Measurement Error Problems Research Articles

Do Customer Emotions Affect Agent Speed? An Empirical Study of Emotional Load in Online Customer Contact Centers

Problem definition: Research in operations management has focused mainly on system-level load, ignoring the fact that service agents and customers express a variety of emotions that may impact service processes and outcomes. We introduce the concept of emotional load—the emotional demands that customer behaviors impose on service agents—to analyze how customer emotions affect service worker’s behavior. Academic/practical relevance: Most theories in organizational behavior literature predict that emotions expressed by customers reduce agent’s cognitive abilities and therefore, should reduce the agent’s speed (e.g., by increasing the service time required to serve an angry customer). We aim to shed light on the magnitude of that phenomenon while addressing important econometric challenges. We also investigate an important mechanism that drives this relation, namely agent effort. We discuss practical opportunities that arise from measuring emotional load and how it can be used to enhance productivity. Methodology: We measure the emotional load of agents using sentiment analysis tools that quantify positive/negative customer emotion expressions in an online chat-type contact center and link it to agent behavior: response time (RT) and the length and number of messages required to complete a service request. Identifying a causal effect of customer emotion on agent behavior using observational data is challenging because there are confounding factors associated with the complexity of service requests, which are related to both customer emotions and agent behavior. Our identification strategy uses panel data and exploits the variation across messages within a focal request using fixed effects to control for unobserved factors associated with case complexity. Instrumental variables are also used to address issues of measurement error and other endogeneity problems; the instruments are based on exogenous shocks to agent performance indicators that have been studied in the service operations literature. Results: Analyses show that emotional load created by negative customer emotions increases agent RT, the length of the agent messages (a measure of effort), and the required number of messages needed to complete a service request. Emotional load and agent RT reciprocally affect each other, with long agent RTs and a high number of messages producing more negative customer emotion. Managerial implications: We suggest that the emotional content in customer communications should be an important factor to consider when assigning workload to agents in a service system. Our study provides a rigorous methodology to measure the emotional content from customer text messages and objectively evaluate its associated workload. We discuss how this can be used to improve staffing decisions and dynamic workload routing through real-time monitoring of emotional load.

A wavelet-based approach for Johansen’s likelihood ratio test for cointegration in the presence of measurement errors: An application to CO2 emissions and real GDP data

We suggest a wavelet filtering technique as a remedy to the problem of measurement errors when testing for cointegration using Johansen’s (1988) likelihood ratio test. Measurement errors, which more or less are always present in empirical economic data, essentially indicates that the variable of interest (the true signal) is contaminated with noise, which may induce biased and inconsistent estimates and erroneous inference. Our Monte Carlo experiments demonstrate that measurement errors distort the statistical size of Johansen’s cointegration test in finite samples; the test is significantly oversized. A contribution and major finding of this article is that the proposed wavelet-based technique significantly improves the statistical size of the traditional Johansen test in small and medium sized samples. Since Johansen’s test is a standard cointegration test, and we demonstrate that the constantly present measurement errors in empirical data over sizes the test, this simple alteration can be used in most situations with more reliable finite sample inference. We empirically examine the long-run relation between CO2 emissions and the real GDP in the G7 countries. The traditional Johansen tests provide evidence of an equilibrium relation for Canada and weak evidence for the US. However, the suggested size-unbiased wavelet-filtering approach consistently indicates no evidence of cointegration for all six countries.

Efficient semiparametric inference for two-phase studies with outcome and covariate measurement errors.

In modern observational studies using electronic health records or other routinely collected data, both the outcome and covariates of interest can be error-prone and their errors often correlated. A cost-effective solution is the two-phase design, under which the error-prone outcome and covariates are observed for all subjects during the first phase and that information is used to select a validation subsample for accurate measurements of these variables in the second phase. Previous research on two-phase measurement error problems largely focused on scenarios where there are errors in covariates only or the validation sample is a simple random sample of study subjects. Herein, we propose a semiparametric approach to general two-phase measurement error problems with a quantitative outcome, allowing for correlated errors in the outcome and covariates and arbitrary second-phase selection. We devise a computationally efficient and numerically stable expectation-maximization algorithm to maximize the nonparametric likelihood function. The resulting estimators possess desired statistical properties. We demonstrate the superiority of the proposed methods over existing approaches through extensive simulation studies, and we illustrate their use in an observational HIV study.

On Liu-type biased estimators in measurement error models

ABSTRACT This paper considers the shrinkage estimation of parameters of measurement error models when it is suspected that the parameters may belong to a linear subspace. The class of Liu type estimators is proposed by choosing five quasi-empirical Bayes estimators in the presence of measurement errors in the data. This class of estimator combines the sample and prior information together along with the good properties of ridge estimators and chosen five quasi-empirical Bayes estimators. The advantages of the proposed class of estimators over the classical ridge regression estimator is that the quasi-empirical Bayes estimators are a linear function of the tuning parameter. When data has problems of measurement errors and multicollinearity, then these estimators can handle both the issues simultaneously. The asymptotic properties of the estimators are derived and analyzed. A Monte Carlo simulation is conducted and its findings are reported.

Stochastic Revealed Preferences with Measurement Error

AbstractA long-standing question about consumer behaviour is whether individuals’ observed purchase decisions satisfy the revealed preference (RP) axioms of the utility maximization theory (UMT). Researchers using survey or experimental panel data sets on prices and consumption to answer this question face the well-known problem of measurement error. We show that ignoring measurement error in the RP approach may lead to overrejection of the UMT. To solve this problem, we propose a new statistical RP framework for consumption panel data sets that allows for testing the UMT in the presence of measurement error. Our test is applicable to all consumer models that can be characterized by their first-order conditions. Our approach is non-parametric, allows for unrestricted heterogeneity in preferences and requires only a centring condition on measurement error. We develop two applications that provide new evidence about the UMT. First, we find support in a survey data set for the dynamic and time-consistent UMT in single-individual households, in the presence of nonclassical measurement error in consumption. In the second application, we cannot reject the static UMT in a widely used experimental data set in which measurement error in prices is assumed to be the result of price misperception due to the experimental design. The first finding stands in contrast to the conclusions drawn from the deterministic RP test of Browning (1989, International Economic Review, 979–992). The second finding reverses the conclusions drawn from the deterministic RP test of Afriat (1967, International Economic Review, 8, 6–77) and Varian (1982, Econometrica, 945–973).

The Fast Attitude Estimation Method Based on Quaternion and Generalized Multivectors

Aiming at the problem of low output accuracy and large attitude measurement error of a micro-electromechanical systems (MEMS) inertial sensor, a fast attitude estimation method based on quaternion and generalized multivectors is proposed. In the algorithm, the attitude estimation method based on the deterministic vector with only one nonzero element is analyzed. Then, by constructing a new deterministic vector, the attitude updating equations are analyzed for the case that the deterministic vector contains two nonzero elements and three nonzero elements. In this article, all forms of the deterministic vectors are analyzed, and the attitude updating equations are analyzed according to the different deterministic vectors. In view of the existence of measurement noise and measurement interference in the observations of the deterministic vectors, this algorithm provides a compensation method. The experimental results show that the method has high accuracy and efficiency.

An error reduction method of a binocular measurement system under vibrating conditions

To solve the problem of large measurement errors caused by vibrations of the surrounding environment, an error reduction method of a binocular measurement system is proposed. A light pen with a V-8 layout is designed to estimate the pose, and coded markers are used to establish a dynamic reference coordinate system to reduce the measurement error mainly caused by vibrations. The experimental results indicate that the light pen is highly accurate in pose estimation, and a root mean square error of the standard blocks and standard balls can be achieved within 0.02 mm when the measurement distance is 1.5 m. By measuring the straightness error of an ABB robot’s manipulator in a comparative experiment, the effective establishment of the dynamic reference coordinate system is preliminarily proven. The experiments are then conducted under vibrating conditions to calculate the standard deviations of the light pen probe center on the X-, Y-, and Z-axes with different quantities (3 and 4) of coded markers, which verify the high accuracy and real-time requirement of the method with a standard deviation below 0.040 mm and a processing speed above 45 frames per second when the measurement distance is 2 m.

The Relationship between Discretionary Revenues and Book-Tax Difference

Purpose: This study looks at the relevance between discretionary revenue and book-tax differences (hereafter BTDs). While the study of earnings management, which focused on discretionary accruals and real earnings management, has largely made, it has not yet been actively researched on discretionary revenues. Therefore, it was believed that discretionary revenue would expand the preceding study by looking at its relevance to BTD, known as financial reporting quality and measures of tax avoidance. In general, prior research suggested that earnings management make BTDs larger. Thus, the relationship between discretionary revenue and the amount of BTD is predicted positive. Research design, data and methodology: To this end, the method of discretionary revenues was used and BTDs measured in four ways. First, Earnings before income tax - estimated taxable income divided by total asset (BTD). Second is fractional rank variable of BTDs (FBTD). Third is Indicator variable equals 1 if the firm-year has a positive BTD, 0 otherwise (PBTD). Fourth is that Indicator variable equals 1 if the firm-year has a BTDs in top(bottom) quartile, 0 otherwise (LPBTD, LNBTD). 4,251 samples were analyzed in the Korean Security market (KOSPI) from 2003 to 2014. Results Empirical analysis shows that BTDs increases as discretionary revenue increases. These results were equally observed when BTDs was measured as a ranking variable or as a indicating variable. These results indicate that earnings management through the revenue of managers exacerbate the quality of financial reporting. Conclusions: In sum, discretionary revenues can be used as an indicator of making BTDs larger and meaningful as the first study of the Korean capital market where discretionary revenues affect accounting information quality. Investors need to increase interest in discretionary revenues because intervention in financial reporting through revenue accounts by managers can increase information asymmetry and agency costs. This means that studies on discretionary revenues that have been relatively small should be expanded. The results also provide important implications for the relevant authorities and investors. Despite these benefits, however, measurement error problems with estimates still appear as limited points, and prudent interpretations are required, and additional follow-up studies are needed in that variables that are not yet considered in this study may affect our findings.

Measurement errors in agricultural data and their implications on marginal returns to modern agricultural inputs

AbstractThe low uptake of modern agricultural technologies in sub‐Saharan African countries has encouraged researchers to revisit the returns to (or profitability of) these agricultural inputs. A related strand of literature is exploring the allocative efficiency of these factors of production in African agriculture. However, all these studies rely on self‐reported agricultural data, which are prone to nonclassical measurement errors, the errors in these data are correlated with the true values of variables of interest. In this article we investigate the implication of measurement errors in self‐reported agricultural input and production data on marginal returns to these modern agricultural inputs. We consider a generic two‐sided measurement error problem where both production and inputs can be measured with error, and these errors can be correlated. We employ both self‐reported and objective measures of production and plot size to compute output elasticities under these alternative measurement scenarios. We find that using self‐reported production and plot size overestimates output elasticities and hence marginal returns to modern agricultural inputs (including chemical fertilizer and improved seed). These results are noteworthy in terms of informing conventional technology diffusion strategies as well as in view of revisiting existing presumptions about the profitability of modern agricultural inputs.

Development of equation-error adaptive IIR-filter-based active noise control system

An infinite impulse response (IIR) filter benefits to identify complex primary and secondary paths information with short filter length for active noise control (ANC) system. Conventional output-error (OE)-based system uses adaptive IIR filter with the filtered-U recursive least mean square (FURLMS) algorithm to cancel undesired noise. This paper develops an ANC system using equation-error (EE)-based IIR filter and analyzes its step size bound, global minimum and computation complexity. The EE-based ANC system can also solve measurement error problem for the error sensor. Several simulations show effectiveness and enhancement of the developed EE-based system.

Corporate Disclosure and Capital Markets

Corporate disclosures are critical for functioning of capital markets demanding financial information. Firms provide disclosures through structured and regulated reports, including financial statements, disclosure notes, management recommendations, other compliance filings. In addition, firms voluntarily communicate their proforma financial statements and business data arising from forecasts or corporate reports prepared for investors. Lastly, there are certain disclosures demanded by financial and market intermediaries, industry experts and media. This paper introduces questions and empirical methods that are pertinent to capital markets research in accounting (hereinafter, CMRA). In section II, the literature review is discussed. Forces giving rise to demand for disclosures in modern capital-market economy, and the institutions increasing the credibility of disclosures are addressed. In the same section, I inform about the main research topics used for studying of financial reporting and disclosures associated with capital markets. I use the disclosure framework to identify research questions within each topic. Empirical evidence and research methodology related with CMRA are reviewed in section III. Section IV discusses reflections on constraints of studying financial reporting issues and their impact on capital markets. Previous studies in CMRA documented disclosures associated with stock performance, bid-ask spreads, transactions, cost of capital, analyst coverage, executive compensation and institutional ownership (Healy, P. et al. 2001). Also, section IV evaluates the previous studies falling short due to endogeneity and measurement error problems and complicating the interpretation of research findings. This paper will aid potential researchers in identification of many open, i.e. unanswered, questions in the CMRA field. Section V summarizes the main ideas, methodology and desired results for the research. Finally, section VI lists bibliography used for the references.

Measurement of raised pavement marker’s photometric performance

Large error exists in the photometric performance test of raised pavement marker. This paper reports a set of retroreflective measurement standard devices built to measure raised pavement markers based on the principle of direct measurement, and the photometric performance of the raised pavement markers was tested. The study also involved calibration of a portable meter using the standard device. According to the experimental comparison, the method proposed in this paper provides a new direction for the development of retroreflection measurement technology. The results measured by the proposed method showed high accuracy. This method can solve the large measurement error problems with indication errors of 2% at most.

The R Package hmi: A Convenient Tool for Hierarchical Multiple Imputation and Beyond

Applications of multiple imputation have long outgrown the traditional context of dealing with item nonresponse in cross-sectional data sets. Nowadays multiple imputation is also applied to impute missing values in hierarchical data sets, address confidentiality concerns, combine data from different sources, or correct measurement errors in surveys. However, software developments did not keep up with these recent extensions. Most imputation software can only deal with item nonresponse in cross-sectional settings and extensions for hierarchical data - if available at all - are typically limited in scope. Furthermore, to our knowledge no software is currently available for dealing with measurement error using multiple imputation approaches. The R package hmi tries to close some of these gaps. It offers multiple imputation routines in hierarchical settings for many variable types (for example, nominal, ordinal, or continuous variables). It also provides imputation routines for interval data and handles a common measurement error problem in survey data: biased inferences due to implicit rounding of the reported values. The user-friendly setup which only requires the data and optionally the specification of the analysis model of interest makes the package especially attractive for users less familiar with the peculiarities of multiple imputation. The compatibility with the popular mice package (Van Buuren and Groothuis-Oudshoorn 2011) ensures that the rich set of analysis and diagnostic tools and post-imputation functions available in mice can be used easily, once the data have been imputed.

Finding our balance? Revisiting the randomization revolution in development economics ten years further on

Ten years ago, we offered reflections on the power and pitfalls of randomized controlled trials in development economics, arguing that the research community had lost its balance between theory, observational data and randomized experiments. We remain convinced of both the importance and the limits of RCTs for development economics research. But with another decade of RCTs under our collective belts, three issues now strike us as having become increasingly important. First, ethical risks still loom large. Second, increasing evidence that many interventions have highly heterogeneous impacts, places a premium on reintegrating ex ante theorizing with RCT methods to understand the heterogeneity. In some cases, heterogeneity may imply RCTs are less desirable than other research methods. Third, the increasing use of RCTs to study informational, behavioral, and other perceptions-mediated interventions creates an opportunity for non-classical measurement error problems that undercut the statistical power of seemingly well-designed studies in ways that remain underappreciated.

Effects of health status on work exit and absenteeism among the older working population in China: a secondary analysis of a cohort sample

ObjectivesTo analyse the effects of health status on work exit and absenteeism among the older working population in China.DesignSecondary analysis of a cohort sample.Setting and participantsCommunity samples who engaged in...

Partial Identification of Economic Mobility: With an Application to the United States

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the well-acknowledged problem of measurement error in self-reported and even administrative data. Existing methods of addressing measurement error are complex, rely on numerous strong assumptions, and often require data from more than two periods. In this article, we investigate what can be learned about economic mobility as measured via transition matrices while formally accounting for measurement error in a reasonably transparent manner. To do so, we develop a nonparametric partial identification approach to bound transition probabilities under various assumptions on the measurement error and mobility processes. This approach is applied to panel data from the United States to explore short-run mobility before and after the Great Recession.

Special Issue on Probabilistic Robotics and SLAM

Intelligent mobile robots need self-localization, map generation, and the ability to explore unknown environments autonomously. Probabilistic processing can be applied to overcome the problems of movement uncertainties and measurement errors. Probabilistic robotics and simultaneous localization and mapping (SLAM) technologies are therefore strongly related, and they have been the focus of many studies. As more and more practical applications are found for intelligent mobile robots, such as for autonomous driving and cleaning, the applicability of these techniques has been increasing. In this special issue, we provide a wide variety of very interesting papers ranging from studies and developments in applied SLAM technologies to fundamental theories for SLAM. There are five academic papers, one each on the following topics: first visit navigation, controls for following rescue clues, indoor localization using magnetic field maps, a new solution for self-localization using downhill simplex method, and object detection for long-term map management through image-based learning. In addition, in the next number, there will be a review paper by Tsukuba University’s Prof. Tsubouchi, who is famous for the Tsukuba Challenge and research related to mobile robotics. We editors hope this special issue will help readers to develop mobile robots and use SLAM technologies and probabilistic approaches to produce successful applications.

Locally efficient semiparametric estimators for a class of Poisson models with measurement error

AbstractThe presence of measurement error may cause bias in parameter estimation and can lead to incorrect conclusions in data analyses. Despite a large body of literature on general measurement error problems, relatively few works exist to handle Poisson models. In this article we thoroughly study Poisson models with errors in covariates and propose consistent and locally efficient semiparametric estimators. We assess the finite sample performance of the estimators through extensive simulation studies and illustrate the proposed methodologies by analyzing data from the Stroke Recovery in Underserved Populations Study. The Canadian Journal of Statistics 47: 157–181; 2019 © 2019 Statistical Society of Canada

Public pension wealth and household asset holdings: new evidence from Belgium

AbstractIt has been long suggested that public pension wealth may crowd out household savings. However, there remains controversy about the extent of this displacement effect. In this paper we use an original microsimulation model based on retrospective survey data collected through the third wave of the Survey of Health, Ageing and Retirement in Europe (SHARE) to estimate the displacement effect of public pension wealth on other wealth in Belgium. Combining this rich dataset with an accurate estimation of the individual pension entitlements allows us to circumvent some of the main measurement error problems faced by previous studies. We estimate that an extra euro of public pension wealth is associated with about 14–25 cent decline in households’ non-pension wealth.

How Serious is the Measurement-Error Problem in a Popular Risk-Aversion Task?

This paper uses the test/retest data from the Holt and Laury (2002) experiment to provide estimates of the measurement error in this popular risk-aversion task. Maximum likelihood estimation suggests that the variance of the measurement error is approximately equal to the variance of the number of safe choices. Simulations confirm that the coefficient on the risk measure in univariate OLS regressions is approximately half of its true value. Unlike measurement error, the discrete transformation of continuous risk aversion is not a major issue. We discuss the merits of a number of different solutions: increasing the number of observations, IV and the ORIV method developed by Gillen et al. (2019).