Comparison of Forced-Choice and Subjective Probability Scales Measuring Behavioral Intentions

2000 ◽  
Vol 86 (1) ◽  
pp. 321-332 ◽  
Author(s):  
Laura T. Flannelly ◽  
Kevin J. Flannelly ◽  
Malcolm S. McLeod
Keyword(s):  
Subjective Probability ◽  
Measurement Errors ◽  
Behavioral Intentions ◽  
Cognitive Theory ◽  
Sampling Error ◽  
Forced Choice ◽  
Practical Application ◽  
Support Theory ◽  
Don't Know Responses ◽  
Source Of Error

Three surveys compared the accuracy of predictions based on forced-choice and subjective probability scales. The latter produced significantly more accurate election predictions and significantly reduced the percentage of undecided, or “Don't Know” responses, compared to forced-choice scales in all three surveys. Analysis indicates subjective probability scales decrease sampling error and confirms there is an inherent source of error in traditional forced-choice questions about voting intentions not attributable to sampling error. The results are discussed with respect to (1) sampling and measurement errors in forced-choice and subjective probability scales measuring behavioral intentions, (2) their practical application, and (3) cognitive theory, especially support theory.

How to improve allometric equations to estimate forest biomass stocks? Some hints from a central African forest

2014 ◽  
Vol 44 (7) ◽  
pp. 685-691 ◽  
Author(s):  
Quentin Moundounga Mavouroulou ◽  
Alfred Ngomanda ◽  
Nestor Laurier Engone Obiang ◽  
Judicaël Lebamba ◽  
Hugues Gomat ◽  
...  
Keyword(s):  
Measurement Error ◽  
Bayesian Model Averaging ◽  
Sampling Error ◽  
Forest Biomass ◽  
Model Averaging ◽  
Allometric Equation ◽  
Allometric Equations ◽  
Data Set ◽  
Equation Error ◽  
Source Of Error

Predicting the biomass of a forest stand using forest inventory data and allometric equations involves a chain of propagation of errors going from the sampling error to the tree measurement error. Using a biomass data set of 101 trees in a tropical rain forest in Gabon, we compared two sources of error: the error due to the choice of allometric equation, assessed using Bayesian model averaging, and the biomass measurement error when tree biomass is calculated from tree volume rather than directly weighed. Differences between allometric equations resulted in a between-equation error of about 0.245 for log-transformed biomass compared with a residual within-equation error of 0.297. Because the residual error is leveled off when randomly accumulating trees whereas the between-equation error is incompressible, the latter turned out to be a major source of error at the scale of a 1 ha plot. Measuring volumes rather than masses resulted in an error of 0.241 for log-transformed biomass and an average overestimation of the biomass by 19%. These results confirmed the choice of the allometric equation as a major source of error but unexpectedly showed that measuring volumes could seriously bias biomass estimates.

scholarly journals Potential Survey Error Due to a Panel Design: A Review and Evaluation of the National Crime Victimization Survey

2020 ◽  
Author(s):  
Marcus E. Berzofsky ◽  
Andrew Moore ◽  
G. Lance Couzens ◽  
Lynn Langton ◽  
Chris Krebs
Keyword(s):  
Sampling Error ◽  
The United States ◽  
National Crime Victimization Survey ◽  
Crime Victimization ◽  
Longitudinal Surveys ◽  
Victimization Survey ◽  
Mode Effects ◽  
Survey Error ◽  
Using Data ◽  
Source Of Error

We use a total survey error approach to examine and make recommendations on how to adjust for non-sampling error in longitudinal, mixed-mode surveys. Using data from the National Crime Victimization Survey (NCVS), we examine three major sources of non-sampling error: telescoping, mode effects, and fatigue. We present an assessment of each source of error from a total survey error perspective and propose alternative adjustments to adjust better for this error. Findings suggest that telescoping and fatigue are likely sources of error in the NCVS, but the use of mixed-modes is not. Furthermore, both telescoping and fatigue are present in longitudinal surveys and accounting for one but not the other results in estimates that under- or overestimate the measures of interest—in this case, the rate of crime in the United States.

scholarly journals Study of measurement errors of Cabinet Office opinion survey

Impact ◽  
2021 ◽  
Vol 2021 (2) ◽  
pp. 6-7
Author(s):  
Harumasa Yoshimura
Keyword(s):  
Public Opinion ◽  
Measurement Errors ◽  
Japanese Society ◽  
Social Research ◽  
Sampling Error ◽  
Opinion Survey ◽  
Community Surveys ◽  
Public Opinion Surveys ◽  
Survey Results ◽  
Negative Impacts

Public opinion surveys are important for gauging the feelings and behaviours of societies. However, there is the possibility of error, which means that the data collected may not accurately reflect the thoughts and opinions of society, which can have dangerous repercussions. In order to minimise such error, with a specific focus on the Japanese Government's Cabinet Office public opinion survey, Professor Harumasa Yoshimura, Nara University, Japan, is investigating measurement error and, in doing so, he hopes to more accurately reflect the true opinions of Japanese society. This research involves integrating the different factors that can affect the reliability of survey results and looking at non-sampling error, which refers to human mistakes. Yoshimura is proposing a new style of social research that integrates psychometric research with sociological community surveys and believes this is the key to enhancing the reliability of public opinion surveys. Ultimately, improving the accuracy of public opinion surveys will have far-reaching benefits that include more accurately depicting thoughts and behaviours and therefore improving awareness of Japanese society, as well as preventing the negative impacts that inaccurate opinion survey results can have, including the political utilisation of academic endeavours.

scholarly journals Marks: A New Interval Tool for Uncertainty, Vagueness and Indiscernibility

Mathematics ◽  
2021 ◽  
Vol 9 (17) ◽  
pp. 2116
Author(s):  
Miguel A. Sainz ◽  
Remei Calm ◽  
Lambert Jorba ◽  
Ivan Contreras ◽  
Josep Vehi
Keyword(s):  
Dynamical Systems ◽  
Measurement Errors ◽  
Practical Application ◽  
Modal Interval ◽  
The Difference ◽  
Systems Simulation ◽  
And Control ◽  
Physical Quantities ◽  
Successive Calculation

The system of marks created by Dr. Ernest Gardenyes and Dr. Lambert Jorba was first published as a doctoral thesis in 2003 and then as a chapter in the book Modal Interval Analysis in 2014. Marks are presented as a tool to deal with uncertainties in physical quantities from measurements or calculations. When working with iterative processes, the slow convergence or the high number of simulation steps means that measurement errors and successive calculation errors can lead to a lack of significance in the results. In the system of marks, the validity of any computation results is explicit in their calculation. Thus, the mark acts as a safeguard, warning of such situations. Despite the obvious contribution that marks can make in the simulation, identification, and control of dynamical systems, some improvements are necessary for their practical application. This paper aims to present these improvements. In particular, a new, more efficient characterization of the difference operator and a new implementation of the marks library is presented. Examples in dynamical systems simulation, fault detection and control are also included to exemplify the practical use of the marks.

Use of Randomized Branch and Importance Sampling to Estimate Loblolly Pine Biomass

1989 ◽  
Vol 13 (4) ◽  
pp. 181-184 ◽  
Author(s):  
Roger A. Williams
Keyword(s):  
Importance Sampling ◽  
Loblolly Pine ◽  
Sampling Method ◽  
Sampling Error ◽  
Total Biomass ◽  
Tree Biomass ◽  
Individual Tree ◽  
Wide Range ◽  
Pinus Taeda L ◽  
Source Of Error

Abstract A previously developed sampling method utilizing randomized branch and importance sampling for the purpose of quickly estimating tree biomass was tested on five loblolly pine (Pinus taeda L.) trees. Results show a wide range of per-tree sampling error, ranging from 5.3 to 28.9%. Largevariation in foliage content among selected branches per treee may be a major source of error. However, the sampling error for the total biomass of the five trees tested was only 3.3%. This sampling method appears to be reliable and efficient in obtaining precise estimates of the total biomassof a population of trees. Increased sampling intensity per tree is necessary to obtain precise estimates of individual tree biomass. South. J. Appl. For. 13(4):181-184.

All that Glitters is Not Gold: Examining the Perils and Obstacles in Collecting Data on the Internet

2005 ◽  
Vol 10 (1) ◽  
pp. 115-130 ◽  
Author(s):  
Cha Yeow Siah
Keyword(s):  
Data Collection ◽  
Measurement Errors ◽  
Research Method ◽  
Sampling Error ◽  
Reliability And Validity ◽  
The Internet ◽  
The Past ◽  
Areas Of Concern ◽  
Conducting Research

AbstractThe speed, ease and cost of conducting an internet-based study has attracted an increasingly large number of researchers to the medium for data collection. The lure of conducting research on the internet warrants heightened awareness of the practical problems one may encounter in the course of design and data collection. Researchers should also be attuned to the various threats of reliability and validity that may affect the quality of their data. This article surveys the past literature and identifies four main areas of concern in internet-based research: (1) sampling error and generalizability; (2) subject fraud; (3) measurement errors resulting from extraneous factors, and (4) the ethics of conducting research on the internet. Before carrying out their research on the internet, researchers should carefully weigh the sometimes hidden costs against the obvious benefits to consider whether the results obtained will be seriously compromised by the problems currently existing with this relatively new medium. However, a more productive approach recognizes that this research method is here to stay and thus greater attention needs to be given to refining and clearing the hurdles that internet-based researchers currently face.

Luminous intensity correction method for misalignment-induced measurement error of light-emitting diode arrays

2018 ◽  
Vol 51 (3) ◽  
pp. 432-446
Author(s):  
HY Wu ◽  
HB Cheng ◽  
YP Feng ◽  
X Chen
Keyword(s):  
Intensity Distribution ◽  
Measurement Errors ◽  
Light Emitting Diode ◽  
Correction Method ◽  
Luminous Intensity ◽  
Practical Application ◽  
Light Emitting ◽  
Physical Measurements ◽  
Test Distance ◽  
Operating Methods

To eliminate axial misalignment-induced measurement errors of discrete light-emitting diode arrays in the far-field condition, a robust and effective method for correcting the measured luminous intensity distribution is proposed. The precision of the correction can be determined beforehand by setting a criterion which can also be used to determine the required test distance. To validate the feasibility and practicability of the proposed approach, numerical simulations of light-emitting diode arrays with three kinds of typical luminous intensity distributions were performed. In addition, the test distances as a function of the light-emitting diode luminous intensity distribution, packing density and dimensions under translational misalignment were analysed. Some beneficial operating methods and rules for practical application are summarised. Finally, physical measurements of several experimental examples were collected. The correction results agreed with the desired data and again proved the utility of the presented method.

Accuracy in LP electromagnetic seismograph calibration by least-squares inversion of the calibration pulse

1980 ◽  
Vol 70 (6) ◽  
pp. 2261-2273
Author(s):  
Robert W. McGonigle ◽  
Paul W. Burton
Keyword(s):  
Transfer Function ◽  
Least Squares ◽  
Gaussian Noise ◽  
Measurement Errors ◽  
Transfer Functions ◽  
Phase Delay ◽  
Noise Distribution ◽  
Dominant Source ◽  
Electromagnetic Seismograph ◽  
Source Of Error

abstract Uncertainties in the instrumental constants for the standard LP instruments of the WWSSN, arising from errors in amplitude measurements of the calibration pulse, are analytically quantified assuming that such measurement errors may be approximated by a perturbing Gaussian noise distribution. More importantly, for the practicing seismologist, uncertainties in magnification and phase delay of the entire instrumental transfer function are deduced from least-squares inversion of the seismogram calibration pulse. It is emphasized that reliable estimates of instrumental transfer functions are obtained by least-squares inversion of the calibration pulse, particularly for underdamped or near critically damped seismographs, where the Gaussian noise distribution is the dominant source of error.

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