Survey Research In The Digital Age

The Next AAPOR Webinar is Coming Soon:
Survey Research In The Digital Age
February 21, 2019
Noon - 1:30 PM CST
Presented by Matthew Salganik

In the past several years, we have witnessed the birth and rapid spread of social media, smart phones, and numerous other digital marvels. In addition to changing how we live, these tools enable us to collect and process data about human behavior on a scale never before imaginable. In this talk, I’ll describe how survey research fits into this new data landscape. Further, I’ll use specific examples to illustrate how survey researchers can harness the tools of the digital age to collect data in new ways. Throughout the talk I will emphasize ways that big data sources and surveys can serve as compliments rather than substitutes.

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Preventing cancer: mere rhetoric or a promising plan?

In 2018, @mats_julius and @MortenValberg were finalists in our annual writing competition. Today we've published their article, "Preventing cancer: mere rhetoric or a promising plan?" https://t.co/eer5FNk2MR. Please read, and follow the links to learn about our 2019 competition. pic.twitter.com/cccLb2Dhnp

— Significance (@signmagazine) 13 februari 2019

Statistical Paradises in Big Data

From Annals of Applied Statistics and tweet by Karin Nelsson

Statisticians are increasingly posed with thought-provoking and even paradoxical questions, challenging our qualifications for entering the statistical paradises created by Big Data. By developing measures for data quality, this article suggests a framework to address such a question: “Which one should I trust more: a 1% survey with 60% response rate or a self-reported administrative dataset covering 80% of the population?” 

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A comprehensive approach to problems of performance measurement

https://youtu.be/VH754ZImCzU

The paper describes a comprehensive approach to problems of performance measurement that can be used to tackle a wide range of situations, including designing monthly board and leadership reports in enterprises, assessing research quality and monitoring the efficiency and effectiveness of government programmes. It provides a review of various methods for tackling these problems and outlines some current areas of research. Although technical statistical issues are buried somewhat below the surface, statistical thinking is very much part of the main line of argument, meaning that performance measurement should be an area attracting serious attention from statisticians. 

Author: N. I. Fisher, University of Sydney and ValueMetrics, Sydney

”Statistik ger chans till bättre levnadsvillkor”

Foto: Colourbox

Dagens Samhälle
Folkhögskolerektorernas påstående att staten vill skapa register över människor med funktionsnedsättningar är fel. Genom statistik går det att åstadkomma en förändring till det positiva för dessa personer, skriver fyra företrädare för funktionshinderrörelsen.

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Can social media data improve official statistics? Not yet, suggests new work on tourism

National Statistical
While ONS (Office for National Statistics, UK) strategy is focused on making more use of government-held data to improve official statistics, researchers in the Data Science Campus are also exploring the value of social media data. There’s plenty of it and its potential seems obvious. But who is “citizen of the world” and where is “Black Pudding Land”? As well as privacy concerns some big limitations in the data need addressing too, conclude Lanthao Benedikt and Emily Tew.

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Top 9 ebooks that can level you up in data science

Robotics & Automation
A ton of books for Data Science is available to help you get started with it and build a career you have always dreamt of. Not all of the books are for beginners as some are for data science paragons.

It is always recommended that a beginner should do mind mapping, concerning books that can guide through the basics of data science. What if you get a curated list of books that have been cull out by experts to give beginners a kickstart in data science, big data, Python, R, and so on?

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How the BBC Visual and Data Journalism team works with graphics in R

BBC
Over the past year, data journalists on the BBC Visual and Data Journalism team have fundamentally changed how they produce graphics for publication on the BBC News website. In this post, we explain how and why we have used R’s ggplot2 package to create production-ready charts, document our process and code and share what we learned along the way.

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Is 'Data Scientist' the 'Sexiest Job of the 21st Century'? And How Do You Get One of Your Own?

Image credit: Sirinarth Mekvorawuth | EyeEm | Getty Images

Entrepreneur Europe
Even if you're not versed in advanced analytics and data science, you can understand the thought process data scientists go through.

When you hear the word “data scientist,” what does that term mean to you?

Is it the “sexiest job” of the 21st century as the Harvard Business Review suggested? Does it describe a really smart person with advanced degrees in computer science, applied math, statistics, economics? Someone who analyzes and extracts business value from big data?

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Hans Rosling måste förstås på ett djupare plan

DN - Replik

Hans Rosling gjorde inte anspråk på att lösa 2000-talets stora frågor utan beskrev världen utifrån ett epidemiologiskt synsätt, replikerar Roslings tidigare medarbetare Daniel Lapidus i debatten om Roland Paulsens artikel.

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New EU directive to create public data pool

Global Government Forum
The European Parliament, Commission and Council have agreed a new directive to increase the availability and re-use of public sector data across the EU.

The agreement is designed to create a huge pool of anonymised information for use by businesses and public bodies, supporting the European data economy to become more competitive and spurring “the development and uptake of advanced technologies such as artificial intelligence, cloud computing and many others”, the European Council said.

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Predictive Analytics: A Review Essay

Predictive Analytics Times - machine learning & data science news
In 2013, as the worst effects of the crash had begun to reverberate out of the system, analysts like myself, and of dozens of other stripes—statisticians, biostatisticians, econometricians, financial quants, psycho-sociological researchers, etc., etc. (not in any order)—were exposed to the first wave of evangelism for Big Data, and what it meant. Characterized by ready enthusiasm, it radiated a minimum of science and a maximum of advertising-as-pseudo-science.

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Global Temperature Report for 2018

Berkeley Earth

Berkeley Earth, a California-based non-profit research organization, has been preparing independent analyses of global mean temperature changes since 2013. The following is our report on global mean temperature during 2018.

We conclude that 2018 was likely the fourth warmest year on Earth since 1850. Global mean temperature in 2018 was colder than 2015, 2016, and 2017, but warmer than every previously observed year prior to 2015. Consequently, 2016 remains the warmest year in the period of historical observations. The slight decline in 2018 is likely to reflect short-term natural variability, but the overall pattern remains consistent with a long-term trend towards global warming.

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What to Look for in Data – Part 2

Stats With Cats Blog
What to Look for in Data – Part 1 discusses how to explore data snapshots, population characteristics, and changes. Part 2 looks at how to explore patterns, trends, and anomalies. There are many different types of patterns, trends, and anomalies, but graphs are always the best first place to look.

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(Full) Excerpt of Excursion 4 Tour I: The Myth of “The Myth of Objectivity”

Error Statistics Philosophy

4.1 Dirty Hands: Statistical Inference Is Sullied with Discretionary Choices

If all flesh is grass, kings and cardinals are surely grass, but so is everyone else and we have not learned much about kings as opposed to peasants. (Hacking 1965, p.211)

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4.2 Embrace Your Subjectivity

The classical position of the subjective Bayesian aims at inner coherence or consistency rather than truth or correctness. Take Dennis Lindley:

I am often asked if the method gives the right answer: or, more particularly, how do you know if you have got the right prior. My reply is that I don’t know what is meant by ‘right’ in this context. The Bayesian theory is about coherence, not about right or wrong. (Lindley 1976, p. 359)

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Göteborg och SND värd för global IASSIST-konferens 2020

Svensk Nationell Datatjänst

IASSIST, som står för International Association for Social Science Information Services and Technology, är en internationell organisation som arbetar för att främja det samhällsvetenskapliga forskarsamhället genom informationsteknologi och datarelaterade tjänster. Varje år anordnar IASSIST tillsammans med ett av sina medlemsländer en konferens för sitt globala nätverk. Förra veckan kom beslutet att Göteborg blir värdstad för den 46e upplagan av den internationella konferensen.

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Better and better? A comment on Hans Rosling

https://youtu.be/OoIcsj9ysvs

Is the world only becoming better and better? How are increases in wealth and life expectancy affected by social inequalities? Why do we hear so little about the global increase in mental illness and its development over time? These are some of the questions that Roland Paulsen, associate professor of sociology, discusses in this video, which is also a comment on some of the claims of Hans Rosling and other New Optimists.

Read more.... och se även Global Politics och Johan Norberg i DN och Daniel Lapidus i DN och DN Ledare

Statistical insights: Are international productivity gaps as large as we thought?

OECD ECOSCOPE
Labour productivity is a key indicator of economic wellbeing, and raising it – producing more goods and services from the same or less work (labour input) – is one of the main drivers of sustainable economic growth.

Historically, comparisons of productivity across countries have shown substantial gaps, even between similar-sized economies at a similar stage of development – leaving many analysts struggling to understand the causes. However, a new OECD study has found that at least a part of these gaps disappears once we adjust for differences in how countries measure labour input.

In the case of the United Kingdom for instance, the study reveals that the gap in labour productivity levels with the United States, is around 8 percentage points smaller than was previously thought – closing from 24% to 16%. The gap with Germany shrinks from 22% to 14% and with France from 20% to 11%.

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Statistikfrämjandets nyhetsbrev nr 2 januari 2019

Innehåll: Missa inte Kvalitetsseminariet den 7/2 NOMINERA TILL CRAMÉRPRISET 2019 SENAST 8/2 The Baltic-Nordic Conference on Survey Statistics 2019 (BaNoCoSS 2019), June 16 – 20, Örebro, Sweden Statisticon bjuder in till diskussion… UseR!2019 Datathon for young statisticians ENBES invites to two establishment statistics workshops

Det händer mycket när det gäller utveckling av stickprovsteori

Det händer mycket när det gäller utveckling av stickprovsteori och det är glädjande att se att nya sätt att göra tillförlitliga undersökningar utvecklas.

Det är viktigt att statistiken fortsatt är pålitlig. Detta är bara ett av många pågående intiativ. #mrxse #ResearchMethods https://t.co/GTjCpnkeN9

— karin nelsson (@karinnelsson) 19 januari 2019

Sifo - ”Vilket parti skulle du rösta på i riksdagsvalet om det vore val i dag?”

Länk till Wikipedia

Statistical Self-Defense

Blog about Stats
No day without numbers in (social) media, in everyday life. And they not only want to inform us, they also want to orient us in one direction or the other.

And every day are among them deliberately or unintentionally false or misleading numbers.

Therefore, statistics must arm themselves against incorrect use of data and repeatedly teach the correct handling of statistical data.

There have long been numerous works on this subject. Here is another quite basic presentation by the Dutch journalist Sanne Blauw.

She picks out five statistical sins. Read more.... ....and look at https://youtu.be/mJ63-bQc9Xg

Why Digital Marketers Need a Solid Understanding of Statistics

Towards Data Science
Most of the companies states that we are data-driven but how well do we understand the statistical tests available to us. Let’s start from the study of data :).

In 2012, “ Marketers Flunk the Big Data Test” on Harvard Review pointed out that vast majority of marketers are still gut-based decision making during big-data explosion era, based on a study polled nearly 800 marketers at Fortune 1000 companies.

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Democratizing data science

MIT researchers are hoping to advance the democratization 

of data science with a new tool for nonprogrammers that 

automatically generates models for analyzing raw data.

Image: Christine Daniloff, MIT, MIT News

MIT News:
Tool for nonstatisticians automatically generates models that glean insights from complex datasets.

MIT researchers are hoping to advance the democratization of data science with a new tool for nonstatisticians that automatically generates models for analyzing raw data.

Democratizing data science is the notion that anyone, with little to no expertise, can do data science if provided ample data and user-friendly analytics tools. Supporting that idea, the new tool ingests datasets and generates sophisticated statistical models typically used by experts to analyze, interpret, and predict underlying patterns in data.

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YouGov lanserar nytt undersökningsverktyg

Dagensanalys.se
Marknadsundersökningsföretaget YouGov lanserar ett nytt gör-det-själv-verktyg för onlineundersökningar.

Det nya verktyget, YouGov Collaborate, tillåter användare bygga egna onlineundersökning via företagets Omnibus-service, där man får möjlighet att se och analysera resultaten i realtid.

I verktyget kommer användare även få tillgång till riktlinjer och stöd från forskare ifall de behöver.

Ben Glanville, UK head of data på YouGov, kommenterar: “The online dashboard and live reporting will save time and allow users to gain insights quicker, which are easy to share across the business.”

 

Cooking up statistics: The science and the art

Significance magazine
Letisha Smith – winner of the 2018 Award for Statistical Excellence in Early-Career Writing – started the year with the resolve to eat smarter, with less food and less money going to waste. She turned to machine learning to help streamline her meal plans.

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Mementos from Excursion 4: Objectivity & Auditing: Blurbs of Tours I – IV

Error Statistics Philosophy
Excursion 4 Tour I: The Myth of “The Myth of Objectivity”

Blanket slogans such as “all methods are equally objective and subjective” trivialize into oblivion the problem of objectivity. Such cavalier attitudes are at odds with the moves to take back science The goal of this tour is to identify what there is in objectivity that we won’t give up, and shouldn’t. While knowledge gaps leave room for biases and wishful thinking, we regularly come up against data that thwart our expectations and disagree with predictions we try to foist upon the world. This pushback supplies objective constraints on which our critical capacity is built. Supposing an objective method is to supply formal, mechanical, rules to process data is a holdover of a discredited logical positivist philosophy. Discretion in data generation and modeling does not warrant concluding: statistical inference is a matter of subjective belief. It is one thing to talk of our models as objects of belief and quite another to maintain that our task is to model beliefs. For a severe tester, a statistical method’s objectivity requires the ability to audit an inference: check assumptions, pinpoint blame for anomalies, falsify, and directly register how biasing selection effects–hunting, multiple testing and cherry-picking–alter its error probing capacities.

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R Coding Style Guide

R-bloggers
Language is a tool that allows human beings to interact and communicate with each other. The clearer we express ourselves, the better the idea is transferred from our mind to the other. The same applies to programming languages: concise, clear and consistent codes are easier to read and edit. It is especially important, if you have collaborators, which depend on your code. However, even if you don’t, keep in mind that at some point in time, you might come back to your code, for example, to fix an error. And if you did not follow consistently your coding style, reviewing your code can take much longer, than expected. In this context, taking care of your audience means to make your code as readable as possible.

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Why Ice Cream Is Linked to Shark Attacks – Correlation/Causation Smackdown

From Dr. Data Show Video: Predictive Analytics Times - machine learning & data science news



Why are soda and ice cream each linked to violence? Watch this episode of The Dr. Data Show, which delivers the final word on what people mean by “correlation does not imply causation.”

CLICK HERE TO VIEW THE FULL EPISODE

About the Dr. Data Show. This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics.

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Dare to Compare – part 4

Stats With Cats Blog

Part 3 of Dare to Compare shows how one-population statistical tests are conducted. Part 4 extends these concepts to two-population tests.

To review, this flowchart summarizes the the process of statistical testing.

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