André Calero Valdez

Summer semester 2020

Fri, 01 May 2020 00:00:00 +0100

The summer semester 202020 focuses on using R, ggplot2 and shiny.

Summer semester 2019

Sun, 05 May 2019 00:00:00 +0100

The summer semester 2019 focused on using R and ggplot2.

Todo: Youtube Playlist

Winter semester 2019/20

Sun, 05 May 2019 00:00:00 +0100

Important links

Videos

Was sind Methoden?

Qualitative und Quantitative Daten und Methoden

Wissenschaftstheorie

Messtheorie und Skalen

Deskriptive Statistik

Wahrscheinlichkeit, Verteilungen und Stichproben

Inferenz und t-test

Varianzanalyse

Korrelation

Lineare Regression

Faktorenanalyse und Reliabilität

Winter semester 2019/20

Sun, 05 May 2019 00:00:00 +0100

Slides

Slides can be found at the Github-Repository or below.

https://github.com/rwth-comm/EmpirischeForschungsmethoden

Videos

Git und Versionierung

Forschungsthemen

Omnibusfragebogen

Data Cleaning

Deskriptive Statistik

Boxplot und Barplot

p-Werte

Anova (Flipped Classroom)

Correlation (Flipped Classroom)

Lineare Regression (Flipped Classroom)

Principal Component Analyses (Flipped Classroom)

Summer semester 2018

Sun, 05 May 2019 00:00:00 +0100

Summer semester 2018/19

Sun, 05 May 2019 00:00:00 +0100

In this seminar students were introduced to the programming environment Processing. Students should develop their own information visualization using processing that utilized an open-data API. The whole process was design around the design study methodology.

As a requirement students had to submit an application and a term paper describing the applications and an evaluation.

Video-Lectures for the seminar

All videos can be seen in a youtube playlist.

Installing processing

How to learn processing

Pixels and rectangles

Comments and first examples

Shape Primitives

Exercise drawing faces

Text and Bar-Charts

Variables

Variables (simplified)

Setup and draw

Writing functions

Mouse interaction

A drawing app

Window resizing

Arrays

For loops

If statements

Tabular data 1

Tabluar data 2

Object orientation

Object orientation 2

Random

Dynamic data

Flummis

Error-Messages

Summer semester 2019

Sun, 05 May 2019 00:00:00 +0100

Summer semester 2020

Sun, 05 May 2019 00:00:00 +0100

Winter semester 2018/19

Sun, 05 May 2019 00:00:00 +0100

WS 18/19 Video Material

Was sind Forschungsmethoden

Qualitative und quantitative Methoden

Wissenschaftstheorie und Empirie

Forschungsinstrument, Messtheorie, Skalen

Deskriptive Statistik

Verteilungen, Zufall und Stichproben

Leider ohne Ton.

Nachreichung mit Ton.

Inferenz, Hypothesen, t-Test, Fehler 1. und 2. Art

Alpha-Fehler Kummulierung, Bonferroni, ANOVA

Kovarianz und Korrelation

Audio fehlt. Zweite Aufzeichnung wird nachgereicht :(

Mit Audio als Kurzzusammenfassung

Lineare Regression

Wiederholung und Likert Skalen

Video-Vorlesung: Conjoint-Software

Faktorenanalyse

Winter semester 2018/19

Sun, 05 May 2019 00:00:00 +0100

Slides

Slides can be found at the Github-Repository or below.

Slides 1 - Link
Slides 2 - Link
Slides 3 - Link
Slides 4 - Link

Döring/Bortz - Forschungsmethoden und Evaluation
Online books:
- R for Data Science, Link: http://r4ds.had.co.nz (free)
- R for Social Science, Link: http://socviz.co/ (free)
- Modern Dive: https://moderndive.com/ (free)

Coursebook

The coursebook for empricial methods of social sciences can be found here.

The relevant chapters are:

Chapter 1
Chapter 2

Video Lecture

Introduction

Literature Search and factor space

Project Organization and Git

Questionnaire design using surveymonkey

Data Cleaning using dplyr

Descriptive Statistics and ggplot2

Tutorial on GIT, Boxplots and Esquisse

T-Test and P-Values

Übung - ANOVA, MANOVA

Tutorial 4 - Mittelwertsunterschiede mit ggplot2

Tutorial 5 - Korrelation und Lineare Regression

Likert-Plots

Older courses

Sun, 05 May 2019 00:00:00 +0100

Video-Lectures Flipped Classroom

The following videos are based on the old course that relies on SPSS.

All videos can be watched in a playlist here.

Model

Mittelwert und Standardabweichung

Deskriptive Statistik und Korrelation

Induktive und Deduktive Verfahren

Explorative Statistik

Signifikanz und Bedeutsamkeit

Stichprobe und Grundgesamtheit

Lineare Regression

t-Test

ANOVA

infoXpand

Fri, 01 Jul 2022 22:12:34 +0200

Information, opinion, mobility, behavior, and Bayesian inference in infectious disease modeling - Subproject C.

The COVID-19 pandemic was accompanied by an “infodemic,” i.e., an excess of information about the virus, protective measures, and government intervention. In particular, misinformation and conspiracy theories disseminated on the Internet were blamed for increasing polarization of opinion, radicalization, and declining trust in institutions. It was warned that this had fueled the pandemic and made it even more difficult to manage. However, models of disease spread, such as those used to predict pandemic dynamics, ignore the fact that information and the pandemic form a complex interaction. While these models take into account that, for example, low vaccination rates increase hospitalizations, they fail to consider that knowledge of increasing hospitalizations motivates people to get vaccinated. The main goal in infoXpand is to understand this feedback loop between pandemic and information dissemination and to derive suggestions for future decision makers. To this end, we have formed an interdisciplinary consortium with unique expertise in pandemic modeling, opinion dynamics, mobility, and human behavior. We closely develop and analyze agent-based models and compartmental models that capture both classical disease dynamics and opinion dynamics. We calibrate critical model assumptions with data from social science survey studies and behavioral experiments, as well as extensive mobility data.

Project partners

Dr. Viola Priesemann - Max Planck Institute for Dynamics and Self-Organization
Prof. Dr. Mirjam Kretzschmar - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht
Prof. Dr. Michael Mäs - Karlsruhe Institute of Technology
Prof. Dr. Kai Nagel - Technische Universität Berlin

OptimAgent

Fri, 01 Jul 2022 22:12:34 +0200

Optimized strategies for epidemic control in highly heterogeneous populations - Subproject D.

The SARS-CoV-2 pandemic poses an unprecedented challenge to society and policy-making. The goal of OptimAgent is to develop a standardized model-based framework to support public health decision-making processes that can evaluate a wide range of infection control interventions. The focus is on the design of an agent-based model (ABM) that goes substantially beyond simulation approaches used to date. Through a flexible modular structure and an extensive consultation process with national and international modeling experts, the model will be tailored in particular to inform health policy decision-making during future pandemics. In addition, it will also be adaptable for endemic pathogens. The model will realistically represent the socio-demographic and regional structures of Germany. Agents will have demographic, socio-economic, sociological as well as psychological characteristics that influence individual contact behavior, risk of infection and disease. Based on the results of comprehensive and sophisticated analyses of contact behavior, specific model modules will be developed on selective target-directed contact restraints in different settings, contact tracking and testing strategies. The flexible modular model design will also provide opportunities for easy integration of additional components. The focus of the project is to analyze the impact of different dimensions of heterogeneity in the population as well as their interaction on the incidence of infection. This will provide new insights into the importance of heterogeneity in the spread of severe respiratory infectious diseases in the population and the effectiveness of pandemic control measures.

Projektpartner

Prof. Dr. Rafael Mikolajczyk, Jun.-Prof. Dr. Alexander Kuhlmann, Dr. Johannes Horn - Martin-Luther-Universität Halle-Wittenberg
Prof. Dr. André Karch, Dr. Veronika Jäger - Westfälische Wilhelms-Universität
Prof. Dr. Vitaly Belik, Freie Universität Berlin
Prof. Dr. Markus Scholz, Universität Leipzig
Prof. Dr. Jan Pablo Burgard & Prof. Dr. Ralf Münnich; Universität Trier
Dr. Wolfgang Bock, Technische Universität Kaiserslautern
Prof. Dr. Bernd Hellingrath, Westfälische Wilhelms-Universität Münster
Prof. Dr. Tyll Krüger, Technische Universität Breslau
Prof. Dr. Mirjam E. Kretzschmar, Universität Utrecht
Dr. Berit Lange, Helmholtz-Zentrum für Infektionsforschung
Prof. Dr. Wolfgang Greiner, Universität Bielefeld
Prof. Dr. Uwe Siebert & Prof. Dr. Beate Jahn; UMIT – University for Health Sci- ences, Medical Informatics and Technology
Dr. Sten Rüdiger, NET CHECK GmbH

Perceptions of behaviour efficacy, not perceptions of threat, are drivers of COVID-19 protective behaviour in Germany

Thu, 24 Mar 2022 00:00:00 +0000

Interplay between risk perception, behaviour, and COVID-19 spread

Tue, 15 Feb 2022 00:00:00 +0000

Fear, Behavior, and the COVID-19 Pandemic: A City-Scale Agent-Based Model Using Socio-Demographic and Spatial Map Data.

Tue, 01 Feb 2022 00:00:00 +0000

The benefits, costs and feasibility of a low incidence COVID-19 strategy

Tue, 01 Feb 2022 00:00:00 +0000

Applying an extended UTAUT2 model to explain user acceptance of lifestyle and therapy mobile health apps: survey study

Tue, 18 Jan 2022 00:00:00 +0000

Europe must come together to confront omicron

Sat, 01 Jan 2022 00:00:00 +0000

Rethinking Pension Communication–The Role of Metaphors in Information Visualization

Sat, 01 Jan 2022 00:00:00 +0000

The Influence of User Diversity on Motives and Barriers when Using Health Apps-A Conjoint Investigation of the Intention-Behavior Gap

Sat, 01 Jan 2022 00:00:00 +0000

The pension story-Data-driven storytelling with pension data

Sat, 01 Jan 2022 00:00:00 +0000

Visualizing Large Collections of URLs Using the Hilbert Curve

Sat, 01 Jan 2022 00:00:00 +0000

The winter dilemma

Mon, 04 Oct 2021 00:00:00 +0000

Taigers

Thu, 23 Sep 2021 22:12:34 +0200

Transparency in Artificial Intelligence: Considering Explainability, User and System Factors

The increased use of artificial intelligence, shortform AI, systems leads to a variety of interdisciplinary challenges in the field of human-system interaction. One of the biggest challenges is the influence of transparency on trust, acceptance and usage of AI. While users, politicians and developers desire transparency, a user-centered definition is highly dependent on context, system factors, and user factors. Lack of transparency, on the other hand, leads to non-acceptance, algorithm aversion and poorly performing systems.

The project TAIGERS addresses the research gap of a user-centric definition of transparency, as well as a transfer into practicable recommendations for AI developers. In a close interdisciplinary cooperation of the chair of information management in mechanical engineering, shortform IMA and the chair of communication science, an AI transparency map is build based on user factors and system factors. This typology is consolidated in an AI transparency framework by a representative survey study and experimental investigations. Herein, questions of construct validity and user diversity will be addressed, as well as questions of information architecture in prospective and retrospective transparency settings. After discussions with AI developers, engineers and computer scientists, the framework will include the technical perspective as well.

The result of TAIGERS is an interdisciplinary evaluated framework of development recommendations that can be used and extended across all domains in AI research. The TAIGERS project thus lays a foundation for inter- and transdisciplinary collaboration between social science, engineering, and computer science in the highly relevant topic of transparent AI.

Towards a European strategy to address the COVID-19 pandemic

Sat, 04 Sep 2021 00:00:00 +0000

A look into the future of the COVID-19 pandemic in Europe: an expert consultation

Wed, 01 Sep 2021 00:00:00 +0000

SEMinR: Domain-specific language for building, estimating, and visualizing structural equation models in R

Fri, 06 Aug 2021 00:00:00 +0000

Expectation, Perception, and Accuracy in News Recommender Systems: Understanding the Relationships of User Evaluation Criteria Using Direct Feedback

Sat, 24 Jul 2021 00:00:00 +0000

Fairness and the Need for Regulation of AI in Medicine, Teaching, and Recruiting

Sat, 24 Jul 2021 00:00:00 +0000

Finding a Structure: Evaluating Different Modelling Languages Regarding Their Suitability of Designing Agent-Based Models

Sat, 24 Jul 2021 00:00:00 +0000

The history of agent-based modeling in the social sciences

Sat, 24 Jul 2021 00:00:00 +0000

A look into the future of the COVID-19 pandemic in Europe: an expert consultation

Thu, 01 Jul 2021 00:00:00 +0000

Benchmarking crisis in social media analytics: a solution for the data-sharing problem

Wed, 31 Mar 2021 00:00:00 +0000

Defend your enemy. A qualitative study on defending political opponents against hate speech online

Mon, 26 Oct 2020 00:00:00 +0000

Filter Bubbles and Content Diversity? An Agent-Based Modeling Approach

Sun, 19 Jul 2020 00:00:00 +0000

Investigating key factors for social network evolution and opinion dynamics in an agent-based simulation

Sun, 19 Jul 2020 00:00:00 +0000

Making reproducible research simple using RMarkdown and the OSF

Sun, 19 Jul 2020 00:00:00 +0000

Netlogo vs. Julia: Evaluating Different Options for the Simulation of Opinion Dynamics

Sun, 19 Jul 2020 00:00:00 +0000

User behavior and awareness of filter bubbles in social media

Sun, 19 Jul 2020 00:00:00 +0000

Opinion formation on the internet: The influence of personality, network structure, and content on sharing messages online

Thu, 02 Jul 2020 00:00:00 +0000

KISTRA - Use of Artificial Intelligence for Early Detection of Crimes

Wed, 01 Jul 2020 22:12:34 +0200

Research on Technical Solutions for the Detection of Hate Crimes as well as Socio-Scientific, Ethical and Legal Considerations on the Topic “Hate on the Internet”

On July 1, 2020, the research project KISTRA – Use of AI for the Early Detection of Crimes was launched. The aim is to research the possibilities and framework conditions for the ethically and legally justifiable use of artificial intelligence by security authorities for the early detection and prevention of hate crime. A consortium of nine partners, led by the Central Office for Information Technology in Security, shortform ZITiS, is participating in the three-year project. KISTRA is characterized by the interdisciplinary integration of science, economy and end users. In addition to ZITiS and the Federal Criminal Police Office, or BKA for short, which is both partner and end user, seven other partners are involved in the project: Johannes Gutenberg University Mainz, Ludwig Maximilian University Munich, Munich Innovation Labs GmbH, RWTH Aachen University, Technical University Berlin, Technical University Darmstadt and University Duisburg-Essen. The research project has a total budget of 2.98 million euros. KISTRA’s results will include socio-scientific, ethical and legal expertises as well as technical solutions, for example software demonstrators. In addition to the direct application at the BKA, other authorities with security tasks can also benefit from the results: on the one hand, through the function of ZITiS as a central office with the mandate to support the German security authorities by researching and developing tools in the digital space, on the other hand, through the BKA’s central office function for the federal and state police forces.

Basis

The project is based on the “Protection against Crime and Terrorism” initiative of the national security research programme at the Federal Ministry of Education and Research, shortform BMBF, and aims to promote the development of legally compliant AI and procedures for the collection and evaluation of security-relevant content on the Internet.

Background and Objectives

Police crime statistics have shown a significant increase in politically motivated crimes with an Internet connection in recent years. The Federal Government is countering this development, among other things, with a legislative initiative to combat right-wing extremism and hate crime, which was adopted by the Bundestag on 18 June 2020. This is intended to counteract the brutalization of communication, which is increasingly being observed particularly in social networks, if the content is criminally relevant. Social networks with at least two million registered users in the Federal Republic of Germany are to be obliged to report certain criminal content that they become aware of in the course of user complaints to the Federal Criminal Police Office so that the BKA can initiate criminal prosecution with the competent security authorities. In order to implement this plan, the BKA is currently in charge of setting up a new Central Internet Reporting Office for hate crime, among other things. Here too, ZITiS will provide support by providing information technology capabilities, for example by researching and developing trustworthy AI. Artificial intelligence, or AI for short, will be a key technology in dealing with the challenges of hate crime. KISTRA is investigating its application to support investigators at security authorities. Hate crime stands for politically motivated crimes which are directed against people of an actual or ascribed social group, for example in the form of death threats, sedition or insults. The amount of data that is exchanged daily on the Internet is growing continuously and can hardly be processed without the support of AI methods. Deep learning in particular has proven to be a proven method for classifying large amounts of data of different data types.

KISTRA is investigating the possible application of AI in security authorities in a holistic approach: technical, social scientific, ethical and legal considerations are all taken into account in the project. The overriding goals include:

the consideration of the legality and the ethical justifiability of the intended AI solutions and the resulting methods for security authorities,
the identification and sociological examination of politically motivated hate speech and “hate crime” on the Internet,
the development and implementation of adaptive AI methods to support the police criminal law assessment of hate crime incidents, and
the holistic consideration of the individual technical components and scientific results and their transfer into a technical overall solution, a so-called framework.

Evaluation of a financial portfolio visualization using computer displays and mixed reality devices with domain experts

Tue, 21 Apr 2020 00:00:00 +0000

Masken für alle? Die Wissenschaft sagt ja.

Tue, 14 Apr 2020 00:00:00 +0000

Übersetzt: André Calero Valdez (+ DeepL).

Soll man nun Masken tragen oder nicht? Sicher, es ist kompliziert. Aber nicht so kompliziert, wie manche Leute unterstellen. Wir haben uns die Forschung angeschaut (siehe unsere Veröffentlichungen “Face Masks Against COVID-19: An Evidence Review” - mit 84 Referenzen! - und “Face masks for the public during the covid-19 crisis”). Hier ist eine Zusammenfassung der verschiedenen Evidenzströme und unsere Einschätzung, was das alles bedeutet.

Die Epidemiologie der Krankheitsverbreitung

Sie haben wahrscheinlich schon die Videos von dicht gedrängten Dominosteinen und Mausefallen gesehen, wo ein einzelner Gegenstand eine riesige Kettenreaktion auslöst. Je näher die Dominosteine (oder Mausefallen) beieinander liegen, desto mehr Chaos entsteht. Jede ansteckende Krankheit hat eine Übertragungsrate (R0). Eine Krankheit mit einer R0 von 1,0 bedeutet, dass jede infizierte Person im Durchschnitt eine andere Person ansteckt. Eine Krankheit, deren R0 kleiner als 1,0 ist, wird aussterben. Der Grippestamm, der die Pandemie von 1918 verursachte, hatte einen R0 von 1,8. Der R0 des Virus, der COVID-19 verursacht, wurde von Forschern des Imperial College auf 2,4 geschätzt, obwohl einige Untersuchungen darauf hindeuten, dass er bis zu 5,7 betragen könnte. Das bedeutet, dass sich COVID-19 ohne Eindämmungsmaßnahmen weit und schnell ausbreiten wird. Wichtig ist, dass COVID-19-Patienten in der Frühphase der Krankheit am ansteckendsten sind (To et al. 2020; Zou et al. 2020; Bai et al. 2020; Zhang et al. 2020; Zhang et al. 2020; Doremalen et al. 2020; Wei 2020), in der sie in der Regel wenige oder keine Symptome haben.

Die Physik der Tröpfchen und Aerosole

Wenn Sie sprechen, werden winzige Mikrotröpfchen aus Ihrem Mund ausgestoßen. Wenn Sie infektiös sind, enthalten diese Viruspartikel. Nur die allergrößten Tröpfchen überleben am Ende mehr als 0,1 Sekunde, bevor sie austrocknen und sich in Tröpfchenkerne verwandeln (Wells 1934; Duguid 1946; Morawska et al. 2009), die 3-5 Mal kleiner sind als das ursprüngliche Tröpfchen selbst, aber immer noch etwas Virus enthalten.

Das bedeutet, dass es viel einfacher ist, Tröpfchen zu blockieren, sobald sie aus dem Mund kommen, wenn sie viel größer sind, als sie zu blockieren, wenn sie sich dem Gesicht einer nicht infizierten Person nähern, die diese Tröpfchen aufnimmt. Aber das ist nicht das, was die meisten Forscher untersucht haben…

Die Materialwissenschaft der Masken

In den Debatten über die Wirksamkeit von Masken wird oft davon ausgegangen, dass der Zweck der Maske darin besteht, den Träger zu schützen, denn das ist es, was alle Ärzte im Medizinstudium lernen. Tuchmasken sind dabei relativ schlecht (wenn auch nicht ganz unwirksam). Für einen 100%igen Schutz benötigt der Träger ein richtig angepasstes medizinisches Atemschutzgerät (wie z.B. ein N95 Maske oder eine FFP3-Maske). Aber Stoffmasken, die von einer infizierten Person getragen werden, schützen die Menschen um sie herum sehr wirksam. Dies wird als “Quellenkontrolle” bezeichnet. Und es ist die Quellenkontrolle, die in der Debatte darüber, ob die Öffentlichkeit Masken tragen sollte, eine Rolle spielt.

Wenn Sie COVID-19 haben und jemanden aus 20 cm Entfernung anhusten, reduziert das Tragen einer Baumwollmaske die Virusmenge, die Sie auf diese Person übertragen, um das 36fache und ist sogar noch wirksamer als eine chirurgische Maske. Seltsamerweise hielten die Forscher, die diese Tatsache entdeckten, eine 36-fache Reduktion für “unwirksam”. Wir sind anderer Meinung. Das bedeutet, dass Sie nur 1/36-mal so viel Virus übertragen, wie Sie es sonst getan hätten, wodurch die Viruslast verringert wird, was wahrscheinlich zu einer geringeren Infektionswahrscheinlichkeit und weniger Symptomen führt, wenn Sie infiziert werden.

Die Mathematik der Ansteckung

Die mathematische Modellierung unseres Teams, unterstützt durch andere Forschungsarbeiten (Yan et al. 2019), legt nahe, dass, wenn die meisten Menschen in der Öffentlichkeit eine Maske tragen, die Übertragungsrate (“effektiver R-Wert”) unter 1,0 sinken kann, wodurch die Ausbreitung der Krankheit vollständig gestoppt wird. Die Maske muss nicht jedes einzelne Viruspartikel blockieren, aber je mehr Partikel sie blockiert, desto niedriger ist die effektive R.

Modellierte Auswirkungen der Verwendung von Masken auf die Reproduktionsrate

Wie wirksam das Tragen von Masken ist, hängt von drei Dingen ab, die in der Grafik dargestellt sind: wie gut die Maske das Virus blockiert (“Wirksamkeit” - Efficacy: horizontale Achse), welcher Anteil der Bevölkerung Masken trägt (“Adhärenz” - Adherence: vertikale Achse) und die Übertragungsrate der Krankheit (R0: die schwarzen Linien in der Grafik). Der blaue Bereich des Diagramms zeigt einen R0 unter 1,0 an, was wir erreichen müssen, um die Krankheit auszulöschen. Wenn die Maske 100% der Partikel blockiert (ganz rechts in der Grafik), führen selbst geringe Adhärenzraten zur Eindämmung der Krankheit. Selbst wenn die Maske einen viel geringeren Anteil an Viruspartikeln blockiert, könnte die Krankheit dennoch eingedämmt werden - aber nur, wenn die meisten oder alle Menschen Masken tragen.

Die Politikwissenschaft des Maskentragens

Wie bringt man alle oder die meisten Menschen dazu, Masken zu tragen? Nun, man kann sie aufklären und versuchen, sie zu überzeugen, aber ein effektiverer Ansatz ist es, von ihnen zu verlangen, dass sie eine Maske tragen, entweder in bestimmten Umgebungen wie öffentlichen Verkehrsmitteln oder Lebensmittelgeschäften oder sogar jederzeit außerhalb des Hauses. Forschungsarbeiten zum Thema Impfung (Bradford und Mandich 2015) zeigen, dass Rechtsordnungen, die eine höhere Messlatte für Impfausnahmen setzen, höhere Impfraten aufweisen. Der gleiche Ansatz wird jetzt auch zur Erhöhung der Maskentragepflicht angewandt, und erste Ergebnisse (Leffler et al. 2020) deuten darauf hin, dass diese Gesetze wirksam dazu beitragen, die Einhaltung der Vorschriften zu erhöhen und die Ausbreitung von COVID-19 zu verlangsamen oder zu stoppen.

Experimente zum Tragen von Masken: künstlich und natürlich

Ein künstliches Experiment liegt vor, wenn ein Forscher Menschen (in der Regel nach dem Zufallsprinzip - daher der Begriff “randomisierte kontrollierte Studie” - randomized controlled trial oder RCT) entweder eine Maske tragen oder keine Maske tragen (die Kontrollgruppe). In COVID-19 gab es keine RCTs zum Maskentragen in der Öffentlichkeit. RCTs zum Maskentragen zur Vorbeugung anderer Krankheiten (wie Grippe oder Tuberkulose) zeigten tendenziell einen kleinen Effekt, der in vielen Studien statistisch nicht signifikant war. In den meisten dieser Studien trugen die der Personen der Gruppe “Maskentragen” nicht immer ihre Masken.

Ein natürliches Experiment ist, wenn wir etwas untersuchen, das wirklich passiert - zum Beispiel wenn ein Land eine Politik des Maskentragens einführt. In Südkorea zum Beispiel gab es eine rasche Ausbreitung in der Gemeinschaft ähnlich wie in den ersten Wochen in Italien. Dann, Ende Februar 2020, stellte die Regierung jedem Bürger regelmäßig Masken zur Verfügung. Von diesem Zeitpunkt an änderte sich alles. Als die Zahl der Todesopfer in Italien auf ein entsetzliches Ausmaß anstieg, nahm die Zahl der Todesopfer in Südkorea sogar ab. Hier ist die Zahl der aktiven Fälle in Südkorea (rot) und in Italien (blau); sehen Sie sich genau an, was Anfang März geschah, als die Auswirkungen der Maskenverteilung einsetzten (diese südkoreanische Analyse ist Hyokon Zhiang und der Visualisierung von Reshama Shaik zu verdanken):

Vergleich der COVID-19-Fälle zwischen Korea und Italien

Natürliche Experimente sind wissenschaftlich unvollkommen, weil es keine direkte Kontrollgruppe gibt, so dass wir nicht sicher sein können, dass irgendeine Veränderung auf die Masken zurückzuführen ist. In einigen Ländern, die das Maskentragen einführten, fanden andere Maßnahmen wie striktes “Social Distancing”, Schulschließungen und die Absage von öffentlichen Veranstaltungen etwa zur gleichen Zeit statt. Selbst in diesen Fällen können wir relevante Vergleiche finden. Beispielsweise führten die europäischen Nachbarn Österreich und Tschechien zum gleichen Zeitpunkt Vorschriften zum “Social Distancing” ein, aber auch Tschechien führte das obligatorische Tragen von Masken ein. Die Fallzahlen in Österreich setzten ihren Aufwärtstrend fort, während sie sich in Tschechien abflachten. Erst als Österreich Wochen später ebenfalls Maskengesetze einführte, gingen die beiden Länder wieder auf ähnliche Bahnen.

Vergleich der COVID-19-Fälle zwischen Tschechien und Österreich

Wichtig ist, dass in jedem Land und in jedem Zeitraum, in dem die Verwendung von Masken durch Gesetze gefördert wurde oder in dem den Bürgern Masken zur Verfügung gestellt wurden, die Fall- und Todesraten gesunken sind.

Die Verhaltenswissenschaft des Maskentragens

Einige haben behauptet, dass es riskantes Verhalten (Brosseau et al. 2020) fördert (z.B. mehr ausgehen, weniger Hände waschen), wenn man Menschen dazu bringt (oder stark dazu ermutigt), Masken zu tragen. Dies führe zu einem negativen Nettoergebnis, und dieser Effekt wurde in einigen experimentellen Versuchen mit Masken beobachtet. Ähnliche Argumente wurden bereits früher für HIV-Präventionsstrategien (Cassell et al. 2006; Rojas Castro, Delabre und Molina 2019) und Motorradhelmgesetzen (Ouellet 2011) angeführt. Praktische Untersuchungen zu diesen Themen ergaben jedoch, dass, obwohl einige Personen mit riskantem Verhalten reagierten, auf Bevölkerungsebene insgesamt eine Verbesserung der Sicherheit und des Wohlbefindens zu verzeichnen war (Peng et al. 2017; Houston und Richardson 2007).

Die Ökonomie des Maskentragens

Ökonomische Analysen berücksichtigen, wie viel es kostet, Masken zur Verfügung zu stellen im Vergleich zu dem Wert den sie stiften (sowohl finanzieller als auch nicht-finanzieller Art). Solche ökonomischen Studien (Abaluck et al. 2020) weisen darauf hin, dass jede von einer Person getragene Maske (die fast nichts kostet) einen wirtschaftlichen Nutzen von Tausenden von Dollar erzeugen und viele Leben retten könnte.

Die Anthropologie des Maskentragens

Das Tragen von Masken durch die Öffentlichkeit hat sich in vielen asiatischen Ländern normalisiert, teils aus individuellen Gründen (zum Schutz vor Umweltverschmutzung), teils aus kollektiven Gründen (als Folge der jüngsten MERS- und SARS-Epidemien). “Meine Maske schützt Dich, Deine schützt mich.” In den meisten dieser Länder war es jedoch die Norm, nur dann eine Maske zu tragen, wenn Sie Symptome haben; erst in den letzten Wochen, seit das Bewusstsein für die asymptomatische Ausbreitung besser verstanden wird, ist es üblich geworden, die Maske unabhängig von den Symptomen zu tragen.

Schlussfolgerung

Zwar sprechen nicht alle wissenschaftlichen Erkenntnisse für das Tragen von Masken, aber die meisten weisen in die gleiche Richtung. Unsere Bewertung dieser Beweise führt uns zu einer klaren Schlussfolgerung: Behalten Sie Ihre Tröpfchen für sich - tragen Sie eine Maske.

Sie können sich zu Hause eine Maske anfertigen, aus einem T-Shirt, Taschentuch oder Papiertuch, oder Sie können sich auch einfach einen Schal oder ein Kopftuch um Ihr Gesicht wickeln. Idealerweise verwenden Sie dicht gewebten Stoff, durch den Sie noch atmen können. Forscher empfehlen, eine Lage Papierhandtuch als Einwegfilter beizufügen; Sie können es einfach zwischen zwei Stofflagen schieben. Es gibt keine Anhaltspunkte dafür, dass Ihre Maske mit besonderer Sachkenntnis oder Sorgfalt hergestellt werden muss, um eine wirksame Quellenkontrolle zu gewährleisten. Sie können eine Stoffmaske waschen und wiederverwenden, genauso wie Sie ein T-Shirt wiederverwenden.

Wenn sich herausstellt, dass Sie an COVID-19 erkranken, werden die Menschen, die Ihnen wichtig sind, froh sein, dass Sie eine Maske tragen.

Epilog: Jeremys Illustration der Quellenkontrolle

Hier ist eine kleine Illustration der Quellenkontrolle von Jeremy!

Referenzen

Abaluck, Jason, Judith A. Chevalier, Nicholas A. Christakis, Howard Paul Forman, Edward H. Kaplan, Albert Ko, and Sten H. Vermund. 2020. “The Case for Universal Cloth Mask Adoption and Policies to Increase Supply of Medical Masks for Health Workers.” SSRN Scholarly Paper ID 3567438. Rochester, NY: Social Science Research Network. https://papers.ssrn.com/abstract=3567438.

Bai, Yan, Lingsheng Yao, Tao Wei, Fei Tian, Dong-Yan Jin, Lijuan Chen, and Meiyun Wang. 2020. “Presumed Asymptomatic Carrier Transmission of Covid-19.” Jama. Bradford, W David, and Anne Mandich. 2015. “Some State Vaccination Laws Contribute to Greater Exemption Rates and Disease Outbreaks in the United States.” Health Affairs 34 (8): 1383–90.

Brosseau, Lisa M., ScD, Margaret Sietsema, PhD Apr 01, and 2020. 2020. “COMMENTARY: Masks-for-All for COVID-19 Not Based on Sound Data.” CIDRAP. https://www.cidrap.umn.edu/news-perspective/2020/04/commentary-masks-all-covid-19-not-based-sound-data.

Cassell, Michael M, Daniel T Halperin, James D Shelton, and David Stanton. 2006. “Risk Compensation: The Achilles’ Heel of Innovations in Hiv Prevention?” Bmj 332 (7541): 605–7.

Doremalen, Neeltje van, Trenton Bushmaker, Dylan H. Morris, Myndi G. Holbrook, Amandine Gamble, Brandi N. Williamson, Azaibi Tamin, et al. 2020. “Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1.” New England Journal of Medicine 0 (0): null. https://doi.org/10.1056/NEJMc2004973.

Duguid, JP. 1946. “The Size and the Duration of Air-Carriage of Respiratory Droplets and Droplet-Nuclei.” Epidemiology & Infection 44 (6): 471–79. Houston, David J, and Lilliard E Richardson. 2007. “Risk Compensation or Risk Reduction? Seatbelts, State Laws, and Traffic Fatalities.” Social Science Quarterly 88 (4): 913–36.

Leffler, Christopher, Edsel Ing, Craig A. McKeown, Dennis Pratt, and Andrzej Grzybowski. 2020. “Country-Wide Mortality from the Novel Coronavirus (COVID-19) Pandemic and Notes Regarding Mask Usage by the Public.”

Morawska, LJGR, GR Johnson, ZD Ristovski, Megan Hargreaves, K Mengersen, Steve Corbett, Christopher Yu Hang Chao, Yuguo Li, and David Katoshevski. 2009. “Size Distribution and Sites of Origin of Droplets Expelled from the Human Respiratory Tract During Expiratory Activities.” Journal of Aerosol Science 40 (3): 256–69.

Ouellet, James V. 2011. “Helmet Use and Risk Compensation in Motorcycle Accidents.” Traffic Injury Prevention 12 (1): 71–81.

Peng, Yinan, Namita Vaidya, Ramona Finnie, Jeffrey Reynolds, Cristian Dumitru, Gibril Njie, Randy Elder, et al. 2017. “Universal Motorcycle Helmet Laws to Reduce Injuries: A Community Guide Systematic Review.” American Journal of Preventive Medicine 52 (6): 820–32.

Rojas Castro, Daniela, Rosemary M Delabre, and Jean-Michel Molina. 2019. “Give Prep a Chance: Moving on from the ‘Risk Compensation’ Concept.” Journal of the International AIDS Society 22: e25351.

To, Kelvin Kai-Wang, Owen Tak-Yin Tsang, Wai-Shing Leung, Anthony Raymond Tam, Tak-Chiu Wu, David Christopher Lung, Cyril Chik-Yan Yip, et al. 2020. “Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study.” Lancet Infect. Dis. 0 (0). https://doi.org/10.1016/S1473-3099(20)30196-1.

Wei, Wycliffe E. 2020. “Presymptomatic Transmission of SARS-CoV-2” Singapore, January 23 “March 16, 2020.” MMWR. Morbidity and Mortality Weekly Report 69. https://doi.org/10.15585/mmwr.mm6914e1.

Wells, WF. 1934. “On Air-Borne Infection: Study Ii. Droplets and Droplet Nuclei.” American Journal of Epidemiology 20 (3): 611–18.

Yan, Jing, Suvajyoti Guha, Prasanna Hariharan, and Matthew Myers. 2019. “Modeling the Effectiveness of Respiratory Protective Devices in Reducing Influenza Outbreak.” Risk Analysis 39 (3): 647–61. https://doi.org/10.1111/risa.13181.

Zhang, Juanjuan, Maria Litvinova, Wei Wang, Yan Wang, Xiaowei Deng, Xinghui Chen, Mei Li, et al. 2020. “Evolving Epidemiology and Transmission Dynamics of Coronavirus Disease 2019 Outside Hubei Province, China: A Descriptive and Modelling Study.” The Lancet Infectious Diseases 0 (0). https://doi.org/10.1016/S1473-3099(20)30230-9.

Zou, Lirong, Feng Ruan, Mingxing Huang, Lijun Liang, Huitao Huang, Zhongsi Hong, Jianxiang Yu, et al. 2020. “SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients.” New England Journal of Medicine 382 (12): 1177–9. https://doi.org/10.1056/NEJMc2001737.

Using Netlogo for Disease Modeling

Sun, 15 Mar 2020 00:00:00 +0000

Overview

Since the term “Simulation” or modeling was used frequently in the media recently to get an understanding of the spread of covid-19, I have created an instructive agent-based model for the simulation of disease spread and limited hospital capacities. Since this model is only “instructive” for the purpose of understanding modeling, I have also created a video to explain the model and some implications due to the simplicity of the design.

Please watch the video first, before playing around with the simulation: English Version

German Version

You can test the Simulation yourself here: http://bit.ly/NLinfection

If you are interested in seeing the current case numbers in comparison to other countries, have a look at this shiny application that I made: https://andrecalerovaldez.shinyapps.io/CovidTimeSeries/

Human-Algorithm Interaction at Data Community Meetup Deutsche Bahn

Tue, 18 Feb 2020 00:00:00 +0000

Human-Algorithm Interaction at Dataviscamp 2020

Mon, 17 Feb 2020 00:00:00 +0000

Testing regular expressions with regexr.com

Sat, 04 Jan 2020 00:00:00 +0000

The website regexr.com provides a well developed tool for testing regular expression on the fly. Even better, it has a set of working patterns and lets you store your own patterns for later reuse. I highly recommend to test it: https://regexr.com/

Using the apastats package to write reproducible reports

Sat, 04 Jan 2020 00:00:00 +0000

Overview

Writing a research report using RMarkdown allows you to start your analysis even before you have collected the data. This is especially helpful in report writing and thesis writing. When you, web-crawler, or a panel are still collecting data, you can already start writing the thesis. However, writing results before having the data is somewhat challenging. Keep in mind that writing a discussion before having collected the data is a lot harder.

What we’re going to need two things:

a package that formats output in a way that we need it. In our case apastats.
string manipulation: stringr and glue

Installing apastats

You can install the apastats package using the following command:

devtools::install_github('achetverikov/apastats',subdir='apastats')

Now, we can start. Let’s assume we have already collected the following data about iris flowers 😉.

df <- iris %>% sample_n(5) 
df %>% kable()

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.8	2.7	4.1	1.0	versicolor
6.4	2.8	5.6	2.1	virginica
4.4	3.2	1.3	0.2	setosa
4.3	3.0	1.1	0.1	setosa
7.0	3.2	4.7	1.4	versicolor

The analysis

Deciding on our hypothesis

We have found in literature that the setosa species has a rather small petal length of only about 3-4 cm. Since we are sampling from random flowers we want to test whether our sample is also smaller than, let us say, 5cm. Thus, our null hypothesis would be $H_0: PL > 5$.

Selecting a test

We would test this using a one-sample t-test on the subset of data.

res <- df %>% 
  filter(Species == "setosa") %>% 
  pull(Petal.Length) %>% 
  t.test(mu = 5)
res

## 
##  One Sample t-test
## 
## data:  .
## t = -38, df = 1, p-value = 0.01675
## alternative hypothesis: true mean is not equal to 5
## 95 percent confidence interval:
##  -0.07062047  2.47062047
## sample estimates:
## mean of x 
##       1.2

Well… This is not our final data yet, so we should not bee too happy about this result already. And this looks nothing like a report. How can we write the test and the interpretation that it incorporates the results?

Using apastats to describe

The describe.ttest function returns the correctly formatted markdown for reporting a t-test. So the following would always generate the correct output.

tresult <- apastats::describe.ttest(res)
tresult

## [1] "_t_(1.0) = -38.00, _p_ = .017"

Meh. This is not really helpful either, right? We must include the text result as an inline result using the `r tresult`. Then we can say:

Setosa iris have petal leaves whose length is statistically significant different from 5 (t(1.0) = -38.00, p = .017).

Preparing for both outcomes

This is nice, but we don’t know whether our sample of $n=2$ actually represents the population. Therefore, we must prepare for both cases. Here the glue package is helpful in filling in the blanks in pre-written output.

library(glue)


# test the p-value
alpha_level <- .05

# default is not significant
have_txt <- "do not have"
if (res$p.value < alpha_level) {
  have_txt <- "have"
} 

phrase <- glue("> Setosa iris {have_txt} petal leaves whose length is ", 
               "statistically significant different from 5 ({tresult}).")

This we can now output using: `r phrase`.

Setosa iris have petal leaves whose length is statistically significant different from 5 (t(1.0) = -38.00, p = .017).

Collecting the final data and running our analysis

Now, we can run the full script again, but with the full data set. I have omitted unnecessary output in the following code chunk and directly construct the output phrase.

# run the test
res <- iris %>% 
  filter(Species == "setosa") %>% 
  pull(Petal.Length) %>% 
  t.test(mu = 5)

# describe results
tresult <- apastats::describe.ttest(res)

# generate phrase
have_txt <- "do not have"
if (res$p.value < alpha_level) {
  have_txt <- "have"
} 

# you can use markdown here as well
phrase <- glue("> Setosa iris {have_txt} petal leaves whose length is ",  
               "statistically significant different from 5 ({tresult}).")

The final outcome of our analysis now is:

Setosa iris have petal leaves whose length is statistically significant different from 5 (t(49.0) = -144.06, p < .001).

rmdtemplates package update

Fri, 03 Jan 2020 00:00:00 +0000

Today I updated the rmdtemplates package on github. It now supports PocketMods: Tiny little 8 paged books that you can now author in Markdown.

Check it out at http://www.github.com/statisticsforsocialscience/rmd_templates

Upgrade to Kickstart Academic 4.7

Fri, 03 Jan 2020 00:00:00 +0000

New year, new version

I have started to migrate my website to version 4.7. Since I was using version 2.4, almost all changes seem to be breaking changes. I’ll try to find a way to improve publication import using some automation. Other things I’ll try this time:

Move all course material to github and their own repositories.
Have a RStudio.cloud workspace prepared for the website (done!)
- including Github connection (done!)
Post more frequently!
Maybe use the slides feature?
Keep things more simple :)

Human and algorithmic contributions to misinformation online-identifying the culprit

Wed, 01 Jan 2020 00:00:00 +0000

On the Importance of Context: Privacy Perceptions ofPersonal vs. Health Data in Health Recommender Systems.

Wed, 01 Jan 2020 00:00:00 +0000

Computational Methods in Professional Communication

Mon, 01 Jul 2019 00:00:00 +0000

Hey, Siri, Ok, Google, Alexa. Acceptance-Relevant Factors of Virtual Voice-Assistants

Mon, 01 Jul 2019 00:00:00 +0000

Trust in Cyber Security Recommendations

Mon, 01 Jul 2019 00:00:00 +0000

Would I Lie to You? How Users Evaluate Faked Online Content Depending on Its Publication Type

Mon, 01 Jul 2019 00:00:00 +0000

Bubble Trouble: Strategies Against Filter Bubbles in Online Social Networks

Sat, 01 Jun 2019 00:00:00 +0000

Follow me: The Impact of Opinion Majorities in Social Networks and the Role of Digital Maturity

Sat, 01 Jun 2019 00:00:00 +0000

Towards an Understanding of Opinion Formation on the Internet: Using a Latent Process Model to Understand the Spread of Information on Social Media

Sat, 01 Jun 2019 00:00:00 +0000

Trends and Changes in the Field of HCI the Last Decade from the Perspective of HCII Conference

Sat, 01 Jun 2019 00:00:00 +0000

Attitude - mHealth Apps and Users' Insights: An Empirical Approach to Understand the Antecedents of Attitudes towards mHealth Applications

Wed, 01 May 2019 00:00:00 +0000

Who Shares Fake News in Online Social Networks?

Mon, 01 Apr 2019 00:00:00 +0000

Human and algorithmic contributions to disinformation in social media. Identifying the culprit.

Wed, 27 Feb 2019 00:15:20 +0200

Slides

Tue, 05 Feb 2019 00:00:00 +0000

Create slides in Markdown with Academic

Academic | Documentation

Features

Efficiently write slides in Markdown
3-in-1: Create, Present, and Publish your slides
Supports speaker notes
Mobile friendly slides

Controls

Next: Right Arrow or Space
Previous: Left Arrow
Start: Home
Finish: End
Overview: Esc
Speaker notes: S
Fullscreen: F
Zoom: Alt + Click
PDF Export: E

Code Highlighting

Inline code: variable

Code block:

porridge = "blueberry"
if porridge == "blueberry":
    print("Eating...")

Math

In-line math: $x + y = z$

Block math:

$$ f\left( x \right) = ;\frac{{2\left( {x + 4} \right)\left( {x - 4} \right)}}{{\left( {x + 4} \right)\left( {x + 1} \right)}} $$

Fragments

Make content appear incrementally

{{% fragment %}} One {{% /fragment %}}
{{% fragment %}} **Two** {{% /fragment %}}
{{% fragment %}} Three {{% /fragment %}}

Press Space to play!

One Two Three

A fragment can accept two optional parameters:

class: use a custom style (requires definition in custom CSS)
weight: sets the order in which a fragment appears

Speaker Notes

Add speaker notes to your presentation

{{% speaker_note %}}
- Only the speaker can read these notes
- Press `S` key to view
{{% /speaker_note %}}

Press the S key to view the speaker notes!

Themes

black: Black background, white text, blue links (default)
white: White background, black text, blue links
league: Gray background, white text, blue links
beige: Beige background, dark text, brown links
sky: Blue background, thin dark text, blue links

night: Black background, thick white text, orange links
serif: Cappuccino background, gray text, brown links
simple: White background, black text, blue links
solarized: Cream-colored background, dark green text, blue links

Custom Slide

Customize the slide style and background

{{< slide background-image="/img/boards.jpg" >}}
{{< slide background-color="#0000FF" >}}
{{< slide class="my-style" >}}

Custom CSS Example

Let’s make headers navy colored.

Create assets/css/reveal_custom.css with:

.reveal section h1,
.reveal section h2,
.reveal section h3 {
  color: navy;
}

Questions?

Ask

Documentation

What happens when decision support systems fail? — the importance of usability on performance in erroneous systems

Fri, 01 Feb 2019 00:00:00 +0000

Industry 4.0 and its future staff. Matching millennials perceptions of a perfect job with the requirements of digitalization.

Wed, 30 Jan 2019 00:00:00 +0000

Predicting Acceptance of Novel Technology from Social Network Data - An Agent-based Simulation-Approach

Wed, 30 Jan 2019 00:00:00 +0000

Predicting Acceptance of Novel Technology from Social Network Data

Sat, 26 Jan 2019 22:55:24 +0200

Political Opinions of Us and Them and the Influence of Digital Media Usage

Sat, 01 Dec 2018 00:00:00 +0000

Similarity of Search Results in the Datenspende BTW17 Dataset - CSS Data Challenge 2018

Sat, 01 Dec 2018 00:00:00 +0000

Talk at RWTH 5vor 12

Fri, 09 Nov 2018 21:16:35 +0200

During this years “Wissenschaftsnacht” our research group presented our research to the open public. The room was packed although we were schedulded at 11:15 P.M.

You can see the talk in the following youtube clip.

Interdisciplinary Cooperation Management in Research Clusters: A Review of Twelve Years

Thu, 01 Nov 2018 00:00:00 +0000

New Course - Empirical Methods using R

Mon, 15 Oct 2018 21:16:35 +0200

This winter semester I am starting a new course on empirical methods in social sciences. The whole course will be based on using the R ecosystem. Students will learn R using RStudio and have access to a dedicated course room at datacamp. In addition, I’m developing shiny applications that will allow students to “play” with the data.

All slides are generated using xaringan and the coursebook is being developed using bookdown. Even the final exam will be generated using R and the exams package.

I’m looking forward to this new course that provides massive amounts of material provided by the R community.

Starting a new course on empirical methods this semester using #rlang and the #tidyverse at @RWTH with support from @DataCamp. We're also building online exercises using the #exams PKG and some online tools using #shiny. Will post updates and repositories.
— André Calero Valdez (@Sumidu) October 14, 2018

Requirements for Reproducibility of Research in Situational and Spatio-Temporal Visualization

Wed, 03 Oct 2018 21:58:20 +0200

Studying Biases in Visualization Research: Framework and Methods

Mon, 01 Oct 2018 00:00:00 +0000

User preferences in recommendation algorithms: the influence of user diversity, trust, and product category on privacy perceptions in recommender algorithms

Mon, 01 Oct 2018 00:00:00 +0000

e-Coaching the Elderly Recommender Systems in Health

Thu, 27 Sep 2018 00:13:00 +0200

Acceptance of a Digital Paper-based Diabetes Diary

Thu, 27 Sep 2018 00:10:20 +0200

Eliciting Mental Models for a Mobile Diabetes Living Assistant

Wed, 26 Sep 2018 23:42:18 +0200

HCI for Recommender Systems - The Past, the Present, the Future.

Wed, 26 Sep 2018 23:00:22 +0200

Requirements for Reproducibility of Research in Situational andSpatio-Temporal Visualization

Wed, 26 Sep 2018 23:00:00 +0200

Trump, Brexit und die AFD – Verändert Facebook unsere Demokratie?

Wed, 26 Sep 2018 22:59:52 +0200

Trump, Brexit und die AFD – Verändert Facebook unsere Demokratie?

Wed, 26 Sep 2018 22:59:10 +0200

Human Factors in the Age of Algorithms. Understanding the Human-in-the-loop Using Agent-based Modeling

Wed, 26 Sep 2018 22:59:01 +0200

‘Industrie 4.0’ and an Aging Workforce – A Discussion from a Psychological and a Managerial Perspective.

Wed, 26 Sep 2018 22:58:46 +0200

Measuring Insight Into Multi-dimensional Data from a Combination of a Scatterplot Matrix and a HyperSlice Visualization.

Wed, 26 Sep 2018 22:58:25 +0200

Towards Interactive Recommender Systems with the Doctor in the Loop.

Wed, 26 Sep 2018 22:58:16 +0200

Workshop Introduction HFIDSS

Wed, 26 Sep 2018 22:57:58 +0200

An Open-Source Object-Graph-Mapping Framework for Neo4j and Scala: Renesca.

Wed, 26 Sep 2018 22:57:48 +0200

Strategic Knowledge Management for Interdisciplinary Teams - Overcoming Barriers of Interdisciplinary Work Via an Online Portal Approach.

Wed, 26 Sep 2018 22:57:36 +0200

Visualizing Opportunities of Collaboration in a Research Cluster

Wed, 26 Sep 2018 22:57:25 +0200

Using Liferay as a Scientific Cooperation Portal

Wed, 26 Sep 2018 22:57:16 +0200

Preparing Production Systems for the Internet of Things. Stellenbosch, South Africa

Wed, 26 Sep 2018 22:57:08 +0200

Reducing Complexity with Simplicity.

Wed, 26 Sep 2018 22:56:57 +0200

What should I read next?

Wed, 26 Sep 2018 22:56:49 +0200

What do my colleagues know?

Wed, 26 Sep 2018 22:56:42 +0200

Older users rejection of mobile health apps

Wed, 26 Sep 2018 22:56:30 +0200

On the visual design of enterprise resource systems

Wed, 26 Sep 2018 22:56:16 +0200

Orchestrating collaboration - Visual collaboration suggestions in large research clusters

Wed, 26 Sep 2018 22:56:02 +0200

User-centered design of business communities. The influence of user diversity on motivation to use.

Wed, 26 Sep 2018 22:55:50 +0200

Reasons for using social networks professionally

Wed, 26 Sep 2018 22:55:44 +0200

Enhancing interdisciplinary collaboration in large research clusters

Wed, 26 Sep 2018 22:55:34 +0200

INDUSTRY 4.0 AND ITS FUTURE STAFF. Matching millennials perceptions of a perfect job with the requirements of digitalization.

Wed, 26 Sep 2018 22:55:24 +0200

Scientific Cooperation Engineering

Wed, 26 Sep 2018 22:55:24 +0200

Graph entropy measures in publication network data

Wed, 26 Sep 2018 22:55:13 +0200

Information disclosure in social networks

Wed, 26 Sep 2018 22:55:07 +0200

Netiquette for the work environment

Wed, 26 Sep 2018 22:54:56 +0200

Social Networking Sites in the work environment

Wed, 26 Sep 2018 22:54:43 +0200

Task-Performance in mobile and ambient interfaces for an electronic diabetes assistant

Sun, 23 Sep 2018 23:18:27 +0200

Effects of Aging and Domain Knowledge on Usability in a Diabetes Small Screen Device

Sun, 23 Sep 2018 23:13:42 +0200

Belivability of Fake News

Sun, 23 Sep 2018 22:37:47 +0200

Shinystats

Sun, 23 Sep 2018 22:13:17 +0200

In this project Shiny applications will be developed to support statistics courses.

SmartIdentifikation

Sun, 23 Sep 2018 22:12:24 +0200

In the SmartIdentifikation project, a German-Austrian project consortium is addressing challenges of migration management in Austria and Germany, which have become increasingly important as a result of the 2015 / 16 refugee movement. In addition to the identification of individuals based on their smartphone data, the focus is on the recognition of (unaccompanied) minors and the identification and analysis of smuggling routes. In the context of these goals, the interdisciplinary project teams pursue ethical, legal and acceptance-relevant questions on an equal footing with the technical challenges and goals. The scientific and technical work objectives refer to the following aspects:

Development of a system for systematic evaluation of smartphone data for the identification of individuals
Determining the age of persons on photos and adaptation to the application for the detection of minor
Development of tools for trend analysis and visualization of smuggling routes
Interface for secure data exchange
Processing of data protection issues with regard to the use of personal and private data
Review and implementation of applicable ethical frameworks
Analyzing the importance of privacy for citizens in a field study

Partners:

Lehrstuhl für Communication Science, RWTH Aachen
Goethe-Universität, Frankfurt am Main
Hochschule der Medien, Stuttgart
Bundespolizeipräsidium Potsdam
G2K Group GmbH, München
SBA Research GmbH, Wien
T3K-Forensics GmbH, Wien
Kibosec GmbH, Wien
Universität Wien
Bundesministerium für Inneres, Wien
T3K Forensics GmbH (assoziierter Partner)
SAP Deutschland SE & Co. KG (assoziierter Partner)

Digitale Mündigkeit

Sun, 23 Sep 2018 22:03:19 +0200

Project website: https://www.digitale-mündigkeit.de

Believability of News - Understanding Users Perceptions of Fake News and the Effectiveness of Fact-Checking Badges

Sun, 23 Sep 2018 21:58:20 +0200

Human Factors in the Age of Algorithms. Understanding the Human-in-the-loop Using Agent-Based Modeling

Sun, 01 Jul 2018 00:00:00 +0000

Political Opinions of Us and Them and the Influence of Digital Media Usage

Sun, 01 Jul 2018 00:00:00 +0000

Evaluating Strategies to Restore Trust in Decision Support Systems in Cross-Company Cooperation

Fri, 01 Jun 2018 00:00:00 +0000

Akzeptanz autonomer Robotik

Thu, 01 Mar 2018 00:00:00 +0000

The users’ perspective on the privacy-utility trade-offs in health recommender systems

Thu, 01 Mar 2018 00:00:00 +0000

Acceptance of a Digital Paper-based Diabetes Diary

Mon, 01 Jan 2018 00:00:00 +0000

Decisions About Medical Data Disclosure in the Internet: An Age Perspective

Mon, 01 Jan 2018 00:00:00 +0000

Elitism, trust, opinion leadership and politics in social protests in Germany

Mon, 01 Jan 2018 00:00:00 +0000

Get Well Soon! Human Factors’ Influence on Cybersickness After Redirected Walking Exposure in Virtual Reality

Mon, 01 Jan 2018 00:00:00 +0000

Human Interaction Under Risk in Cyber-Physical Production Systems

Mon, 01 Jan 2018 00:00:00 +0000

Priming and anchoring effects in visualization

Mon, 01 Jan 2018 00:00:00 +0000

The diversity of why: a meta-analytical study of usage motivation in enterprise social networks

Mon, 01 Jan 2018 00:00:00 +0000

You spin my head right round: Threshold of limited immersion for rotation gains in redirected walking

Mon, 01 Jan 2018 00:00:00 +0000

“Should I Trust or Should I Go?” or What Makes Health-Related Websites Appear Trustworthy?

Mon, 01 Jan 2018 00:00:00 +0000

Second workshop on health recommender systems:(healthrecsys 2017)

Tue, 01 Aug 2017 00:00:00 +0000

The Interaction of Causal Attribution of Performance and Compliance with Decision Support Systems in Cyber-Physical Production Systems-An Empirical Study Using a Business Simulation Game

Tue, 01 Aug 2017 00:00:00 +0000

How Correct and Defect Decision Support Systems Influence Trust, Compliance, and Performance in Supply Chain and Quality Management

Sat, 01 Jul 2017 00:00:00 +0000

That’s so Meta! Usability of a Hypergraph-Based Discussion Model

Sat, 01 Jul 2017 00:00:00 +0000

A Framework for Studying Biases in Visualization Research

Sun, 01 Jan 2017 00:00:00 +0000

Augmenting research cooperation in production engineering with data analytics

Sun, 01 Jan 2017 00:00:00 +0000

Domestic Robots for Homecare: A Technology Acceptance Perspective

Sun, 01 Jan 2017 00:00:00 +0000

Hybrid collaboration recommendation from bibliometric data

Sun, 01 Jan 2017 00:00:00 +0000

Measuring Insight into Multi-dimensional Data from a Combination of a Scatterplot Matrix and a HyperSlice Visualization

Sun, 01 Jan 2017 00:00:00 +0000

Scientific Cooperation Engineering

Sun, 01 Jan 2017 00:00:00 +0000

Towards health (aware) recommender systems

Sun, 01 Jan 2017 00:00:00 +0000

User Groups and Different Levels of Control in Recommender Systems

Sun, 01 Jan 2017 00:00:00 +0000

‘Industrie 4.0’and an Aging Workforce--A Discussion from a Psychological and a Managerial Perspective

Sun, 01 Jan 2017 00:00:00 +0000

On Studying Human Factors in Complex Cyber-Physical Systems

Sat, 01 Oct 2016 00:00:00 +0000

Cyber - Space Perception in Cyberspace

Fri, 23 Sep 2016 22:12:34 +0200

The Perception of Space in Virtual Reality. With the broader availability of low-cost VR-devices, the diversity of applications as well as user groups is growing. At the same time, AR and VR applications areleaving the (professional) lab environment and are entering domains where lesser assumptions can be made about the technical setup. In this project we want to address the particular problem that space restrictions in the real environment bear the risk to completely break the illusion of virtual reality if the dimensions of the virtual environment don’t match. More concretely, for the perception of space in virtual environments it is essential to be able to freely move/walk around. If the real environment (eg. the trackable area in an office) does not provide enough space, the illusion breaks down as soon as the user hits a (real) obstacle.Our methodological approach to overcome these problems is to exploit the fact that a user wearing a head mounted display can only see the virtual environment but senses the real environment with his body. Mismatches between real body movements and visual responses on the display are un-noticeable if they are small and the user’s attention is reduced (eg. because of head oreye move-ment). Hence our goal is to track the user and to rate the current level of distraction. Then, if reduced attention is detected, we can slightly shift the virtual environment relative to the real one in order to reduce the probability of an obstacle hit. In the first phase, we will restrict to passive measures, like head and eye tracking. In the second phase we will explore active methods such as adding obstacles in the virtual environment or letting virtual characters cross the user’s way for distraction.

HCI for recommender systems: the past, the present and the future

Thu, 01 Sep 2016 00:00:00 +0000

Defective still deflective--how correctness of decision support systems influences user’s performance in production environments

Fri, 01 Jul 2016 00:00:00 +0000

Strategic knowledge management for interdisciplinary teams-overcoming barriers of interdisciplinary work via an online portal approach

Fri, 01 Jul 2016 00:00:00 +0000

An open-source object-graph-mapping framework for Neo4j and scala: renesca