In the last few decades the role played by models and modeling activities has become a central topic in the scientific enterprise. In particular, it has been highlighted both that the development of models constitutes a crucial step for understanding the world and that the developed models operate as mediators between theories and the world. Such perspective is exploited here to cope with the issue as to whether error-based and uncertainty-based modeling of measurement are incompatible, and thus alternative (...) with one another, as sometimes claimed nowadays. The crucial problem is whether assuming this standpoint implies definitely renouncing to maintain a role for truth and the related concepts, particularly accuracy, in measurement. It is argued here that the well known objections against true values in measurement, which would lead to refuse the concept of accuracy as non-operational, or to maintain it as only qualitative, derive from a not clear distinction between three distinct processes: the metrological characterization of measuring systems, their calibration, and finally measurement. Under the hypotheses that (1) the concept of true value is related to the model of a measurement process, (2) the concept of uncertainty is related to the connection between such model and the world, and (3) accuracy is a property of measuring systems (and not of measurement results) and uncertainty is a property of measurement results (and not of measuring systems), not only the compatibility but actually the conjoint need of error-based and uncertainty-based modeling emerges. (shrink)

In the United States, the racial and ethnic statistics published by the National Center for Health Statistics (NCHS) assume that each member of the U.S. population has a race and ethnicity and that if a member is black or white with respect to his risk of one disease, he is the same race with respect to his risk of another. Such an assumption is mistaken. Race and ethnicity are taken by the NCHS to be an intrinsic property of members of (...) a population, when they should be taken to depend on interest. The actual or underlying race or ethnicity of members of a population depends on the risk whose variation within the population we wish to describe or explain. (shrink)

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling (SEM), where latent variables are approximated by weighted composites of indicators. It has no formal mechanism to incorporate errors in indicators, which in turn renders components prone to the errors as well. We propose to extend GSCA to account for errors in indicators explicitly. This extension, called GSCA_M, considers both common and unique parts of indicators, as postulated in common factor analysis, and estimates a weighted composite of (...) indicators with their unique parts removed. Adding such unique parts or uniqueness terms serves to account for measurement errors in indicators in a manner similar to common factor analysis. Simulation studies are conducted to compare parameter recovery of GSCA_M and existing methods. These methods are also applied to fit a substantively well-established model to real data. (shrink)

The definition and measurement of the quality audit work has been the subject of many studies. Since the quality of auditors' work could not be observed directly except in ex post audit failures, prior researches adapted different proxies for measuring it. These proxies include firm size, reputation, auditor tenure, audit fees and other measures. This article reviews empirical studies over the past decades from all over the world in order to assess what researchers have done about measuring the quality (...) of auditors' work. Prior measurements are classified into two main groups; direct and indirect measure of quality audit and actual quality audit versus perceptions. This article contributes to the quality audit literature by proposing new measurement, which is developed based on the quality of auditors' work definition and theory of planned behaviour. (shrink)

Articulated arm coordinate measuring machine is a specific robotic structural instrument, which uses D-H method for the purpose of kinematic modeling and error compensation. However, it is difficult for the existing error compensation models to describe various factors, which affects the accuracy of AACMM. In this paper, a modeling and error compensation method for AACMM is proposed based on BP Neural Networks. According to the available measurements, the poses of the AACMM are used as the input, and (...) the coordinates of the probe are used as the output of neural network. To avoid tedious training and improve the training efficiency and prediction accuracy, a data acquisition strategy is developed according to the actual measurement behavior in the joint space. A neural network model is proposed and analyzed by using the data generated via Monte-Carlo method in simulations. The structure and parameter settings of neural network are optimized to improve the prediction accuracy and training speed. Experimental studies have been conducted to verify the proposed algorithm with neural network compensation, which shows that 97% error of the AACMM can be eliminated after compensation. These experimental results have revealed the effectiveness of the proposed modeling and compensation method for AACMM. (shrink)

By using the concept of a uniformity, the Structuralists have given us a most useful means of representing approximations. In the second section of this paper, I have made use of this technique to show how we can deal with errors of measurement — imprecise explananda — in the context of theoretical explanation. As well as (I hope) providing further demonstration of the power of the Structuralist approach, this also serves to support the ontic conception of explanation by showing (...) that it can help us resolve substantial problems in the theory of explanation. (shrink)

What is the correct concept behind measures of inflation? Does money cause business activity or is it the other way around? Shall we stimulate growth by raising aggregate demand or rather by lowering taxes and thereby providing incentives to produce? Policy-relevant questions such as these are of immediate and obvious importance to the welfare of societies. The standard approach in dealing with them is to build a model, based on economic theory, answer the question for the model world and then (...) apply the results to economic phenomena outside. Data come in, if at all, only in testing a limited number of the model's consequences. Despite some critical voices, economic methodology too has by and large subscribed to a "theory first" approach to applied economics. Error in Economics systematically develops an alternative to the theory-based orthodoxy. It places the methodical study of evidence at the centre of the scientific enterprise and thus provides a foundation for a methodology of evidence-based economics. But the book does not stop at the truism that claims should be based on the best available evidence. Rather, detailed studies in the areas of measurement, causal inference and policy analysis show what it means for a claim to be evidence-based in the context of a concrete case. The examples discussed concern topics as diverse as consumer price indices, radio spectrum auctions, the transmission mechanism, natural experiments on minimum wages and the evaluation of counterfactuals for policy. Error in Economics is essential reading for economic methodologists, philosophers of science and anyone interested in how claims about socio-economic matters are validated. (shrink)

The measurement of sensory intensity has had a long history, attracting the attention of investigators from many disciplines including physiology, psychology, physics, mathematics, philosophy, and even chemistry. While there has been a continuing doubt by some that sensation has the properties necessary for measurement, experiments designed to obtain estimates of sensory intensity have found that a general rule applies: Equal stimulus ratios produce equal sensory ratios. Theories concerning the basis for this simple psychophysical rule are discussed, with emphasis (...) given to the physical correlate theory, which considers judgments of subjective magnitudes to be based upon estimates of physical dimensions that vary regularly with changes in degree of stimulation. For the most thoroughly investigated sensory scales, brightness and loudness, the physical correlate is considered to be distance. Our “tacit knowledge” of the sensory effects of changing distance plays an essential role in matching motor activities to environmental conditions and in ensuring accurate perceptual evaluations through brightness and loudness constancies. In psychophysical experiments, subjects apparently use this same tacit knowledge when required to estimate relative subjective magnitudes. The evidence related to the physical correlate theory is summarized, and it is concluded that, while under appropriate conditions we demonstrate considerable skill in evaluating environmental relationships, we are quite unable to estimate the neurophysiological nature or quantity of sensory response. A psychophysics devoted to studying conditions required for accuracy and conditions producing errors in the perception of environmental relationships would seem to be more valuable than one devoted to subjective magnitudes. (shrink)

The error statistical account of testing uses statistical considerations, not to provide a measure of probability of hypotheses, but to model patterns of irregularity that are useful for controlling, distinguishing, and learning from errors. The aim of this paper is (1) to explain the main points of contrast between the error statistical and the subjective Bayesian approach and (2) to elucidate the key errors that underlie the central objection raised by Colin Howson at our PSA 96 Symposium.

Measurement is said to be the basis of exact sciences as the process of assigning numbers to matter (things or their attributes), thus making it possible to apply the mathematically formulated laws of nature to the empirical world. Mathematics and empiria are best accorded to each other in laboratory experiments which function as what Nancy Cartwright calls nomological machine: an arrangement generating (mathematical) regularities. On the basis of accounts of measurement errors and uncertainties, I will argue for two (...) claims: 1) Both fundamental laws of physics, corresponding to ideal nomological machine, and phenomenological laws, corresponding to material nomological machine, lie, being highly idealised relative to the empirical reality; and also laboratory measurement data do not describe properties inherent to the world independently of human understanding of it. 2) Therefore the naive, representational view of measurement and experimentation should be replaced with a more pragmatic or practice-based view. (shrink)

Motor proficiency reflects the ability to perform precise and coordinated movements in different contexts. Previous research suggests that different profiles of motor proficiency may be associated with different cognitive functioning characteristics thus suggesting an interaction between cognitive and motor processes. The current study investigated this interaction in the general population of healthy adults with different profiles of motor proficiency by focusing on error-related cognitive control and behavioral adaptation mechanisms. In addition, the impact of these processes was assessed in terms (...) of trait anxiety and worries. Forty healthy adults were divided into high and low motor proficiency groups based on an assessment of their motor skills. Using electroencephalography during a flanker task, error-related negativity was measured as the neural indicator of cognitive control. Post-error slowing was measured to represent behavioral adaptation. Participants also completed an anxiety assessment questionnaire. Participants in the high motor proficiency group achieved better task accuracy and showed relatively enhanced cognitive control through increased ERN. Contrastingly, individuals in the lower motor proficiency group achieved poorer accuracy whilst showing some evidence of compensation through increased PES. Trait anxiety reflecting general worries was found to be correlated with motor functioning, but the study could not provide evidence that this was related to cognitive or behavioral control mechanisms. The interaction between cognitive and motor processes observed in this study is unique for healthy and sub-clinical populations and provides a baseline for the interpretation of similar investigations in individuals with motor disorders. (shrink)

The way metrologists conceive of measurement has undergone a major shift in the last two decades. This shift can in great part be traced to a change in the statistical methods used to deal with the expression of measurement results, and, more particularly, with the calculation of measurement uncertainties. Indeed, as we show, the incapacity of the frequentist approach to the calculus of uncertainty to deal with systematic errors has prompted the replacement of the customary frequentist methods (...) by fully Bayesian procedures. The epistemological ramifications of the Bayesian approach merge with a deep empiricist mood tantamount to an “epistemic turn”: measurement results are analysed in terms of degrees of belief, and central concepts such as error and accuracy are called into question. We challenge the perspective entailed by this epistemic turn: we insist on the centrality of the concepts of error and accuracy by underlining the intentional character of measurement that is intimately linked to the process of correction of experimental data. We further circumvent the difficulties posed by the classical analysis of measurement by stressing the social rather than the epistemic dimension of measurement activities. (shrink)

Once upon a time, logic was the philosopher’s tool for analyzing scientific reasoning. Nowadays, probability and statistics have largely replaced logic, and their most popular application—Bayesianism—has replaced the qualitative deductive relationship between a hypothesis h and evidence e with a quantitative measure of h’s probability in light of e.

Measures of algorithmic bias can be roughly classified into four categories, distinguished by the conditional probabilistic dependencies to which they are sensitive. First, measures of "procedural bias" diagnose bias when the score returned by an algorithm is probabilistically dependent on a sensitive class variable (e.g. race or sex). Second, measures of "outcome bias" capture probabilistic dependence between class variables and the outcome for each subject (e.g. parole granted or loan denied). Third, measures of "behavior-relative error bias" capture probabilistic dependence (...) between class variables and the algorithmic score, conditional on target behaviors (e.g. recidivism or loan default). Fourth, measures of "score-relative error bias" capture probabilistic dependence between class variables and behavior, conditional on score. Several recent discussions have demonstrated a tradeoff between these different measures of algorithmic bias, and at least one recent paper has suggested conditions under which tradeoffs may be minimized. -/- In this paper we use the machinery of causal graphical models to show that, under standard assumptions, the underlying causal relations among variables forces some tradeoffs. We delineate a number of normative considerations that are encoded in different measures of bias, with reference to the philosophical literature on the wrongfulness of disparate treatment and disparate impact. While both kinds of error bias are nominally motivated by concern to avoid disparate impact, we argue that consideration of causal structures shows that these measures are better understood as complicated and unreliable measures of procedural biases (i.e. disparate treatment). Moreover, while procedural bias is indicative of disparate treatment, we show that the measure of procedural bias one ought to adopt is dependent on the account of the wrongfulness of disparate treatment one endorses. Finally, given that neither score-relative nor behavior-relative measures of error bias capture the relevant normative considerations, we suggest that error bias proper is best measured by score-based measures of accuracy, such as the Brier score. (shrink)

Though he maintained a significant interest in theoretical aspects of measurement, Henry E. Kyburg, Jr. was critical of the representational theory that in many ways has come to dominate discussions concerning the foundations of measurement. In particular, Kyburg (in Savage and Ehrlich (eds) Philosophical and foundational issues in measurement theory, 1992 ) asserts that the representational theory of measurement, as introduced in (Scott and Suppes, Journal of Symbolic Logic, 23:113–128, 1958 ) and developed in (Krantz et (...) al., Foundations of measurment: additive and polynomial representations. Academic Press, 1971 ), cannot account for the measurement of error. The present work examines and responds to this charge. (shrink)

Adaptive behavior critically depends on performance monitoring, the ability to monitor action outcomes and the need to adapt behavior. PM-related brain activity has been linked to guiding decisions about whether action adaptation is warranted. The present study examined whether PM-related brain activity in a flanker task, as measured by electroencephalography, was associated with adaptive behavior in daily life. Specifically, we were interested in the employment of self-control, operationalized as self-control failures, and measured using ecological momentary assessment. Analyses were conducted using (...) an adaptive elastic net regression to predict SCFs from EEG in a sample of 131 participants. The model was fit using within-subject averaged response-locked EEG activity at each electrode and time point within an epoch surrounding the response. We found that higher amplitudes of the error-related negativity were related to fewer SCFs. This suggests that lower error-related activity may relate to lower recruitment of interventive self-control in daily life. Altered cognitive control processes, like PM, have been proposed as underlying mechanisms for various mental disorders. Understanding how alterations in PM relate to regulatory control might therefore aid in delineating how these alterations contribute to different psychopathologies. (shrink)

This article discusses the ways in which nineteenth-century geodesists reflected on precision as an epistemic virtue in their measurement practice. Physical geodesy is often understood as a quintessential nineteenth-century precision science, stimulating advances in instrument making and statistics, and generating incredible quantities of data. Throughout most of the nineteenth century, geodesists indeed pursued their most prestigious research problem – the exact determination of the earth’s polar flattening – along those lines. Treating measurement errors as random, they assumed that (...) remaining discordances could be overcome by manufacturing better instruments and extending statistical analysis to a larger amount of data. In the second half of the nineteenth century, however, several German geodesists developed sophisticated methodological critiques of their discipline, in which they diagnosed a too-narrow focus on precision among their peers. On their account, geodesists urgently needed to identify and anticipate the causes of the remaining measurement errors that arose from the earth’s little understood interior constitution. While mostly overlooked in the literature, these critiques paved the way for many empirical successes in late nineteenth- and early twentieth-century geodesy, including the first convergent measurements of the earth’s polar flattening. (shrink)

This paper considers two subjective measures of the existence of unconscious mental states - the guessing criterion, and the zero correlation criterion - and considers the assumptions underlying their application in experimental paradigms. Using higher order thought theory the impact of different types of biases on the zero correlation and guessing criteria are considered. It is argued that subjective measures of consciousness can be biased in various specified ways, some of which involve the relation between first order states and second (...) order thoughts, and hence are not errors in measurement of the conscious status of mental states; but other sorts of biases are measurement errors, involving the relation between higher order thoughts and their expression. Nonetheless, it is argued this type of bias does not preclude subjective measures - both the guessing criterion and the zero correlation criterion - as being amongst the most appropriate and useful tools for measuring the conscious status of mental states. (shrink)

This work develops an epistemology of measurement, that is, an account of the conditions under which measurement and standardization methods produce knowledge as well as the nature, scope, and limits of this knowledge. I focus on three questions: (i) how is it possible to tell whether an instrument measures the quantity it is intended to? (ii) what do claims to measurement accuracy amount to, and how might such claims be justified? (iii) when is disagreement among instruments a (...) sign of error, and when does it imply that instruments measure different quantities? Based on a series of case studies conducted in collaboration with the US National Institute of Standards and Technology (NIST), I argue for a model-based approach to the epistemology of physical measurement. To measure a physical quantity, I argue, is to estimate the value of a parameter in an idealized model of a physical process. Such estimation involves inference from the final state (‘indication’) of a process to the value range of a parameter (‘outcome’) in light of theoretical and statistical assumptions. Contrary to contemporary philosophical views, measurement outcomes cannot be obtained by mapping the structure of indications. Instead, measurement outcomes as well as claims to accuracy, error and quantity individuation can only be adjudicated relative to a choice of idealized modelling assumptions. (shrink)

Recent research shows that in reasoning tasks, subjects usually produce an initial intuitive answer, accompanied by a metacognitive experience, which has been called feeling of rightness. This paper is aimed at exploring the complimentary experience of feeling of error, that is, the spontaneous, subtle sensation of cognitive uneasiness arising from conflict detection during thinking. We investigate FOE in two studies with the “bat-and-ball” reasoning task, in its standard and isomorphic control versions. Study 1 is a generation study, in which (...) participants are asked to generate their own response. Study 2 is an evaluation study, in which participants are asked to choose between two conflicting answers. In each study, the FOE is measured by the FOE questionnaire. Results show that the FOE is significantly present in the standard B&B task when participants give a wrong answer, that our questionnaire can measure it, and furthermore, that it is diagnostic of genuine error. (shrink)

This paper argues that epistemic errors rooted in group- or identity- based biases, especially those pertaining to disability, are undertheorized in the literature on medical error. After sketching dominant taxonomies of medical error, we turn to the field of social epistemology to understand the role that epistemic schemas play in contributing to medical errors that disproportionately affect patients from marginalized social groups. We examine the effects of this unequal distribution through a detailed case study of ableism. There are (...) four primary mechanisms through which the epistemic schema of ableism distorts communication between nondisabled physicians and disabled patients: testimonial injustice, epistemic overconfidence, epistemic erasure, and epistemic derailing. Measures against epistemic injustices in general and against schema-based medical errors in particular are ultimately issues of justice that must be better addressed at all levels of health care practice. (shrink)

The current concerns with laggard growth and income inequality have led to a widespread set of demands for more regulation and higher taxation to reverse the trend. These two approaches move matters exactly in the wrong direction. The correct response is to find ways to reduce tax burdens and barriers to entry, and to reduce the political uncertainty associated with new government measures. It may well be too late, worldwide, for a substantial rollback in the welfare state. But the current (...) proposals will only prolong the dismal results on both fronts in any arena in which they are tried. (shrink)

Exact measurements are a central practice of modern physics. In certain cases, they are essential for determining values of coefficients, for confirming theories, and for detecting the existence of effects. The history of piezoelectricity at the end of the nineteenth century reveals two different methods of exact measurement: a mathematical versus an “artisanal” approach. In the former, a scientist first carried out the experiment and later employed mathematical methods to reduce error. In the latter, a scientist physically manipulated (...) the experimental apparatus to bypass possible sources of error before its performance. These two approaches were related to German theoreticians and to French experimentalists, respectively. However, affiliation with a particular school rather than nationality was the decisive factor in the differences between the two approaches. Despite differences, adherents of both approaches sought to attain high precision and to eliminate even small experimental errors. This history exhibits the complexity and flexibility of experiments and their analysis. It supports the claim of the “New Experimentalism” that theory does not supply complete directions for practical experimental decisions. An example for this is found in the way an experimental error was discovered only by a comparison to an earlier experiment rather than to a theory. (shrink)

Recently, Timothy Williamson has argued that considerations about margins of errors can generate a new class of cases where agents have justified true beliefs without knowledge. I think this is a great argument, and it has a number of interesting philosophical conclusions. In this note I’m going to go over the assumptions of Williamson’s argument. I’m going to argue that the assumptions which generate the justification without knowledge are true. I’m then going to go over some of the recent arguments (...) in epistemology that are refuted by Williamson’s work. And I’m going to end with an admittedly inconclusive discussion of what we can know when using an imperfect measuring device. (shrink)

In both axiomatic theories and the practice of extensive measurement, it is assumed that a series of replicas of any given object can be found. The replicas give rise to a standard series, the "multiples" of the given object. The numerical value assigned to any object is determined, approximately, by comparisons with members of a suitable standard series. This prescription introduces unspecified errors, if the comparison process is somewhat insensitive, so that "replicas" are not really equivalent. In this paper, (...) it is assumed that the comparison process leads only to a semiorder, which allows for such insensitivity. It is shown that, nevertheless, extensive measurement can be carried out, provided that a certain set of (plausible) axioms is valid. Approximate measures, and their limits of error, can be derived from finite sets of semiorder observations. These approximate measures converge to ratio-scale exact measurement. (shrink)

The Cognitive Reflection Test (CRT) is a measure of analytical reasoning that cues an intuitive but incorrect response that must be rejected for successful performance to be attained. The CRT yields two types of errors: Intuitive errors, which are attributed to Type 1 processes; and non-intuitive errors, which result from poor numeracy skills or deficient reasoning. Past research shows that participants who commit the highest numbers of errors on the CRT overestimate their performance the most, whereas those with the lowest (...) error-rates tend to slightly underestimate. This is an example of the Dunning-Kruger Effect (DKE). The present study examined how intuitive vs. non-intuitive errors contribute to overestimation in the CRT at different levels of performance. Female undergraduate students completed a seven-item CRT test and subsequently estimated their raw score. They also filled out the Faith in Intuition (FI) questionnaire, which is a dispositional measure of intuitive thinking. Data was separated into quartiles based on level of performance on the CRT. The results demonstrated the DKE. Additionally, intuitive and non-intuitive errors predicted miscalibration among low, but not high performers. However, intuitive errors were a stronger predictor of miscalibration. Finally, FI was positively correlated with CRT self-estimates and miscalibration, indicating that participants who perceived themselves to be more intuitive were worse at estimating their score. These results taken together suggest that participants who perform poorly in the CRT and also those who score higher in intuitive thinking disposition are more susceptible to the influences of heuristic-based cues, such as answer fluency, when judging their performance. (shrink)

Abstract In Myth & Measurement: The New Economics of the Minimum Wage, David Card and Alan Krueger assemble a variety of evidence purporting to weaken the case that minimum wages lead to unemployment among low?wage workers. Although the authors succeed in casting doubt on some previous studies that supported the standard view, they fail to provide compelling evidence for their alternative model. The methodological errors in their showcase study of minimum wages in New Jersey and Pennsylvania render it nearly (...) worthless, and the remainder of the book's arguments are not weighty enough to reverse the conventional economic wisdom on minimum wages. (shrink)

This paper is based on ethnographic research of data practices in a public health project called Weather Health and Air Pollution. I examine two different kinds of practices that make air pollution data, focusing on how they relate to particular modes of sensing and articulating air pollution. I begin by describing the interstitial spaces involved in making measurements of air pollution at monitoring sites and in the running of a computer simulation. Specifically, I attend to a shared dimension of these (...) practices, the checking of a numerical reading for error. Checking a measurement for error is routine practice and a fundamental component of making data, yet these are also moments of interpretation, where the form and meaning of numbers are ambiguous. Through two case studies of modelling and monitoring data practices, I show that making a ‘good’ measurement requires developing a feeling for the instrument–air pollution interaction in terms of the intended functionality of the measurements made. These affective dimensions of practice are useful analytically, making explicit the interaction of standardised ways of knowing and embodied skill in stabilising data. I suggest that environmental data practices can be studied through researchers’ materialisation of error, which complicate normative accounts of Big Data and highlight the non-linear and entangled relations that are at work in the making of stable, accurate data. (shrink)