A news service focusing on developments in monitoring and evaluation methods relevant to development programmes with social development objectives. Managed by Rick Davies, since 1997
Virginia Eubanks, (2018), New York, NY: St. Martin’s Press
Unfortunately, a contents list does not seem to be available online. But here is a lengthy excerpt from the book.
And here is a YouTube interview with the author, a University of Albany political scientist Virginia Eubanks discusses her new book “Automating Inequality: How High Tech Tools Profile, Police, and Punish the Poor.” (Taped: 12/05/2017)
by Howard White David A. Raitzer. Published by Asian Development Bank. 2017. Available as a pdf (3.12Mb)
The publisher says “This book offers guidance on the principles, methods, and practice of impact evaluation. It contains material for a range of audiences, from those who may use or manage impact evaluations to applied researchers”
“Impact evaluation is an empirical approach to estimating the causal effects of interventions, in terms of both magnitude and statistical significance. Expanded use of impact evaluation techniques is critical to rigorously derive knowledge from development operations and for development investments and policies to become more evidence-based and effective. To help backstop more use of impact evaluation approaches, this book introduces core concepts, methods, and considerations for planning, designing, managing, and implementing impact evaluation, supplemented by examples. The topics covered range from impact evaluation purposes to basic principles, specific methodologies, and guidance on field implementation. It has materials for a range of audiences, from those who are interested in understanding evidence on “what works” in development, to those who will contribute to expanding the evidence base as applied researchers.”
Contents
Rick Davies’ comments: I have only scanned, not read, this book. But some of the sections that I found of interest included:
The Tyranny of Metrics, by Jerry Z Muller, Princeton University Press, RRP£19.95/ $24.95, 240 pages
See Tim Harford’s review of this book in the Financial Times, 24, January 2018
Some quotes: Muller shows that metrics are often used as a substitute for relevant experience, by managers with generic rather than specific expertise. Muller does not claim that metrics are always useless, but that we expect too much from them as a tool of management. ….
The Tyranny of Metrics does us a service in briskly pulling together parallel arguments from economics, management science, philosophy and psychology along with examples from education, policing, medicine, business and the military.
In an excellent final chapter, Muller summarises his argument thus: “measurement is not an alternative to judgement: measurement demands judgement: judgement about whether to measure, what to measure, how to evaluate the significance of what’s been measured, whether rewards and penalties will be attached to the results, and to whom to make the measurements available”.
The book does not engage seriously enough with the possibility that the advantages of metric-driven accountability might outweigh the undoubted downsides. Tellingly, Muller complains of a university ratings metric that rewards high graduation rates, access for disadvantaged students, and low costs. He says these requirements are “mutually exclusive”, but they are not. They are in tension with each other,
Nor does this book reckon with evidence that mechanical statistical predictions often beat the subjective judgment of experts.
…and perhaps most curiously, there is no discussion of computers, cheap sensors, or big data. In this respect, at least, the book could have been written in the 1980s.
Table of Contents
Introduction 1
I THE ARGUMENT
1 The Argument in a Nutshell 17
2 Recurring Flaws 23
II THE BACKGROUND
3 The Origins of Measuring and Paying for Performance 29
4 Why Metrics Became So Popular 39
5 Principals, Agents, and Motivation 49
6 Philosophical Critiques 59
III THE MISMEASURE OF ALL THINGS? Case Studies
7 Colleges and Universities 67
8 Schools 89
9 Medicine 103
10 Policing 125
11 The Military 131
12 Business and Finance 137
13 Philanthropy and Foreign Aid 153
EXCURSUS
14 When Transparency Is the Enemy of Performance: Politics, Diplomacy, Intelligence, and Marriage 159
IV CONCLUSIONS
15 Unintended but Predictable Negative Consequences 169
16 When and How to Use Metrics: A Checklist 175
Acknowledgments 185
Notes 189
Index 213
Search inside this book using a Google Books view
To be published in Jan 2018. SECOND EDITION. Published by Sage
Stephen P Borgatti – University of Kentucky, USA
Martin G Everett – Manchester University, UK
Jeffrey C Johnson – University of Florida, USA
Publishers blurb: “Designed to walk beginners through core aspects of collecting, visualizing, analyzing, and interpreting social network data, this book will get you up-to-speed on the theory and skills you need to conduct social network analysis. Using simple language and equations, the authors provide expert, clear insight into every step of the research process—including basic maths principles—without making assumptions about what you know. With a particular focus on NetDraw and UCINET, the book introduces relevant software tools step-by-step in an easy to follow way.
In addition to the fundamentals of network analysis and the research process, this new Second Edition focuses on:
Detailed contents list here
by Cass R. Sunstein, Cambridge University Press, 2016
Contents:
1. The age of behavioral science;
2. Choice and its architecture;
3. ‘As judged by themselves’;
4. Values;
5. Fifty shades of manipulation;
6. Do people like nudges? Empirical findings;
7. Green by default? Ethical challenges for environmental protection;
8. Mandates – a very brief recapitulation;
Appendix A. American attitudes toward thirty-four nudges;
Appendix B. Survey questions;
Appendix C. Executive Order 13707: using behavioral science insights to better serve the American people;
Amazon blurb: “In recent years, ‘nudge units’ or ‘behavioral insights teams’ have been created in the United States, the United Kingdom, Germany, and other nations. All over the world, public officials are using the behavioral sciences to protect the environment, promote employment and economic growth, reduce poverty, and increase national security. In this book, Cass R. Sunstein, the eminent legal scholar and best-selling co-author of Nudge (2008), breaks new ground with a deep yet highly readable investigation into the ethical issues surrounding nudges, choice architecture, and mandates, addressing such issues as welfare, autonomy, self-government, dignity, manipulation, and the constraints and responsibilities of an ethical state. Complementing the ethical discussion, The Ethics of Influence: Government in the Age of Behavioral Science contains a wealth of new data on people’s attitudes towards a broad range of nudges, choice architecture, and mandates.
Book Review by Roger Frantz (pdf)
Cristina Bicchieri, Oxford University Press, 2016. View Table of Contents
Publisher summary:
Amazon blurb: “The philosopher Cristina Bicchieri here develops her theory of social norms, most recently explained in her 2006 volume The Grammar of Society. Bicchieri challenges many of the fundamental assumptions of the social sciences. She argues that when it comes to human behavior, social scientists place too much stress on rational deliberation. In fact, many choices occur without much deliberation at all. Bicchieri’s theory accounts for these automatic components of behavior, where individuals react automatically to cues–those cues often pointing to the social norms that govern our choices in a social world
Bicchieri’s work has broad implications not only for understanding human behavior, but for changing it for better outcomes. People have a strong conditional preference for following social norms, but that also means manipulating those norms (and the underlying social expectations) can produce beneficial behavioral changes. Bicchieri’s recent work with UNICEF has explored the applicability of her views to issues of human rights and well-being. Is it possible to change social expectations around forced marriage, genital mutilations, and public health practices like vaccinations and sanitation? If so, how? What tools might we use? This short book explores how social norms work, and how changing them–changing preferences, beliefs, and especially social expectations–can potentially improve lives all around the world.”
3rd Edition by Douglas W. Hubbard (Author)
pdf copy of 2nd edition available here
Building up from simple concepts to illustrate the hands-on yet intuitively easy application of advanced statistical techniques, How to Measure Anything reveals the power of measurement in our understanding of business and the world at large. This insightful and engaging book shows you how to measure those things in your business that until now you may have considered “immeasurable,” including technology ROI, organizational flexibility, customer satisfaction, and technology risk.
Offering examples that will get you to attempt measurements-even when it seems impossible-this book provides you with the substantive steps for measuring anything, especially uncertainty and risk. Don’t wait-listen to this book and find out:
Amazon.com Review Now updated with new research and even more intuitive explanations, a demystifying explanation of how managers can inform themselves to make less risky, more profitable business decisions This insightful and eloquent book will show you how to measure those things in your own business that, until now, you may have considered “immeasurable,” including customer satisfaction, organizational flexibility, technology risk, and technology ROI.
How to Measure Anything, Second Edition illustrates how the author has used his approach across various industries and how any problem, no matter how difficult, ill defined, or uncertain can lend itself to measurement using proven methods.
See also Julia Galef’s podcast interview with the author: http://rationallyspeakingpodcast.org/show/rs-197-doug-hubbard-on-why-people-think-some-things-cant-be.html …
WHAT IS PRISM?
PRISM is a toolkit that aims to support small/medium-sized conservation projects to effectively evaluate the outcomes and impacts of their work.
The toolkit has been developed by a collaboration of several conservation NGOs with additional input from scientists and practitioners from across the conservation sector.
The toolkit is divided into four main sections:
Introduction and Key Concepts: Provides a basic overview of the theory behind evaluation relevant to small/medium-sized conservation projects
Designing and Implementing the Evaluation: Guides users through a simple, step by step process for evaluating project outcomes and impacts, including identifying what you need to evaluate, how to collect evaluation data, analysing/interpreting results and deciding what to do next.
Modules: Provides users with additional guidance and directs users towards methods for evaluating outcomes/impacts resulting from five different kinds of conservation action:
Method factsheets: Outlines over 60 practical, easy to use methods and supplementary guidance factsheets for collecting, analysing and interpreting evaluation data
Toolkit Website: https://conservationevaluation.org/
PDF copy of manual- Download request form: https://conservationevaluation.org/download/
Recently noted papers of interest on my Twitter feed:
This is a question posed by Michael Bamberger in his blog posting on the MERL Tech website, titled Building bridges between evaluators and big data analysts. There he puts forward eight reasons (4 main ones and 4 subsidiary points). None of which I disagree with. But I have my own perspective on the same question and posted the following points as a Comment underneath his blog posting.
My take on “Why have evaluators been slow to adopt big data analytics?”
1. “Big data? I am having enough trouble finding any useful data! How to analyse big data is ‘a problem we would like to have’” This is what I suspect many evaluators are thinking.
2. “Data mining is BAD” – because data mining is seen as by evaluators something that is ad hoc and non-transparent. Whereas the best data mining practices are systematic and transparent.
3. “Correlation does not mean causation” – many evaluators have not updated this formulation to the more useful “Association is a necessary but insufficient basis for a strong causal claim”
4. Evaluators focus on explanatory models and do not give much attention to the uses of predictive models, but both are useful in the real world, including the combination of both. Some predictive models can become explanatory models, through follow-up within-case investigations.
5. Lack of appreciation of the limits of manual hypothesis formulation and testing (useful as it can be) as a means of accumulating knowledge. In a project with four outputs and four outcomes there can be 16 different individual causal links between outputs and outcomes, but 2 to the power of 16 possible combinations of these causal links. That’s a lot of theories to choose from (65,536). In this context, search algorithms can be very useful.
6. Lack of knowledge and confidence in the use of machine learning software. There is still work to be done to make this software more user friendly. Rapid Miner, BigML, and EvalC3 are heading in the right direction.
7. Most evaluators probably don’t know that you can use the above software on small data sets. They don’t only work with large data sets. Yesterday I was using EvalC3 with a data set describing 25 cases only.
8. The difficulty of understanding some machine learning findings. Decision tree models (one means of machine learning) are eminently readable, but few can explain the internal logic of specific prediction models generated by artificial neural networks (another means of machine learning, often used for classification of images). Lack of explainability presents a major problem for public accountability. Public accountability for the behavior and use of algorithms is shaping up to be a BIG issue, as highlighted in this week’s Economist Leader article on advances in facial recognition software: What machines can tell from your face
Update: 2017 09 19: See Michael Bamberger’s response to my comments above in the Comment section below. They are copied from his original response posted here http://merltech.org/building-bridges-between-evaluators-and-big-data-analysts/