How scientists can stop fooling themselves over statistics
Behaviour change 101: How to do a Rapid Review | LinkedIn
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.
Development and Testing of a Short Form of the Patient Activation Measure
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)
Nonequivalent Groups Analysis | Research Methods Knowledge Base
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.
six rules of thumb for determining sample size and statistical power
xkcd: Curve-Fitting Methods and the Messages They Send
A Practical Guide to Conducting a Barrier Analysis
Using Twitter as a data source: an overview of social media research tools (2019) | Impact of Social Sciences
Chapter 4 Using Twitter as a Data Source: An Overview of Ethical, Legal, and Methodological Challenges - White Rose Research Online
Text as Data
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
Course: Open Online Introduction to R Course [Wolfgang Viechtbauer]
PsychArchives: COVID-19 Snapshot MOnitoring (COSMO Standard): Monitoring knowledge, risk perceptions, preventive behaviours, and public trust in the current coronavirus outbreak - WHO standard protocol
FORMATIVE RESEARCH FOR ASSISTING BEHAVIOR CHANGE: A PRACTICAL GUIDE FOR FIELD WORKERS
Catalogue for predictive models in the humanitarian sector – The Centre for Humanitarian Data
Social Influence Scale for Technology Design and Transformation | SpringerLink
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
Evaluating digital health products - GOV.UK
Human-centred policy? Blending ‘big data’ and ‘thick data’ in national policy - Policy Lab
Emotion, empathy and ethnography in policy-making
Criteria for good data visualization, according to design and statistics — Quartz
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.
Tapped out or barely tapped? Recommendations for how to harness the vast and largely unused potential of the Mechanical Turk participant pool
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.
Humanitarian Data Exchange
If You Want to Change the World, Design Your Data to do These Four Things
Causal Inference Book | Miguel Hernan | Harvard T.H. Chan School of Public Health
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.
10 data storytelling mistakes to avoid - Techerati
How to prevent cheating in online surveys and experiments
Science Forum: Ten common statistical mistakes to watch out for when writing or reviewing a manuscript | eLife
Writing Publishable Mixed Research Articles: Guidelines for Emerging Scholars in the Health Sciences and Beyond
How effective is nudging? A quantitative review on the effect sizes and limits of empirical nudging studies - ScienceDirect
Hello, and Thanks for All the Fish: Tips for effective research recruiting
Data Playbook Toolkit | Global Disaster Preparedness Center
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:
What’s Wrong With Your Survey? How to Reduce Error and Increase Reliability
Your Friendly Guide to Colors in Data Visualisation | Chartable
Evaluating Effect Size in Psychological Research: Sense and Nonsense - David C. Funder, Daniel J. Ozer, 2019
Daniel J. O’Keefe PUBLICATIONS AND PAPERS
research on health comm messaging effects
How to spot a statistical problem: advice for a non-statistical reviewer | BMC Medicine | Full Text
4 Ways to Turn Eye-Glazing Data Into Eye-Opening Stories | Inc.com
Unconventional Techniques for Better Insights from Satisfaction Surveys
What science reporters should know about meta-analyses before covering them
Reference Collection to push back against “Common Statistical Myths“ - data analysis - Datamethods Discussion Forum
How to use Screening Questions to Select the Right Participants for User Research
Social and Behavior Change Monitoring Guidance | Breakthrough ACTION and RESEARCH
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.
Understanding how and why people change - Journal of Marketing Management
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).
How do I delete my search history? And other questions | The Behavioural Insights Team
Design and statistical considerations in the evaluation of digital behaviour change interventions | UCL CBC Digi-Hub Blog
The Question Protocol: How to Make Sure Every Form Field Is Necessary :: UXmatters
Learning what our target audiences think and do: extending segmentation to all four bases
The aim of this study was to establish if distinct segments were evident in a sexual health context drawing from measures sourced from four segmentation bases extending application of segmentation to all recommended bases . This study indicates how researchers can use two-step cluster analysis to identify segments, which are represented by a group of individuals who share similar characteristics that differ from other groups in the larger heterogeneous target audience. Further, this study demonstrates how available information can be used delivering a dashboard to inform program design and planning.
How to Visualize Statistically Significant P-Values with Squares | Depict Data Studio
What Does Probability Mean in Your Profession? – Math with Bad Drawings
Healthcare access: A sequence-sensitive approach - ScienceDirect
•Despite its sequential nature, healthcare seeking is often analysed as single event. •We demonstrate the value of sequential healthcare data analysis. •Descriptive analysis exposes otherwise neglected behavioural patterns. •Sequence-insensitive indicators can be inconsistent and misleading. •Sequence-sensitive evaluation hints at adverse behaviours of wealthy patients.
Graphics Principles Cheat Sheet v1.0 (pdf)
Effective visualizations communicate complex statistical and quantitative information facilitating insight, understanding, and decision making. But what is an effective graph? This cheat sheet provides general guidance and points to consider.