How to select which hypotheses to test?

I have been reviewing an evaluation that has made use of QCA (Qualitative Comparative Analysis). An important part of the report is the section on findings, which lists a number of hypotheses that have been tested and the results of those tests. All of these are fairly complex, involving a configuration of different contexts and interventions, as you might expect in a QCA oriented evaluation.  There were three main hypotheses, which in the results section were dis-aggregated into six more specific hypotheses. The question for me, which has  much wider relevance, is how do you select hypotheses for testing, given limited time and resources available in any evaluation?

The evaluation team have developed three different data sets, each will 11 cases, and with 6, 6 and 9 attributes of these cases (shown in columns), known as "conditions" in QCA jargon. This means there are 2⁶ + 2⁶  + 2⁹ = 640  possible combinations of these conditions that could be associated with and cause the outcome of interest. Each of the hypotheses being explored by the evaluation team represents one of these configurations. In this type of situation, the task of choosing an appropriate hypotheses seems a little like looking for a needle in a haystack

It seems there are at least three options, which could be combined. The first is to review the literature and find what claims (supported by evidence) are made there about "what works" and select from these those that are worth testing e.g. one that seems to have wide practical use, and/or one that could have different and significant program design implications if it is right or wrong. This seems to be the approach that the evaluation team has taken, though I am not so sure to what extent they have used the programming implications as an associated filter.

The second approach is to look for constituencies of interest among the staff of the client who has contracted the evaluation.There have been consultations, but it is not clear what sort of constituencies each of the tested hypotheses have. There were some early intimations that some of the hypotheses that were selected are not very understandable. That is clearly an important issue, potentially limiting the usage of the evaluation findings.

The third approach is an inductive search, using QCA or other software, for configurations of conditions associated with an outcome that have both high level of consistency (i.e. they are always associated with the presence (or the absence ) of an outcome) and  coverage (i.e. they apply to a large proportion of the outcomes of interest). In their barest form these configurations can be be considered as hypotheses. I was surprised to find that this approach had not been used, or at least reported on, in the evaluation report I read. If it had been used but no potentially useful configurations found then this should have been reported (as a fact, not a fault).

Somewhat incidentally, I have been playing around with the design of an Excel worksheet and managed to build in a set of formula for automatically testing how well different configurations of conditions of particular interest (aka hypotheses) account for a set of outcomes of interest, for a given data set. The tests involve measures taken from QCA (consistency and coverage, as above) and from machine learning practice (known as a Confusion Matrix). This set-up provides an opportunity to do some quick filtering of a larger number of hypotheses than an evaluation team might initially be willing to consider (i.e. the 6 above). It would not be as efficient a search as the QCA algorithm, but it would however be a search that could be directed according to specific interest. Ideally this directed search process would identify configurations that are both necessary and sufficient (for more than a small minority of outcomes). A second best result would be those that are necessary but insufficient, or vice versa. (I will elaborate on these possibilities and their measurement in another blog posting)

The wider point to make here is that with the availability of a quick screening capacity the evaluation team, in its consultations with the client, should then be able to broaden the focus of useful discussions away from what are currently quite specific hypotheses,  and towards the contents of a menu of a limited number of conditions that can not only make up these hypotheses but also other alternative versions. It is the choice of these particular conditions that will really make the difference, to the scale and usability of the results of a QCA oriented evaluation. More optimistically, the search facility could even be made available online, for continued use by those interested in the evaluation results, and their possible variants

The Excel file for quick hypotheses testing is here: http://wp.me/afibj-1ux

In defense of the (careful) use of algorithms and the need for dialogue between tacit (expertise) and explicit (rules) forms of knowledge

This blog posting is a response to the following paper now available online

Greenhalgh, T., Howick, J., Maskrey, N., 2014. Evidence based medicine: a movement in crisis? BMJ 348, http://www.bmj.com/content/348/bmj.g3725

Background: Chris Roche passed this very interesting paper on to me, received via "Kate", who posted a comment on  Chris's posting on "What has cancer taught me about the links between medicine and development? which can be found on Duncan Green's "From Poverty to Power" blog. 

The paper is interesting in the first instance because both the debate and practice about evidence based policy and practice seems to be much further ahead in the field of medicine than it is in the field of development aid (...broad generalisation that this is...).

It is also of interest to reflect on the problems and solutions copied below and to think how many of these kinds of issues can also be seen in development aid programs.

 According to the paper, the problems with the current version of evidence based medicine include:

1. Distortion of the evidence based brand ("The first problem is that the evidence based “quality mark” has been misappropriated and distorted by vested interests. In particular, the drug and medical devices industries increasingly set the research agenda. They define what counts as disease ... They also decide which tests and treatments will  be compared in empirical studies and choose (often surrogate) outcome measures for establishing “efficacy.”
2. Too much evidence:  The second aspect of evidence based medicine’s crisis (and yet, ironically, also a measure of its success) is the sheer volume of evidence available. In particular, the number of clinical guidelines is now both unmanageable and unfathomable. One 2005 audit of a 24 hour medical take in an acute hospital, for example, included 18 patients with 44 diagnoses and identified 3679 pages of national guidelines (an estimated 122 hours ofreading) relevant to their immediate care"
3. Marginal gains and a shift from disease to risk: "Large trials designed to achieve marginal gains in a near saturated therapeutic field typically overestimate potential benefits (because trial samples are unrepresentative and, if the trial is overpowered, effects may be statistically but not clinically significant) and underestimate harms (because adverse events tend to be under detected or under reported)."
4. Overemphasis on following algorithmic rules: "Well intentioned efforts to automate use of evidence through computerised decision support systems, structured templates, and point of care prompts can crowd out the local,individualised, and patient initiated elements of the clinical consultation"
5. Poor fit for multi-morbidity. "Multi-morbidity (a single condition only in name) affects every person differently and seems to defy efforts to produce or apply objective scores, metrics, interventions, or guidelines"

The paper's proposed solutions or ways forward include:

1. Individualised for the patient: Real evidence based medicine has the care of individual patients as its top priority, asking, “what is the best course of action for this patient, in these circumstances, at this point in their illness or condition?” It consciously and reflexively refuses to let process (doing tests, prescribing medicines) dominate outcomes (the agreed goal of management in an individual case). 
2. Judgment not rules. Real evidence based medicine is not bound by rules.  
3. Aligned with professional, relationship based care.  Research evidence may still be key to making the right decision—but it does not determine that decision. Clinicians may provide information, but they are also trained to make ethical and technical judgments, and they hold a socially recognised role to care, comfort, and bear witness to suffering.
4. Public health dimension . Although we have focused on individual clinical care, there is also an important evidence base relating to population level interventions aimed at improving public health (such as pricing and labelling of consumables, fluoridation of water, and sex education). These are often complex, multifaceted programmes with important ethical and practical dimensions, but the same principles apply as in clinical care. 
5. Delivering real evidence based medicine. To deliver real evidence based medicine, the movement’s stakeholders must be proactive and persistent. Patients (for whose care the movement exists) must demand better evidence, better presented, better explained, and applied in a more personalised way with sensitivity to context and individual goals.
6. Training must be reoriented from rule following Critical appraisal skills—including basic numeracy, electronic database searching, and the ability systematically to ask questions of a research study—are prerequisites for competence in evidence based medicine. But clinicians need to be able to apply them to real case examples.
7. Evidence must be usable as well as robust. Another precondition for real evidence based medicine is that those who produce and summarise research evidence must attend more closely to the needs of those who might use it
8. Publishers must raise the bar. This raises an imperative for publishing standards. Just as journal editors shifted the expression of probability from potentially misleading P values to more meaningful confidence intervals by requiring them in publication standards, so they should now raise the bar for authors to improve the usability of evidence, and especially to require that research findings are presented in a way that informs individualised conversations.
9. ...and more

While many of these complaints and claims that make a lot of sense, I think there is also a risk"throwing the baby out with the bathwater" if care is not taken with some. I will focus on a couple of ideas that run through the paper.

The risk lies in seeing two alternative modes of practice as exclusive choices. One is rule based, focused on average affects when trying to meet common needs in populations and the other is expertise focused on the specific and often unique needs of individuals. Parallels could be drawn between different type of aid programs, e.g. centrally planned and nationally rolled out services meeting basic needs like water supply or education and much more person centered participatory rural development programs

Alternatively, one can see these two approaches as having complementary roles that can help and enrich each other. The authors describe one theory of learning which probably applies in many fields, including medicine: The first stage " ...beginning with the novice who learns the basic rules and applies them mechanically with no attention to context. The next two stages involve increasing depth of knowledge and sensitivity to context when applying rules. In the fourth and fifth stages, rule following gives way to expert judgments, characterised by rapid, intuitive reasoning informed by imagination, common sense, and judiciously selected research evidence and other rules"  During this process a lot of explicit knowledge become tacit, and almost automated, with conscious attention left for the more case specific features of a situation. It is an economic use of human cognitive powers. Michael Polanyi wrote about this process years ago (1966, The Tacit Dimension).

The other side of this process is when tacit knowledge gets converted into explicit knowledge. That's what some anthropologists and ethnographers do. They seek to get into the inner world of their subjects and to make it accessible to others. One practitioner whose work interests me in particular is Christina Gladwin, who wrote a book on Ethnographic Decision Trees in 1989. This was all about eliciting how people, like small farmers in west Africa, made decisions about what crops to plant. The result was a decision tree model, that summarised all the key choices farmers could make, and the final outcomes those different choices would lead to. This was not  a model of how they actually thought, but a model of how different combinations of choices were associated with different outcomes of interest. These decision trees are not so far removed from those used in medical practice today.

A new farmer coming into the same location could arguably make use of such a decision tree to decide what to crops to plant. Alternatively they could work with one of the farmers for a number of seasons, which then might cover all the eventualities in the decision tree, and learn from that direct experience. But this would take much more time. In this type of setting explicit rule based knowledge is an  easier and quicker means of transferring knowledge between people. Rule based knowledge that can be quickly and reliably communicated is also testable knowledge.  Following the same pattern of rules may or may not always lead to the expected outcome in another context.

And now a word about algorithms. An algorithm is a clearly defined sequence of steps that will lead to a desired end, sometimes involving some iteration until that end state gets closer. A sequence of choices in a decision tree is an algorithm. At each choice point the answer will dictate what choices to be made next. These are the rules mentioned in the paper above. There are also algorithms for constructing such algorithms. On this blog I have made a number of postings about QCA and (automated) Decision Tree models, both of which are means of constructing testable causal models. Both involve computerised processes for finding rules that best predict outcomes of interest. I think they have a lot of potential in the field of development aid.

But returning to the problems of evidence based medicine, it is very important to note that algorithms are means of achieving specific goals. Deciding which goals need to be pursued remains a very human choice. Even within the use of both QCA and (automated) Decision tree modeling users have to decide the extent to which they want to focus on finding rules that are very accurate or those which are less accurate but which apply to a wider range of circumstances (usually simple rather than complex rules).

So, in summary, in any move towards evidence based practice, we need to ensure that tacit and explicit forms of knowledge build upon each other rather than getting separated as different and competing forms of knowledge. And while we should develop, test and use good algorithms, we should remember they are always means to an end, and we remain responsible for choosing the ends we are trying to achieve.

Postscript 2015 05 04: Please also read this recent cautionary analysis of the use of algorithms for the purposes of public policy implementation. The author points out that algorithms can embody and perpetuate cultural biases. How is that possible? It is possible because all evidence-based algorithms are developed using historical data i.e. data sets of what has happened in the past. Those data sets, e.g. of arrest and conviction data in a given city reflect historical practice by human institutions in that city, with all their biases, conscious and not so conscious. They don't reflect ideal practice, simply the actual practice at the time. Where an algorithm is not based on analysis of historical data then it may have its origins in a more ethnographic study of the practice of human experts in the domain of interest. Their practice, and their interpretations of their practice, are also equally subject to cultural biases. The analysis by Virginia Eubanks include four useful suggestions to counter these risks, one of which is that "We need to learn more about how policy algorithms work" by demanding more transparency about the design of a given algorithm and its decisions. But this may not be possible, or in some cases publicly desirable. One alternative method of interest is the algorithmic audit.

A mistaken criticism of the value of binary data

When reviewing a recent evaluation report I came across the following comment:

"Crisp set QCA where binary codings are used to establish the presence or absence of certain conditions does not facilitate a nuanced or granular analysis."

(PS: Look here for the basics of QCA) 

Wrong. Simply wrong.

A DFID strategy for promoting "improved governance" could be coded  as present or absent. This does seem crude, given the varieties of ways in which a governance strategy could actually be implemented. But the answer is not to ditch binary coding, but to extend it.

This can be done by breaking down the concept of "a strategy for improved governance"  into a number of component parts or attributes, and then coding for their presence/absence. The initial conception of the governance strategy is then deemed present if all 10 attributes are present. But it only takes a single change in one attribute at a time to produce 9 new versions of almost the same strategy. If you change two attributes at a time, there are ( 1 think... 1-(10 x 10) =) 100 new versions. If any number of attributes can be changed then this means there are 2 to the power of 10 possible configurations of the strategy, some of which may be very different from the present strategy. Basically it does not take much tweaking of the initial configuration before you will have nuances by the bucketful!

The limitations of the dis-aggregation-into-components approach have nothing to do with the nature of binary coding, but rather whether there are enough cases available to allow identification of the kinds of outcomes associated with the different varieties of configurations arising from the more micro-level coding of attributes.

If there are enough cases available, then learning about what works through the emergence (or planned development) of variations in the initial configuration then becomes possible. Some of these new versions of a governance strategy may work more effectively than the initial model, and others less so. Incremental exploration becomes possible.

For more on the idea of exploring adjacent variations in causal configurations see Andreas Wagner's very interesting (2014) book titled "The Arrival of the Fittest" which explores a theory of how innovation is possible in biological systems. Here is a review of the book, in the Times Higher Education website.

There is also a connection here, I think, with Stuart Kauffman's concept of "the adjacent possible", an idea also taken up by Stephen Johnson in his book "Where do good ideas come from: The natural history of innovation" Here is a review of the book in the Guardian

Postscript 2015 05 14: I heard the same"binary is crude"  criticism again today from a person attending a QCA presentation at the UK Evaluation Society Conference in London.

This time I will present another response. Binary judgments can be and often are derived from a dichotomised scale that captures graduations of the phenomena of interest. As Carroll Patterson pointed out today, with current QCA software it is now possible to experiment with varying the location of the cut-off point on such scales, and observe the consequences for the quality of the configurations that are then identified as the best fitting solutions The same approach is also possible with searches for best-fitting configurations using an evolutionary algorithm, which is another approach I have been experimenting with recently. It is also possible to go much further into the specific details of the underlying concept being measured by a scale by basing it on the aggregated output of a weighted checklist, like the kind I have described elsewhere. Basically, the limit to what is possible is defined by the imagination of the researcher/evaluator, not any inherent limitation of binary measures.

Postscript  2015 05 17: I tried to post a Comment below in reply to Anon's comment below, but Blogger.com wont accept any HTML formatting, so I will place the comment here instead.

RE "If, to combat the reductiveness of binary coding, you introduce a scale of 4-6 points, you still face the same problem in coding something more complex – a remote non expert is reducing a complex context and process to a number in an arbitrary way. "

Coding for QCA (and other purposes, such as when using NVIVO) should always be done in a way that is transparent and replicable, with attention to inter-rate reliability. It should certainly not be done in an “arbitrary way”

RE "Grading a large, diverse and complicated country on a scale of 0-1 or 1-5 on 'improved governance' is just ridiculous. Anyone who has studied the way people actually behave, governance, how decisions are really made or projects succeed or fail, will tell you that this reductiveness does not helpfully or accurately reflect reality."

QCA has been used in a field of Political Science since the 1980s and many of these applications have been cross-country analyses of political systems.

RE QCA is not qualitative – as it seeks to reduce a complex qualitative issue to a quantitative score - a number.

In crisp-set QCA data set the “number” 0 or 1 is actually a category not a numerical value. QCA could be done just as well by replacing  the 0’s and 1’s with the words “absent” and “present” 

RE "QCA is not comparative – the serious comparative part comes afterwards in some form of qualitative analysis, which researchers can choose. Looking at the truth table for patterns is the only form of comparison that QCA offers."

There are two levels of analysis involved in QCA: within-case analysis and between-case analysis.  At the beginning within-case analysis informs the selection of conditions to be included in a data set. When inconsistencies are found in an examination of configurations in a data set good practice advises a return to within-case analysis to identify missing conditions that can resolve these inconsistencies.  When these have been resolved and set of configurations has been identified that accounts for all case in the most parsimonious way possible,  these then need to be interpreted by reference to   the details of specific cases, with particular attention to more detailed process that connect the conditions making up the configurations.

RE "In my view QCA is a quantitative form of data management and pattern identification."

It does depend on what you mean by quantitative. It is based on a form of mathematics known as set theory, but that is about logical relationships, not quantities. In case there is any reservation about its significance, pattern identification is very important. In a data set with 10 different conditions there are 2 to the 10 different possible combinations of these that might be consistently associated with an outcome of interest. Finding these is like looking for a needle in a haystack. QCA and other methods like decision tree algorithms, help us find what part of the haystack the needle is most likely to be found. But as I said at the end of my section of the UKES presentation, finding a plausible configuration is not enough. It is necessary but not sufficient  for a strong causal claim. There also needs to be a plausible account of the likely causal mechanisms at work that connect the conditions in the configurations. These will only be found and confirmed through detailed within-case investigations, using methods like (but not only) process tracing. And the pattern finding has to be systematic, and transparent in the way it has been done. This is the case with QCA and Decision Tree modeling, where there are specifics algorithms used, both with their known limitations

There is a useful reference that may be of interest: Wagemann, C., Schneider, C.Q., 2007. Standards of Good Practice in Qualitative Comparative Analysis (qca) and Fuzzy-Sets. http://www.compasss.org/wpseries/WagemannSchneider2007.pdf

Comparing QCA and Decision Tree models - an ongoing discussion

This blog is a continuation of a dialogue that is based on Michaela Raab and Wolfgang Stuppert's  EVAW blog. I would have preferred to post my response below via their blog's Comment facility, but it cant cope with long responses or hypertext links. They in turn have had difficulty posting comments on my YouTube site where this EES presentation (Triangulating the results of Qualitative Comparative Analyses (EES Dublin 2014)  can be seen. It was this presentation that prompted their response here on their blog.

Hi Michaela and Wolfgang

Thanks for going to the trouble of responding in detail to my EES presentation.

Before responding in detail I should point out to readers that the EES presentation was on the subject of triangulation, and how to compare QCA and Decision Tree models, when applied to the same data set. In my own view I think it is unlikely that either of these methods will produce the “best” results in all circumstances. The interesting challenge is to develop ways of thinking about how to compare and choose between specific models generated by these, and what may be other comparable methods of analysis. The penultimate slide (#17)  in the presentation highlights the options I think we can try out when faced with different kinds of differences between models.

The rest of this post responds to particular points that have been made by Michaela and Wolfgang, and then makes a more general conclusion.

Re  “1. The  decision tree analysis is not based on the same data set as our QCA” This is correct. I was in a bit of a quandary because while the original data set was fuzzy set (i.e. there intermediate values between 0 and 1) the solutions that were found were described in binary form i.e. the conditions and outcomes either were or were not present. I did produce a Decision Tree with the fuzzy set data but I had no easy means of comparing the results with the binary results of the QCA model. That said, Michaela and Wolfgang are right in expecting that such a model would be more complex and have more configurations.

Re “2. Decision tree analysis is compared with a type of QCA solution that is not meant to maximise parsimony.”  I agree that “If the purpose was to compare the parsimony of QCA results with those of decision trees, then the 'parsimonious' QCA solution should be used” But the intermediate solution was the solution that was available to me, and parsimony was not the only criteria of interest in my presentation. Accuracy (or consistency in QCA terms) was also of interest. But it was the difference in parsimony that stood out the most in this particular model comparison.

Re “3. The decision tree analysis performs less well than stated in the presentation” Here I think I disagree. The focus of the presentation is on consistency of those configurations that predict effective evaluations only (indicated in the tree diagram by squares with 0.0 value rather than 1.0 value ), not the whole model.  Among the three configurations that predict effective evaluations the consistency was 82%. Slide 15 may have confused the discussion because the figures there refer to coverage rather than consistency (I should have made this clear).

Re “none of the paths in our QCA is redundant”. The basis for my claim here was some simple color coding of each case according to which QCA configuration applied to them. Looking back at the Excel file it appears to me that cases 14 and 16 were covered by two configurations and cases 16 and 32 by another two configurations. BUT bear in mind this was done with the binary (crisp) data, not the fuzzy valued data. (The two configurations that did not seem to cover unique cases were  quanqca*sensit*parti_2  and qualqca*quanqca*sensit*compevi_3). The important point here is not that redundancy is “bad” but where it is found it can prompt us to think about how to investigate such cases if and when they arise (including when two different models provide alternate configurations for the same cases).

4. “The decision tree consistency measure is less rigorous than in QCA”       I am not sure that this matters in the case of the comparison at hand but it may matter when other comparisons are made. I say this because on the measures given on slide 13 the QCA model actually seems to perform better than the Decision Tree model. BUT again, a possibly confounding factor is the use of crisp versus fuzzy values behind the two measures. There is nevertheless a positive message here though, which is to look carefully into how the consistency measures are calculated for any two models being compared. On a wider note, there is an extensive array of performance measures for Decision Tree (aka classification) models that can be summarised in a structure known as a Confusion Matrix. Here is a good summary of these: http://www.saedsayad.com/model_evaluation_c.htm

Moving on, I am pleased that Michaela and Wolfgang have taken this extra step: “Intrigued by the idea of 'triangulating' QCA results with decision tree analysis, we have converted our QCA dataset into a binary format (as Rick did, see point 1 above) and conducted a csQCA with that data”. Their results show that the QCA model does better in three of four comparisons (twice on consistency levels and once on number of configurations). However, we differ in how to measure the performance of the Decision Tree model. Their count of configurations seem to involve double counting (4+4 for both types of outcome), whereas I count 3 and 2, reflecting a total of the 5 that exist in the tree. On this basis I see the Decision Tree model doing better on parsimony for both types of outcome but the QCA model doing better on consistency for both types of outcomes.

What would be really interesting to explore,  now that we have two more comparable models, is how much overlap there was in the contents of the configurations found by the two analyses, and the actual contents of those configurations i.e. the specific conditions involved. That is what will probably be of most interest to the donor (DFID) who funded the EVAW work. The findings could have operational consequences.

In addition to exploring the concrete differences between models based on the same data I think one other area that will be interesting to explore is how often the best levels of parsimony and accuracy can be found in one model versus one being available at the cost of the other in any given model. I suspect QCA may privilege consistency whereas Decision Tree algorithms might not do so. But this may simply reflect variations in analysis settings given for a particular analysis. This question has some wider relevance, since some parties might want to prioritise accuracy whereas others might want to prioritise parsimony. For example, a stock market investor could do well with a model that has 55% accuracy, whereas a surgeon might need 98%. Others might want to optimise both.

And a final word of thanks is appropriate, to Michaela and Wolfgang for making their data set publicly available for others to analyse. This is all too rare an event, but hopefully one that will become more common in the future, encouraged by donors and modeled by examples such as theirs.

Where is no common outcome measure...

The previous posting on this topic has now been removed but is still available as pdf  It was removed because I thought the solution it was exploring was too complex and would not really work very well, if at all!

Following some useful discussions with Comic Relief staff I have worked out a much simpler process, which I will describe below

The problem:

1. How do you make summary descriptive statements about the overall performance of a portfolio of activities, if there is no quantitative measure that can be applied to all projects in the portfolio? This kind of problem is likely to be present in projects with complex social development objectives e.g. those relating to accountability, empowerment, governance, etc.
2. How to identify the causal factors contributing to an outcome that seems to be unmeasurable because of its complexity? There are methods that can manage causal complexity, such as QCA and Decision Tree modelling which i have discussed elsewhere on this blog, but each of these are only practicable when there is some form of consistent coding of the type of outcomes that have occurred. 

The suggested approach to the outcome measurement problem: A multi-dimensional measure (MDM) for a given project = (The scale of achievement of the project specific outcomes) X (a weighting for the relative importance of that package of outcomes associated with a given project)

Project specific outcomes: Both DFID and DFAT (ex-AusAID) use a relatively simple annotated rating scale to assess the likely or actual achievement of a project’s objectives. By themselves these ratings can’t be sensibly aggregated, because the contents of the outcomes being achieved may be quite different. But this type of score can be used as an input to a larger calculation.

Where these rating systems are not in place a project specific rating can be generated through one of more types of pair comparison process. See Postscript 1 below.

Weightings: There are many different ways of developing weightings, some of which I have explored elsewhere. These weight individual aspects of performance then summarize these for each entity having those aspects. For example, the Basic Necessities Survey weights the importance of individual items households may posses, then sums the weights of all the items a household has into an aggregate score.

There is an alternate approach using a variant of the Hierarchical Card Sorting (HCS) process. This identifies clusters of performance attributes, then ranks them. Entities such as projects will have an outcome score that reflects their particular cluster of performance attributes.

• First stage: Participants are asked to sort projects in the portfolio of interest into two piles, to  “what they see as the most significant difference in the outcomes being sought by the projects, in the light of the overall objective of the portfolio, as they see it”. 

As with normal use of HCS, the same question is then re-iterated with each newly created group of projects to generate sub-groups of projects and then further sub-sub-groups.

The process stops when participants can no longer identify any significant differences, or when there is only one project left in any sub-group.

In facilitating this process care needs to be taken to ensure that participants do not start to report differences in the intervention, as distinct from outcomes. These are relevant to a causal analysis, but not to measurement of outcomes , which is the focus here.

The results from this first stage will be a nested classification in the form of a tree with various branches, each representing one or more projects pursuing a particular set of outcomes, as described by the multiple distinctions made at each point in the branch.

Here is an example of a hierarchical card sorting of projects funded in Bangladesh by an Australian NGO in the early 1990s [Caveat: It was developed way before the idea for this blog posting emerged, but it gives an idea of the type of tree structure that can be produced using a Hierarchical Card Sort. It is more focused on means rather than ends, so please bear this in mind.]

• Second stage: Participants are then asked to make choices at each branching point in the tree, starting from the base of the tree. They are asked to identify which type of outcome (represented by the two diverging branches) they think it is more important for the portfolio owner to be seeking to achieve. When this question is re-iterated down all branches of the tree this will enable a complete ranking of outcome configurations (branches) to be identified. 

Score construction: A simple table would then be generated in Excel where rows = projects and columns detailed (a) project specific ratings, (b) outcome weightings (i.e. the ranking of the branch that the project belonged to), (c) the product of the rating and weighting values.

Next: Now I need some real life examples, to show how this works in practice…and/or to discover the practical difficulties of using this approach. Any offers?


Postscript 1: Generating project ratings from pair comparisons. In my earlier version of this blog I explored the potential of a pair comparison method as a means of coming up with an overall ranking of project outcomes in a portfolio. The downside of this, as pointed out by Tom Thomas reflecting on PRA experiences, was that pair comparisons can be very time consuming and the time cost rises exponentially as the number of entities being compared increases.The number of pair comparaiosn = N to the power of N.

In the process of exploring this approach I ended up reading some of the literature on sorting algorithms. Processing cost (i.e. time taken to make comparisons of items) is one of the criteria that is used to assess the value of a sorting algorithm. Not surprisingly perhaps there is a huge variety of sorting algorithms. One which I have developed is described in this short Word file (NB: It was probably already developed by someone else many years ago!)

More recently still (April 2015), I have just finished reading Computational Fairy Tales by Jeremy Kubica, which I recommend to beginners in this area (such as me). In that book the author describes something called a QuickSort sorting algorithm, which sounds very useful for minimising the number of pair comparison needed to generate a complete ranking of a set of cases of interest. On average it works well, but in the worst case it can require N to the power of No comparisons. But this worst case wont apply when humans are doing the sorting because they can pick what are called "pivot" cases more purposively, whereas the computerised algorithm uses random choices. Good human choices of pivot cases with approximate median values should mean the sorting process is as quick as it can be with this type of algorithm.

The challenges of using QCA

This blog posting is a response to my reading of the Inception Report written by the team who are undertaking a review of evaluations of interventions relating to violence against women and girls. The process of the review is well documented in a dedicated blog – EVAW Review

The Inception Report is well worth reading, which is not something I say about many evaluation reports! One reason is to benefit from the amount of careful attention the authors have given to the nuts and bolts of the process. Another is to see the kind of intensive questioning the process has been subjected to by the external quality assurance agents and the considered responses by the evaluation team. I found that many of the questions that came to my mind while reading the main text of the report were dealt with when I read the annex containing the issues raised by SEQUAS and the team’s responses to them.

I will focus on one issue that is challenge for both QCA and data mining methods like Decision Trees (which I have discussed elsewhere on this blog). That is the ratio of conditions to cases. In QCA conditions are attributes of the cases under examination that are provisionally considered as possible parts of causal configurations that explain at least some of the outcomes. After an exhaustive search and selection process the team has ended up with a set of 39 evaluations they will use as cases in a QCA analysis. After a close reading of these and other sources they have come up with a list of 20 conditions that might contribute to 5 different outcomes. With 20 different conditions there are 2²⁰ (i.e. 1,048,576) different possible configurations that could explain some or all of the outcomes. But there are only 39 evaluations, which at best will represent only 0.004% of the possible configurations. In QCA the remaining 1,048,537 are known as “logical remainders”. Some of these can usually be used in a QCA analysis through a process using explicit assumptions e.g. about particular configurations plus outcomes which by definition would be impossible to occur in real life. However, from what I understand of QCA practice, logical remainders would not usually exceed 50% of all possible configurations.

The review team has dealt with this problem by summarising the 20 conditions and 5 outcomes into 5 conditions and one outcome. This means there are 2⁵ (i.e. 32) possible causal configurations, which is more reasonable considering there are 39 cases available to analyse. However there is a price to be paid for this solution, which is the increased level of abstraction/generality in the terms used to describe the conditions. This makes the task of coding the known cases more challenging and it will make the task of interpreting the results and then generalising from them more challenging as well. You can see the two versions of their model in the diagram below, taken from their report.

 

What fascinated me was the role of evaluation method in this model (see “Convincing methodology”). It is only one of five conditions that could explain some or all of the outcomes. It is quite possible therefore that all or some of the case outcomes could be explained without the use of this condition. This is quite radical, considering the centrality of evaluation methodology in much of the literature on evaluations. It may also be worrying to DFID in that one of their expectations of this review was it would “generate a robust understanding of the strengths, weaknesses and appropriateness of evaluation approaches and methods”. The other potential problem is that even if methodology is shown to be an important condition, its singular description does not provide any means to discriminating between forms which are more or less helpful.

The team seems to have responded to this problem by proposing additional QCA analyses, where there will be an additional condition that differentiates cases according to whether they used qualitative or quantitative methods.  However reviewers have still questioned whether this is sufficient. The team in return have commented that they will “add to the model further conditions that represent methodological choice after we have fully assessed the range of methodologies present in the set, to be able to differentiate between common methodological choices” It will be interesting to see how they go about doing this, while avoiding the problem of “insufficient diversity” of cases already mentioned above.

One possible way forward has been illustrated in a recent CIFOR Working Paper (Sehring et al, 2013) and which is also covered in Schneider and Wagemann (2012). They have illustrated how it is possible to do a “two-step QCA”, which differentiates between remote and proximate conditions. In the VAWG review this could take the form of an analysis of conditions other than methodology first, then a second analysis focusing on a number of methodology conditions. This process essentially reduces a larger number of remote conditions down to a smaller number of configurations that do make a difference to outcomes, which are then included in the second level of the analysis which uses the more proximate conditions. It has the effect of reducing the number of logical remainders. It will be interesting to see if this is the direction that the VAWG review team are heading.

PS 2014 03 30: I have found some further references to two-level QCA:

•  Mannewitz, Tom. ‘Two-Level Theories in QCA: A Discussion of Schneider and Wagemann’s Two-Step Approach’. Compasss Working Paper 2011 (2011). http://www.compasss.org/wpseries/Mannewitz2011.pdf.
•  Pattyn, Valérie. ‘Why Organizations (do Not) Evaluate: A Search for Necessary and Sufficient Conditions’. Comnpass Working Paper 2012 (2012). http://www.compasss.org/wpseries/Pattyn2012.pdf.  This is an example of two level analysis in use

 And for people wanting a good introduction to QCA, see

•  Legewie, Nicolas. ‘An Introduction to Applied Data Analysis with Qualitative Comparative Analysis’. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research 14, no. 3 (26 September 2013). http://www.qualitative-research.net/index.php/fqs/article/view/1961.