This paper defends the need for evidential diversity and the mix of methods that that can in train require. The focus is on causal claims, especially ‘singular’ claims about the effects of causes in a specific setting—either what will happen or what has happened. I do so by offering a template that categorises kinds of evidence that can support these claims. The catalogue is generated by considering what needs to happen for a causal process to carry through from putative cause at the start to the targeted effect at the end. The usual call for mixed methods focusses on a single overall claim and argues that we increase certainty by the use of different methods with compensating strengths and weaknesses. My proposals instead focus on the evidence that supports the great many subsidiary claims that must hold if the overall one is to be true. As is typical for singular causal claims, the mix of methods that will generally be required to collect the kinds of evidence I urge will usually have little claim to the kind of rigour that is now widely demanded in evidencing causal claims, especially those for policy/treatment effectiveness. So I begin with an exploration of what seems to be intended by ‘rigour’ in such discussions, since it is seldom made clear just what makes the favoured methods especially rigorous. I then argue that the emphasis on rigour can be counterproductive. Rigour is often the enemy of evidential diversity, and evidential diversity—lots of it—can make for big improvements in the reliability of singular causal predictions and post hoc evaluations. I illustrate with the paragon of rigour for causal claims, randomised controlled trials (RCTs), rehearsing at some length what they can and cannot do to make it easier to assess the importance of rigour in warranting singular causal claims. I defend the need for evidential diversity by offering a template that categorises kinds of evidence needed for tracing a causal process from cause to effect.This paper defends the need for evidential diversity and the mix of methods that that can in train require. The focus is on causal claims, especially ‘singular’ claims about the effects of causes in a specific setting—either what will happen or what has happened there. Or, to put this more carefully since I will be dealing with multivalued variables: I focus on ‘what a cause As with most methodologies for singular causal hypotheses, the mix of methods that it would take to do what I urge has little claim to the kind of rigour that is now widely demanded in evidencing causal hypotheses, especially those for policy/treatment effectiveness. This can seem like a damming charge against this and almost any methodologies using mixed methods. But I argue that it will not seem so damming if we look seriously at what rigour might amount to for methods that are praised for being most rigorous in establishing causal claims. This is especially so considering what conclusions can be rigorously established by these methods if successfully implemented and how many things often not mentioned can get in the way of successful implementation.I aim for this paper to make a number of interconnected contributions.catalogue of different kinds of information that can serve as evidence for singular causal claims.First, I provide a defence that these are evidence. The catalogue is based in a widely acceptable account of what it takes for a casual process to carry through from the intervention at the start to the targeted effect at the end. Everything I say to back up their claim to be evidence is straightforward. Each different kind of evidence has an advocate in some current school of thought about causality, each fits with common sense, each has solid philosophical credentials and each is readily acknowledged by researchers across the natural and social sciences. Yet this catalogue is different from the currently dominant ways of approaching the issue and, once all the different types are put together, is richer than the offerings of other minority approaches.Second I provide a RCTs are rigorous and scientific: ‘This rhetoric is rarely if ever justified. The adjectives  are used as code words for RCTs. Frequently so.’  that randomised controlled trials (RCTs) are the most rigorous way to support causal conclusions. But I have failed to find anything like a characterisation of what is meant by ‘rigour’, nor, in train, a defence of why what might be meant deserves the honorific ‘rigour’, nor a proper argument that RCTs indeed meet the criteria supposed in the characterisation. As Angus Deaton, who won the 2015 Nobel prize in economics for his analysis of consumption, poverty, and welfare, remarks under the heading  Deaton, , p 8. HeAn anonymous referee suggests dropping this analysis on the grounds that it is already well-known, including from my own paper with Angus Deaton The call for rigour in science is widespread. The social sciences are no exception. Consider education for example. The US National Research Council’s, rigorous testing.2Scientific explanations are accepted as reliable only when they have been subjected to This view is not peculiar to the US. Here, for example, is what the Ministry of Education in New Zealand has to say:Rigour has substantial charm as a road to securing objectivity and making results more certain. But it has a substantial drawback: you can’t do much with it. The kinds of results that can be secured rigorously cannot support much on their own. To build secure support for the kinds of outcomes that matter both in practice and in principle, rigorous designs must be woven together with a vast amount of other scientific work that uses a panoply of different methods—‘a tangle of science’.RCTs) as my exemplar of a method for warranting causal hypotheses that is widely praised for its rigour. Ever since the ‘credibility revolution’ in economics and the spreading influence of JPALI am going to illustrate with the case of randomized controlled trials  and all the evidence that provides credible estimates of those effects. The critical feature of the research methods in this regard is the ability of the basic design to yield an unbiased estimate of the effects on the target outcomes relative to a defined counterfactual condition, that is, the internal validity of the research design... With rare exceptions, the best evidence by this standard is provided by randomized controlled trials (RCTs). :ATE) in the population studied. Causal claims of interest are seldom about the populations enrolled in a study but about other populations—or individual units—elsewhere. RCTs can be part of an evidence base for those, especially if you have a lot of information about the relations between the causal principles at work in the new population and the arrangement of causal factors that obtains there compared with those of the study population. That already creates a need for a good mix of methods. And at that you will still probably only be able to estimate some kind of average whereas you are frequently interested in causal predictions about a single unit—this client, this pupil, this village, this school, this state.What an RCT by itself can give you is, as the Campbell quote notes, an unbiased estimate. What can thus be estimated is the average treatment effect (pToC) for that population or that unit.I propose a different, more direct way of warranting a causal hypothesis about a specific population or a specific unit: by building a special kind of theory of change—a causal-process-tracing theory of change (ToCs) are by now standard fare for predicting, implementing and evaluating the effects of policy interventions. They map the steps by which the cause is supposed to produce the effect. The special causal-process-tracing versions of these that I advocate contain much more information than standard ToCs and are thereby much more useful.Theories of change  association of C and E. If there were no such bias, you could infer causation. Well, you could—supposing a lot of metaphysics, like every event has a causal explanation, that for a given event in a given population there is a given set of features that can cause it, and that there is a probability measure over the features in what is called the ‘potentuial outcome equation’ (POE) for the population studied.First you need the context. Suppose you want to know about whether a specific factor C causes a targeted outcome E in members of a given population P and you are in the happy situation of knowing that in P the expectation of E is greater with C than without C (Exp(E/C) > Exp(E/-C)). Still, we know that goes little way to ensuring a causal connection because of the possibility of statistical bias , that is, put loosely, C might be ‘correlated with’ other causal factors for E—the dependence of E on C might be statistically biased—and potential outcomes equation and ends with the conclusion that Exp(E/C)-Exp(E/-C) (in an RCT this will be the expectation of E in the test arm minus the expectation of E in the control arm) is an unbiased estimate of the average treatment effect of C on E. I’ll sketch this briefly. For much more discussion see Deaton and Cartwright  and the net effect of all interactive/moderator variables.Suppose (i) the generation of y in the units {uindividual treatment effect for unit u is the size of the outcome y(u) when the cause is present minus when it is absent, all other causes held constant. From the proof you see that the average treatment effect (ATE) in P, which is the expectation of the individual treatment effects, is Exp(β), i.e. the ATE for population P = Exp(β).Note, as just above, that the Suppose (ii) orthogonality of the treatment with the net effect of the other causal factors, defined thus:Conditioning on x = 1 and x = 0, taking expectations across the equation and subtracting yieldsthe difference in observed mean outcomes is an unbiased estimate of the study population ATE. If the average of individual effects of C on E across P is positive then C must have a positive effect on E in at least some individual units in P.Thus we see that individual treatment effect for u on y. A POE goes beyond this in distinguishing among the other causal factors for y those that are interactive with x in the production of y—i.e., factors that affect whether or how much x contributes to y for u—represented by β, and those that are linear, i.e. x contributes the same to y for u independent of the values they take for u, represented all told as w.For those readers who are more familiar with the intervention/manipulation or counterfactual approaches to singular causation than with a POE approach I should point out that these all make essentially the same basic assumptions. All three suppose that in most individual cases there will be more than one factor that can causally influence an effect y. All count x as a cause of y for a given individual u if were x to take a different value for u, y would too, supposing all other factors causally relevant to the production of y for u stay fixed except the downstream effects of the change in the value of x. This is the question of whether x has an POEs also suppose that for each case there is a given magnitude for how much x contributes to y, which is a function in part of the magnitude of its interactive variables:So all three approaches are in essential agreement in identifying a positive individual treatment effect of x on y for u as the arbiter of whether ‘x causes y in u’ holds.7premises of the proof. Any method can be backed by a valid argument if we are allowed to help ourselves to the right premises for it. But not any method is deemed rigorous. So there must be some requirement on premises.So now, what is so special about RCTs that would make the laudatory but undefined label ‘most rigorous’ appropriate? Three things stand out. First, RCTs estimate an average across quantities that we cannot individually measure, which to me seems astounding, though it may not have all that much to do with rigour. The next do seem the kinds of thing we can expect of a process labelled ‘rigorous’. First is that there is a valid proof that shows why the observed results support a causal conclusion and just what form that causal conclusion takes. The immediate causal conclusion is of the form ‘The ATE for the study population is φ’, and the observed results (the difference between the observed outcome mean in the treatment wing minus that in the control wing) are an unbiased estimate of that. The other is this matter of RCTs being claimed to be the only thing that controls for statistical bias. That has to do with the credible. Consider comments from Joshua Angrist and Jörn-Steffen Pishke In this they are following on from the work of Edward Leamer  in the deductive argument. RCTs are supposed to achieve the required orthogonality in the population P by random assignment of C versus -C to members of P. Great! Except that that only secures orthogonality at the time of assignment. Much can happen after that to confound results. RCTs do have devices to ease that problem—‘blinding’ wherever possible, so that the units in the population, those administering the randomization process, those administering C, those measuring whether E occurs, those doing the statistical analysis, none of these know which units are Cs and which are not.Great again. But that only eliminates statistical bias due to bias in the sense of prejudice, implicitly or explicitly behaving in ways that affect outcomes by virtue of knowledge of assignment. A lot of time passes between C’s occurrence and E’s, and much can happen in the meantime that affects those units who receive C differently from those who do not that has nothing to do with bias of that kind. Just what these factors might be depends on the specific nature of C and E and of the population P and its setting during the period before and from C to E. What makes it credible in the sense demanded that all these have been guarded against? Further information is needed and what methods can secure that will depend on just what we need to know and again on the nature of C, E, P and the setting. There’s no way to avoid diversity of methods if the premises required for the RCT are to be credible, and the methods are seldom themselves up to securing credibility in the required sense.That’s all about premise (ii). Now look at premise (i). There’s a lot of metaphysics piled into that. There is first an assumption like that of Donald Davidson  that eveNow, as to rigour, I don’t see how any of these assumptions can be made credible by methods meeting the requirements identified for rigour. I do not mean to imply by this that we seldom/never have good enough warrant for them. There are many specific cases where we can mount very good arguments warranting the assumptions for that case. But there are no such good cases that I have seen constructed from results or collections of results supplied by rigorous methods alone. Rather they all build on a great diversity of methods, both to establish different facets of the same assumption (as in the pToC discussion below) and to approach the same assumption in a variety of ways relying on a variety of different assumptions themselves.The lesson that I take to follow from these considerations about premises (i) and (ii) is that RCTs—supposed to be a paragon of rigour—do not after all very often establish the very results they are best at with rigour.So far what I have been discussing is about what is called ‘internal validity’—about how well the method properly applied secures the targeted conclusion. The notion of rigour I’ve adumbrated implies that rigorous methods will be high in internal validity. In the case of RCTs, granting all the metaphysics in premise i), the lynchpin with respect to rigour and internal validity is orthogonality, which cannot always be banked on even with sound randomisation procedures and successful blinding at all relevant stages. That is half my worry about them. The other half is the familiar worry that goes under the misleading title ‘external validity’.Suppose you want these conclusions about other population or about my focus—what happens in a single case—to be drawn rigorously, in a sense similar to that in which RCTs are rigorous. For that you would need a good argument showing why  the observable results from the RCT coupled with other presumably well-established information you use are evidence of the conclusion drawn. That, as always, is easy if there’s no constraints on the premises. If we are willing to assume that a target population in Birmingham England is sufficiently like a study population in Birmingham Alabama in just the right ways,Lessons. I propose that what is meant by a rigorous methodI have looked at one method highly touted for its rigour.There is no reason to think that the RCT is especially open to problems in these regards. It is after all supposed to be the gold standard for one important type of conclusion, and despite all the explicit acknowledgement of the ‘problem of external validity’ it is widely held in development economics and throughout the evidence-based policy movement to be THE method to use to warrant causal claims even if they are about populations other than that enrolled in the study or about individual units. I suggest that what is true of the RCT is true of any single method once you unpack the details of the defence of it: few applications of any one method will meet the standards assumed for rigour and those that do often don’t establish what we want to know.How does this support diversity of methods? If as RCT advocates suggest all the other methods are even less good at securing results  why turn to them?Part of the answer is that this supposition is not entirely right. Many of these other methods can deliver results as rigorously as the RCT in the right epistemic circumstances. That’s because the trick is not in getting a valid argument to back the method but rather in finding premises that are credible in the case at hand. Consider a well-rehearsed example. We may stratify in an observational study to control for confounders. That could provide orthogonality, in which case the same proof as the one I cited shows that the observational study can estimate the ATE for the population in the study. But how secure is the orthogonality assumption? What about those unknown unknowns? In some cases we may have very good reasons arguing that unknown confounders are not responsible for the observed correlations. In others, somewhat good reason. In others, little at all. Just as with an RCT we may often have very good reasons arguing that assignment was genuinely by a random procedure that was not undermined and there was no serious confounding post random assignment. In others, somewhat good reason. In others, little at all. It all depends on what can be called on as already well-established (or that we are in a position to get established in time) that can play the role as premise for this or that method. And that is a highly local matter. Case by case we will have different bits of deemed-secure knowledge that we can press into service as premises for different methods. So better to choose our methods to fit our local knowledge than to despair because we do not have the right knowledge for our most beloved method.The second part of the answer is that, as I stress in the next section, when we bet on a singular causal claim being true, we are betting on a lot of facts happening. That is generally the case for whatever kind of empirical conclusion we want to draw. Surely it is better to have some evidence about whether these facts will occur or not rather than none at all, even if that evidence cannot be rigorously obtained and all told does not rigorously establish the targeted conclusion.The third part is that we often must make decisions about what to do for individual units—should we introduce mobile phone nutrition technology in this local Indonesian village clinic, shall we install alley gates to reduce crime in Headington Quarry, shall we prescribe timolol drops for this glaucoma patient? These are both singular and predictive. So we cannot have RCT evidence directly about them, and what it takes to show the evidential relevance of any RCT results we have from elsewhere will often be substantially lacking in credibility. I discuss this a little more fully just below.Together these three reasons argue that we should have a panoply of methods in our tool bag, not just one golden hammer.Moving on. There is more to be said in favour of diverse methods in the case of causal claims than just the observation that generally it’s better to have a good choice of tools for problem solving rather than trying to adjust your needs to fit the one tool you prefer, which anyway seems obvious on the face of it. One of the reasons I first got so exercised about RCTs and their endorsement for evidence-based policy is that the choice of an RCT to learn about averages in a study population as the instrument to use to make predictions about a different population or about an effect on an individual unit seems perverse. Really, the best way to warrant causal claims in one population is to establish them in another?Crime Reduction Toolkit since it devotes a section to ‘In which contexts does it work best?’Toolkit itself remarks: ‘Within the review, a variety of contextual factors, which could impact upon the effectiveness of alley gates were discussed… The absence of data in the primary studies meant that these could not be tested.’Doing that would make sense if the kinds of claims at stake could plausibly be taken to be universal. We can measure the charge on a handful of electrons and to the extent we can take our results on those to be accurate, we’ve learned about them all. But that hardly seems true for the effects of installing alley gates on crime, especially burglaries, as, for instance the UK’s What Works Centre on Crime (The College of Policing) makes clear in its Toolkit’s overall evaluation that the evidence for positive impact is reasonably strong. They tell us this for instance: ‘How strong is the evidence? The review was sufficiently systematic that most forms of bias that could influence the study conclusions can be ruled out.’there then to find out about here now or in the future. In the next section I sketch a more direct way to tackle this problem that I’ve recently (with co-authors) worked out in detail   coupled with Mackie’s  movementMackie’s  INUS accMackie’s  argumentMackie’s  account Mackie’s  on tendeMackie’s  discussiPerhaps it is worth clarifying before we start that ‘singular’ is opposed here to ‘universal’, ‘fairly general’, ‘widespread under X conditions’, etc. The claims I have in view are ex ante predictions or post hoc evaluations about a given population or a given individual  at a given time and place. This does not imply that the effect in question need be what we might think of as individual. It may for instance be about the average improvement in attainment in 12- year-olds in a school district, which will of course involve a number of pupils.individual unit, both ex ante and post hoc.They can provide good evidence about effects on an causal predictions about a specific population in a specific setting not yet studied without having to rely on claims about how this population in this setting and its causal structure compare with already-studied populations elsewhere.They can provide good evidence for Frederica Russo and Jon Williamson have famously argued that group-comparison statistical ‘difference-making’ evidence of the kind RCTs provide is seldom enough alone to secure causal conclusions. It should generally be combined with mechanistic evidence, where ‘mechanism’ is usually taken to refer to the causal process connecting cause and effect. To the extent that their arguments apply, then pToCs have much to offer even for post hoc evaluation where RCTs are available since they provide a catalogue of evidence types for warranting causal-process/ mechanistic claims.23It will be apparent that the conglomerate of methods that will need to be employed to vouchsafe the diverse kinds of evidence represented in a pToC cannot sensibly be argued to rigorously establish an overall causal claim with anything like the degree of rigour that a good RCT can provide for estimating a study population ATE (in the sense of ‘rigour’ adopted here). But there are three special things they can do:Figure The significant steps by which the programme is to produce the outcome.The casual principles by which each step is to lead to the next. The principles are not actually pictured in the figure but are meant to be listed alongside. They should be a big help in figuring out the next three ingredients.Support (interactive) factors. This supposes, following Mackie, that the causes that are highlighted in typical causal principles are seldom if ever enough on their own to produce the indicated effect. Rather to do so, they need to operate in tandem with other factors—support factors. In Fig. Derailers. These are anything that can intervene and stop or diminish a process once it is in train. These are represented by jaggedy ovals.Safeguards. Safeguards can be thought of as ‘walls’ that prevent derailers from intruding. When safeguards cannot be implemented against every likely derailer, we should be wary of predicting programme success.Like the defence of the RCT, the structure suggested for a pToC makes both the Davidson-like assumption that, at least where prediction and warranted retrodiction are possible, causes do not produce their effects willy-nilly but do so in accord with principles that hold more widely than the single case (though those principles may not describe the factors in the same way that we do) and the Mackie-like assumption that causes are INUS conditions.The causal principles that are supposed to work at each step and that they can be called into play and at the right timeAt each step the support factors will take appropriate values to allow that step to produce the nextAt each step derailers will be absent or guarded against.The pToC for how the programme evolves provides a framework for categorizing the kinds of evidence that, all told, can make effectiveness predictions reliable. This includes evidence forThere is no rigorous way to establish these separate kinds of evidence nor to assess what they amount to all told. The kinds of information that are helpful are varied, so too are the methods that can provide evidence for them. What is clear is that, if you want to avail yourself of this additional evidence—as you should—you will have to employ a great variety of different methods and a good deal of judgment. This kind of pToC, well done and well-supported, can yield reliable predictions about programme success. It can do so even though none of the pieces, nor their overall impact, are rigorously supported. Conversely, I have never seen any way to do the same job rigorously.Let’s look at the first couple steps in this pToC to illustrate. Here is the key for them:Principle 1,1’-2: Health workers tend do what they can in their clients’ best interest.Community health workers have the capacity to use mHealth.Community health workers agree that using mHealth is good for their clientsMothers and children attend the community health clinic on a regular basisDerailers:I’a. External pressure to not perform the task or other priorities prevailSupport factors:Principle 2–3: mHealth technology does accurate calculations of growth status2a. mHealth technology is well designed for the job2b. Community health workers input the correct data in the correct formatDerailers:IIa. Technology fails to operateSupport factors:The two causal principles invoked here are typical for this kind of enterprise. The causal principles that programme design and prediction rely on are generally ‘middle or low level’—neither high theory meant to obtain everywhere nor so narrow as to hold almost only for the case at hand, and they often come in the form of generics, without quantifiers or explicit range of application. They are what JS Mill  labels ‘25The two are quite different with respect to the kinds of methods needed for warranting them. The first, that perceiving an action to be in their client’s best interest disposes health workers to do that action, is common knowledge. The issue is not establishing it but rather establishing that it will be called into play in this case and then not be overwhelmed by competing dispositions. This places the bulk of work on warranting the support factors and derailers that are identified and evidencing their presence or absence. This is a good reminder that for making and warranting singular causal claims like these effectiveness predictions not all the methods to be used are taught in science classrooms or methodology courses. ‘Call on common knowledge’ is an important tool in our methodology tool bag.The second principle employed does take warranting. It says that inputting a child’s weight and other information into the phone app will produce an accurate judgment about the child’s growth status. The methods to warrant this include a great deal of theorical derivation and use of local knowledge about what the right answer is and about the mechanism by which the app does the calculation as well as trials under various conditions that it works properly, and more. Not all these methods need be employed in the immediate defense of the expectation that the causal process will carry through start-to-finish. Sometimes it can reasonably be taken on faith that they have been employed, and employed properly, by someone else somewhere else. Still that should not blind us to the recognition that they (or a good substitute for them) are all necessary if the prediction is to be genuinely warranted. Some are of immediate importance though. It would generally be foolish to send phones that have been shipped from abroad out to community health centres without some method that checks that they work properly.Beyond the principles, a host of other things pictured in the pToC need to be considered—all the support factors, derailers and safeguards. Why can you assume that the community health workers are able to use the apps? Perhaps because of background knowledge that they are accredited nurses and also use equipment like this all the time and that they have the time to use the phones properly. But in Indonesia that was not the case. The community health workers were for the most part untrained volunteers. Perhaps then because there are records that they have participated in a training course or from questionnaires or from test results. And so forth.could operate in that setting, and (b) the support factors and derailers at each step are appropriate to that causal principle. The pToC then shows you what facts should be evidenced as part of a large body of evidence about the overall cause/effect claim. You then want whatever evidence is possible, gathered by whatever methods are appropriate to it, that each and every support factor and requisite safeguard is (or was) likely to be in place and each and every derailer that is not safeguarded against is (or was) likely to be absent. That’s a lot of evidence to gather. But these are after all the facts that have to obtain if the cause is to lead to the effect in the way pictured, and to the extent that any have no evidence in their favor, to that extent our case in favour of the cause/effect claim is weakened. Would that it were easier. But there are few singular causal claims that can be supported in any less demanding way.26So, to evidence a singular causal claim I propose that you first construct a plausible pToc for the targeted cause/effect pair in the setting under consideration. The pToc should contain the 5 types of information I listed above. It is plausible when (a) each step is connected to the previous by a casual principle for which there are reasons in support of the assumption that that principle I advocate construction of the best pToC possible to use as a guide for figuring what facts count as evidence for or against the overall prediction that C causes/will cause E in S. But there are several cautions to note about its use.a. How do we come up with support factors and derailers for the pToC? The causal principles suggest what’s needed. That’s why it is important to have principles that we have good reason to suppose apply and can be triggered in the setting and to express these as concretely (‘low level’) as possible. In the Indonesian setting there was good reason to suppose that community health workers were positively disposed to act in the children's interests and also that World Vision Indonesia and MOTECH had done a proper job in vetting the phone app technology. Supposing we are relatively confident that the causal principles can operate in the setting, it then takes a great deal of local knowledge to figure out what supports and derailers would be there. 1b.—that the health workers believe that using the technology is in the children’s best interests—seems an obvious necessity. But 1.a. and 1.c., which are in a sense equally obvious, might not have been thought of by programme designers accustomed to trained staff and parents who don’t face severe difficulties  in getting children to clinics.show why a given fact is evidence for a given hypothesis is that facts don’t come labelled as evidence. Assumptions are always needed to turn them into evidence—like the assumptions in premises (i) and (ii) in the RCT argument that turn facts about observed mean outcomes in the treatment and control groups into evidence for the size of the study population ATE. But supposing that a given pToC is correct can seem a very big assumption.The presence of the intermediate steps and support factors and the absence of the derailers represented in the pToC counts as evidence only if these genuinely are necessary intermediates, supports and derailers. That is, these are evidence only supposing the pToC is right. It is not surprising that we need to make some suppositions. The reason we need arguments that This need not be the case though. Part of the point of breaking the envisaged process into small steps and trying to see what principles would need to operate at each step is to allow us to invoke not just general but local knowledge of what those principle might amount to in the setting and what might be needed—e.g., knowing that the health clinic workers are untrained volunteers from rural villages, helps in recognising that it is important to explicitly note that it is necessary that they be able to use the app technology properly.The starting point for a setting-specific pToC of the kind I urge will often in cases of policy interventions be a more general ToC constructed by the programme developers who have thought through why they expect the programme to work and how. A general ToC for a programme that is hoped to work in more than one setting will usually assume causal principles expressed at a more middle, abstract level—e.g. ‘People respond to incentives’— with concomitantly abstractly expressed supports and derailersb. It is important to keep in mind that one may have excellent evidence for the singular causal claim that C will cause E in S and indeed the process actually carries through as envisaged, but yet E may not obtain. An anonymous referee has raised worries about a problem that Gerhardt Hesslow  made appMunro Review of Child Protection argues about child welfare. When a child welfare tragedy occurs, she explains,[t]he standard response is to hold an inquiry, looking in detail at the case and trying to get a picture of the causal sequence of events that ended in the child’s death…We are tracing a chain of events back in time to understand how it happened.… Munro, , 381Instead, she urges, ‘Child protection is a systems problem.’ And she quotes the US National Academy of Science’s To Err Is Human: Building a Safer Health System  c. I have offered a catalogue of diverse evidence types. I do not here hazard suggestions about how then to assess the sum total of evidence gathered along with estimates of the accuracy of the pToC that justifies these as evidence and estimates of the accuracy of the evidence claims themselves in order to arrive at an overall verdict on whether C will in fact cause E in S. Since these pToCs involve a kind of process tracing, standard advice from there can be of help, e.g. thinking in terms of van Evera, Equally important, as another referee points out, one may very well establish, properly with a great body of diverse evidence, that C caused an unwanted E in S, but that does not mean that removing C-type events is a good strategy for preventing future E’s. Often what’s needed is a structural change to make it difficult for C-to-E type processes to occur. This is what Eileen Munro, author of the UK  Munro Red. The mention of process-tracing evaluation methods brings to the fore an important kind of evidence made much of there as well as generally in theories of warrant, a kind that is not directly represented in a pToC. A pToC lays out facts that are evidentially relevant to directly confirming or disconfirming the hypothesis in view. It does not say anything about evidence that could count for or against alternative hypotheses about what will happen if C occurs, which we would also very much like to have. This kind of evidence does enter to some extent in the consideration of support factors and derailers, as Munslow  argues. e. One might wonder how to use a setting-specific pToC to learn about what might happen elsewhere. I don’t have much to offer here. This is a general question for case-study and single-case process-tracing research so we can look to these two areas for guidance, for example the classic George and Bennett , Guala , Steel . What I f. An anonymous referee asks about how pToCs can contribute to questions about the size of an effect in a new setting. I can say at least this. As we see from the proof in Sect. Before concluding, let me in response to a referee’s query turn to the metaphysics behind a pToC. One of the nice things about a pToC is that it makes only very common assumptions about causality that can hold on almost any metaphysics of causation.see the cat lapping up the milk just as she could see the cat and the milk themselves, that is, she could see the cat causing the milk to disappear. If a true singular causal connection requires a counterfactual truth, then seeing the cause produce its effect must count as evidence of the counterfactual truth—it is an indirect way to establish that the requisite counterfactual is true. Or, closer to our pToCs, suppose C occurs and E occurs but none of the support factors necessary for C to produce E are there or there is good evidence that serious derailers operated. Again, if C causes E just in case the requisite counterfactual holds, then we have ample evidence that the requisite counterfactual did not hold since the casual claim can’t be true if required support factors are missing.I should note at the start that though there is a close connection between metaphysics and method it is not so close that metaphysics dictates method. For instance, a counterfactual characterisation of causation requires that there be the right kind of truth about what would happen if the cause were absent, other causes held fixed. But this does not imply that the only evidence that such a counterfactual claim is true in a given case is by a direcrt frontal attack: seeing its actually obtaining in the given case, contrary to possibility, or seeing its actually obtaining in a case that is ‘identical’ to the given one. GEM Anscombe  claimed This is one of the things I like about pToCs. They make essentially only three fundamental assumptions about causality and each is consistent with all the standard accounts of causality that I know and some or all are actually presupposed by many of these accounts.First the pToC approach supposes that where C at one place and time contributes to producing E at another place and time, there is a causal process with steps in between connecting the two. We need not even suppose one way or another whether that process is continuous or discrete. This assumption need not hold universally—perhaps it fails in some kinds of case, as is sometimes argued about wave-packet reduction in quantum mechanics. No matter. In these cases it simply turns out that a pToC is not a useful tool for cataloguing evidence.Second it supposes that the effect cited at step n that was supposed to have been partially caused by the factor cited at n-1 will not usually be sufficient on its own for the effect cited at n + 1 which must figure as a cause of n + 2. It needs the presence of support factors and the absence of derailers (or something that stops the derailers interfering). That is just the familiar ‘insufficient but necessary part of a sufficient condition’ for a contribution that we know from JL Mackie. This assumption too fits with all the metaphysical accounts of causality that I am familiar with.Third is more metaphysically controversial. The pToc as we have constructed it supposes that the singular cause/effect relation that occurs at each step does so in accord with some general causal principle. It is neutral though about whether this is a governing principle or just descriptive of what does in fact often cause what. It is equally non-committal about what ‘causes’ in the causal principle amounts to, for instance is it a kind of very active pushing the effect into existence or instead just a report of some complex regularity of the kind we see in standard Humean approaches or in some probabilistic theories of causality?I personally resisted this assumption for a long while.  But that was before I started to think in terms of smaller steps. One of my favourite examples is of the Rube Goldberg machine where flying a kite causes a pencil to be sharpened. That cause/effect pair does not fall under any general causal principle. BUT—the process in between can be broken down into steps each one of which does fall under a familiar causal principle. Indeed that is why we can be so sure without building it that the machine would operate as expected. Nevertheless, we needn’t make the strong assumption that all singular causings occur in accord with a principle. In cases where some one or another of the steps between C and E do not, then that’s a case where we cannot use a principle as a tool for figuring our what support factors and derailers are likely to be and hence for figuring out what kinds of evidence to gather about whether that step will carry through.When you bet on a singular causal claim—C causes E in setting S—like the policy effectiveness predictions used here for illustration, you are betting that the causal process connecting C and E will carry through successfully in S. You are betting on that whether your defence for doing so is that a similar process must have carried through elsewhere because there is good evidence (perhaps from RCTs) that C has caused E elsewhere or because you have evidence that directly supports that prediction about S or you are just betting without much warrant.The pToC for the process lays out what must be in place for it to carry through. Again, whether you detail the features in the pToC and provide warrant about whether they will be present or not, their presence or absence will determine success or failure. Given that you are betting on success, evidence seems in order. And producing that evidence, whether done by you or someone else somewhere else, requires a panoply of diverse kinds of methods. There’s no way to warrant singular causal claims with only a single method—or a handful of them.Few of these methods will be able to secure the information aimed for rigorously in the sense of ‘rigour’ I’ve identified for RCTs. And there certainly is no rigorous way to figure what all the information together amounts to. This is not to recommend giving up on rigour when you can have it. Just do not overestimate what it can do for you—and definitely don’t let it dictate what methods can be used.The logo for this paper


Internet of Things (IoT) devices, particularly those used for sensor networks, are often latency-sensitive devices. The topology of the sensor network largely depends on the overall system application. Various configurations include linear, star, hierarchical and mesh in 2D or 3D deployments. Other applications include underwater communication with high attenuation of radio waves, disaster relief networks, rural networking, environmental monitoring networks, and vehicular networks. These networks all share the same characteristics, including link latency, latency variation (jitter), and tail latency. Achieving a predictable performance is critical for many interactive and latency-sensitive applications. In this paper, a two-stage tandem queuing model is developed to estimate the average end-to-end latency and predict the latency variation in closed forms. This model also provides a feedback mechanism to investigate other major performance metrics, such as utilization, and the optimal number of computing units needed in a single cluster. The model is applied for two classes of networks, namely, Edge Sensor Networks (ESNs) and Data Center Networks (DCNs). While the proposed model is theoretically derived from a queuing-based model, the simulation results of various network topologies and under different traffic conditions prove the accuracy of our model. The exponential growth in wireless and mobile devices has resulted in producing an unprecedented amount of traffic that needs to be processed by various nodes, sometimes in real time. The advancements in sensing, data processing, and cloud communication technology have enabled the systems to interact with the environment and optimize processes. The Internet of Things (IoT) is evolving from vertical to polymorphic applications, supporting both personal and industry users. To provide a seamless experience to the end-users, many challenges need to be addressed, including technology standards, interoperable module components supporting heterogeneous applications and requirements at several layers. In general, scalability is a limiting factor both vertically and horizontally. In the vertical scalability, computing resources are added or deleted, and in the horizontal scalability, network nodes are added or deleted. Security, privacy, self-organization, and energy efficiency are also other challenges in IoT deployment. The vehicular networks applications, such as Vehicular to Vehicular V2V), Vehicular to Pedestrian (V2P), and Vehicular to Infrastructure (V2I) networks are latency-sensitive networks. Some healthcare and wearable devices have stringent requirements in terms of latency and latency variation (jitter). The authors in  provide V, VehicuSeveral billion active IoT and sensor devices around the world continuously require data connections to data centers to operate and connect with other devices. These connections to data centers yield a significant ever-growing amount of data continuously being updated as the use of IoT increases. How is this affecting data centers around the world? Data storage is a very important consideration. With such a huge rise in data consumption and traffic, the connections from devices to data centers will need to be improved drastically in terms of response time, particularly for real-time applications. Real-time applications include environmental sensor networks, weather forecasting networks, healthcare, and epidemic networks, etc. With more smart houses being built than ever before and more IoT devices being implemented, data centers will need to respond within a constrained time.response latency, latency variation (jitter), and tail latency. Optimizing data center networks for tail latency is a shift from the previous designs where the performance metrics of interest were throughput or average latency. Moreover, optimizing for tail latency is another key performance metric and has already been considered in vertical designs with new operating systems, cluster managers, and data services [These applications service several millions of queries and instructions on several thousands of machines and are concerned with services ,4,5. OptNetwork latency is directly related to the two other troublesome network complications, packet loss and jitter. Thus, identifying the root causes of the latency problem and what latency means to the applications is necessary. First, request latency reflects how long it takes from sending a request to receiving the reply, including the time spent waiting in the queue and the time spent executing the corresponding instructions. Second, a significant portion of latency is dominated by the network transfer time which in turn is dominated by the queuing probabilistic delay. There is also the variation in the network latency, which is caused by various network traffic types, including the background traffic and the request/response traffic with tight deadlines. The background traffic can be latency-sensitive packets, short control messages, or large replicated transfer files. Third, the absence of priority scheduling for latency-sensitive traffic when latency-sensitive and non-latency-sensitive traffic share the network equally. Additionally, uneven load balancing and burst traffic all contribute to variation in network transfer times. Fourth, increasing buffer size to reduce loss rate introduces variable buffer latency. As predictability of response time becomes more critical for latency-sensitive cloud services, finding a balance between the expected response time and deployment of computation/communication resources becomes more critical.In this paper, an analytical model was developed to reduce the stringent task service delays for sensor and IoT devices, particularly when they must obtain or update data from/to cloud data centers. Eliminating the sources of latency and latency variability in large-scale systems is impractical, especially in shared environments. However, predictions and mitigation of latencies are possible through queuing analysis.We developed a two-stage tandem queuing model for Edge Sensor Networks (ESNs) and Data Center Networks (DCNs), as well as feedback mechanisms to investigate key performance indicators, such as latency, delay variation (jitter), utilization, and the optimal number of computing units needed in a cluster.We derived closed-form formulas for the above key performance indicators and analyzed tail latency under various traffic scenarios. We extended the result presented in  for the We formulated the compound latency accumulated by ESNs and DCNs, which can be used to suggest the optimal workload allocations among schedulers and servers, thereby minimizing the turn-around time required by applications.These findings were verified by simulating the system with the traffic obtained from synthetic data as well as IoT traffic traces. The distinction between the model presented in this paper and the previous models ,8,9,10 is models ,8 and frOur main contributions can be summarized as follows:Generally, the network-specific real-time analytics, heterogeneous resources, scalability of many detailed data, tail latency, and sub-millisecond response time requirements represent most of the critical challenges facing data centers. Short-running jobs are normally held disproportionately and become subordinate to long-running jobs . DespitA query or response initiated by a sensor or an IoT node suffers a cascade of three latencies. The edge latency is associated with an edge distribution network consisting of edge devices such as computers, Wi-Fi access points, and desktop and wiring closet switches, also known as hosts or end systems, which are connected at the edge of the network. The core network latency is associated with operation in data centers. Data centers have their edge networks in racks that aggregate server traffic. Finally, network latency is often dominated by propagation (distance) delay between the edge network and core network (data centers). The first mile (edge network) and the last mile (data center or core network) contribute significantly to the latency and its variation. In the following sections, we develop models for edge and core networks to estimate and predict the latency and latency variation in each network. The edge network model consisting of sensors and IoT devices is called the Edge Sensor Network (ESN), and the core network model associated with a cloud center is called the Data Center Network (DCN).An analytical model is a valuable tool that provides insights into a system’s behavior under normal and extreme conditions. One can draw a trajectory of the system’s characteristics by varying the system’s parameters with extreme or boundary values. For example, one can increase the load or aggregation level to project tail latency. This is particularly useful when schedulers target real-time data processing in which jobs are latency sensitive. The Data Center Network (DCN) is modeled based on tandem queuing networks and fork-join systems to analyze parallel job latency with distributed schedulers in data center cluster networks. We consider conserving policies in which a job demands system resources if the job is ready for execution. Work conserving policy states that the total backlog of work is independent of the scheduling policy at any given time. FCFS is an example of work conserving policies. Multiplexing J jobs to a single processor such that each job receives 1/J-th of the resources (or time) is another example.The rest of the paper is organized as follows. Recent research in the core and edge networking has focused on several aspects of underlying cluster architecture such as creating various layer 2 network topologies, designing transport protocols, virtualizing network resources, latency analysis, and so on. Both edge and core networks play a significant role in supporting enterprise networks and cloud computing services, including, but not limited to large-scale computations, web searching, email, online gaming, and social networking. As far as latency, which is a high priority in both networks, some work took an experimental approach and others tried to develop a generalized analytical model to explain and draw a trajectory of the behavior of a cluster of nodes under various workloads. For example, Flexplane  is an exIn terms of analysis, the work in  surveys C is the maximum queue length of a server. A load balancer, the point of entry for the system, is modeled as a An approximate solution for steady-state queue length in a ribed in . A cloudThe authors in  presentsTo improve the difficulty of building large data centers in a dense metro area, the authors in  proposedIn terms of transport layer latency, the authors of  report oWhile the N  i, a packet will proceed to node j with a probability A Jackson queuing network  is a netj and j. Let N be the number of nodes in a Jackson [flow conservation principle, in which the total sum of arrival rates entering the system is equal to the total departure rate under steady-state conditions.In this model,  Jackson  network, network , where γj, i.e., In the steady state,k sensors or IoT nodes communicating with edge servers or gateways. We derive closed-form formulas for the key features and performance measures, including mean job response time (latency) and latency variation. Consider the sensor network in In this section, we present a two-stage tandem queuing model to characterize the Edge Sensor Network, which consists of In this ESN network, there are 15 sensor nodes at level 1 , or destination node.Source nodes generate packets (traffic) whose destination is a randomly chosen destination node that exists in the network. Any given node generates traffic independently to other nodes.Each node maintains a forwarding queue where the packets are stored.Periodically, a node checks its forwarding queue. If there are packets in the node’s queue, it pick up a packet from the head of the queue and forwards the packet either to the destination node (if directly connected to it) or to the next-hop node along the path to the destination.Upon receiving a packet at the destination, the packet is processed and then removed from the network.To test and validate our mathematical model, we developed a simulator based on the Abstract Network Simulator (anx) , which inode_pkt_rate: represents the of arrival rate . The aggregated packets at each gateway were transmitted to the Data Center Network (DCN). The simulation was executed 10 times for each load  Routing . Figure Simulation experiments, similar to those in As it can be seen in From the user’s perspective, latency and its variation (jitter) are primary performance metrics. From the provider’s perspective, utilization and cost are primary performance metrics. There are a few questions this article tries to answer, including how tail latency can be mitigated without over-provisioning, how to schedule different user requests, and how these decisions help the desire to minimize energy consumption. While the answers to these questions often come from detailed experiments, it is of great value to have an analytical framework that can identify major trade-offs and challenges supporting latency-sensitive services in data center networks.The delay analysis of the Data Center Network (DCN) is similar to the analysis of the Edge Sensor Network (ESN), except in the DCN there are several layers of tandem queuing systems. However, we can use the same Equation  to derivj in stage ℓ. Given the regularity and hierarchical structure of the fat-tree, we can directly compute the load on each node at each level. Assuming a uniform distribution of traffic governed by the load balancers, we can formulate the aggregated traffic and the corresponding latency at each level (Equation (n is the number of hosts and Let Equation ).(10)λℓTo illustrate the above formulation with an example, consider an The average end-to-end latency can be estimated by adding the latencies from Equations  and 13)13).Recent data center traffic traces ,29 indicTo investigate the impact of variability in job latency, we consider a system with a given inter-arrival time and service time distribution. We can use the squared coefficient of variation (The  formula .(16)Tj=With tail latency, we are not concerned solely about the average latency. Rather, we care about the full latency distribution. At the very least, we should care about the tail. Usually, between the 90th and 99th percentiles of the tail distribution is considered. For a given fanout, how fast the children nodes have to be to get 99th percentile of latency.Techniques such as resource provisioning, dividing jobs and parallelizing tasks, eliminating head-of-line blocking, and caching helps in reducing the tail latency. Even with a maximum level of parallelism in its finest form, the slowest instance of tasks (straggler) dominates the response time. Smaller task partitioning (micropartition) helps to reduce head-of-line blocking and achieves smoother latency distribution percentiles. At the 95th percentile latency, the scheduler sends the same request to multiple replicas (hedged requests) and using the result from the first responder. With a Canary request, one sends normal requests but falls back to hedged request if the Canary request did not finish the task in a reasonable time. More fine-grained scheduling also helps.s) of a task when a fraction (f) of the computation is accelerated by a factor (k).While parallelism mitigates tail latency, there is still a limit on how much tail latency can be improved when microservices with fork and join are used. Amdahl’s law describes the speedup M/M/1 quen Google  and Alibn Google  show thaIn distributed IoT, sensors, and data center network applications, achieving predictable performance is critical, particularly in interactive jobs. Resource provisioning, such as parallel storage and multi-core servers, can pull tail latency. However, resource provisioning may be insufficient in the absence of identifying traffic characteristics and network bottlenecks. This paper, first, proposed a two-stage tandem queuing model, which is theoretically derived from a queuing-based model. This theoretical queuing network model provides a way to estimate the average end-to-end latency and predict the latency variation in closed forms. Additionally, this model introduces a feedback mechanism to investigate other major networking performance metrics, such as utilization and the optimal number of computing units needed in a single cluster. Second, a model for sensor and IoT networks was implemented, and the simulation results validate the accuracy of this model. Third, the model was extended and applied for two classes of networks, namely, Edge Sensor Networks (ESNs) and Data Center Networks (DCNs).A significant amount of tail latency can be due to queuing effects. So, reducing tandem queuing delays through distributed scheduling reduces tail latency.Queuing effects increase super-linearly with utilization.In With distributed scheduling, it is possible to keep the servers busy, yet keep the tail latency low.While parallelism reduces tail latency, there is still a limit on how much tail latency can be improved when microservices with fork and join. The takeaways from this study can be summarized as follows.


Developing physical literacy at population levels provides a transformative appeal for those working in sport, health, education, recreation and physical activity settings. Interdisciplinary approaches to development of policy in this area is recommended. The purpose of this study was to gather empirical data from key stakeholders working with young people in areas related to physical literacy across the Republic of Ireland and Northern Ireland, to capture their current understanding and awareness of the physical literacy to help inform the development of the first all-island consensus statement for physical literacy.A total of 1,241 participants , from a range of stakeholder groups  completed a researcher developed physical literacy questionnaire. A one-way MANOVA was carried out to investigate differences across stakeholder grouping in terms of perceived importance of three domains of physical literacy. Overlap of independent confidence intervals was analysed to determine importance of the physical literacy domains within stakeholder grouping.A majority (63%) of respondents indicated they were aware of an existing definition of physical literacy, but this varied by stakeholder group . Participants working in higher education (69%), or working as physical education specialists (67%), were more likely to rate themselves as experts or near experts in physical literacy, while coaches, education generalists, and decision makers were more likely rate themselves as having no expertise . Non-specialist teachers and physical education teachers rated the importance of all domains of physical literacy significantly higher than decision makers, and significantly higher than coaches in the cognitive and affective domains. All stakeholders significantly rated the importance of the physical/psychomotor domain of physical literacy higher than the affective or cognitive domains of physical literacy.Differences observed across stakeholder groups underline the importance of developing a shared vision for physical literacy, and the need to clarify and gain consensus on a definition of the term and its domains. Engaging and understanding the voice of stakeholders is critical in ensuring the relevance, ownership of and commitment to physical literacy statement operationalisation. Physical literacy is a concept which has gained increased attention in recent years, with many countries working to develop and release consensus statements regarding the definition of the concept, including Australia and Canada. Margaret Whitehead’s work , 2 proviA recent systematic review by Edwards et al. noted that physical literacy was being heavily promoted in sport, health-related physical activity and recreation contexts, but each offered various representations of the concept and limited consensus on its central tenets  . CriticsIn order to confirm the claims made about the potential for physical literacy, and to better understand its determinants and correlates, and how physical literacy can be effectively operationalised, clarity and agreement on its definition is needed , 17, 18.The development of an all-island statement on physical literacy represents a logical first step towards the development of physical literacy policy that will lead to physical literacy being theoretically understood, and practically employed in a strategic and coherent way, across a range of sectors and populations on the island of Ireland. As part of a broader piece of research commissioned by Sport Ireland and Sport Norther Ireland, the purpose of this study was to gather empirical data from key stakeholders from across the island of Ireland (those working with young people) on their current understanding, and awareness of the construct of physical literacy to inform the development of the first all-island consensus statement for physical literacy, and to offer guidance for the subsequent strategic /policy direction and implementation.The target cohort of this study were stakeholders from across the island of Ireland working or volunteering with young people in areas related to physical literacy; including sport, education, physical activity, recreation, health. The decision to focus the research on this cohort was taken, in consultation with Sport Ireland, as it was considered that this cohort were likely to be those most involved in operationalisation of the all-island consensus statement across the island of Ireland. Participants were recruited to this study through the networks of contacts that the research team had in their individual institutions, along with the network of contacts existing through Sport Ireland (SI) and Sport Northern Ireland (SNI) databases and networks. The questionnaire, which included a plain language statement and consent form, was disseminated electronically directly by members of the research team, SI and SNI, to their networks. Recipients were also asked to further share it with others across RoI and NI whom they knew worked or volunteered in an area related to PL. Ethical approval for this study was granted by the University of Ulster School of Sport Filter Committee (FC06 2019-20).A researcher developed questionnaire was created through consultation with all members of the research team along with SI and SNI, to ensure that questions captured the key areas required to help inform development of Ireland’s all island statement on physical literacy. The questionnaire was piloted with a small group prior to national dissemination, to ensure readability and appropriate timing, with some minor changes subsequently made to wording. The pilot group consisted of a convenience sample of 20 people working or volunteering with young people in areas related to physical literacy. The questionnaire was completed online by participants and took approximately 10 minutes to complete. Questions relating to participant demographics, including gender, age, and the role/area in which participants work  were included at the start of the questionnaire. Respondents were then invited to respond to an open-ended  question; ‘Definitions of physical literacy vary across the world depending on the specific environment and context. What does Physical Literacy mean to you?’. The purpose of this question was to allow participants the opportunity to articulate potential components of physical literacy that may be important to them, but that may not have been included later in the closed questions later in questionnaire How would you rate your expertise in the area of physical literacy? Not a component of Physical Literacy’, 2 ‘Small Component of Physical Literacy’, 3 ‘Important Component of Physical Literacy’, and 4 ‘Vital Component of Physical Literacy’).Consider the following list [see Table Participants were then asked the following closed questions relating specifically to physical literacy including;The components of physical literacy included in the questionnaire, as shown in Table Participants work role was collapsed into five broad categories as shown in Table Descriptive statistics  were calculated for all physical literacy components and domains. To understand if there was an interaction between stakeholder groups a one-way multivariate analysis of variance (MANOVA) was run with stakeholder group as the independent variable and physical literacy domains  considered separately as three dependent variables. Follow up analyses were conducted using univariate two-way ANOVAs and the main effect of gender and stakeholder were considered separately. Tukey pairwise comparisons were run for stakeholder and gender, when necessary, to highlight differences in physical literacy domain importance. A one-way MANOVA was chosen over a series of ANOVAs for protection against inflated Type I error due to multiple testing . Bonferrp < .05) were considered statistically significant, and referred to as a proportional overlap [p < 0.001). Simply, if the upper confidence interval of the smaller mean does not extend 50% over the lower confidence interval of the greater mean, then statistical difference at the p < .05 level is observed [For a statistically significant comparison of domains, , upper and lower confidence intervals were compared; those that overlapped by less than 50% , to 55+ years (16%); 6% 19 – 24 years, 11% 25 - 30 years, 15% 31-36 years, 20% 37 – 42 years, 14% 43-48 years, and 15% 49 – 54 years. Participants reported as 52% male, 47.7% female, and 0.3% non-binary gender. Most respondents reported working in the RoI (54%), with 24% working in NI, and 22% working across both jurisdictions. The greatest proportion of respondents indicated they worked in urban areas (30%), followed by rural areas (23%) and then suburban areas (19%), with 28% of the respondents indicating they work across all three areas.n = 1034 for this question) identified as holding a range of different roles relating to PL with over 50% of the respondents identifying themselves as post-primary PE teachers, or coaches/instructors. A large percentage of participants indicated they work with post-primary school aged children (84%), with 63% indicating they work with primary school aged children, and 19% working with preschool aged children (again noting participants could select more than one response option as appropriate).As shown in Table n = 909), 63% of respondents indicated that they were aware of a definition (see Table n = 844) identifying as ‘experts’ in physical literacy, and 9% indicating they have no expertise. This perceived expertise level varied across work grouping with 1% of those in Higher Education and 3% of those in PE rating their expertise as ‘Not at all’, while these figures were higher for the other domains .When considering awareness of an existing definition of physical literacy (respondents to this question n = 913). Figure Participants ranking of the perceived importance of the potential components of physical literacy is shown in Table r| < 0.7). There were 42 univariate outliers in the data, as assessed by inspection of a boxplot for values greater than 1.5 box-lengths (34 univariate outliers) and 3 box-lengths (8 extreme univariate outliers) from the edge of the box. There were ten multivariate outliers in the data, as assessed by Mahalanobis distance compared against a chi-square (χ2) distribution 16.27 (p > .001). All outliers were assessed for data entry and measurement errors and deemed genuinely unusual values [p < .05). The one-way MANOVA is robust to deviations from normality with respect to Type I error [p = .029), and homogeneity of variances, as assessed by Levene's Test of Homogeneity of Variance (p > .05).There was a linear relationship between the dependent variables, as assessed by scatterplot. There was no evidence of multicollinearity, as assessed by Pearson correlation  effect on the combined dependent variables , F = 2.924, p < .001, Pillai’s Trace = .041, partial η2 = .014. There was a statistically significant main effect of stakeholder for the three physical literacy domains; Physical/Psychomotor  = 6.741, p < .001, partial η2 = .031), Cognitive  = 4.507, p = .001, partial η2 = .021), or Affective  = 4.582, p = .001, partial η2 = .021). Table p < .0005) or Decision Maker (p < .0005). For Cognitive scores, Tukey post-hoc tests (see Table p < .0005) or Decision Maker (p < .0005). For Affective scores, Tukey post-hoc tests showed that Non-Specialist Teachers and PE Teachers had statistically significantly higher mean scores than Decision Maker (p < .0005), and Non-Specialist Teachers had statistically significantly higher mean scores than Coaches (p < .0005). Table *indicates the mean difference is significant at the 0.01 level).There was a statistically significant stakeholder group N =  effect op < .001) was observed between all three domains of physical literacy at the population level  than the Affective or Cognitive domains of physical literacy. In addition, the only stakeholder group that placed significantly higher importance on the Affective domain of physical literacy the Cognitive domain was Coaches, as highlighted by a proportion overlap of the affective lower confidence interval and the cognitive upper confidence interval .The mean of the three physical literacy domains  were compared for population, and subsequently for stakeholder. The corresponding 95% confidence intervals were estimated via bias corrected bootstrap . A proportion gap  Movement vocabulary and safety, with particular consideration of safe and effective movement  Lifelong journey, representing the value and impact of physical literacy across the lifecourse,  Personal benefits, representing an holistic appreciation of the innate value of physical literacy for an individual .These themes were i) Social benefits, representing a variety of interpersonal and other skills, indicated they work in an area directly related to physical literacy, just 43% considered themselves experts or near experts in the area of physical literacy. Unsurprisingly, and consistent with the findings by Goss et al.in a sample of ‘academic/practitioner experts’ , those wImportant Component of Physical Literacy’). All other physical literacy components scored a mean of 3 or above, with Physical Activity, Fundamental Movement Skills and Enjoyment ranking highest.Mindful of the concerns raised in previous research regarding the over-simplification of physical literacy , 17, or Different stakeholder-perspectives are apparent in the results, with those working in an educational paradigm  scoring all three domains of physical literacy significantly higher in importance than decision makers, and significantly higher than coaches in the Cognitive and Affective domains. The higher level of importance placed on physical literacy domains by this cohort may be attributed to initial teacher education, which is known to play a role in shaping teachers beliefs , and encwithin each Stakeholder group, with the exception being the Coaches group, who place significantly higher importance on the Affective domain than the Cognitive domain. Results of the within-stakeholder analysis in this study indicate however that all stakeholder groups rated the importance of the Physical/Psychomotor domain of physical literacy significantly higher than the Affective or Cognitive domains. Work carried out in Canada, which used factor analysis to help refine the Canadian Assessment of Physical Literacy (CAPL), found the cognitive domain was deemed to have lower relative importance to physical literacy assessment overall, which led to the revision of the Knowledge and Understanding (Cognitive) domain within CAPL-2 [For the most part the Affective and Cognitive domains are held in equal importance n CAPL-2 . CAPL-2 n CAPL-2  and Warnn CAPL-2 ), albeitn CAPL-2 .It could be argued that the perceptions of participants in the current study simply reflect a lack of understanding of the holistic nature of the concept, which has been observed in previous research in early years , school As well as ranking the importance of the dimensions included in physical literacy, participants were asked, in an open-ended question , about their own interpretation of physical literacy. A number of common themes were identified, as are presented in Table The vision of physical literacy as a concept supporting lifelong engagement not only in physical activity, but also in society, is notable, particularly when considering participants in this study were those working within the youth context. This highlights that although participants work within a youth sector, they recognise their role, and the role of physical literacy, as a lifelong influence. The lifelong nature of physical literacy is consistently a core defining feature of the concept, although the majority of research to date has been predominantly focussed on a youth context . It is eFindings from the current study, in line with the Australian consensus statement , and worabstract and inaccessible’ , it is In developing a consensus statement for the island of Ireland, and more particularly when operationalising this statement, emphasis will need to be placed on addressing the ‘poorer’ status of both the affective and cognitive domains when compared with the physical domain. Specifically, some of the ‘personal benefits’ highlighted by stakeholders when asked ‘What does physical literacy mean to you?’ Table , includiMore accessible language may assist in addressing the imbalances currently evident in the prioritising of domains, and help remove ambiguity as to what each domain means (and what components it may represent). As previously discussed however, this is a balance, as there is a need to be mindful of the work of Young et al.  who warnCrucially, findings of this study suggest that currently those working in education settings are more comfortable with concepts relating to physical literacy, and as such future work to grow expertise across all sectors will be vital. Findings also suggest that, currently the physical/psychomotor domain is better understood and prioritised amongst Irish stakeholders, and so efforts to develop and encourage a more holistic approach with specific emphasis on elevating the status of both the affective and cognitive (knowledge and understanding) domains is warranted; developing physical literacy as a lifelong journey is an important perception of the concept amongst stakeholders. Study findings support that there is a level of readiness for such developments. Consideration and adoption of the ‘social benefits’ of physical literacy also has importance to Irish stakeholders and deserves attention in the development physical literacy across the island.Strengths of the current study include the nationally representative sample, from NI and the RoI, and representing a broad range of stakeholders involved in delivering physical literacy related programmes to young people. As stated, it was beyond the scope of this project to focus on other life stages, although the importance of physical literacy as a lifelong journey was conveyed in responses. This is the first study that this research team is aware of to investigate and incorporate a range of stakeholders perceptions of physical literacy in the formative stages of national consensus statement development. The information obtained regarding how physical literacy is understood, how understanding differs, and indeed the priority placed on the different domains of physical literacy by stakeholder groups, provides critical information which can inform the development of Ireland’s all-island consensus statement. Early stakeholder involvement can hopefully prevent some of the ‘uncertainty, confusion, and resistance’ (pp.1 ) to the The researcher developed questionnaire used in this study is a limitation, as no existing validated questionnaire was available to meet the needs of the study. Nevertheless, the data gathered in this study supports the development of a statement for physical literacy for the island of Ireland, that is culturally and socially relevant, and provides an insight into how physical literacy is interpreted, how important various components are perceived to be, and how it is prioritised by those working in various roles and sectors on the island of Ireland. The open-ended question item, which was posed at the start of the survey was optional, with 48% of participants choosing to respond. While a lower response rate to open ended questions due to burden placed on participants is to be expected , it is pThis study is one of the first to engage with ‘on the ground’ stakeholders to gather their perceptions regarding physical literacy, with a view to informing the development of a physical literacy statement. This study included a wide array of potentially relevant physical literacy components, thus providing a broad and comprehensive investigation of participants understanding and perceived importance of same. Findings demonstrate that different stakeholder groups have a wide variation of expertise and experience in physical literacy, and place different levels of importance on the various physical literacy domains/components; underlining the need for the all-island statement on physical literacy to ‘understand the audience’ and to continue to engage with stakeholders, to ensure that the statement is effectively operationalised in a cross- sectoral, collaborative manner.This paper offers valuable new insights, which have not been published related to previous physical literacy consensus statements internationally. The definitions, interpretations and level of importance placed upon physical literacy in society will continue to transcend, as the evidence for policy level physical literacy implementation improves. The stakeholder data from the RoI and NI that informed the current study has critically evaluated physical literacy as a holistic construct, and presents cutting-edge information on societal perspectives for those working with children and youth, and critically, informs the development and operationalisation of the all-island consensus statement on physical literacy. Continued engagement with stakeholders during the development of the statement, and throughout its operationalisation, is critical to achieve sustained, meaningful and impactful implementation. These findings lay an important foundation, but the operationalisation, and evaluation of the implementation of the resulting statement is key . This on