Tag Archives: evaluation

Big data or big hype: a MERL Tech debate

by Shawna Hoffman, Specialist, Measurement, Evaluation and Organizational Performance at the Rockefeller Foundation.

Both the volume of data available at our fingertips and the speed with which it can be accessed and processed have increased exponentially over the past decade.  The potential applications of this to support monitoring and evaluation (M&E) of complex development programs has generated great excitement.  But is all the enthusiasm warranted?  Will big data integrate with evaluation — or is this all just hype?

A recent debate that I chaired at MERL Tech London explored these very questions. Alongside two skilled debaters (who also happen to be seasoned evaluators!) – Michael Bamberger and Rick Davies – we sought to unpack whether integration of big data and evaluation is beneficial – or even possible.

Before we began, we used Mentimeter to see where the audience  stood on the topic:

Once the votes were in, we started.

Both Michael and Rick have fairly balanced and pragmatic viewpoints; however, for the sake of a good debate, and to help unearth the nuances and complexity surrounding the topic, they embraced the challenge of representing divergent and polarized perspectives – with Michael arguing in favor of integration, and Rick arguing against.

“Evaluation is in a state of crisis,” Michael argued, “but help is on the way.” Arguments in favor of the integration of big data and evaluation centered on a few key ideas:

  • There are strong use cases for integration. Data science tools and techniques can complement conventional evaluation methodology, providing cheap, quick, complexity-sensitive, longitudinal, and easily analyzable data.
  • Integration is possible. Incentives for cross-collaboration are strong, and barriers to working together are reducing. Traditionally these fields have been siloed, and their relationship has been characterized by a mutual lack of understanding of the other (or even questioning of the other’s motivations or professional rigor).  However, data scientists are increasingly recognizing the benefits of mixed methods, and evaluators are seeing the potential to use big data to increase the number of types of evaluation that can be conducted within real-world budget, time and data constraints. There are some compelling examples (explored in this UN Global Pulse Report) of where integration has been successful.
  • Integration is the right thing to do.  New approaches that leverage the strengths of data science and evaluation are potentially powerful instruments for giving voice to vulnerable groups and promoting participatory development and social justice.   Without big data, evaluation could miss opportunities to reach the most rural and remote people.  Without evaluation (which emphasizes transparency of arguments and evidence), big data algorithms can be opaque “black boxes.”

While this may paint a hopeful picture, Rick cautioned the audience to temper its enthusiasm. He warned of the risk of domination of evaluation by data science discourse, and surfaced some significant practical, technical, and ethical considerations that would make integration challenging.

First, big data are often non-representative, and the algorithms underpinning them are non-transparent. Second, “the mechanistic approaches offered by data science, are antithetical to the very notion of evaluation being about people’s values and necessarily involving their participation and consent,” he argued. It is – and will always be – critical to pay attention to the human element that evaluation brings to bear. Finally, big data are helpful for pattern recognition, but the ability to identify a pattern should not be confused with true explanation or understanding (correlation ≠ causation). Overall, there are many problems that integration would not solve for, and some that it could create or exacerbate.

The debate confirmed that this question is complex, nuanced, and multi-faceted. It helped to remind that there is cause for enthusiasm and optimism, at the same time as a healthy dose of skepticism. What was made very clear is that the future should leverage the respective strengths of these two fields in order to maximize good and minimize potential risks.

In the end, the side in favor of integration of big data and evaluation won the debate by a considerable margin.

The future of integration looks promising, but it’ll be interesting to see how this conversation unfolds as the number of examples of integration continues to grow.

Interested in learning more and exploring this further? Stay tuned for a follow-up post from Michael and Rick. You can also attend MERL Tech DC in September 2018 if you’d like to join in the discussions in person!

Evaluating ICT4D projects against the Digital Principles

By Laura Walker McDonald,  This post was originally published on the Digital Impact Alliance’s Blog on March 29, 2018.

As I have written about elsewhere, we need more evidence of what works and what doesn’t in the ICT4D and tech for social change spaces – and we need to hold ourselves to account more thoroughly and share what we know so that all of our work improves. We should be examining how well a particular channel, tool or platform works in a given scenario or domain; how it contributes to development goals in combination with other channels and tools; how the team selected and deployed it; whether it is a better choice than not using technology or using a different sort of technology; and whether or not it is sustainable.

At SIMLab, we developed our Framework for Monitoring and Evaluation of Technology in Social Change projects to help implementers to better measure the impact of their work. It offers resources towards a minimum standard of best practice which implementers can use or work toward, including on how to design and conduct evaluations. With the support of the Digital Impact Alliance (DIAL), the resource is now finalized and we have added new evaluation criteria based on the Principles for Digital Development.

Last week at MERL Tech London, DIAL was able to formally launch this product by sharing a 2-page summary available at the event and engaging attendees in a conversation about how it could be used. At the event, we joined over 100 organizations to discuss Monitoring, Evaluation, Research and Learning related to technology used for social good.

Why evaluate?

Evaluations provide snapshots of the ongoing activity and the progress of a project at a specific point in time, based on systematic and objective review against certain criteria. They may inform future funding and program design; adjust current program design; or to gather evidence to establish whether a particular approach is useful. They can be used to examine how, and how far, technology contributes to wider programmatic goals. If set up well, your program should already have evaluation criteria and research questions defined, well before it’s time to commission the evaluation.

Evaluation criteria provide a useful frame for an evaluation, bringing in an external logic that might go beyond the questions that implementers and their management have about the project (such as ‘did our partnerships on the ground work effectively?’ or ‘how did this specific event in the host country affect operations?’) to incorporate policy and best practice questions about, for example, protection of target populations, risk management, and sustainability. The criteria for an evaluation could be any set of questions that draw on an organization’s mission, values, principles for action; industry standards or other best practice guidance; or other thoughtful ideas of what ‘good’ looks like for that project or organization. Efforts like the Principles for Digital Development can set useful standards for good practice, and could be used as evaluation criteria.

Evaluating our work, and sharing learning, is radical – and critically important

While the potential for technology to improve the lives of vulnerable people around the world is clear, it is also evident that these improvements are not keeping pace with the advances in the sector. Understanding why requires looking critically at our work and holding ourselves to account. There is still insufficient evidence of the contribution technology makes to social change work. What evidence there is often is not shared or the analysis doesn’t get to the core issues. Even more important, the learnings from what has not worked and why have not been documented and absorbed.

Technology-enabled interventions succeed or fail based on their sustainability, business models, data practices, choice of communications channel and technology platform; organizational change, risk models, and user support – among many other factors. We need to build and examine evidence that considers these issues and that tells us what has been successful, what has failed, and why. Holding ourselves to account against standards like the Principles is a great way to improve our practice, and honor our commitment to the people we seek to help through our work.

Using the Digital Principles as evaluation criteria

The Principles for Digital Development are a set of living guidance intended to help practitioners succeed in applying technology to development programs. They were developed, based on some pre-existing frameworks, by a working group of practitioners and are now hosted by the Digital Impact Alliance.

These nine principles could also form a useful set of evaluation criteria, not unlike OECD evaluation criteria, or Sphere standards. Principles overlap, so data can be used to examine more than one criterion, and ot every evaluation would need to consider all of the Digital Principles.

Below are some examples of Digital Principles and sample questions that could initiate, or contribute to, an evaluation.

Design with the User: Great projects are designed with input from the stakeholders and users who are central to the intended change. How far did the team design the project with its users, based on their current tools, workflows, needs and habits, and work from clear theories of change and adaptive processes?

Understand the Existing Ecosystem: Great projects and programs are built, managed, and owned with consideration given to the local ecosystem. How far did the project work to understand the local, technology and broader global ecosystem in which the project is situated? Did it build on existing projects and platforms rather than duplicating effort? Did the project work sensitively within its ecosystem, being conscious of its potential influence and sharing information and learning?

Build for Sustainability: Great projects factor in the physical, human, and financial resources that will be necessary for long-term sustainability. How far did the project: 1) think through the business model, ensuring that the value for money and incentives are in place not only during the funded period but afterwards, and 2) ensure that long-term financial investments in critical elements like system maintenance and support, capacity building, and monitoring and evaluation are in place? Did the team consider whether there was an appropriate local partner to work through, hand over to, or support the development of, such as a local business or government department?

Be Data Driven: Great projects fully leverage data, where appropriate, to support project planning and decision-making. How far did the project use real-time data to make decisions, use open data standards wherever possible, and collect and use data responsibly according to international norms and standards?

Use Open Standards, Open Data, Open Source, and Open Innovation: Great projects make appropriate choices, based on the circumstances and the sensitivity of their project and its data, about how far to use open standards, open the project’s data, use open source tools and share new innovations openly. How far did the project: 1) take an informed and thoughtful approach to openness, thinking it through in the context of the theory of change and considering risk and reward, 2) communicate about what being open means for the project, and 3) use and manage data responsibly according to international norms and standards?

For a more complete set of guidance, see the complete Framework for Monitoring and Evaluating Technology, and the more nuanced and in-depth guidance on the Principles, available on the Digital Principles website.

MERL Tech London 2018 Agenda is out!

We’ve been working hard over the past several weeks to finish up the agenda for MERL Tech London 2018, and it’s now ready!

We’ve got workshops, panels, discussions, case studies, lightning talks, demos, community building, socializing, and an evening reception with a Fail Fest!

Topics range from mobile data collection, to organizational capacity, to learning and good practice for information systems, to data science approaches, to qualitative methods using mobile ethnography and video, to biometrics and blockchain, to data ethics and privacy and more.

You can search the agenda to find the topics, themes and tools that are most interesting, identify sessions that are most relevant to your organization’s size and approach, pick the session methodologies that you prefer (some of us like participatory and some of us like listening), and to learn more about the different speakers and facilitators and their work.

Tickets are going fast, so be sure to snap yours up before it’s too late! (Register here!)

View the MERL Tech London schedule & directory.

 

Moving from “evaluation” to “impact management”

by Richa Verma, Resident Entrepreneur at Social Cops. This post originally appeared on the Social Cops blog on August 28, 2017.

When I say that Impact Evaluation is history, I mean it. Some people will question this. After all, Impact Evaluation just became mainstream in the last decade, driven by great improvements in experimental design methods like randomized control trials (RCTs). So how can I say that it’s already a thing of the past? It’s not Impact Evaluation’s fault. The world changed.

Methodologies like RCTs came from medical science, where you can give patients a pill and assess its impact with randomized trials. However, development is not a space where one pill will work for everyone. In development, the patients change faster, the illness evolves faster, and the pill needs to keep pace with both the patients and the illness. That’s where Impact Management comes in.

What Is Impact Management?

New Philanthropy Capital‘s 2017 Global Innovation in Measurement and Evaluation Report counts Impact Management as one of the top 7 innovations of 2017.

So what is Impact Management? Let me first explain what it is not. It’s not a one-time evaluation. It’s not collecting data for answering a limited set of questions. It’s not a separate activity from your program. It’s not just monitoring and evaluation.

It’s a way of making data-driven decisions at every step of your program. It’s about keeping a pulse on your program every day and finding new questions to answer, rather than just focusing on specific questions predetermined by your monitoring and evaluation team or funders.

“The question that’s being asked more and more is, ‘How does evaluation feed into better management decisions?’ That’s a shift from measurement of impact, to measurement for impact.”
– Megan Campbell (Feedback Labs)

How Does Impact Management Work?

Impact Management uses the basic components of monitoring and evaluation, but with an outlook shift. It involves frequent data collection, regular reporting and monitoring of your data, and iteratively updating your program indicators and metrics as data comes in and the program changes.

Impact Management differs from Impact Assessment in that it promotes course correction on a daily basis. Organizations collect data on their programs as they conduct activities, analyze that information on a regular basis, and make changes to the program.

With an outlook that encourages frequent changes, as if you were trading in stocks, organizations will have the ability to A/B test their programs with real-time data to make decisions immediately; rather than wait to compare and contrast two different surveys. They can test out new things and make changes as they receive data in servers, even at the end of the day rather than waiting for the official year-end review. It becomes a way of deciding how they should execute a program daily rather than only seeing strategic changes through.

“[Data collection] should be ongoing — it’s a value driver not a compliance requirement.”
– Tom Adams (Acumen)

In many ways, this is how decisions are made on Wall Street or Dalal Street in India. Analysts don’t wait until the end of the year to make investments by reviewing annual reports. They watch daily as the market fluctuates and strike as soon as they see new potential.

Impact Management works exactly the same. You should strive to increase your impact as soon as opportunity arrives, rather than waiting for a year-end external evaluation or approval.

How Can You Implement Impact Management?

To make Impact Management possible, switch from static data files to a flexible data system.

Today, most of your program officers and even your beneficiaries are armed with mini-computers in their pockets (read: smartphones). Leverage these to create a network of data ingestion devices, continuously tracking and measuring the impact of your programs. Use mobile data collection apps to add forms, deploy them to the field, and reach out not just to your field force but also your beneficiaries — not just at the end of the month or quarter, but as frequently as possible.

Then don’t let this data sit in Excel files. Use today’s technologies to create your own data management system, one that will link your beneficiaries, connect your programs, and answer queries. Have someone with an analytical bent look at this data regularly, or draw on machine power to analyze this data and generate meaningful insights or reports in real time.

“We’re moving away from a static data world, where you work on datasets, and you write reports, to a dynamic data world where data is always being generated and created and it helps you do your job better.”
– Andrew Means (beyond.uptake)

Lastly, it’s crucial to tie this flexible data system back to your decisions. Make real-time data — rather than guesses or last year’s data — the basis of every program decision and the foundation of even weekly catch-ups. And don’t hesitate to test out new things. Data will tell you whether something worked or not.

Many of our partners are using our platform to make Impact Management possible and track their programs in real time. The platform lets them create and tweak data collection forms, and monitor incoming data in real time on their computer, in regular reports, or even on map-based dashboards. They are asking new questions about how their programs are doing and answering them with data.

If we really want to create the best development programs, we’ll have to think differently and use evidence not just once every month or year, but as we make crucial decisions every day. All backed by the tenets of Impact Management: test, fail, improve, repeat.

Join us at MERL Tech London on March 19-20 – where we’ll be debating this topic!

MERL Tech 101: Google forms

by Daniel Ramirez-Raftree, MERL Tech volunteer

In his MERL Tech DC session on Google Forms, Samhir Vesdev from IREX led a hands-on workshop on Google Forms and laid out some of the software’s capabilities and limitations. Much of the session focused on Google Forms’ central concepts and the practicality of building a form.

At its most fundamental level, a form is made up of several sections, and each section is designed to contain a question or prompt. The centerpiece of a section is the question cell, which is, as one would imagine, the cell dedicated to the question. Next to the question cell there is a drop down menu that allows one to select the format of the question, which ranges from multiple-choice to short answer.


At the bottom right hand corner of the section you will find three dots arranged vertically. When you click this toggle, a drop-down menu will appear. The options in this menu vary depending on the format of the question. One common option is to include a few lines of description, which is useful in case the question needs further elaboration or instruction. Another is the data validation option, which restricts the kinds of text that a respondent can input. This is useful in the case that, for example, the question is in a short answer format but the form administrators need the responses to be limited numerals for the sake of analysis.

The session also covered functions available in the “response” tab, which sits at the top of the page. Here one can find a toggle labeled “accepting responses” that can be turned off or on depending on the needs for the form.

Additionally, in the top right corner this tab, there are three dots arranged vertically, and this is the options menu for this tab. Here you will find options such as enabling email notifications for each new response, which can be used in case you want to be alerted when someone responds to the form. Also in this drop down, you can click “select response destination” to link the Google Form with Google Sheets, which simplifies later analysis. The green sheets icon next to the options drop-down will take you to the sheet that contains the collected data.

Other capabilities in Google Forms include the option for changing the color scheme, which you can access by clicking the palette icon at the top of the screen. Also, by clicking the settings button at the top of the screen you can limit the response amount to restrict people’s ability to skew the data by submitting multiple responses, or you can enable response editing after submission to allow respondents to go in and correct their response after submitting it.

Branching is another important tool in Google Forms. It can be used in the case that you want a particular response to a question (say, a multiple choice question) to lead the respondent to another related question only if they respond in a certain way.

For example, if in one section you ask “did you like the workshop?” with the answer options being “yes” and “no,” and if you want to know what they didn’t like about the workshop only if they answer “no,” you can design the sheet to take the respondent to a section with the question “what didn’t you like about the workshop?” only in the case that they answer “no,” and then you can design the sheet to bring the respondent back to the main workflow after they’ve answered this additional question.

To do this, create at least two new sections (by clicking “add section” in the small menu to the right of the sections), one for each path that a person’s response will lead them down. Then, in the options menu on the lower right hand side select “go to section based on answer” and using the menu that appears, set the path that you desire.

These are just some of the tools that Google Forms offers, but with just these it is possible to build an effective form to collect the data you need. Samhir ended with a word of caution that Google has been known to shut down popular apps, so you should be wary about building an organization strategy around Google Forms.

M&E Squared: Evaluating M&E Technologies

by Roger Nathanial Ashby, Co-Founder & Principal Consultant, OpenWise

The universe of MERL Tech solutions has grown exponentially. In 2008 monitoring and evaluating tech within global development could mostly be confined to mobile data collection tools like Open Data Kit (ODK), and Excel spreadsheets to analyze and visualize survey data. In the intervening decade a myriad of tools, companies and NGOs have been created to advance the efficiency and effectiveness of monitoring, evaluation, research and learning (MERL) through the use of technology. Whether it’s M&E platforms or suites, satellite imagery, remote sensors, or chatbots, new innovations are being deployed every day in the field.

However, how do we evaluate the impact when MERL Tech is the intervention itself? That was the question and task put to participants of the “M&E Squared” workshop at MERL Tech 2017.

Workshop participants were separated into three groups that were each given a case study to discuss and analyze. One group was given a case about improving the learning efficiency of health workers in Liberia through the mHero Health Information System (HIS). The system was deployed as a possible remedy to some of the information communication challenges identified during the 2014 West African Ebola outbreak. A second group was given a case about the use of RapidPro to remind women to attend antenatal care (ANC) for preventive malaria medicine in Guinea. The USAID StopPalu project goal was to improve the health of infants by increasing the percent of women attending ANC visits. The final group was given a case about using remote images to assist East African pastoralists. The Satellite Assisted Pastoral Resource Management System (SAPARM) informs pastoralists of vegetation through remote sensing imagery so they can make better decisions about migrating their livestock.

After familiarizing ourselves with the particulars of the case studies, each group was tasked to present their findings to all participants after pondering a series of questions. Some of the issues under discussion included

(1) “How would you assess your MERL Tech’s relevance?”

(2) “How would you evaluate the effectiveness of your MERL Tech?”

(3) “How would you measure efficiency?” and

(4) “How will you access sustainability?”.

Each group came up with some innovative answers to the questions posed and our facilitators and session leads (Alexandra Robinson & Sutyajeet Soneja from USAID and Molly Chen from RTI) will soon synthesize the workshop findings and notes into a concise written brief for the MERL Tech community.

Before the workshop closed we were all introduced to the great work done by SIMLab (Social Impact Lab) in this area through their SIMLab Monitoring and Evaluation Framework. The framework identifies key criteria for evaluating M&E including:

  1. Relevance – The extent to which the technology choice is appropriately suited to the priorities and capacities of the context of the target group or organization.
  2. Effectiveness – A measure of the extent to which an information and communication channel, technology tool, technology platform, or a combination of these attains its objectives.
  3. Efficiency – Measure of the outputs (qualitative and quantitative) in relation to the inputs.
  4. Impact – The positive and negative changed produced by technology introduction, change in a technology tool, or platform on the overall development intervention (directly or indirectly; intended or unintended).
  5. Sustainability – Measure of whether the benefits of a technology tool or platform are likely to continue after donor funding has been withdrawn.
  6. Coherence – How related is the technology to the broader policy context (development, market, communication networks, data standards & interoperability mandates, and national & international law) within which the technology was developed and implemented.

While it’s unfortunate that SIMLab stopped most operations in early September 2017, their exceptional work in this and other areas lives on and you can access the full framework here.

I learned a great deal in this session from the facilitators and my colleagues attending the workshop. I would encourage everyone in the MERL Tech community to take the ideas generated during this workshop and the great work done by SIMLab into their development practice. We certainly intend to integrate much of these insights into our work at OpenWise. Read more about “The Evidence Agenda” here on SIMLab’s blog. 

 

 

 

Making (some) sense of data storage and presentation in Excel

By Anna Vasylytsya. Anna is in the process of completing her Master’s in Public Policy with an emphasis on research methods. She is excited about the role that data can play in improving people’s lives!

At the MERL Tech Conference, I attended a session called “The 20 skills that solve 80% of M&E problems” presented by Dr. Leslie Sage of DevResults. I was struck by the practical recommendations Leslie shared that can benefit anyone that uses Excel to store and/or present data.

I boiled down the 20 skills presented in the session into three key takeaways, below.

1. Discerning between data storage and data presentation

Data storage and data presentation serve two different functions and never the two shall meet. In other words, data storage is never data presentation.

Proper data storage should not contain merged cells, subheadings, color used to denote information, different data types within cells (numbers and letters), more than one piece of data in a cell (such as disaggregations). Additionally, in proper data storage, columns should be the variables and rows as the observations or vice versa. Poor data storage practices need to be avoided because they mean that you cannot use Excel’s features to present the data.

A common example of poor data storage:

Excel 1

 

One of the reasons that this is not good data storage is because you are not able to manipulate this data using Excel’s features. If you needed this data in a different format or you wanted to visualize it, you would have to do this manually, which would be time consuming.

Here is the same data presented in a “good” storage format:

2Good_Data_Storage

 

Data stored this way may not look as pretty, but it is not meant to be presented or read in within the sheet. This is an example of good data storage because each unique observation gets a new row in the spreadsheet. When you properly store data, it is easy for Excel to aggregate the data and summarize it in a pivot table, for example.

2. Use Excel’s features to organize and clean data

You do not have to use precious time to organize or clean data manually. Here are a few recommendations on Excel’s data organization and cleaning features:

  • To join to cells that have text into one cell, use the concatenate function.
  • To split text from one cell into different cells, use the text to columns
  • To clean text data, use Excel’s functions: trim, lower, upper, proper, right, left, and len.
  • To move data from rows into columns or columns into rows, use Excel’s transpose feature.
  • There is a feature to remove duplicates from the data.
  • Create a macro to automate simple repetitive steps in Excel.
  • Insert data validation in an excel spreadsheet if you are sending a data spreadsheet to implementers or partners to fill out.
    • This restricts the type of data or values that can be entered in certain parts of the spreadsheet.
    • It also saves you time from having to clean the data after you receive it.
  • Use the vlookup function in Excel in your offline version to look up a Unique ID
    • Funders or donors normally require that data is anonymized if it is made public. While not the best option for anonymizing data, you can use Excel if you haven’t been provided with specific tools or processes.
    • You can create an “online” anonymized version that contains a Unique ID and an “offline version” (not public) containing the ID and Personally Identifiable Information (PII). Then, if you needed to answer a question about a Unique ID, for example, your survey was missing data and you needed to go back and collect it, you can use vlookup to find a particular record.

3. Use Excel’s features to visualize data

One of the reasons to organize data properly so that you can use Excel’s Pivot Table feature.

Here is an example of a pivot table made from the data in the good data storage example above (which took about a minute to make):

3Pivot_Table

Using the pivot table, you can then use Excel’s Create a Chart Feature to quickly make a bar graph:

4BarGraph

In the Future

I have fallen prey to poor data storage practices in the past. Now that I have learned these best practices and features of Excel, I know I will improve my data storage and presentation practices. Also, now that I have shared them with you; I hope that you will too!

Please note that in this post I did not discuss how Excel’s functions or features work or how to use them. There are plenty of resources online to help you discover and explore them. Some helpful links have been included as a start. Additionally, the data presented here are fictional and created purely for demonstration purposes.

You can’t have Aid…without AI: How artificial intelligence may reshape M&E

by Jacob Korenblum, CEO of Souktel Digital Solutions

Photo: wikipedia.org/

Potential—And Risk

The rapid growth of Artificial Intelligence—computers behaving like humans, and performing tasks which people usually carry out—promises to transform everything from car travel to personal finance. But how will it affect the equally vital field of M&E? As evaluators, most of us hate paper-based data collection—and we know that automation can help us process data more efficiently. At the same time, we’re afraid to remove the human element from monitoring and evaluation: What if the machines screw up?

Over the past year, Souktel has worked on three areas of AI-related M&E, to determine where new technology can best support project appraisals. Here are our key takeaways on what works, what doesn’t, and what might be possible down the road.

Natural Language Processing

For anyone who’s sifted through thousands of Excel entries, natural language processing sounds like a silver bullet: This application of AI interprets text responses rapidly, often matching them against existing data sets to find trends. No need for humans to review each entry by hand! But currently, it has two main limitations: First, natural language processing works best for sentences with simple syntax. Throw in more complex phrases, or longer text strings, and the power of AI to grasp open-ended responses goes downhill. Second, natural language processing only works for a limited number of (mostly European) languages—at least for now. English and Spanish AI applications? Yes. Chichewa or Pashto M&E bots? Not yet. Given these constraints, we’ve found that AI apps are strongest at interpreting basic misspelled answer text during mobile data collection campaigns (in languages like English or French). They’re less good at categorizing open-ended responses by qualitative category (positive, negative, neutral). Yet despite these limitations, AI can still help evaluators save time.

Object Differentiation

AI does a decent job of telling objects apart; we’ve leveraged this to build mobile applications which track supply delivery more quickly & cheaply. If a field staff member submits a photo of syringes and a photo of bandages from their mobile, we don’t need a human to check “syringes” and “bandages” off a list of delivered items. The AI-based app will do that automatically—saving huge amounts of time and expense, especially during crisis events. Still, there are limitations here too: While AI apps can distinguish between a needle and a BandAid, they can’t yet tell us whether the needle is broken, or whether the BandAid is the exact same one we shipped. These constraints need to be considered carefully when using AI for inventory monitoring.

Comparative Facial Recognition

This may be the most exciting—and controversial—application of AI. The potential is huge: “Qualitative evaluation” takes on a whole new meaning when facial expressions can be captured by cameras on mobile devices. On a more basic level, we’ve been focusing on solutions for better attendance tracking: AI is fairly good at determining whether the people in a photo at Time A are the same people in a photo at Time B. Snap a group pic at the end of each community meeting or training, and you can track longitudinal participation automatically. Take a photo of a larger crowd, and you can rapidly estimate the number of attendees at an event.

However, AI applications in this field have been notoriously bad at recognizing diversity—possibly because they draw on databases of existing images, and most of those images contain…white men. New MIT research has suggested that “since a majority of the photos used to train [AI applications] contain few minorities, [they] often have trouble picking out those minority faces”. For the communities where many of us work (and come from), that’s a major problem.

Do’s and Don’ts

So, how should M&E experts navigate this imperfect world? Our work has yielded a few “quick wins”—areas where Artificial Intelligence can definitely make our lives easier: Tagging and sorting quantitative data (or basic open-ended text), simple differentiation between images and objects, and broad-based identification of people and groups. These applications, by themselves, can be game-changers for our work as evaluators—despite their drawbacks. And as AI keeps evolving, its relevance to M&E will likely grow as well. We may never reach the era of robot focus group facilitators—but if robo-assistants help us process our focus group data more quickly, we won’t be complaining.

Visualizing what connects us: Social Network Analysis (SNA) in M&E

by Anne Laesecke (IREX) and Danielle de García (Social Impact). This post also appears on the Social Impact blog and  the IREX blog.

SNA, or Social Network Analysis, continues to gain momentum in the M&E space. This year at MERL Tech, we held an SNA 101 session, giving a quick-and-dirty overview of what it is, how it can contribute to M&E, and useful tips and tools for conducting an SNA. If you missed it, here’s what you need to know:

What is SNA?

SNA is a way to analyze social systems through relationships. Analyzing and visualizing networks can reveal critical insights for understanding relationships between organizations, supply chains; social movements; and/or between individuals. It’s a very versatile tool which can be used throughout the program cycle to measure things like trust and social capital, information flows, resources, collaboration, and disease spread, among other things.

SNA uses a different vocabulary than other types of analyses. For example, the relationships we measure are called ties or links, and the entities that make up a network are called nodes or actors. These can be organizations, people, or even whole networks themselves. We can study nodes more closely by looking at their attributes – things that characterize them (like demographic information), and we can learn more about how nodes interact and cluster by studying communities or modalities within networks. Various measures of the roles nodes play in a network, as well as measures that characterize the networks themselves, can reveal a lot about the systems and hidden relationships at play. For example, we can determine who has the most ties with other actors; who is relatively cut off from the network, or who is connected to the most well-connected actors.

Why would you use SNA for M&E?

The term “social network analysis” often triggers associations with social media, but SNA uses data from a variety of platforms (including but not limited to social media!). For instance, SNA can identify key influencers in systems – important for programs that rely on thinking politically. SNA can also be a useful tool in processing big data with applications for cybersecurity as well as creating biological and epidemiological projections. Beyond looking at networks of individuals, SNA can explore relationships with concepts through analysis of qualitative data and concept mapping. It can also look at organizational risks and processes (think about comparing an organizational chart with who people actually go to within an organization for information).

How do you do SNA?

Conducting SNA mostly follows the same procedure as other analysis.

  1. Determine your purpose and questions. What decisions do you need to make based on the data you get? Who is your audience and what do they need to know? Answering these questions can help you decided what you are trying to measure and how.
  2. Collect your data. SNA can incorporate lots of different data forms, including customized surveys and interviews asking actors in your network about the links they have, external data such as census information or other public records to further inform attributes or triangulate your custom data; and mapping of key locations or concepts. One thing to consider while conducting an SNA – data cleaning is usually a heavier lift than for other types of analysis.
  3. Crunch those numbers. SNA uses matrices to calculate various measures – from types of centrality to network density and beyond. Lucky for us, there are plenty of tools that take on both the analysis and visualization portions of SNA. However, another consideration as you analyze your data is that network data is often not generalizable in the same way as some statistical analysis. If you miss a key node in the network, you may miss an entire portion that is only linked through that node.
  4. Visualize the network. Network visualizations are one of the most distinctive features of SNA and can be incredibly useful as tools to engage partners about your findings. There is a wealth of analysis and visualization tools that can help you do this. We created a worksheet that outlines several, but a few of the most popular are UCINet, Gephi, and NodeXL.
  5. Interpret your results. You now have a beautiful graph that shows what nodes are important in your network. So what? How does it relate to your program? Your interpretation should answer the questions around the purpose of your analysis, but beyond interpretation can serve to improve your programming. Often, SNA results can help make projections for program sustainability based on who key players are and who can continue championing work, or projecting where trends seem to be going and anticipating activities around those areas.

Conclusions and resources

We barely scratched the surface of what SNA can do and there are so many more applications! Some great resources to learn more are the SNA TIG of the American Evaluation Association, Stephen Borgatti’s course website on SNA, and a site of his dedicated completely to designing surveys for SNA.

MERL Tech Round Up | November 1, 2017

It’s time for our second MERL Tech Round Up, a monthly compilation of MERL Tech News!

On the MERL Tech Blog:

We’ve been posting session summaries from MERL Tech DC. Here are some posts you may have missed in October:

Stuff we’re reading/watching/bookmarking:

There’s quite a bit to learn both in our “MERL / Tech” sector and in related sectors whose experiences are relatable to MERL Tech. Some thought-provoking pieces here:

Events:

Jobs

Head over to ICT4DJobs for a ton of tech related jobs. Here are some interesting ones for folks in the MERL Tech space:

If you’re not already signed up to the Pelican Initiative: Platform for Evidence-based Learning & Communication for Social Change, we recommend doing that. You will find all kinds of MERL and MERLTech related jobs and MERL-related advice. (Note: the Platform is an extremely active forum, so you may want to adjust your settings to receive weekly compilations).

Tag us on Twitter using #MERLTech if you have resources, events, or other news you’d like us to include here!

Don’t forget to submit your session ideas for MERL Tech London by November 10th!