scholarly journals Using Ancillary Information to Reduce Sample Size in Discovery Sampling and the Effects of Measurement Error

2005 ◽  
Author(s):  
M Axelrod
Keyword(s):  
Measurement Error ◽  
Sample Size ◽  
Ancillary Information ◽  
Discovery Sampling

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 Product Acceptance Determination with Measurement Error Using the Neutrosophic Statistics

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Aslam
Keyword(s):  
Measurement Error ◽  
Sample Size ◽  
Optimization Problem ◽  
Sampling Plan ◽  
Measurement Process ◽  
Comparison Study ◽  
Classical Statistics ◽  
Neutrosophic Statistics ◽  
Variable Data ◽  
Uncertainty Environment

The variable data is obtained from the measurement process which is not fully complete or clear in nature due to measurement error. The neutrosophic statistics which is the extension of classical statistics can be applied in the industry for the lot senescing when observations or parameters are uncertain or indeterminate or unclear. In this manuscript, a new sampling plan for the measurement error using the neutrosophic statistics is designed. The proposed sampling plan has two neutrosophic parameters, namely, sample size and acceptance number. The neutrosophic operating function is also given. The neutrosophic plan parameters will be determined through the neutrosophic optimization problem. Some tables are given for some specified parameters. From the comparison study, it is concluded that the proposed sampling plan is more flexible, adequate, and effective in the uncertainty environment as compared to the existing sampling plan under the classical statistics. A real example is given for the illustration purpose.

Evaluating Structural Equation Models with Unobservable Variables and Measurement Error

1981 ◽  
Vol 18 (1) ◽  
pp. 39-50 ◽  
Author(s):  
Claes Fornell ◽  
David F. Larcker
Keyword(s):  
Measurement Error ◽  
Sample Size ◽  
Structural Equation ◽  
Structural Model ◽  
Structural Equation Models ◽  
Statistical Tests ◽  
Explanatory Power ◽  
Measurement Model ◽  
Measurement Properties ◽  
Chi Square

The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addition to the known problems related to sample size and power, is that it may indicate an increasing correspondence between the hypothesized model and the observed data as both the measurement properties and the relationship between constructs decline. Further, and contrary to common assertion, the risk of making a Type II error can be substantial even when the sample size is large. Moreover, the present testing methods are unable to assess a model's explanatory power. To overcome these problems, the authors develop and apply a testing system based on measures of shared variance within the structural model, measurement model, and overall model.

RE: “THE IMPACT OF DIETARY MEASUREMENT ERROR ON PLANNING SAMPLE SIZE REQUIRED IN A COHORT STUDY”

1991 ◽  
Vol 134 (12) ◽  
pp. 1470-1472 ◽  
Author(s):  
Lawrence H. Kushi ◽  
Daniel Zelterman ◽  
David R. Jacobs, ◽  
John D. Potter
Keyword(s):  
Cohort Study ◽  
Measurement Error ◽  
Sample Size ◽  
The Impact

THE IMPACT OF DIETARY MEASUREMENT ERROR ON PLANNING SAMPLE SIZE REQUIRED IN A COHORT STUDY

1990 ◽  
Vol 132 (6) ◽  
pp. 1185-1195 ◽  
Author(s):  
LAURENCE S. FREEDMAN ◽  
ARTHUR SCHATZKIN ◽  
YOHANAN WAX
Keyword(s):  
Cohort Study ◽  
Measurement Error ◽  
Sample Size ◽  
The Impact

Penny-wise and pound-foolish: the impact of measurement error on sample size requirements in clinical trials

2000 ◽  
Vol 47 (8) ◽  
pp. 762-766 ◽  
Author(s):  
Diana O Perkins ◽  
Richard Jed Wyatt ◽  
John J Bartko
Keyword(s):  
Clinical Trials ◽  
Measurement Error ◽  
Sample Size ◽  
The Impact

Power and sample size calculations for generalized regression models with covariate measurement error

2003 ◽  
Vol 22 (7) ◽  
pp. 1069-1082 ◽  
Author(s):  
Tor D. Tosteson ◽  
Jeffrey S. Buzas ◽  
Eugene Demidenko ◽  
Margaret Karagas
Keyword(s):  
Measurement Error ◽  
Sample Size ◽  
Regression Models ◽  
Covariate Measurement Error ◽  
Sample Size Calculations ◽  
Generalized Regression

scholarly journals Modifying Spearman’s Attenuation Equation to Yield Partial Corrections for Measurement Error—With Application to Sample Size Calculations

2017 ◽  
Vol 78 (1) ◽  
pp. 70-79 ◽  
Author(s):  
W. Alan Nicewander
Keyword(s):  
Measurement Error ◽  
Sample Size ◽  
Correlation Coefficient ◽  
Measurement Errors ◽  
Statistical Tests ◽  
Classical Test Theory ◽  
Test Theory ◽  
Sample Size Calculations ◽  
Correction For Attenuation ◽  
Partial Correction

Spearman’s correction for attenuation (measurement error) corrects a correlation coefficient for measurement errors in either-or-both of two variables, and follows from the assumptions of classical test theory. Spearman’s equation removes all measurement error from a correlation coefficient which translates into “increasing the reliability of either-or-both of two variables to 1.0.” In this inquiry, Spearman’s correction is modified to allow partial removal of measurement error from either-or-both of two variables being correlated. The practical utility of this partial correction is demonstrated in its use to explore increasing the power of statistical tests by increasing sample size versus increasing the reliability of the dependent variable for an experiment. Other applied uses are mentioned.

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