“significant” p-value ≠ “significant” finding: The significance of statistical evidence for the true X (i.e., statistical significance of the p-value for the estimate of the true X) says absolutely nothing about the practical/scientific significance of the true X. That is, significance of evidence is not evidence of significance. Increasing your sample size in no way increases the practical/scientific significance of your practical/scientific hypothesis. “significant” p-value = “discernible” finding: The significance of statistical evidence for the true X does tell us how well the estimate can discern the true X. That is, significance of evidence is evidence of discernibility. Increasing your sample size does increase how well your finding can discern your practical/scientific hypothesis.

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.

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” [1]. 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.

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

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:

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.

A Formative Research Handbook for California HIV Prevention Programs