. We want to choose which dataset we’re interested in and well as the variables we’re interested in.
On the left hand side, you can see all the surveys that you’re allowed to have access to. For our purposes, we’re interested in the current population survey. We’ll just find that survey, open it up, and then choose the most recent set of data. You can see that they gather fresh data with this survey every single month. So if you want to make sure your data is as accurate as possible, you could come here every month and generate the newest results.
When you click on the dataset you want, you’ll see a menu to view the variables. Once you choose that option, then the list of variables on the right side will appear. For our purposes, we want to see the household, geography, and demographic variables. It will take the ferret a minute or two to find those variables but then it will list them for you. Sometimes, you’ll even get thousands of variables.
Because the list is only 75 variables, it’s simple enough to just scroll through and find the ones you want. In our case, we want age, sex, and region. Sex is down just a little further. And when I double click on it.
I get this box. You just need to click on the select box and then the ok box. Then you can do it again for the age variable and the region variable.
Now we can move on to step 2 where we work with the databasket or create a table. Here you can see the three variables that we’ve chosen, sex, age, and region. What I’m most interested in doing here is two things.
What I really need to do is recode the age variable into two brand new variables. The original variable that the census department gathers is actual individual age. I need to create a variable that groups people according to their age. And, I need to make sure that it uses the 3 age groups that I’m interested in. So, I’m going to create an age group variable like this.
The second variable I need to create is for subsample purposes. It seems a little redundant but you’ll see why we’re doing this in just a minute. In this case, I’m going to create three separate groups. Two of the groups identify people that I’m NOT interested in. One group, the 16 to 29 group, identifies the group of people that I AM interested in. So, I’m going to use this variable to identify who is in my target group.
Let’s hop back into dataferret now. You’ll see on the right side, there is an option to recode variables. So first I’ll click on the age variable and then I’ll click on recode variable. A popup will appear where you can create the groupings for age. I’ll do this twice, once to create the 3 age groups I’m interested in and once to create the subsample groups.
Now all the variables are set up and we just need to select databasket/make a table.
That gives us this blank spreadsheet. All we’re going to do know is click and move each variable onto the spreadsheet into the spot we want. Just remember how we set up the sampling matrix before and we’re going to replicate that here.
Our sampling matrix had sex, age, and region. Just make sure you choose the grouping variable that we created for age. The one that splits the ages 16 to 29 into 3 groups.
The next thing we’re going to do is pick out our subsample. Remember we’re only interested in people who are aged 16 to 29. Here’s where we choose the second age variable, the one we created just for the purposes of sampling. We’ll pop that variable into the top row.
We’re almost there now. First, we’ll tell the ferret that we want to see percentages so we’ll click on the percentage button. Make sure to choose the column percentage button, not the row percentage button. Now we can click on go get data.
And this is our wonderful result!
You’ll see four columns but we’re really only interested in one of the columns. The third column that shows the percentages for people who are in our target group aged 16 to 29.
What this table tells us is that, in the USA, among people who are aged 16 to 29, 50.2% of them are male and 49.8% of them are female. And, 27.7% of them are aged 16 to 19 while 36.8% of them are aged 20 to 24. THIS is what our group of people really looks like so this is what we need to include in our sample.
For those of you who are familiar with weighting, this is also the column that you’re going to weight your results to once you get your data back.
Now once you have this sampling matrix based on real data from the US census, you can just pop it into whatever sampling system you’re using
In this case, we’re looking at the peanut labs samplify system. We can just type the percentages we got from dataferret here and then the sampling system will do its job to pull a collection of people who like just like this.
Of course, there is an unlimited number of sampling matrices you might need. Here is another one. In this case, I’ve added the metropolitan variable which basically means people who live in rural vs urban areas. I’ve also added in Hispanic as a variable.
Let’s think back to 20 minutes ago when we first started to think about our sampling matrix. We tried to make things fair by saying that we wanted 25% of people to come from the northeast. But then we realized that wasn’t very fair at all and we guess that the number should maybe be 20%. But, when we looked at real census data we discovered that that number should actually be 17.9%. It doesn’t seem like a big deal but there are probably many cases where you just can’t imagine what the breakouts should be. Well, now we’ve seen just how easy it is to get the right numbers instead of asking all your colleagues to give their best guestimate.
And, now that we know what the true census targets are, we can do any weighting that might be necessary
That brings me to the end of my talk. If you have any questions, please do feel free to type them in the box and I’ll answer as many as I can.
You mentioned weighting? What is that?
Sometimes you’ll find that even the best sampling plan doesn’t work out perfectly in the end. Maybe you were trying to get responses from 100 men aged 16 to 19 but you only got responses from 80. It could bias the results to not include those other 20 young men in your overall results. What we do in this case is make them men we do have count just a little bit more. Instead of letting them each person count as one person each, we’ll make each person count as one and a quarter people. 80 people times 1.25 gives us 100 people. It’s not quite as good as actually having 100 unique people. I’d rather you go back to field and wait to get another 20 people but the real world doesn’t always let that happen.