Systematic Bias of Telephone Surveys: Meta Analysis of 2007 Presidential Election Polls

This paper aims to analyze the efficient stock market hypothesis as responsive to American Presidential Election, 2016. The meta-analysis has been done combining content analysis and event study methodology. The all major newspapers, news channels, public polls, literature and five important indices as Dow Jones Industrial Average (DJIA), NASDAQ Stock Market Composit Indexe (NASDAQ-COMP), Standard & Poor's 500 Index (SPX-500), New York Stock Exchange Composite Index (NYSE-COMP) and Other U.S Indexes-Russell 2000 (RUT-2000) are critically examined and empirically analyzed. The findings from content analysis reflect that stunned winning of Mr Trump from Republican Party worked as shock for American stock market. From event study, findings confirmed that all the major indices reflected a decline on winning of Trump and losing of Ms. Clinton from Democratic. The results are supported empirically and practically through the political event like BREXIT that resulted in shock to Global stock index and loss of $2 Trillion.

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Are perceived prevalences of infection also biased and how? Lessons from large epidemics of mosquito-borne diseases in tropical regions

10.31234/osf.io/zh84a ◽

2017 ◽

Author(s):

Jocelyn Raude

Keyword(s):

Infectious Diseases ◽

Cognitive Biases ◽

Adult Population ◽

Systematic Bias ◽

Tropical Regions ◽

Telephone Surveys ◽

Behavioral Decision ◽

Risk Estimates ◽

Representative Samples ◽

Actual Prevalence

Objectives: Although people have been repeatedly found to underestimate the frequency of risks to health from common diseases, we still do not know much about reasons for this systematic bias, which is also referred to as “primary bias” in the literature. In this study, we take advantage of a series of large epidemics of mosquito-borne diseases to examine the accuracy of judgments of risk frequencies. In this aim, we assessed the perceived versus the observed prevalence of infection by zika, chikungunya or dengue fever during these outbreaks, as well as their variations among different subpopulations and epidemiological settings.Design: We used data drawn from 4 telephone surveys, conducted between 2006 and 2016, among representative samples of the adult population in tropical regions (Reunion, Martinique, and French Guiana). The participants were asked to estimate the prevalence of these infections by using a natural frequency scale.Results: The surveys showed that (1) most people greatly overestimated the prevalence of infection by arbovirus, (2) these risk overestimations fell considerably as the actual prevalence of these diseases increased, (3) the better-educated and male participants consistently yielded less inaccurate risk estimates across epidemics, and (4) that these biases in the perception of prevalence of these infectious diseases are relatively well predicted by probability weighting function.Conclusions: These findings suggest that the cognitive biases that affect perception of prevalence of acute infectious diseases are not fundamentally different from those that characterize other types of probabilistic judgments observed in the field of behavioral decision-making. They also indicate that numeracy may play a considerable role in people’s ability to transform epidemiological observations from their social environment to more accurate risk estimates.

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The 2012 US Presidential Election Polls And Stock Returns

Business and Economic Research ◽

10.5296/ber.v5i2.8005 ◽

2015 ◽

Vol 5 (2) ◽

pp. 66 ◽

Cited By ~ 1

Author(s):

Tamir Levy ◽

Joseph Yagil

Keyword(s):

New York ◽

Stock Returns ◽

Presidential Election ◽

New York Times ◽

Democratic Candidate ◽

Election Poll ◽

Us Presidential Election ◽

Mitt Romney ◽

Election Polls ◽

The Relationship

<p class="ber"><span lang="EN-GB">This study investigates the relationship between daily US presidential election poll results and stock returns. The sample consists of the daily presidential election polls published in the New-York Times for the period between May 31 and November 5, 2012. They include the percentage of support for the Democratic candidate, Barack Obama, and the Republican candidate, Mitt Romney. The findings indicate that stock returns are positively related to the poll results that support the candidate favored to win the election.</span></p>

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Political Ideology, Trust, and Cooperation

Journal of Conflict Resolution ◽

10.1177/0022002716658694 ◽

2016 ◽

Vol 62 (4) ◽

pp. 797-818 ◽

Cited By ~ 15

Author(s):

Daniel Balliet ◽

Joshua M. Tybur ◽

Junhui Wu ◽

Christian Antonellis ◽

Paul A. M. Van Lange

Keyword(s):

Political Party ◽

Presidential Election ◽

Political Ideology ◽

Meta Analysis ◽

Group Versus ◽

Party Affiliation ◽

Two Samples ◽

Group Members ◽

Us Presidential Election ◽

Political Party Affiliation

Theories suggest that political ideology relates to cooperation, with conservatives being more likely to pursue selfish outcomes, and liberals more likely to pursue egalitarian outcomes. In study 1, we examine how political ideology and political party affiliation (Republican vs. Democrat) predict cooperation with a partner who self-identifies as Republican or Democrat in two samples before ( n = 362) and after ( n = 366) the 2012 US presidential election. Liberals show slightly more concern for their partners’ outcomes compared to conservatives (study 1), and in study 2 this relation is supported by a meta-analysis ( r = .15). However, in study 1, political ideology did not relate to cooperation in general. Both Republicans and Democrats extend more cooperation to their in-group relative to the out-group, and this is explained by expectations of cooperation from in-group versus out-group members. We discuss the relation between political ideology and cooperation within and between groups.

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Presidential Election Polls in 2000: A Study in Dynamics

Presidential Studies Quarterly ◽

10.1111/j.1741-5705.2003.tb00022.x ◽

2003 ◽

Vol 33 (1) ◽

pp. 172-186 ◽

Cited By ~ 9

Author(s):

CHRISTOPHER WLEZIEN

Keyword(s):

Presidential Election ◽

Election Polls

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How much do standard area diagrams improve accuracy of visual estimates of plant disease severity? A systematic review and meta-analysis

10.31219/osf.io/h2cxe ◽

2021 ◽

Author(s):

Emerson Medeiros Del Ponte ◽

Luis Ignacio Cazón ◽

Kaique Santos Alves ◽

Sarah J. Pethybridge ◽

Clive H. Bock

Keyword(s):

Systematic Review ◽

Disease Severity ◽

Plant Disease ◽

Multiple Correspondence Analysis ◽

Meta Analysis ◽

Systematic Bias ◽

Constant Bias ◽

Systematic Biases ◽

Visual Assessments ◽

Selection Of

Plant disease severity is commonly estimated visually without or with the aid of standard area diagram sets (SADs). It is generally believed that the use of SADs leads to less biased (more accurate) and thus more precise estimates, but the degree of improvement has not been characterized in a systematic manner. We built on a previous review and screened 153 SAD studies published from 1990 to 2021. A systematic review resulted in a selection of 72 studies that reported three linear regression statistics for individual raters, which are indicative of the two components of bias (intercept = constant bias; slope = systematic bias) and precision (Pearson's correlation coefficient, r), to perform a meta-analysis of these accuracy components. The meta-analytic model determined an overall gain of 0.07 (r increased from 0.88 to 0.95) in precision. Globally, there was a reduction of 2.65 units in the intercept, from 3.41 to 0.76, indicating a reduction in the constant bias. Slope was least affected and was reduced slightly from 1.09 to 0.966, indicating marginally less systematic bias when using SADs. A multiple correspondence analysis suggested an association of less accurate, unaided estimates with diseases that produce numerous lesions and for which maximum severities of 50% are rarely attained. On the other hand, more accurate estimates were observed with diseases that cause only a few lesions and those diseases where the lesions coalesce and occupy more than 50% of the specimen surface. This was most pronounced for specimen types other than leaves. By quantitatively exploring how characteristics of the pathosystem and how SADs affect precision and constant and systematic biases, we affirm the value of SADs for reducing bias and imprecision of visual assessments. We have also identified situations where SADs have greater or lesser effects as an assessment aid.

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Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?

BMC Medical Research Methodology ◽

10.1186/s12874-020-01205-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Loukia M. Spineli ◽

Katerina Papadimitropoulou ◽

Chrysostomos Kalyvas

Keyword(s):

Mixture Model ◽

Simulation Study ◽

Meta Analysis ◽

Credible Interval ◽

Outcome Data ◽

Systematic Bias ◽

Two Stage ◽

Pattern Mixture Model ◽

One Stage ◽

The One

Abstract Background Trials with binary outcomes can be synthesised using within-trial exact likelihood or approximate normal likelihood in one-stage or two-stage approaches, respectively. The performance of the one-stage and the two-stage approaches has been documented extensively in the literature. However, little is known about how these approaches behave in the presence of missing outcome data (MOD), which are ubiquitous in clinical trials. In this work, we compare the one-stage versus two-stage approach via a pattern-mixture model in the network meta-analysis using Bayesian methods to handle MOD appropriately. Methods We used 29 published networks to empirically compare the two approaches concerning the relative treatment effects of several competing interventions and the between-trial variance (τ2), while considering the extent and level of balance of MOD in the included trials. We additionally conducted a simulation study to compare the competing approaches regarding the bias and width of the 95% credible interval of the (summary) log odds ratios (OR) and τ2 in the presence of moderate and large MOD. Results The empirical study did not reveal any systematic bias between the compared approaches regarding the log OR, but showed systematically larger uncertainty around the log OR under the one-stage approach for networks with at least one small trial or low event risk and moderate MOD. For these networks, the simulation study revealed that the bias in log OR for comparisons with the reference intervention in the network was relatively higher in the two-stage approach. Contrariwise, the bias in log OR for the remaining comparisons was relatively higher in the one-stage approach. Overall, bias increased for large MOD. For these networks, the empirical results revealed slightly higher τ2 estimates under the one-stage approach irrespective of the extent of MOD. The one-stage approach also led to less precise log OR and τ2 when compared with the two-stage approach for large MOD. Conclusions Due to considerable bias in the log ORs overall, especially for large MOD, none of the competing approaches was superior. Until a more competent model is developed, the researchers may prefer the one-stage approach to handle MOD, while acknowledging its limitations.

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Pattern-mixture model in network meta-analysis of binary missing outcome data: one-stage or two-stage approach?

10.21203/rs.3.rs-39035/v1 ◽

2020 ◽

Author(s):

Loukia Maria Spineli ◽

Katerina Papadimitropoulou ◽

Chrysostomos Kalyvas

Keyword(s):

Empirical Study ◽

Mixture Model ◽

Simulation Study ◽

Meta Analysis ◽

Outcome Data ◽

Systematic Bias ◽

Two Stage ◽

Pattern Mixture Model ◽

One Stage ◽

The One

Abstract Background Trials with binary outcomes can be synthesised using within-trial exact likelihood or approximate normal likelihood in one-stage or two-stage approaches, respectively. The advantages of the one-stage over the two-stage approach have been documented extensively in the literature. Little is known how these approaches behave in the presence of missing outcome data (MOD) which are ubiquitous in trials. In this work, we compare the one-stage versus two-stage approach via a pattern-mixture model in the network meta-analysis Bayesian framework to handle MOD appropriately. Methods We used 29 published networks to empirically compare the two approaches with respect to the relative treatment effects of several competing interventions and the between-trial variance ( {\tau }^{2} ). We categorised the networks according to the extent and balance of MOD in the included trials. To complement the empirical study, we conducted a simulation study to compare the competing approaches regarding bias and width of the 95% credible interval of the (summary) log odds ratios (OR) and {\tau }^{2} in the presence of moderate and large MOD. Results The empirical study did not reveal any systematic bias between the compared approaches regarding the log OR, but showed systematically larger uncertainty around the log OR under the one-stage approach for networks with at least one small trial or low event risk and moderate MOD. For these networks, the simulation study revealed that the bias in log OR for comparisons with the reference intervention in the network was relatively higher in the two-stage approach. Contrariwise, the bias in log OR for the remaining comparisons was relatively higher in the one-stage approach. Overall, bias increased for large MOD. Furthermore, in these networks, the empirical results revealed slightly higher {\tau }^{2} estimates under the one-stage approach irrespective of the extent of MOD. The one-stage approach also led to less precise log OR and {\tau }^{2} when compared with the two-stage approach for large MOD. Conclusions Due to considerable bias in the log ORs overall, especially for large MOD, none of the competing approaches was superior. Until a more competent model is developed, the researchers may prefer the one-stage approach to handle MOD, while acknowledging its limitations.

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