Enhancing Evaluation Use: Insights from Internal Evaluation Units

Marlène Läubli Loud , John Mayne

John Mayne’s summary (especially for MandE NEWS!)

“The idea for the book was that much written about evaluation in organizations is written by outsiders such as academics and consultants. But in practice, there are those working ‘inside’ an organization who play a key role in helping shape, develop, manage and ultimately make use of the evaluation. The contributions in this book are written by such ‘insiders’. They discuss the different strategies used over a period of time to make evaluation a part of the management of the organization, successes and failures, and the lessons learned. It highlights the commissioners and managers of evaluations, those who seek evaluations that can be used to improve the strategies and operations of the organization. The aim of the book is to help organizations become more focused on using evaluation to improve policies, strategies, programming and delivery of public and communal services.

The chapters cover a wide range of organizations, from government departments in Scotland, new Zealand, Switzerland and Canada, to international organizations such as the World health organization (WHO) and the International labour organization (ILO), to supra-national organizations such as the European Commission.

The book discusses such issues as:

  • The different ways evaluation is set up—institutionalized—in government sectors / organizations, and with what results;
  • why it is so hard to make evaluation a regular aspect of good management;
  • building organizational cultures that support effective evaluation;
  • strategies that are being used to ensure better value for money and enhance utilization of evaluation findings in organizations; and
  • how organizations balance the need for timely, relevant evaluation information with the need for scientific integrity and quality.

The insider perspective and the wide scope of organizations covered is unique in discussion about evaluation in organizations.”

Where there is no single Theory of Change: The uses of Decision Tree models

Eliciting tacit and multiple Theories of Change

Rick Davies, November 2012. Unpublished paper. Available as pdf version available hereand a 4 page summary version

This paper begins by identifying situations where a theory-of-change led approach to evaluation can be difficult, if not impossible. It then introduces the idea of systematic rather than ad hoc data mining and the types of data mining approaches that exist. The rest of the paper then focuses on one data mining method known as Decision Trees, also known as Classification Trees.  The merits of Decision Tree models are spelled out and then the processes of constructing Decision Trees are explained. These include the use of computerised algorithms and ethnographic methods, using expert inquiry and more participatory processes. The relationships of Decision Tree analyses to related methods are then explored, specifically Qualitative Comparative Analysis (QCA) and Network Analysis. The final section of the paper identifies potential applications of Decision Tree analyses, covering the elicitation of tacit and multiple Theories of Change, the analysis of project generated data and the meta-analysis of data from multiple evaluations. Readers are encouraged to explore these usages.

Included in the list of merits of Decision Tree models is the possibility of differentiating what are necessary and/or sufficient causal conditions and the extent to which a cause is a contributory cause (a la Mayne)

Comments on this paper are being sought. Please post them below or email Rick Davies at rick@mande.co.uk

Separate but related:

See also: An example application of Decision Tree (predictive) models (10th April 2013)

Postscript 2013 03 20: Probably the best book on Decision Tree algorithms is:

Rokach, Lior, and Oded Z. Maimon. Data Mining with Decision Trees: Theory and Applications. World Scientific, 2008. A pdf copy is available

Making causal claims

by John Mayne. ILAC Brief, October 2012 Available as pdf

“An ongoing challenge in evaluation is the need to make credible causal claims linking observed results to the actions of interventions. In the very common situation where the intervention is only one of a number of causal factors at play, the problem is compounded – no one factor ’caused’ the result. The intervention on its own is neither necessary nor sufficient to bring about the result. The Brief argues the need for a different perspective on causality. One can still speak of the intervention making a difference in the sense that the intervention was a necessary element of a package of causal factors that together were sufficient to bring about the results. It was a contributory cause. The Brief further argues that theories of change are models showing how an intervention operates as a contributory cause. Using theories of change, approaches such as contribution analysis can be used to demonstrate that the intervention made a difference – that it was a contributory cause – and to explain how and why.”

See also Making Causal Claims by John Mayne at IPDET 2012, Ottawa

RD Comments:

What I like in this paper: The definition of a contributory cause as something neither necessary or sufficient, but a necessary part of a package of causes that is sufficient for an outcome to occur

I also like the view that “theories of change are models of causal sufficiency”

But I query the usefullness of distinguishing between contributory causes that are triggering causes, sustaining causes and enabling causes, mainly on the grounds of the difficulty of reliably identifying them

I am more concerned with the introduction of probablistic statements about “likely” necessity and “likely” sufficiency, because it increases the ease with which claims of casual contribution can be made, perhaps way too much. Michael Patton  recently expressed a related anxiety: “There is a danger that as stakeholders learn about the non-linear dynamics of complex systems and come to value contribution analysis , they will be inclined to always find some kind of linkage between implemented activities and desired outcomes ….In essence, the concern is that treating contribution as the as the criterion (rather than direct attribution) is so weak that a finding of no contribution is extremely unlikely

John Mayne’s paper distinguishes between four approaches to demonstrating causality (adapted from Stern et al., 2012:16-17):

  • “Regularity frameworks that depend on the frequency of association between cause and effect – the basis for statistical approaches making causal claims
  • Counterfactual frameworks that depend on the difference between two otherwise identical cases – the basis for experimental and quasiexperimental approaches to making causal claims
  • Comparative frameworks that depend on combinations of causes that lead to an effect – the basis for ‘configurational’ approaches to making causal claims, such as qualitative comparative analysis
  • Generative frameworks that depend on identifying the causal links and mechanisms that explain effects – the basis for theory-based and realist approaches to making causal claims .”
I would simplify these into two broad categories, with sub-categories:
  • Claims can be made about the co-variance of events
    • Counterfactual approaches: describing the effects of the absence and presence of an intervention on an outcome of interest,  when all other conditions being kept the same
    • Configurational approaches, describing the effects of the presence and absence of multiple conditions (relating to both context and intervention)
    • Statistical approaches, describing the effects of more complex mixes of variables
  • Claims can be made about causal mechanisms underlying each co-variance that is found

Good causal claims contain both 1 and 2: evidence of co-variance and plausable or testable explanations of why each co-variance exists. One without the other is insufficient. You can start with theory (a proposed mechanism) and look for supporting co-variance, or start with a co-variance and look for a supporting mechanism. Currently theory led approaches are in vogue.

For more on causal mechanisms, see Causal Mechanisms in the Social Sciences by Peter Hedstrom and Petri Ylikoski
See also my blog posting on Representing different combinations of causal conditions, for emans of distingishing different configurations of necessary and sufficient conditions
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