Field Tests versus Laboratory Experiments

Field Tests versus Laboratory Experiments: A Question of Goal Instead of Preference

This paper aims to explore two worlds, without explicitly choosing one in the other. We realize of course that making that choice would make our process easier as we would become advocates for one area. However, as we shall see, this might not be the advisable course of action. In knowledge, both field and laboratory tests have specific purposes. We will delineate what some of these purposes are, and the audience will ideally gain an perception and understanding into why this is not simply a subject of preference.

First, it ought to be known that the differentiation between field and laboratory work is a simple division- a great deal such that it has spawned two starkly different packages of research designs, particularly qualitative and quantitative studies. And, much like the controversy on field versus laboratory experiments, the division between qualitative and quantitative has spawned fierce fights within the interested parties, each side advocating the superiority of its research design when in fact both factors have a valid method, albeit for starkly different purposes.

As Trochim (2006) explains, both practices are about gathering and deciphering data- whether it is words, with qualitative research or volumes when working with quantitative methods. Furthermore, Trochim boasts that "All qualitative data can be coded quantitatively" because "[a]nything that is qualitative can be designated meaningful numerical values". When qualitative experts acquire data that is verbal- such as answers to "tell me what you think", they then code these answers and in doing so, split the verbal data into numeric categories. Trochim also assumes the other side of the gold coin, advocating that "[a]ll quantitative data is based on qualitative judgment. " Whenever a quantitative researcher assigns lots, he also assigns interpretation. For instance, if the heartrate of subject matter A is 78 beats each and every minute, that data has to be classified in order for it to get so this means. Is 78 beats each and every minute too high? Could it be too low? Could it be appropriate given the themes' age, health and circumstances? Similarly, when a qualitative researcher makes a study where in fact the answers vary from "excellent" to "poor", those terms need to be defined in order for the data to acquire so this means. Hence, Trochim argues, quantitative methods and qualitative methods are simply just various ways of grappling with the same set of issues.

Likewise, the debates between field and laboratory experiments are merely two means of taking a look at the same subject- knowledge. As Harrison and List (2004) explain, "[F]ield experiments differ from laboratory experiments in lots of ways. Though it is tempting to view field tests as simply less managed variants of lab experiments, we dispute that to take action is always to significantly mischaracterize them. What moves for "control" in laboratory tests might in reality be precisely the opposite if it's artificial to the subject or context of the task. In the end, we see field tests as being methodologically complementary to traditional laboratory experiments. "

Before talking about how both of these methods of experimentation match one another- as Harrison and List plausibly claim that they do- it would be necessary to establish each method. A field experiment is, broadly, a way of investigation that involves "studies with regular people in "real world" settings-studies made to regulate how well a fresh intervention or program works in the real world (i. e. , relative effectiveness) somewhat than how well it works under ideal circumstances (i. e. , effectiveness)" (Dennis, 1990. ) As one might picture, this real world approach is not any easy activity. Indeed, Dennis runs so far as to declare that field studies are "often logistical failures". Awarded, Dennis comes to this view from a medical/drug treatment record, but his warning, and the reasons he gives for this view are broadly applicable.

Dennis gives six reasons for why he looks upon field experiments as failures. First, in a field test, variability is almost inescapable. You are working with human beings in their natural (as opposed to a controlled) environment. Hence, this variation is extremely difficult to regulate and said insufficient control has an effect on whether the experiment is effective or statistically reliable. Second of all, your skin therapy plan devised in the laboratory may easily become affected in the field, whether it is through simple error or even through personal treatment- for example, if two groups in need of medical treatment are out in the field, and you are a control group that's not receiving treatment, their counselor may make an effort to give them the treatment out of a sense of fairness, thus sabotaging the worthiness of the field test. One third trouble spot as it pertains to field tests is "being unable to estimate the expected impact size and, therefore, being unable to estimate the number of units necessary to achieve an acceptable degree of statistical ability because there are no direct data to calculate expected caseflow. " Fourthly, many tests depend on arbitrarily assigning subject matter. This random project is exactly what lends many experiments their credibility in that this way they can show that the treatment has broadly similar results throughout the targeted human population. In the field, random assignments may be breached, either intentionally or elsewhere. A fifth issue with field experiments, particularly those that can last for several years [which is not unheard of] is usually that the changes in the environment, such as "staff turnover, changes in local money, changes in national regulations" make a difference the experiment adversely. Imagine a report which uses two organizations and four trained advisors who have developed both reliability and an mental relationship with the members. If one among those four advisors leaves, which is fairly likely, especially throughout a long running field experiment, that departure can have a significant impact on the findings. Lastly, and connected with this problem, Dennis records that "few programs are static; the majority are continuously changing, and researchers, find it hard to maintain rigid experimental regimens over an extended period of time. "

Nevertheless, Dennis agrees that field experiments are essential for they bring out aspects of a product or hypothesis which are not readily apparent in the course of a laboratory test. Laboratory experiments provide two major purposes. First of all, as was already mentioned, field tests are costly and hard to regulate. However, this can be an debate against field work, not just one and only lab work. The debate made in favour of doing laboratory experiments, specifically in the field of economics is that economics is an inherently theoretical vocation and having said that theories can best be tested in a purely controlled environment that assessments the consequences of economic theories, ideas and stratagems in a "small-scale microeconomic environment in the lab where enough control can be managed. " (Pitt, 1981 at 138. )

This is an essential step in the study process. To be able to proceed to field work, whatever area is being analyzed, ideas must endure controlled scrutiny. Because the field of economics in itself is so flexible and significant, effecting from environmental protection laws and regulations to criminal offenses control regulations, control becomes even more important. As Pitt talks about "control is crucial because it is essential for measurement and so replicability. Replicability, in turn, allows the experimenter to identify systematic romantic relationships between preferences, institutional variables and effects. " (Pitt, 1981 at 142. ) Pitt goes on to give three most important uses for laboratory experiments in neuro-scientific economics, specifically an ability to choose between competing economic theories, the opportunity to exposes economic ideas and models which lack any validity and exposing theory to new types of organization. Many of these are valid known reasons for conducting laboratory tests. In the end, committing a beta mistake- that is, allowing a theory to be implemented widely without sufficient laboratory screening is flirting with disaster. Furthermore, even if an economic theory withstands scrutiny in a given situation, that theory might not exactly necessarily have broad applicability. Given the type of industry of ideas, where individuals and institutions often adjust ideas, principles and theories to suit new purposes, it's important to test theories which may have withstood scrutiny in new conditions and under new circumstances to assure, as much as possible, that these theories can withstand the type of challenges they'll inevitably face once they are from the laboratory and in the overall consciousness.

If laboratory tests in economics are a required filter between organic theory and irresponsible application, then field work must be viewed as another such necessary filtration system. As explained in the intro, we aren't making a choice between two solutions, but instead designating each way as being welcome and appropriate under certain circumstances. As Harrison and List (2004) place it "By examining the nature of field tests, we seek to make it a common surface between research workers. We plan field tests from the point of view of the sterility of the laboratory experimental environment. We do not start to see the notion of a "sterile environment" as a negative, provided one identifies its role in the study discovery process. In a single sense, that sterility we can see in clean relief the consequences of exogenous treatments on patterns. However, lab experiments in isolation are always limited in relevance for predicting field behavior, unless one wishes to demand a priori that those aspects of economic patterns under study are perfectly general in a sense that people will explain. Rather, we see the beauty of laboratory experiments in just a broader context-when they are simply coupled with field data, they permit sharper and even more convincing inference. "

How will field work go with, as Harrison and List insist that it does laboratory work? Thus far, we've seen the role of field work in wide-ranging terms of necessity, as a concession to the brutal certainty of the higher world, where elements do not conform to our targets. But field work is much too valuable to regard as only guard. Indeed, "to see field experiments as simply less handled variants of laboratory experiments. . . would be to very seriously mischaracterize them. " (Harrison and List, 2004. ) Harrison and List make the point that the controls enforced in the laboratory are actually undesirable in the field since imposing said controls would modify the tendencies of the topics and the partnership between variables in a way not within the "real" world- and hence, any look at at such control in the field would execute a serious disservice to the viability of the idea under examination.

Field work in economics is astonishingly mixed. Further, a single economical theory can be tested in a variety of configurations- but as these settings diverge, one must anticipate to see different results under different conditions. Indeed, one major reason for field work is to expose laboratory notions to different conditions and to see how well they endure, just how many exceptions there are and exactly how much these exceptions may have an effect on the idea overall. [Since a theory with a variety of exceptions may not remain a valid and feasible theory for very much longer. ] One of these of this sensation is the Nash equilibrium bidding habit model, that was used showing, back 1961 that no matter which one of four possible public sale formats [British, Dutch, first-price, second-price] can be used by an public sale house, the expected amount of revenue gathered will be practically equal. A lot more than thirty years later, David Lucking-Reiley decided to re run the Nash experiment on the internet- using an internet game called Magic: The Gathering, where game players become "dueling wizards, each with their own libraries of magic spells (symbolized by decks of cards) which could potentially be utilized resistant to the player's opposition. " These credit cards, which represent the magic spells in question, "can be purchased in random assortments, just like baseball cards, at shops ranging from small game and hobby shops to large chains such as Gadgets "R" Us and Waldenbooks. " The game's internet composition and the worthiness of the cards in question have combined to make a thriving market where said credit cards can be bought and sold by players and even auctioned off- an internet version of the real life auctions Nash researched in 1961. Where Nash found that different public sale models made no difference to the bottom line, Lucking-Reiley, by using a medium not existing in Nash's day, spent 2 yrs first observing and then participating in the sort of online cards auctions identified above and concluded that the Dutch auction format, where in fact the price starts off at an inflated level and declines before first, winning bet, produces product prices that are 30 % higher than the prices of the other auction types. (Lucking-Reiley, 1999. )

Lucking-Reiley's work shows how essential field work is to economics and other sciences. It really is through his field work, using other participants behind a computer game that he could disprove, within a narrow set of circumstances, Nash's theory. Will this imply that Nash's theory is no longer valid? Definitely not. Lucking-Reiley limited his field work to a specific medium- the internet- and to a specific format within that medium, particularly internet game players. Not just that, he used a particular game which draws in specific players. Lucking-Reiley is not declaring that all internet auctions would produce the best prices through the Dutch auction format. Nor is he declaring that all internet gamers would reply the way that those who play Magic: The Gathering performed. However, he is challenging Nash's work as well as a theory (that auction platforms would produces approximately the same financial results) which was developed and examined over time. Field work like that of Lucking-Reiley shows how a theory adapts to a new construction or medium- or how it generally does not.

At the beginning of this newspaper we explained that we choose never to choose- that people don't have a clear choice for either laboratory work or field work. In truth, both are essential to economical theory as well as to other sciences. The control provided with a lab will allow a theory to be analyzed in a manner that would show how it does, or doesn't hold up to strict methodical scrutiny. If the idea in question fails in the laboratory, the problem can be regarded as settled- at least until a fresh testing treatment is devised. If the theory succeeds however, the next step is never to simply foist it upon the world but to check it out in the field, and can stand or fail on its own in an environment which is much less sterile and has significantly fewer safeguards. Thus, lab and field work are two attributes of a required filter between ideas and truth.


Dennis, M. L. (1990) "Assessing the Validity of Randomized Field Tests: An Example of Drug Abuse Treatment Research, " Centre for Public Research and Plan Analysis- Research Triangle Institute Analysis Review, Vol. 14 No. 4 (Aug. 1990), 347-373.

Harrison, G. W. and List J. A. (2004) "Field Experiments, " Journal of Economic Literature, Vol. XLII (Dec. 2004), 1009-1055.

Lucking-Reiley D. (1999) "Using Field Experiments to check Equivalence between Auction Platforms: Magic on the Internet, " The American Economic Review, Vol. 89 No. 5 (Dec. 1999), 1063-1080.

Pitt, J. (1981) Philosophy in Economics, Springer-Verlag New York LLC: NY.

Trochim, W. M. K. (2006) "The Qualitative Question, " Research Methods Knowledge Platform: Web Centre for Public Research Methods. Accessed via http://www. socialresearchmethods. net/kb/qualdeb. htm on 15 Apr 2008.

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