Physical Ability-Task Performance Models: Assessing the Risk of Omitted Variable Bias

2008 ◽  
Author(s):  
Jr. Vickers ◽  
Hodgdon Ross R. ◽  
Beckett James A. ◽  
Marcie B.
Keyword(s):  
Task Performance ◽  
Performance Models ◽  
Physical Ability ◽  
Omitted Variable Bias ◽  
Variable Bias

scholarly journals Omitted Variable Bias in GLMs of Neural Spiking Activity

2018 ◽  
Vol 30 (12) ◽  
pp. 3227-3258 ◽  
Author(s):  
Ian H. Stevenson
Keyword(s):  
Neural Activity ◽  
Single Neuron ◽  
Linear Models ◽  
Theory And Practice ◽  
Parameter Estimates ◽  
Omitted Variables ◽  
History Effects ◽  
Omitted Variable Bias ◽  
Wide Range ◽  
Variable Bias

Generalized linear models (GLMs) have a wide range of applications in systems neuroscience describing the encoding of stimulus and behavioral variables, as well as the dynamics of single neurons. However, in any given experiment, many variables that have an impact on neural activity are not observed or not modeled. Here we demonstrate, in both theory and practice, how these omitted variables can result in biased parameter estimates for the effects that are included. In three case studies, we estimate tuning functions for common experiments in motor cortex, hippocampus, and visual cortex. We find that including traditionally omitted variables changes estimates of the original parameters and that modulation originally attributed to one variable is reduced after new variables are included. In GLMs describing single-neuron dynamics, we then demonstrate how postspike history effects can also be biased by omitted variables. Here we find that omitted variable bias can lead to mistaken conclusions about the stability of single-neuron firing. Omitted variable bias can appear in any model with confounders—where omitted variables modulate neural activity and the effects of the omitted variables covary with the included effects. Understanding how and to what extent omitted variable bias affects parameter estimates is likely to be important for interpreting the parameters and predictions of many neural encoding models.

scholarly journals Lock-In and Team Effects

2015 ◽  
Vol 18 (4) ◽  
pp. 376-387
Author(s):  
Trey Dronyk-Trosper ◽  
Brandli Stitzel
Keyword(s):  
Set Covering ◽  
Future Research ◽  
Prior Work ◽  
Omitted Variable Bias ◽  
Specific Effects ◽  
Heterogeneous Effects ◽  
Team Success ◽  
Variable Bias ◽  
Lock In ◽  
Least Squares Approach

How important is recruiting to a football program’s success? While prior research has attempted to answer this question, we utilize an extensive panel set covering 13 years of games along with a two-stage least squares approach to investigate the effects of recruiting on team success. This article also includes new control variables to account for omitted variable bias that prior work may have missed. We also split our sample to investigate whether recruiting displays heterogeneous effects across schools. Additionally, we find evidence that the benefits of recruiting are driven by team-specific effects, indicating that team success may be more heavily derived from the ability of teams to harness and improve their recruits than their ability to utilize each athlete’s raw abilities. This leads to important revelations regarding future research into both the value of recruits and what drives a football team’s success.

The Impact of ICT Investment on Establishment Productivity

2003 ◽  
Vol 184 ◽  
pp. 99-110 ◽  
Author(s):  
Thomas Zwick
Keyword(s):  
Panel Data ◽  
Unobserved Heterogeneity ◽  
Data Set ◽  
Omitted Variable Bias ◽  
Lagged Effects ◽  
Variable Bias ◽  
Employee Characteristics ◽  
The Impact ◽  
Establishment Panel Data

This paper finds substantial effects of ICT investments on productivity for a large and representative German establishment panel data set. In contrast to the bulk of the literature also establishments without ICT capital are included and lagged effects of ICT investments are analysed. In addition, a broad range of establishment and employee characteristics are taken account of in order to avoid omitted variable bias. It is shown that taking into account unobserved heterogeneity of the establishments and endogeneity of ICT investments increases the estimated lagged productivity impact of ICT investments.

scholarly journals Tracking Underreported Financial Flows: China’s Development Finance and the Aid–Conflict Nexus Revisited

2015 ◽  
Vol 61 (5) ◽  
pp. 935-963 ◽  
Author(s):  
Austin M. Strange ◽  
Axel Dreher ◽  
Andreas Fuchs ◽  
Bradley Parks ◽  
Michael J. Tierney
Keyword(s):  
Open Source ◽  
Reliable Information ◽  
African Countries ◽  
Financial Flows ◽  
Development Finance ◽  
Alternative Funding ◽  
Omitted Variable Bias ◽  
Chinese Official ◽  
Variable Bias ◽  
Project Level

China’s provision of development finance to other countries is sizable but reliable information is scarce. We introduce a new open-source methodology for collecting project-level development finance information and create a database of Chinese official finance (OF) to Africa from 2000 to 2011. We find that China’s commitments amounted to approximately US$73 billion, of which US$15 billion are comparable to Official Development Assistance following Organization for Economic Cooperation and Development definitions. We provide details on 1,511 projects to fifty African countries. We use this database to extend previous research on aid and conflict, which suffers from omitted-variable bias due to the exclusion of Chinese development finance. Our results show that sudden withdrawals of “traditional” aid no longer induce conflict in the presence of sufficient alternative funding from China. Our findings highlight the importance of gathering more complete data on the development activities of “nontraditional donors” to better understand the link between aid and conflict.

An introduction to Monte Carlo simulations in criminal psychology: applications in evaluating biased estimators for recidivism

2015 ◽  
Vol 5 (2) ◽  
pp. 149-156 ◽  
Author(s):  
Priscillia Hunt ◽  
Jeremy N.V Miles
Keyword(s):  
Criminal Psychology ◽  
Statistical Tests ◽  
Actual Data ◽  
Content Type ◽  
Criminal Convictions ◽  
Omitted Variable Bias ◽  
Mc Simulation ◽  
Biased Estimators ◽  
Variable Bias ◽  
The Impact

Purpose – Studies in criminal psychology are inevitably undertaken in a context of uncertainty. One class of methods addressing such uncertainties is Monte Carlo (MC) simulation. The purpose of this paper is to provide an introduction to MC simulation for representing uncertainty and focusses on likely uses in studies of criminology and psychology. In addition to describing the method and providing a step-by-step guide to implementing a MC simulation, this paper provides examples using the Fragile Families and Child Wellbeing Survey data. Results show MC simulations can be a useful technique to test biased estimators and to evaluate the effect of bias on power for statistical tests. Design/methodology/approach – After describing MC simulation methods in detail, this paper provides a step-by-step guide to conducting a simulation. Then, a series of examples are provided. First, the authors present a brief example of how to generate data using MC simulation and the implications of alternative probability distribution assumptions. The second example uses actual data to evaluate the impact that omitted variable bias can have on least squares estimators. A third example evaluates the impact this form of heteroskedasticity can have on the power of statistical tests. Findings – This study shows MC simulated variable means are very similar to the actual data, but the standard deviations are considerably less in MC simulation-generated data. Using actual data on criminal convictions and income of fathers, the authors demonstrate the impact of omitted variable bias on the standard errors of the least squares estimator. Lastly, the authors show the p-values are systematically larger and the rejection frequencies correspondingly smaller in heteroskedastic error models compared to a model with homoskedastic errors. Originality/value – The aim of this paper is to provide a better understanding of what MC simulation methods are and what can be achieved with them. A key value of this paper is that the authors focus on understanding the concepts of MC simulation for researchers of statistics and psychology in particular. Furthermore, the authors provide a step-by-step description of the MC simulation approach and provide examples using real survey data on criminal convictions and economic characteristics of fathers in large US cities.

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