scholarly journals Do expert clinicians make the best managers? Evidence from hospitals in Denmark, Australia and Switzerland

BMJ Leader ◽  
2021 ◽  
pp. leader-2021-000483
Author(s):  
Agnes Bäker ◽  
Amanda H Goodall
Keyword(s):  
Potential Role ◽  
Regression Equations ◽  
Clinical Expertise ◽  
Team Leaders ◽  
Leadership Quality ◽  
Natural Case ◽  
Omitted Variable Bias ◽  
Intentions To Quit ◽  
Variable Bias

IntroductionHospital quality rests on the morale and productivity of those who work in them. It is therefore important to try to understand the kinds of team leaders that create high morale within hospitals.MethodsThis study collects and examines data on 3000 physicians in hospitals from Denmark, Australia and Switzerland. It estimates regression equations to study the statistical predictors of levels of doctors’ job satisfaction, their intentions to quit or stay in their current hospital and their assessment of the leadership quality of their immediate manager. A particular concern of this study is to probe the potential role played by clinical expertise among those in charge of other physicians.ResultsWhen led by managers with high clinical expertise, hospital physicians are (1) more satisfied with their jobs, (2) more satisfied with their supervisors’ effectiveness and (3) less likely to wish to quit their current job. These findings are robust to adjustment for potential confounders, including age and job seniority, and pass a variety of statistical checks (including clustering of SEs and checking for omitted variable bias). They are replicated in each of the three nations.ConclusionPhysicians are happier with their jobs when led by outstanding clinical experts. It is not sufficient, it appears from this evidence, for leaders merely to be clinicians. This suggests that—though only an idealised and presumably infeasible randomised experiment could allow complete certainty—there is a natural case for managers within a hospital hierarchy to be drawn from the ranks of those who are themselves outstanding clinicians.

scholarly journals Can Panel Data Methodologies Determine the Impact of Climate Change on Economic Growth?

2021 ◽  
Author(s):  
Richard A. Rosen ◽  
Keyword(s):  
Climate Change ◽  
Economic Growth ◽  
Panel Data ◽  
Regression Equations ◽  
Annual Variations ◽  
Omitted Variable Bias ◽  
The World ◽  
Variable Bias ◽  
One Year ◽  
The Impact

Several major papers have been published over the last ten years claiming to have detected the impact of either annual variations in weather or climate change on the GDPs of most countries in the world using panel data-based statistical methodologies. These papers rely on various multivariate regression equations which include the annual average temperatures for most countries in the world as one or more of the independent variables, where the usual dependent variable is the change in annual GDP for each country from one year to the next year over 30-50 year time periods. Unfortunately, the quantitative estimates derived in these papers are misleading because the equations from which they are calculated are wrong. The major reason the resulting regression equations are wrong is because they do not include any of the appropriate and usual economic factors or variables which are likely to be able to explain changes in GDP/economic growth whether or not climate change has already impacted each country’s economy. These equations, in short, exhibit suffer from “omitted variable bias,” to use statistical terminology.

scholarly journals Omitted variable bias in the link between planning and performance

2004 ◽  
Vol 7 (2) ◽  
pp. 27-31 ◽  
Author(s):  
Kirk C. Heriot ◽  
Noel D. Campbell ◽  
R. Zachary Finney
Keyword(s):  
Planning Process ◽  
Business Plan ◽  
New Venture ◽  
Business Plans ◽  
Business Opportunity ◽  
Omitted Variable Bias ◽  
Variable Bias ◽  
And Performance ◽  
True Relationship

This article argues that existing research poorly specifies the link between planning and performance because of omitted variable bias. Researchers agree planning is a critical part of creating any new venture. Many researchers assess planning by whether a small firm has a written business plan. Unfortunately, efforts empirically to validate this relationship have been inconclusive. This article proposes that researchers should assess business plans both on the quality of the plan (and the planning process that produced it), and on the quality of the underlying business opportunity. Failure to account for both aspects of a business plan amounts to omitted variable bias, frustrating attempts to accurately estimate the true relationship.

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 The Use of Evidence-Based Acupuncture: Current Evidence

2022 ◽  
Author(s):  
Dedi Ardinata
Keyword(s):  
Clinical Evidence ◽  
Scientific Evidence ◽  
Current Evidence ◽  
Systematic Research ◽  
Evidence Based ◽  
Clinical Expertise ◽  
Quality Of Evidence ◽  
Assessment Development ◽  
Evidence Quality

Evidence-based medicine (EBM), which emphasizes that medical decisions must be based on the most recent best evidence, is gaining popularity. Individual clinical expertise is combined with the best available external clinical evidence derived from systematic research in the practice of EBM. The key and core of EBM is the hierarchical system for categorizing evidence. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) system divides evidence quality into four categories: high, moderate, low, and very low. GRADE is based on the lowest quality of evidence for any of the outcomes that are critical to making a decision, reducing the risk of mislabeling the overall evidence quality, when evidence for a critical outcome is lacking. This principle is also used in acupuncture as a complementary and integrative treatment modality, but incorporating scientific evidence is more difficult due to a number of factors. The goal of this chapter is to discuss how to establish a clinical evidence system for acupuncture, with a focus on the current quality of evidence for a variety of conditions or diseases.

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