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Abstract

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The Creation and Analysis of Employer-Employee Matched Data
Type: Book
ISBN: 978-0-44450-256-8

Abstract

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Panel Data Econometrics Theoretical Contributions and Empirical Applications
Type: Book
ISBN: 978-1-84950-836-0

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Article
Publication date: 5 August 2020

Moeti Masiane, Eric Jacques, Wuchun Feng and Chris North

The purpose of this paper is to collect data from humans as they generate insights from the visualised results of computational fluid dynamics (CFD) scientific simulation…

Abstract

Purpose

The purpose of this paper is to collect data from humans as they generate insights from the visualised results of computational fluid dynamics (CFD) scientific simulation. The authors hypothesise the behaviour of their insight errors (IEs) and proceed to quantify the IEs provided by the crowd participants. They then use the insight framework to model the behaviours of the errors. Using the crowd responses and models from the framework, they test the hypotheses and use the results to validate the framework for the speedup of CFD applications.

Design/methodology/approach

The authors use a randomised between-subjects experiment with blocking. CFD grid resolution is the independent variable while IE is the dependent variable. The experiment has one treatment factor with five levels. In case varying timestamps has an effect on insight variance levels, the authors block the responses by timestep. In total, 150 participants are randomly assigned to one of five groups and also randomly assigned to one of five blocks within a treatment. Participants are asked to complete a benchmark and open-ended task.

Findings

The authors find that the variances of insight and perception errors have a U-shaped relationship with grid resolution, that similar to the previously studied visualisation applications, the IE framework is valid for insights generated from CFD results and grid resolution can be used to predict the variance of IE resulting from observing CFD post-processing results.

Originality/value

To the best of the authors’ knowledge, no other work has measured IE variance to present it to simulation users so that they can use it as a feedback metric for selecting the ideal grid resolution when using grid resolution to speedup CFD simulation.

Details

Journal of Engineering, Design and Technology , vol. 19 no. 1
Type: Research Article
ISSN: 1726-0531

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Book part
Publication date: 21 December 2010

Saleem Shaik and Ashok K. Mishra

In this chapter, we utilize the residual concept of productivity measures defined in the context of normal-gamma stochastic frontier production model with heterogeneity to…

Abstract

In this chapter, we utilize the residual concept of productivity measures defined in the context of normal-gamma stochastic frontier production model with heterogeneity to differentiate productivity and inefficiency measures. In particular, three alternative two-way random effects panel estimators of normal-gamma stochastic frontier model are proposed using simulated maximum likelihood estimation techniques. For the three alternative panel estimators, we use a generalized least squares procedure involving the estimation of variance components in the first stage and estimated variance–covariance matrix to transform the data. Empirical estimates indicate difference in the parameter coefficients of gamma distribution, production function, and heterogeneity function variables between pooled and the two alternative panel estimators. The difference between pooled and panel model suggests the need to account for spatial, temporal, and within residual variations as in Swamy–Arora estimator, and within residual variation in Amemiya estimator with panel framework. Finally, results from this study indicate that short- and long-run variations in financial exposure (solvency, liquidity, and efficiency) play an important role in explaining the variance of inefficiency and productivity.

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Maximum Simulated Likelihood Methods and Applications
Type: Book
ISBN: 978-0-85724-150-4

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Book part
Publication date: 29 March 2006

Kajal Lahiri and Fushang Liu

We develop a theoretical model to compare forecast uncertainty estimated from time-series models to those available from survey density forecasts. The sum of the average…

Abstract

We develop a theoretical model to compare forecast uncertainty estimated from time-series models to those available from survey density forecasts. The sum of the average variance of individual densities and the disagreement is shown to approximate the predictive uncertainty from well-specified time-series models when the variance of the aggregate shocks is relatively small compared to that of the idiosyncratic shocks. Due to grouping error problems and compositional heterogeneity in the panel, individual densities are used to estimate aggregate forecast uncertainty. During periods of regime change and structural break, ARCH estimates tend to diverge from survey measures.

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Econometric Analysis of Financial and Economic Time Series
Type: Book
ISBN: 978-0-76231-274-0

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Article
Publication date: 1 March 2013

Hongyu Zhao, Zhelong Wang, Hong Shang, Weijian Hu and Gao Qin

The purpose of this paper is to reduce the calculation burden and speed up the estimation process of Allan variance method while ensuring the exactness of the analysis results.

Abstract

Purpose

The purpose of this paper is to reduce the calculation burden and speed up the estimation process of Allan variance method while ensuring the exactness of the analysis results.

Design/methodology/approach

A series of six‐hour static tests have been implemented at room temperature, and the static measurements have been collected from MEMS IMU. In order to characterize the various types of random noise terms for the IMU, the basic definition and main procedure of the Allan variance method are investigated. Unlike the normal Allan variance method, which has the shortcomings of processing large data sets and requiring long computation time, a modified Allan variance method is proposed based on the features of data distribution in the log‐log plot of the Allan standard deviation versus the averaging time.

Findings

Experiment results demonstrate that the modified Allan variance method can effectively estimate the noise coefficients for MEMS IMU, with controllable computation time and acceptable estimation accuracy.

Originality/value

This paper proposes a time‐controllable Allan variance method which can quickly and accurately identify different noise terms imposed by the stochastic fluctuations.

Details

Industrial Robot: An International Journal, vol. 40 no. 2
Type: Research Article
ISSN: 0143-991X

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Book part
Publication date: 29 August 2005

Kai S. Cortina, Hans Anand Pant and Joanne Smith-Darden

Over the last decade, latent growth modeling (LGM) utilizing hierarchical linear models or structural equation models has become a widely applied approach in the analysis…

Abstract

Over the last decade, latent growth modeling (LGM) utilizing hierarchical linear models or structural equation models has become a widely applied approach in the analysis of change. By analyzing two or more variables simultaneously, the current method provides a straightforward generalization of this idea. From a theory of change perspective, this chapter demonstrates ways to prescreen the covariance matrix in repeated measurement, which allows for the identification of major trends in the data prior to running the multivariate LGM. A three-step approach is suggested and explained using an empirical study published in the Journal of Applied Psychology.

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Multi-Level Issues in Strategy and Methods
Type: Book
ISBN: 978-1-84950-330-3

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Book part
Publication date: 1 January 2005

Kyoungsu Kim

Chen, Mathieu and Bliese (this volume) propose a useful framework for conceptualizing, testing, and validating multi-level constructs. Their framework focuses on the…

Abstract

Chen, Mathieu and Bliese (this volume) propose a useful framework for conceptualizing, testing, and validating multi-level constructs. Their framework focuses on the differences in constructs that occur between individuals and groups. One key question arises with their approach: What happens if the validity of constructs is viewed as potentially varying not only between individuals and between groups but also within individuals and within groups? The focus on within-individuals and within-groups variations is called “frog-pond effects.” Based on such frog-pond effects, this chapter reconsiders the approach of Chen et al. and discusses some of the implications of adding this perspective to multi-level research.

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Multi-level Issues in Organizational Behavior and Processes
Type: Book
ISBN: 978-1-84950-269-6

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Machine Learning and Artificial Intelligence in Marketing and Sales
Type: Book
ISBN: 978-1-80043-881-1

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Article
Publication date: 12 March 2021

Godson A. Tetteh, Kwasi Amoako-Gyampah and Amoako Kwarteng

Several research studies on Lean Six Sigma (LSS) have been done using the survey methodology. However, the use of surveys often relies on the measurement of variables…

Abstract

Purpose

Several research studies on Lean Six Sigma (LSS) have been done using the survey methodology. However, the use of surveys often relies on the measurement of variables, which cannot be directly observed, with attendant measurement errors. The purpose of this study is to develop a methodological framework consisting of a combination of four tools for identifying and assessing measurement error during survey research.

Design/methodology/approach

This paper evaluated the viability of the framework through an experimental study on the assessment of project management success in a developing country environment. The research design combined a control group, pretest and post-test measurements with structural equation modeling that enabled the assessment of differences between honest and fake survey responses. This paper tested for common method variance (CMV) using the chi-square test for the difference between unconstrained and fully constrained models.

Findings

The CMV results confirmed that there was significant shared variance among the different measures allowing us to distinguish between trait and faking responses and ascertain how much of the observed process measurement is because of measurement system variation as opposed to variation arising from the study’s constructs.

Research limitations/implications

The study was conducted in one country, and hence, the results may not be generalizable.

Originality/value

Measurement error during survey research, if not properly addressed, can lead to incorrect conclusions that can harm theory development. It can also lead to inappropriate recommendations for practicing managers. This study provides findings from a framework developed and assessed in a LSS project environment for identifying faking responses. This paper provides a robust framework consisting of four tools that provide guidelines on distinguishing between fake and trait responses. This tool should be of great value to researchers.

Details

International Journal of Lean Six Sigma, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2040-4166

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