The Data Playbook is 120 exercises, games, scenarios, slides and checklists to assist you and your teams on your data journey. The social learning content is designed for teams to have discussions and activities across the data lifecycle in short 30 minute to 1 hour sessions.
EMERGE (Evidence-based Measures of Empowerment for Research on Gender Equality) is a project focused on gender equality and empowerment measures to monitor and evaluate health programs and to track progress on UN Sustainable Development Goal (SDG) 5: To Achieve Gender Equality and Empower All Girls. As reported by UN Women (2018), only 2 of the 14 SDG 5 indicators have accepted methodologies for measurement and data widely available. Of the remaining 12, 9 are indicators for which data are collected and available in only a limited number of countries. This assessment suggests notable measurement gaps in the state of gender equality and empowerment worldwide. EMERGE aims to improve the science of gender equality and empowerment measurement by identifying these gaps through the compilation and psychometric evaluation of available measures and supporting scientifically rigorous measure development research in India.
The Meta-Analysis Learning Information Center (MALIC) believes in equitably providing cutting-edge and up-to-date techniques in meta-analysis to researchers in the social sciences, particularly those in education and STEM education.
For HCI survey research broadly, we recommend using a
question similar to the first question in ’s measure (as
quoted in ) – “Are you…?” with three response options:
“man,” “woman,” “something else: specify [text box]” –
and allowing respondents to choose multiple options. This
question will not identify all trans participants , but is
inclusive to non-binary and trans people and will identify
gender at a level necessary for most HCI research. To
reduce trolling, we recommend providing the fill-in-theblank text box as a second step only for those respondents
who choose the “something else” option.
I love it that one of my students suggested we change the default “Other (please specify“) option to “Not Listed (please specify)“ in a demographic survey. Explicitly *not* “othering“ participants while still asking for the info we want. Any implied failure is on us, not them.
In our work at BehaviourWorks Australia (BWA) we are frequently asked ‘What does the research say about getting audience Y to do behaviour X?’. When our partners need an urgent answer we often provide it using a Rapid Review. In this article I explain Rapid Reviews, why you should do them, and a process that you can follow to conduct one.
What is a Rapid Review?
Rapid Reviews are “a form of knowledge synthesis in which components of the systematic review process are simplified or omitted to produce information in a timely manner” . Indeed, with sufficient resources (e.g., multiple staff working simultaneously) you can do a Rapid Review in less than a day. The outputs of these reviews are, of course, brief and descriptive, but they can be very useful where rapid evidence is needed, for example, in addressing COVID-19.
Rapid Reviews can therefore provide detailed research within reduced timeframes and also meet most academic requirements by being standardised and reproducible. They are often, but not always, publishable in peer-reviewed academic journals.
The Patient Activation Measure is a valid, highly reliable, unidimensional, probabilistic Guttman‐like scale that reflects a developmental model of activation. Activation appears to involve four stages: (1) believing the patient role is important, (2) having the confidence and knowledge necessary to take action, (3) actually taking action to maintain and improve one's health, and (4) staying the course even under stress. The measure has good psychometric properties indicating that it can be used at the individual patient level to tailor intervention and assess changes.
The Research Methods Knowledge Base is a comprehensive web-based textbook that addresses all of the topics in a typical introductory undergraduate or graduate course in social research methods. It covers the entire research process including: formulating research questions; sampling (probability and nonprobability); measurement (surveys, scaling, qualitative, unobtrusive); research design (experimental and quasi-experimental); data analysis; and, writing the research paper. It also addresses the major theoretical and philosophical underpinnings of research including: the idea of validity in research; reliability of measures; and ethics.
This class covers a range of different topics that build on top of each other. For example, in the first tutorial, you will learn how to collect data from Twitter, and in subsequent tutorials you will learn how to analyze those data using automated text analysis techniques. For this reason, you may find it difficult to jump towards one of the most advanced issues before covering the basics.
Introduction: Strengths and Weaknesses of Text as Data
Application Programming Interfaces
Basic Text Analysis
Dictionary-Based Text Analysis
a measurement instrument for evaluating susceptibility to seven social influence principles, namely social learning, social comparison, social norms, social facilitation, social cooperation, social competition, and social recognition
HXLDash is a dashboard and online mapping tool designed for humanitarians and humanitarian contexts. HXLDash's aim is to make creating dashboards possible in less than 2 minutes by leveraging the power of the Humanitarian Exchange Language and linking to the common operation datasets.
In this article, we demonstrated that contrary to the thinking that suggests MTurk is a tapped-out resource, in reality, the opposite is true: MTurk is a vast resource with untapped potential researchers can capitalize on by changing the way they use the platform.
Jamie Robins and I have written a book that provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in several disciplines or confined to technical articles. We expect that the book will be of interest to anyone interested in causal inference, e.g., epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists… The book is divided in 3 parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data.