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. (https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1475-6773.2004.00269.x)
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 Screen-Scraping Basic Text Analysis Dictionary-Based Text Analysis Topic Modeling Text Networks Word Embeddings
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
Emotion, empathy and ethnography in policy-making
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.
The Data Playbook Beta is a recipe book or exercise book with examples, best practices, how to's, session plans, training materials, matrices, scenarios, and resources. The data playbook will provide resources for National Societies to develop their literacy around data, including responsible data use and data protection. The content aims to be visual, remixable, collaborative, useful, and informative. There are nine modules, 65 pieces of content, and a methodology for sharing curriculum across all the sectors and networks. Material has been compiled, piloted, and tested in collaboration with many contributors from across IFRC and National Societies. Each module has a recipe that puts our raw materials in suggested steps to reach a learning objective. To help support you in creating your own recipe, we also include a listing of 'ingredients' for a topic, organised by type:
research on health comm messaging effects
Breakthrough ACTION has distilled guidance on social and behavior change (SBC) monitoring methods into a collection of technical notes. Each note provides an overview of a monitoring method that may be used for SBC programs along with a description of when to use the method and its strengths and weaknesses.
We applied a Hidden Markov Model* (see Figure 1) to examine how and why behaviours did or did not change. The longitudinal repeated measure design meant we knew about food waste behaviour at two points (the amount of food wasted before and after the program), changes in the amount of food wasted reported over time for each household (more or less food wasted) and other factors (e.g. self-efficacy). By using a new method we could extend our understanding beyond the overall effect (households in the Waste Not Want Not program group wasted less food after participating when compared to the control group).