Participatory Modelling: Beginnings of a list

What is Participatory Modelling?

Gray et al (2018) “The field of PM lies at the intersection of participatory approaches to planning, computational modeling, and environmental modeling”

Wikipedia: “Participatory modeling is a purposeful learning process for action that engages the implicit and explicit knowledge of stakeholders to create formalized and shared representation(s) of reality. In this process, the participants co-formulate the problem and use modeling practices to aid in the description, solution, and decision-making actions of the group. Participatory modeling is often used in environmental and resource management contexts. It can be described as engaging non-scientists in the scientific process. The participants structure the problem, describe the system, create a computer model of the system, use the model to test policy interventions, and propose one or more solutions. Participatory modeling is often used in natural resources management, such as forests or water.

There are numerous benefits from this type of modeling, including a high degree of ownership and motivation towards change for the people involved in the modeling process. There are two approaches which provide highly different goals for the modeling; continuous modeling and conference modeling.

Recent references
  • Gray S, Voinov A, Paolisso M, et al. (2018) Purpose, processes, partnerships, and products: four Ps to advance participatory socio-environmental modeling. Ecological Applications 28(1): 46–61. DOI: 10.1002/eap.1627.
  • Hedelin B, Evers M, Alkan-Olsson J, et al. (2017) Participatory modelling for sustainable development: Key issues derived from five cases of natural resource and disaster risk management. Environmental Science & Policy 76: 185–196. DOI: 10.1016/j.envsci.2017.07.001.
  • Basco-Carrera L, Warren A, van Beek E, et al. (2017) Collaborative modelling or participatory modelling? A framework for water resources management. Environmental Modelling & Software 91: 95–110. DOI: 10.1016/j.envsoft.2017.01.014.
  • Eker S, Zimmermann N, Carnohan S, et al. (2017) Participatory system dynamics modelling for housing, energy and wellbeing interactions. Building Research & Information 0(0): 1–17. DOI: 10.1080/09613218.2017.1362919.
  • Voinov A, Kolagani N, McCall MK, et al. (2016) Modelling with stakeholders – Next generation. Environmental Modelling and Software 77: 196220. DOI: 10.1016/j.envsoft.2015.11.016.

Dealing with missing data: A list

In this post “missing data” does not mean absence of whole categories of data, which is a common enough problem, but missing data values within a given data set.

While this is a common problem in almost all spheres of research/evaluation it seems particularly common in more qualitative and participatory inquiry, where the same questions may not be asked of all participants/respondents. It is also likely to be a problem when data is extracted from documentary source produced by different parties e.g. project completion reports.

Some types of strategies (from Analytics Vidhya):

  1. Deletion:
    1. Listwise deletion: Of all cases with missing data
    2. Pairwise deletion: : An analysis is carried out with all cases in which the variable of interest is present. The sub-set of cases used will vary according to the sub-set of variables which are the focus of each analysis.
  2. Substitution
    1. Mean/ Mode/ Median Imputation: replacing the missing data for a given attribute by the mean or median (quantitative attribute) or mode (qualitative attribute) of all known values of that variable. Two variants:
      1. Generalized: Done for all cases
      2. Similar case: calculated separately for different sub-groups e.g. men versus women
    2. K Nearest Neighbour (KNN) imputation: The missing values of an attribute are imputed using those found in other cases with the most similar other attributes (where k = number of other attributes being examined).
    3. Prediction model: Using a sub-set of cases with no missing values, a model is developed that best predicts the presence of the attribute of interest. This is then applied to predict the missing values in the sub-set of cases with the missing values. Another variant, for continuous data:
      1. Regression Substitution: Using multiple-regression analysis to estimate a missing value.
  3. Error estimation (tbc)

References (please help me extend this list)

Note: I would like this list to focus on easily usable references i.e. those not requiring substantial knowledge of statistics and/or the subject of missing data

 

Overview: An open source document clustering and search tool

Overview is an open-source tool originally designed to help journalists find stories in large numbers of documents, by automatically sorting them according to topic and providing a fast visualization and reading interface. It’s also used for qualitative research, social media conversation analysis, legal document review, digital humanities, and more. Overview does at least three things really well.

  • Find what you don’t even know to look for.
  • See broad trends or patterns across many documents.
  • Make exhaustive manual reading faster, when all else fails.

Search is a wonderful tool when you know what you’re trying to find — and Overview includes advanced search features. It’s less useful when you start with a hunch or an anonymous tip. Or there might be many different ways to phrase what you’re looking for, or you could be struggling with poor quality material and OCR error. By automatically sorting documents by topic, Overview gives you a fast way to see what you have .

In other cases you’re interested in broad patterns. Overview’s topic tree shows the structure of your document set at a glance, and you can tag entire folders at once to label documents according to your own category names. Then you can export those tags to create visualizations.

Rick Davies Comment: This service could be quite useful in various ways, including clustering sets of Most Significant Change (MSC) stories, or micro-narratives form SenseMaker type exercises, or collections of Twitter tweets found via a key word search. For those interested in the details, and preferring transparency to apparent magic, Overview uses the k-means clustering algorithm, which is explained broadly here. One caveat, the processing of documents can take some time, so you may want to pop out for a cup of coffee while waiting. For those into algorithms, here is a healthy critique of careless use of k-means clustering i.e. not paying attention to when its assumptions about the structure of the underlying data are inappropriate

It is the combination of searching using keywords, and the automatic clustering that seems to be the most useful, to me…so far. Another good feature is the ability to label clusters of interest with one or more tags

I have uploaded 69 blog postings from my Rick on the Road blog. If you want to see how Overview hierarchically clusters these documents let me know, I then will enter your email, which will then let Overview give you access. It seems, so far, that there is no simple way of sharing access (but I am inquiring).

Research on the use and influence of evaluations: The beginnings of a list

This is intended to be the start of an accumulating list of references on the subject of evaluation use. Particularly papers that review specific sets or examples of evaluations, rather than talk about the issues in a less grounded way

2016

2015

2014

2012

2009

2000

1997

1986

Related docs

  • Improving the use of monitoring & evaluation processes and findings. Conference Report, Centre for Development Innovation, Wageningen, June 2014  
    • “An existing framework of four areas of factors influencing use …:
      1. Quality factors, relating to the quality of the evaluation. These factors include the evaluation design, planning, approach, timing, dissemination and the quality and credibility of the evidence.
      2. Relational factors: personal and interpersonal; role and influence of evaluation unit; networks,communities of practice.
      3. Organisational factors: culture, structure and knowledge management
      4. External factors, that affect utilisation in ways beyond the influence of the primary stakeholders and the evaluation process.
  • Bibliography provided by ODI, in response to this post Jan 2015. Includes all ODI publications found using keyword “evaluation” – a bit too broad, but still useful
  • ITIG- Utilization of Evaluations- Bibliography. International Development  Evaluation Association. Produced circa 2011/12

Livelihoods Monitoring and Evaluation: A Rapid Desk Based Study

by Kath Pasteur, 2014, 24 pages. Found here: http://www.evidenceondemand.info/livelihoods-monitoring-and-evaluation-a-rapid-desk-based-study

Abstract: “This report is the outcome of a rapid desk study to identify and collate the current state of evidence and best practice for monitoring and evaluating programmes that aim to have a livelihoods impact. The study identifies tried and tested approaches and indicators that can be applied across a range of livelihoods programming. The main focus of the report is an annotated bibliography of literature sources relevant to the theme. The narrative report highlights key themes and examples from the literature relating to methods and indicators. This collection of resources is intended to form the starting point for a more thorough organisation and analysis of material for the final formation of a Topic Guide on Livelihoods Indicators. This report has been produced by Practical Action Consulting for Evidence on Demand with the assistance of the UK Department for International Development (DFID) contracted through the Climate, Environment, Infrastructure and Livelihoods Professional Evidence and Applied Knowledge Services (CEIL PEAKS) programme, jointly managed by HTSPE Limited and IMC Worldwide Limited”

Full reference: Pasteur, K. Livelihoods monitoring and evaluation: A rapid desk based study. Evidence on Demand, UK (2014) 24 pp. [DOI: http://dx.doi.org/10.12774/eod_hd.feb2014.pasteur]

Process tracing: A list

  • Understanding Process Tracing, David Collier, University of California, Berkeley. PS: Political Science and Politics 44, No.4 (2011):823-30. 7 pages.
    • Abstract: “Process tracing is a fundamental tool of qualitative analysis. This method is often invoked by scholars who carry out within-case analysis based on qualitative data, yet frequently it is neither adequately understood nor rigorously applied. This deficit motivates this article, which offers a new framework for carrying out process tracing. The reformulation integrates discussions of process tracing and causal-process observations, gives greater attention to description as a key contribution, and emphasizes the causal sequence in which process-tracing observations can be situated. In the current period of major innovation in quantitative tools for causal inference, this reformulation is part of a wider, parallel effort to achieve greater systematization of qualitative methods. A key point here is that these methods can add inferential leverage that is often lacking in quantitative analysis. This article is accompanied by online teaching exercises, focused on four examples from American politics, two from comparative politics, three from international relations, and one from public health/epidemiology”
      • Great explanation of the difference between straw-in-the-wind tests, hoop tests, smoking-gun tests and doubly-decisive tests, using Sherlock Holmes story “Silver Blaze”
  • Case selection techniques in Process-tracing and the implications of taking the study of causal mechanisms seriously, Derek Beach, Rasmus Brun, 2012, 33 pages
    • Abstract: “This paper develops guidelines for each of the three variants of Process-tracing (PT): explaining outcome PT, theory-testing, and theory-building PT. Case selection strategies are not relevant when we are engaging in explaining outcome PT due to the broader conceptualization of outcomes that is a product of the different understandings of case study research (and science itself) underlying this variant of PT. Here we simply select historically important cases because they are for instance the First World War, not a ‘case of’ failed deterrence or crisis decision-making. Within the two theorycentric variants of PT, typical case selection strategies are most applicable. A typical case is one that is a member of the set of X, Y and the relevant scope conditions for the mechanism. We put forward that pathway cases, where scores on other causes are controlled for, are less relevant when we take the study of mechanisms seriously in PT, given that we are focusing our attention on how a mechanism contributes to produce Y, not on the causal effects of an X upon values of Y. We also discuss the role that deviant cases play in theory-building PT, suggesting that PT cannot stand alone, but needs to be complemented with comparative analysis of the deviant case with typical cases”
  • Process-Tracing Methods: Foundations and Guidelines, Derek Beach, Rasmus Brun Pedersen,  The University of Michigan Press (15 Dec 2012), 248 pages.
    • Description: “Process-tracing in social science is a method for studying causal mechanisms linking causes with outcomes. This enables the researcher to make strong inferences about how a cause (or set of causes) contributes to producing an outcome. Derek Beach and Rasmus Brun Pedersen introduce a refined definition of process-tracing, differentiating it into three distinct variants and explaining the applications and limitations of each. The authors develop the underlying logic of process-tracing, including how one should understand causal mechanisms and how Bayesian logic enables strong within-case inferences. They provide instructions for identifying the variant of process-tracing most appropriate for the research question at hand and a set of guidelines for each stage of the research process.” View the Table of Contents here:
  • Mahoney, James. 2012. “Mahoney, J. (2012). The Logic of Process Tracing Tests in the Social Sciences.  1-28.” Sociological Methods & Research XX(X) (March): 1–28. doi:10.1177/0049124112437709.
    • Abstract: This article discusses process tracing as a methodology for testing hypotheses in the social sciences. With process tracing tests, the analyst combines preexisting generalizations with specific observations from within a single case to make causal inferences about that case. Process tracing tests can be used to help establish that (1) an initial event or process took place, (2) a subsequent outcome also occurred, and (3) the former was a cause of the latter. The article focuses on the logic of different process tracing tests, including hoop tests, smoking gun tests, and straw in the wind tests. New criteria for judging the strength of these tests are developed using ideas concerning the relative importance of necessary and sufficient conditions. Similarities and differences between process tracing and the deductive-nomological model of explanation are explored.
  • Goertz, Gary, and James Mahoney. 2012. A Tale of Two Cultures: Qualitative and Quantitative Research in the Social Sciences. Princeton University Press. See chapter 8 on causal mechanisms and process tracing, and the surrounding chapters 7 and 9 which make up a section on within-case analysis
  • Hutchings, Claire. ‘Process Tracing: Draft Protocol’. Oxfam, 2013. Plus an associated blog posting and an Effectiveness Review which made use of the protocol
  • Schneider, C.Q., Rohlfing, I., 2013. Combining QCA and Process Tracing in Set-Theoretic Multi-Method Research. Sociological Methods & Research 42, 559–597. doi:10.1177/0049124113481341
    • Abstract:  Set-theoretic methods and Qualitative Comparative Analysis (QCA) in particular are case-based methods. There are, however, only few guidelines on how to combine them with qualitative case studies. Contributing to the literature on multi-method research (MMR), we offer the first comprehensive elaboration of principles for the integration of QCA and case studies with a special focus on case selection. We show that QCA’s reliance on set-relational causation in terms of necessity and sufficiency has important consequences for the choice of cases. Using real world data for both crisp-set and fuzzy-set QCA, we show what typical and deviant cases are in QCA-based MMR. In addition, we demonstrate how to select cases for comparative case studies aiming to discern causal mechanisms and address the puzzles behind deviant cases. Finally, we detail the implications of modifying the set-theoretic cross-case model in the light of case-study evidence. Following the principles developed in this article should increase the inferential leverage of set-theoretic MMR.”
  • Rohlfing, Ingo. “Comparative Hypothesis Testing Via Process Tracing.” Sociological Methods & Research 43, no. 4 (November 1, 2014): 606–42. doi:10.1177/0049124113503142.
    • Abstract: Causal inference via process tracing has received increasing attention during recent years. A 2 × 2 typology of hypothesis tests takes a central place in this debate. A discussion of the typology demonstrates that its role for causal inference can be improved further in three respects. First, the aim of this article is to formulate case selection principles for each of the four tests. Second, in focusing on the dimension of uniqueness of the 2 × 2 typology, I show that it is important to distinguish between theoretical and empirical uniqueness when choosing cases and generating inferences via process tracing. Third, I demonstrate that the standard reading of the so-called doubly decisive test is misleading. It conflates unique implications of a hypothesis with contradictory implications between one hypothesis and another. In order to remedy the current ambiguity of the dimension of uniqueness, I propose an expanded typology of hypothesis tests that is constituted by three dimensions.
  • Bennett, A., Checkel, J. (Eds.), 2014Process Tracing: From Metaphor to Analytic Tool. Cambridge University Press
  • Befani, Barbara, and John Mayne. “Process Tracing and Contribution Analysis: A Combined Approach to Generative Causal Inference for Impact Evaluation.IDS Bulletin 45, no. 6 (2014): 17–36. doi:10.1111/1759-5436.12110.
  • Abstract: This article proposes a combination of a popular evaluation approach, contribution analysis (CA), with an emerging method for causal inference, process tracing (PT). Both are grounded in generative causality and take a probabilistic approach to the interpretation of evidence. The combined approach is tested on the evaluation of the contribution of a teaching programme to the improvement of school performance of girls, and is shown to be preferable to either CA or PT alone. The proposed procedure shows that established Bayesian principles and PT tests, based on both science and common sense, can be applied to assess the strength of qualitative and quali-quantitative observations and evidence, collected within an overarching CA framework; thus shifting the focus of impact evaluation from ‘assessing impact’ to ‘assessing confidence’ (about impact).

  • Punton, M., Welle, K., 2015. Straws-in-the-wind, Hoops and Smoking Guns: What can Process Tracing Offer to Impact Evaluation?
    • Abstract:  “This CDI Practice Paper by Melanie Punton and Katharina Welle explains the methodological and theoretical foundations of process tracing, and discusses its potential application in international development impact evaluations. It draws on two early applications of process tracing for assessing impact in international development interventions: Oxfam Great Britain (GB)’s contribution to advancing universal health care in Ghana, and the impact of the Hunger and Nutrition Commitment Index (HANCI) on policy change in Tanzania. In a companion to this paper, Practice Paper 10 Annex describes the main steps in applying process tracing and provides some examples of how these steps might be applied in practice.”
  • Weller, N., & Barnes, J. (2016). Pathway Analysis and the search for causal mechanisms. Sociological Methods & Research, 45(3), 424–457.
    • Abstract: The study of causal mechanisms interests scholars across the social sciences. Case studies can be a valuable tool in developing knowledge and hypotheses about how causal mechanisms function. The usefulness of case studies in the search for causal mechanisms depends on effective case selection, and there are few existing guidelines for selecting cases to study causal mechanisms. We outline a general approach for selecting cases for pathway analysis: a mode of qualitative research that is part of a mixed-method research agenda, which seeks to (1) understand the mechanisms or links underlying an association between some explanatory variable, X1, and an outcome, Y, in particular cases and (2) generate insights from these cases about mechanisms in the unstudied population of cases featuring the X1/Y relationship. The gist of our approach is that researchers should choose cases for comparison in light of two criteria. The first criterion is the expected relationship between X1/Y, which is the degree to which cases are expected to feature the relationship of interest
      between X1 and Y. The second criterion is variation in case characteristics or the extent to which the cases are likely to feature differences in characteristics that can facilitate hypothesis generation. We demonstrate how to apply our approach and compare it to a leading example of pathway analysis in the so-called resource curse literature, a prominent example of a correlation featuring a nonlinear relationship and multiple causal mechanisms.
  • Befani, Barbara, and Gavin Stedman-Bryce. “Process Tracing and Bayesian Updating for Impact Evaluation.” Evaluation, June 24, 2016, 1356389016654584. doi:10.1177/1356389016654584.
    • Abstract: Commissioners of impact evaluation often place great emphasis on assessing the contribution made by a particular intervention in achieving one or more outcomes, commonly referred to as a ‘contribution claim’. Current theory-based approaches fail to provide evaluators with guidance on how to collect data and assess how strongly or weakly such data support contribution claims. This article presents a rigorous quali-quantitative approach to establish the validity of contribution claims in impact evaluation, with explicit criteria to guide evaluators in data collection and in measuring confidence in their findings. Coined ‘Contribution Tracing’, the approach is inspired by the principles of Process Tracing and Bayesian Updating, and attempts to make these accessible, relevant and applicable by evaluators. The Contribution Tracing approach, aided by a symbolic ‘contribution trial’, adds value to impact evaluation theory-based approaches by: reducing confirmation bias; improving the conceptual clarity and precision of theories of change; providing more transparency and predictability to data-collection efforts; and ultimately increasing the internal validity and credibility of evaluation findings, namely of qualitative statements. The approach is demonstrated in the impact evaluation of the Universal Health Care campaign, an advocacy campaign aimed at influencing health policy in Ghana.

A review of evaluations of interventions related to violence against women and girls – using QCA and process tracing

In this posting I am drawing attention to a blog by Michaela Raab and Wolf Stuppert, which is exceptional (or at least unusual) in a number of respects.  The blog is called http://www.evawreview.de/

Firstly the blog is not just about the results of a review, but more importantly, about the review process, written as the review process proceeds. (I have not seen many of these kinds of blogs around, but if you know about any others please let me know)

Secondly the blog is about the use of of QCA and process tracing. There have been a number of articles about QCA in the journal Evaluation but generally speaking relatively few evaluators working with development projects know much about QCA or process tracing.

Thirdly, the blog is about the use of QCA and process tracing as a means of doing a review of findings of past evaluations of  interventions related to violence against women and girls. In other words it is another approach to undertaking a kind of systematic review, notably one which does not require throwing out 95% of the available studies because their contents don’t fit the methodology being used to do the systematic review.

Fourthly, it is about combining the use of QCA and process tracing, i.e. combining cross-case comparisons with within-case analyses. QCA can help identify causal configurations of conditions associated with specific outcomes. But once found these associations need to be examined in depth to ensure there are plausible causal mechanisms at work. That is where process tracing comes into play.

I have two hopes for the EVAWG Review blog. One is that it will provide a sufficiently transparent account of the use of QCA to enable new potential users to understand how it works, along with an appreciation of its potentials and difficulties. The other is that the dataset used in the QCA analysis will be made publicly available, ideally via the blog itself. One of the merits of QCA analyses, as published so far, is that the datasets are often published as part of the published articles, which means others can then re-analyse the same data, perhaps from a different perspective. For example, I would like to test the results of the QCA analyses by using another method for generating results which have a comparable structure (i.e. descriptions of one or more configurations of conditions associated with the presence and absence of expected outcomes). I have described this method elsewhere (Decision Tree algorithms, as used in data mining)

There are also some challenges that will face this use of QCA, which I would like to see how the blog’s authors will try to deal with. In RCTs there need to be both comparable interventions and comparable outcomes e.g. cash transfers provided to many people in some standardised manner, and a common measure of household poverty status. With QCA (and Decision Tree) analyses comparable outcomes are still needed, but not comparable interventions. These can be many and varied, as can be the wider context in which they are provided. The challenge with Raab and Stuppert’s work on VAWG is that there will be many and varied outcome measures as well and interventions. They will probably need to do multiple QCA analyses, focusing on sub-sets of evaluations within which there are one or more comparable outcomes. But by focusing in this way, they may end up with too few cases (evaluations) to produce plausible results, given the diversity of (possibly) causal conditions they will be exploring.

There is a much bigger challenge still. On re-reading the blog I realised this is not simply a kind of systematic review of the available evidence, using a different method. Instead it is a kind of meta-evaluation, where the focus is on comparison of the evaluation methods used in the population of evaluation they manage to amass. The problem of finding comparable outcomes is much bigger here. For example, on what basis will they rate or categorise evaluations as successful (e.g. valid and/or useful)? There seems to be a chicken and egg problem lurking here. Help!

PS1: I should add that this work is being funded by DFID, but the types of evaluations being reviewed is not limited to evaluations of DFID projects

PS2 2013 11 07 : I now see from the team’s latest blog posting the the common outcome of interest will be the usefullness of the evaluation. I would be interested to see how they assess usefullness , in some way that is reasonably reliable.

PS3 2014 01 07: I continue to be impressed by the team’s efforts to publicly document the progress of their work. Their Scoping Report is now available online, along with a blog commentary on progress to date (2013 01 06)

PS4 2014 03 27: The Inception Report is now available on the VAWG blog. It is well worth reading, especially the sections explaining the methodology and the evaluation team’s response to comments by the the Specialised Evaluation and Quality Assurance Service (SEQUAS, 4 March 2014) on pages 56-62, some of which are quite tough.

Some related/relevant reading:


AEA resources on Social Network Analysis and Evaluation

American Evaluation Association (AEA) Social Network  Analysis (SNA) Topical Interest Group (TIG) resources

AEA365 | A Tip-a-Day by and for Evaluators

A Bibliography on Evaluability Assessment

PS: This posting and bibliography was first published in November 2012, but has been updated since then, most recently in March 2018. The bibliography now contains 150 items.

An online (Zotero) bibliography was generated in November 2012 by Rick Davies, as part of the process of developing a “Synthesis of literature on evaluability assessments” contracted by the DFID Evaluation Department

[In 2012] There are currently 133 items in this bibliography, listed by year of publication, starting with the oldest first. They include books, journal articles, government and non-government agency documents and webpages, produced between 1979 and 2012. Of these 59% described actual examples of Evaluability Assessments, 13% reviewed experiences of multiple kinds of Evaluability Assessments, 28% were expositions on Evaluability Assessments, with some references to examples, 10% were official guidance documents on how to do Evaluability Assessments and 12% were Terms of Reference for Evaluability Assessments. Almost half (44%) of the documents were produced by international development agencies.

The list is a result of a search using Google Scholar and Google Search to find documents with “evaluability” in the title. The first 100 items in the search result listing were examined. Searches were also made via PubMed, JSTOR and Sciverse. A small number of documents were also identified as a result of a request posted on the MandE NEWS, Xceval and Theory Based Evaluation email lists.

This list is open to further editing and inclusions. Suggestions should be sent to rick.davies@gmail.com

 

M&E Software: A List

Well, the beginnings of a list…

PLEASE NOTE: No guarantee can be given about the accuracy of information provided on the linked websites about the M&E software concerned, and its providers. Please proceed with due caution when downloading any executable programs.

Contents on this page: Stand alone systemsOnline systems | Survey supporting software | Sector specific tools | Qualitative data analysis | Data mining / Predictive ModellingProgram Logic / Theory of Change modelingDynamic models | Excel-based tools | Uncategorised and misc other

If you have any advice or opinions on any of the applications below, please tell us more via this survey.

Stand-alone systems

  • AidProject M+E for Donor-funded aid projects
  • Flamingo and Monitoring Organiser: “In order to implement FLAMINGO, it is crucial to first define the inputs (or resources available), activities, outputs and outcomes”
  • HIV/AIDS  Data Capturing And Reporting Platform[Monitoring and Evaluation System]
  • PacPlan: “Results-Based Planning, Monitoring and Evaluation Software and Process Solution”
  • Prome Web: A project management, monitoring and evaluation software. Adapted for aid projects in developing countries
  • Sigmah: “humanitarian project management open source software”

Online systems

  • Activity Info: “an online humanitarian project monitoring tool, which helps humanitarian organizations to collect, manage, map and analyze indicators. ActivityInfo has been developed to simplify reporting and allow for real-time monitoring”
  • AKVO: “a paid-for platform that covers data collection, analysis, visualisation and reporting”
  • DevResults: “web-based project management tool specially designed for the international development community.” Including M&E, mapping, budgeting, checklists, forms, and collaboration facilities.
  • Granity: “Management and reporting software for Not-for-profits Making transparency easy”
  • IndiKit: Guidance on SMART indicators for relief and development programmes
  • Kashana: An open sourced, web-based Monitoring, Evaluation & Learning (MEL) product for development projects and organisations
  • Kinaki: “Kinaki is a unique and intuitive project design, data collection, analysis, reporting and sharing tool”
  • KI-PROJECTS™ MONITORING AND EVALUATION SOFTWARE:
  • Kobo Toolbox: “a free, more user-friendly way to deploy Open Data Kit surveys. It was developed with humanitarian purposes in mind, but could be used in various contexts (and not just for surveys). There is an Android data collection app that works offline”
  • Logalto:”Collaborative Web-Based Software for Monitoring and Evaluation of International Development Projects”
  • M&E Online: “Web-based monitoring and evaluation software tool”
  • Monitoring and Evaluation Online: Online Monitoring and Evaluation Software Tool
  • SmartME: “SmartME is a tried and tested comprehensive Fund Management and M&E software platform to manage funds better”
  • SocialWell: “SocialWellNet is a digital platform that empowers organizations deliver better social and public services. It also helps take better decisions, by automating data collection and analysis, using SocialWellNet web and mobile apps.”
  • Systmapp: “cloud-based software that uses a patent-pending methodology to connect monitoring, planning, and knowledge management for international development organisations”
  • TCS Aid360: “a web-based system enabling digitisation for the social development sector. It is a modular solution that supports Grant Management, Planning, Monitoring & Evaluation”
  • TolaData “is a program management and M&E platform that helps organisations create data-driven impact through the adaptive and timely management of projects”
  • WebMo: Web-based project monitoring for development cooperation

Survey supporting software

  • CommCare: a mobile data collection platform.
  • EthnoCorder is mobile multimedia survey software for your iPhone
  • HarvestYourData: iPad & Android Survey App for Mobile Offline Data Collection
  • KoBoToolbox is a suite of tools for field data collection for use in challenging environments. Free and open source
  • Magpi (formerly EpiSurvey)  – provides tools for mobile data collection, messaging and visualisation, lets anyone create an account, design forms, download them to phones, and start collecting data in minutes, for free.
  • Open Data Kit (ODK) is a free and open-source set of tools which help organizations author, field, and manage mobile data collection solution
  • REDCap,a secure web application for building and managing online surveys and databases… specifically geared to support online or offline data capture for research studies and operations
  • Sensemaker(c) “links micro-narratives with human sense-making to create advanced decision support, research and monitoring capability in both large and small organisations.”
  • Comparisons

Sector-specific tools

  • Mwater for WASH, which explicitly aims to make the data (in this case water quality). Free and open source
  • Adaptive Management Software for Conservation projects. https://www.miradi.org/

Qualitative data analysis

  • Dedooose, A cross-platform app for analyzing qualitative and mixed methods research with text, photos, audio, videos, spreadsheet data and more
  • Nvivo, powerful software for qualitative data analysis.
  • HyperRESEARCH “…gives you complete access and control, with keyword coding, mind-mapping tools, theory building and much more”.
  • Impact Mapper: “A new online software tool to track trends in stories and data related to social change”

Data mining / predictive modeling

  • RapidMiner Studio. Free and paid for versions. Data Access (Connect to any data source, any format, at any scale), Data Exploration (Quickly discover patterns or data quality issues). Data Blending (Create the optimal data set for predictive analysis), Data Cleansing (Expertly cleanse data for advanced algorithms), Modeling (Efficiently build and delivers better models faster), Validation (Confidently & accurately estimate model performance)
  • BigML. Free and paid for versions. Online service. “Machine learning made easy”
  • EvalC3: Tools for exploring and evaluating complex causal configurations, developed by Rick Davies (Editor of MandE NEWS). Free and available with Skype video support

Program Logic / Theory of Change modeling / Diagramming

  • Changeroo: “Changeroo assists organisations, programs and projects with a social mission to develop and manage high-quality Theories of Change”
  • Coggle:The clear way to share complex information
  • DAGitty: ” a browser-based environment for creating, editing, and analyzing causal models (also known as directed acyclic graphs or causal Bayesian networks)”
  • Decision Explorer: a  tool for managing “soft” issues – the qualitative information that surrounds complex or uncertain situations.
  • DCED’s Evidence Framework – more a way of using a website than software as such, but definitely an approach that is replicable by others.
  • DoView – Visual outcomes and results planning
  • Draw.io:
  • Dylomo: ” a free* web-based tool that you can use to build and present program logic models that you can interact with”
  • IdeaTree – Simultaneous Collaboration & Brainstorming Using Mind Maps
  • Kumu: a powerful data visualization platform that helps you organize complex information into interactive relationship maps.
  • Logframer 1.0 “a free project management application for projects based on the logical framework method”
  • LucidChart: Diagrams done right. Diagram and collaborate anytime on any device
  • Netway: a cyberinfrastructure designed to support collaboration on the development of program models and evaluation plans, provide connection to a virtual community of related programs, outcomes, measures and practitioners, and to provide quick access to resources on evaluation planning
  • Omnigraffle: for creating precise, beautiful graphics: website wireframes, electrical systems, family trees and maps of software classes
  • Theory maker: a free web app by Steve Powell for making any kind of causal diagram, i.e. a diagram which uses arrows to say what contributes to what.
  • TOCO – Theory of Change Online. A free version is available.
  • Visual Understanding Environment (VUE): open source ‘mind mapping’ freeware from Tufts Univ.
  • yEd – diagram editor that can be used to generate drawings of diagrams.  FREE. PS: There is now a web-based version of this excellent network drawing application

Dynamic models

  • CCTools: Map and steer complex systems, using Fuzzy Cognitive Maps and others [ This site is currently under reconstruction]
  • Loopy: A tool for thinking in systems
  • Mental Modeller: FCM modeling software that helps individuals and communities capture their knowledge in a standardized format that can be used for scenario analysis.
  • FCM Expert: Experimenting tools for Fuzzy Cognitive Maps
  • FCMapper: the first available FCM analysis tool based on MS Excel and FREE for non-commercial use.
  • FSDM: Fuzzy Systems Dynamics Model Implemented with a Graphical User Interface

Excel-based tools

  • EvalC3: …tools for developing, exploring and evaluating predictive models of expected outcomes, developed by Rick Davies (Editor of MandE NEWS). Free and available with Skype video support

Uncategorised yet

  • OpenRefine: Formerly called Google Refine is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.
  • Overview is an open-source tool originally designed to help journalists find stories in large numbers of documents, by automatically sorting them according to topic and providing a fast visualization and reading interface. It’s also used for qualitative research, social media conversation analysis, legal document review, digital humanities, and more. Overview does at least three things really well.
Other lists
Other other