scholarly journals An Updated Guideline for Assessing Discriminant Validity

2020 ◽  
pp. 109442812096861
Author(s):  
Mikko Rönkkö ◽  
Eunseong Cho
Keyword(s):  
Monte Carlo ◽  
Measurement Error ◽  
Monte Carlo Simulations ◽  
Empirical Research ◽  
Discriminant Validity ◽  
Ad Hoc ◽  
Validity Assessment ◽  
Two Measures ◽  
Definition Of

Discriminant validity was originally presented as a set of empirical criteria that can be assessed from multitrait-multimethod (MTMM) matrices. Because datasets used by applied researchers rarely lend themselves to MTMM analysis, the need to assess discriminant validity in empirical research has led to the introduction of numerous techniques, some of which have been introduced in an ad hoc manner and without rigorous methodological support. We review various definitions of and techniques for assessing discriminant validity and provide a generalized definition of discriminant validity based on the correlation between two measures after measurement error has been considered. We then review techniques that have been proposed for discriminant validity assessment, demonstrating some problems and equivalencies of these techniques that have gone unnoticed by prior research. After conducting Monte Carlo simulations that compare the techniques, we present techniques called CICFA(sys) and [Formula: see text](sys) that applied researchers can use to assess discriminant validity.

Discriminant validity assessment in marketing research

2018 ◽  
Vol 61 (2) ◽  
pp. 210-222 ◽  
Author(s):  
Joseph M Matthes ◽  
A Dwayne Ball
Keyword(s):  
Measurement Error ◽  
Sample Size ◽  
Discriminant Validity ◽  
Marketing Research ◽  
Size Measurement ◽  
Artificial Data ◽  
Validity Assessment ◽  
Recommendations For Practice ◽  
Artificial Datasets ◽  
Measurement Validity

Establishing discriminant validity has been a keystone of measurement validity in empirical marketing research for many decades. Without statistically showing that constructs have discriminant validity, contributions to marketing literature are likely to foster the proliferation of constructs that are operationally the same as other constructs already present in the literature, thus leading to confusion in the development of theory. This article addresses this concern by evaluating well-established methods for testing discriminant validity through the simulation of artificial datasets (containing varying levels of correlation between constructs, sample size, measurement error, and distribution skewness). The artificial data are applied to six commonly used approaches for testing the existence of discriminant validity. Results strongly suggest that several methods are much more likely than others to yield accurate assessments of whether discriminant validity exists, especially under specific conditions. Recommendations for practice in the assessment of discriminant validity are suggested.

scholarly journals Testing for the presence of measurement error in Stata

2020 ◽  
Vol 20 (2) ◽  
pp. 382-404
Author(s):  
Young Jun Lee ◽  
Daniel Wilhelm
Keyword(s):  
Monte Carlo ◽  
Measurement Error ◽  
Monte Carlo Simulations ◽  
Nonparametric Test ◽  
Parametric Models ◽  
Empirical Application ◽  
Explanatory Variables ◽  
Working Paper ◽  
Linear Regressions

In this article, we describe how to test for the presence of measurement error in explanatory variables. First, we discuss the test of such hypotheses in parametric models such as linear regressions and then introduce a new command, dgmtest, for a nonparametric test proposed in Wilhelm (2018, Working Paper CWP45/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies). To illustrate the new command, we provide Monte Carlo simulations and an empirical application to testing for measurement error in administrative earnings data.

Random-Number Generation

1991 ◽  
Vol 02 (01) ◽  
pp. 296-299
Author(s):  
A. COMPAGNER
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Large Scale ◽  
Random Number ◽  
Statistical Tests ◽  
Random Number Generation ◽  
Operational Definition ◽  
Random Numbers ◽  
The Many ◽  
Definition Of

In large-scale Monte Carlo simulations, reliable random numbers will soon be needed at bit rates of 1 GHz or more. Therefore, existing recipes for the generation of random numbers have to be improved. This is not easy, due to the many unrelated and laborious statistical tests needed to compensate for the lack of an accepted and operational definition of randomness. When however the notion of randomness as a complete absence of all correlations is made precise, a practical approach results.

scholarly journals Dual-Polarized Planar Phased Array Analysis for Meteorological Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Chen Pang ◽  
Peter Hoogeboom ◽  
François Le Chevalier ◽  
Herman W. J. Russchenberg ◽  
Jian Dong ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Measurement Error ◽  
Theoretical Analysis ◽  
Monte Carlo Simulations ◽  
Phased Array ◽  
Antenna Element ◽  
Array Analysis ◽  
Dual Polarized ◽  
Dual Polarized Antenna ◽  
Entire Array

This paper presents a theoretical analysis for the accuracy requirements of the planar polarimetric phased array radar (PPPAR) in meteorological applications. Among many factors that contribute to the polarimetric biases, four factors are considered and analyzed in this study, namely, the polarization distortion due to the intrinsic limitation of a dual-polarized antenna element, the antenna pattern measurement error, the entire array patterns, and the imperfect horizontal and vertical channels. Two operation modes, the alternately transmitting and simultaneously receiving (ATSR) mode and the simultaneously transmitting and simultaneously receiving (STSR) mode, are discussed. For each mode, the polarimetric biases are formulated. As the STSR mode with orthogonal waveforms is similar to the ATSR mode, the analysis is mainly focused on the ATSR mode and the impacts of the bias sources on the measurement of polarimetric variables are investigated through Monte Carlo simulations. Some insights of the accuracy requirements are obtained and summarized.

scholarly journals OPTIMIZATION OF THE HIGUCHI METHOD

2021 ◽  
Vol 9 (11) ◽  
pp. 202-213
Author(s):  
J. Wanliss ◽  
R. Hernandez Arriaza ◽  
G. Wanliss ◽  
S. Gordon
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Fractal Dimension ◽  
Monte Carlo Simulations ◽  
Ad Hoc ◽  
A Priori ◽  
Fractal Dimensions ◽  
Correct Choice ◽  
Tuning Parameter ◽  
Data Set

Background and Objective: Higuchi’s method of determining fractal dimension (HFD) occupies a valuable place in the study of a wide variety of physical signals. In comparison to other methods, it provides more rapid, accurate estimations for the entire range of possible fractal dimensions. However, a major difficulty in using the method is the correct choice of tuning parameter (kmax) to compute the most accurate results. In the past researchers have used various ad hoc methods to determine the appropriate kmax choice for their particular data. We provide a more objective method of determining, a priori, the best value for the tuning parameter, given a particular length data set. Methods: We create numerous simulations of fractional Brownian motion to perform Monte Carlo simulations of the distribution of the calculated HFD. Results: Experimental results show that HFD depends not only on kmax but also on the length of the time series, which enable derivation of an expression to find the appropriate kmax for an input time series of unknown fractal dimension. Conclusion: The Higuchi method should not be used indiscriminately without reference to the type of data whose fractal dimension is examined. Monte Carlo simulations with different fractional Brownian motions increases the confidence of evaluation results.

Low-voltage EDS of magnesium ferrite Dendrites in a FEG-SEM

1996 ◽  
Vol 54 ◽  
pp. 478-479
Author(s):  
Matthew T. Johnson ◽  
Ian M. Anderson ◽  
Jim Bentley ◽  
C. Barry Carter
Keyword(s):  
Monte Carlo ◽  
Quantitative Analysis ◽  
Monte Carlo Simulations ◽  
Cross Section ◽  
Spatial Resolution ◽  
Low Voltage ◽  
Magnesium Ferrite ◽  
X Ray ◽  
Interaction Volume ◽  
Ferrite Spinel

Energy-dispersive X-ray spectrometry (EDS) performed at low (≤ 5 kV) accelerating voltages in the SEM has the potential for providing quantitative microanalytical information with a spatial resolution of ∼100 nm. In the present work, EDS analyses were performed on magnesium ferrite spinel [(MgxFe1−x)Fe2O4] dendrites embedded in a MgO matrix, as shown in Fig. 1. spatial resolution of X-ray microanalysis at conventional accelerating voltages is insufficient for the quantitative analysis of these dendrites, which have widths of the order of a few hundred nanometers, without deconvolution of contributions from the MgO matrix. However, Monte Carlo simulations indicate that the interaction volume for MgFe2O4 is ∼150 nm at 3 kV accelerating voltage and therefore sufficient to analyze the dendrites without matrix contributions.Single-crystal {001}-oriented MgO was reacted with hematite (Fe2O3) powder for 6 h at 1450°C in air and furnace cooled. The specimen was then cleaved to expose a clean cross-section suitable for microanalysis.

MONTE CARLO SIMULATIONS OF ELECTRON DRIFT VELOCITIES IN THE NOBLE GASES AND THEIR MIXTURES

1979 ◽  
Vol 40 (C7) ◽  
pp. C7-63-C7-64
Author(s):  
A. J. Davies ◽  
J. Dutton ◽  
C. J. Evans ◽  
A. Goodings ◽  
P.K. Stewart
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Noble Gases ◽  
Electron Drift
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