Quantifying heterogeneity, heteroscedasticity and publication bias effects on technical efficiency estimates of rice farming: A meta‐regression analysis

2021 ◽  
Author(s):  
Phuc Trong Ho ◽  
Michael Burton ◽  
Chunbo Ma ◽  
Atakelty Hailu
Keyword(s):  
Regression Analysis ◽  
Publication Bias ◽  
Technical Efficiency ◽  
Rice Farming ◽  
Meta Regression ◽  
Meta Regression Analysis

Systematic review and meta-regression analysis of technical efficiency in dairy farms

2016 ◽  
Vol 65 (3) ◽  
pp. 279-301 ◽  
Author(s):  
Taciana Mareth ◽  
Antonio Marcio Tavares Thomé ◽  
Fernando Luiz Cyrino Oliveira ◽  
Luiz Felipe Scavarda
Keyword(s):  
Systematic Review ◽  
Regression Analysis ◽  
Technical Efficiency ◽  
Geographical Location ◽  
Dairy Farms ◽  
Two Dimensions ◽  
Farm Size ◽  
Content Type ◽  
Meta Regression ◽  
Meta Regression Analysis

Purpose – The purpose of this paper is to complement and extend previous literature reviews on Technical Efficiency (TE) in dairy farms, analysing the effects of different methodologies and study-specific characteristics on Mean TE (MTE). Design/methodology/approach – The researchers independently conducted a systematic review of more than 400 abstracts and 85 full-text papers. Original keywords were applied to seven key electronic databases. Results from a meta-regression analysis of 85 published papers totalling 443 TE distributions in dairy farms worldwide are discussed. Findings – The variation in the MTE indexes reported in the literature can be explained by the methodology of estimations (method of estimation, functional form of frontier models, model dimensionality), the farms geographical location and farm size. Additionally, the results suggest that, given the state of technology prevailing in each country at the time that the studies on TE were conducted, dairy farmers in the sample could increase milk output by 20.9 per cent (level of inefficiency), on average, if they produce on their frontiers. Originality/value – This study makes two important contributions: first, it updates and compares previous works on frontier estimation of TE in dairy farms; and second, it adds two dimensions of dairy farms, size (herd and land area) and economic development, to the known differentials of TE measurement.

scholarly journals Technical efficiency in farming: a meta-regression analysis

2006 ◽  
Vol 27 (1) ◽  
pp. 57-72 ◽  
Author(s):  
Boris E. Bravo-Ureta ◽  
Daniel Solís ◽  
Víctor H. Moreira López ◽  
José F. Maripani ◽  
Abdourahmane Thiam ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Technical Efficiency ◽  
Meta Regression ◽  
Meta Regression Analysis

Relationship Between Smoking and Pressure Injury Risk: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 67 (9) ◽  
pp. 34-46
Author(s):  
Bing-Bing Wu ◽  
Dong-Zhou Gu ◽  
Jia-Ning Yu ◽  
Li-Peng Feng ◽  
Rong Xu ◽  
...  
Keyword(s):  
Systematic Review ◽  
Regression Analysis ◽  
Publication Bias ◽  
Injury Risk ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
China National Knowledge Infrastructure ◽  
Pressure Injury ◽  
Meta Regression ◽  
Meta Regression Analysis

BACKGROUND: Smoking is a risk factor for many diseases. PURPOSE: This study explored the relationship between current or past smoking and pressure injury (PI) risk through a systematic review and meta-analysis. METHODS: The databases PubMed, Web of Science, and China National Knowledge Infrastructure were searched for the years between 2001 and 2020. Quality of evidence was estimated by the Newcastle-Ottawa Scale. The random effects model was applied to assess the odds ratios (OR) and 95% confidence intervals (CI); pooled adjusted OR and 95% CI, subgroup analysis, publication bias, sensitivity analyses, and meta-regression analysis were performed. RESULTS: Fifteen (15) studies (12 retrospective and 3 prospective) comprising data on 11 304 patients were eligible for inclusion in the review. The meta-analysis demonstrated that smoking increased the risk of PI (OR = 1.498; 95% CI, 1.058-2.122), and the pooled adjusted OR (1.969) and 95% CI (1.406-2.757) confirmed this finding. Publication bias was not detected by funnel plot, Begg’s test (P = .322), or Egger’s test (P = .666). Subgroup analyses yielded the same observations in both retrospective (OR = 1.607; 95% CI, 1.043-2.475) and prospective (OR = 1.218; 95% CI, 0.735-2.017) studies. The results were consistent across sensitivity analyses (OR = 1.07; 95% CI, 1.043-2.475). Relevant heterogeneity moderators were not identified by meta-regression analysis with PI incidence (P = .466), years of patient data included (P = .637), mean patient age (P = .650), and diabetes mellitus diagnosis (P = .509). CONCLUSION: This study found that individuals who are current or formers smokers have an almost 1.5 times higher risk of PI development than do those who do not smoke.

Family firms and financial performance: a meta-regression analysis

2019 ◽  
Vol 32 (3) ◽  
pp. 345-372
Author(s):  
Maximiliano Gonzalez ◽  
Juan David Idrobo ◽  
Rodrigo Taborda
Keyword(s):  
Regression Analysis ◽  
Financial Performance ◽  
Publication Bias ◽  
Family Firms ◽  
Meta Analysis ◽  
Research Process ◽  
Content Type ◽  
The Family ◽  
Meta Regression ◽  
Meta Regression Analysis

Purpose The purpose of this paper is to carry out a meta-regression analysis upon the literature that examines the relationship between family firms and financial performance. Design/Methodology/Approach Information of publication and study characteristics from 61 primary studies, comprising 726 size effects was collected. In particular, three leading factors highlighted in narrative literature reviews analyzed were: the financial performance measures, the family–firm definitions and the estimation methodologies. Findings Overall, a positive relationship between family involvement and financial performance was found. A series of results, those linked to return on assets (ROA) – earnings before interest, taxes, depreciation and amortization (EBITDA), suggest positive publication bias from family definition and negative publication bias when OLS is used. Tobin’s Q estimates show no linkage to certain traits and aspects of the research process. Originality/value Existing literature review and meta-analysis studies show not concluding results on the family effect upon firm performance. The meta-regression analysis used in this paper allows to examine simultaneously effect size and publication bias. The latter effect is particularly salient in the approach and findings, and not present in previous studies.

Tourism and Economic Growth: A Meta-regression Analysis

2019 ◽  
Vol 59 (3) ◽  
pp. 404-423 ◽  
Author(s):  
Robin Nunkoo ◽  
Boopen Seetanah ◽  
Zameelah Rifkha Khan Jaffur ◽  
Paul George Warren Moraghen ◽  
Raja Vinesh Sannassee
Keyword(s):  
Economic Growth ◽  
Regression Analysis ◽  
Publication Bias ◽  
Theory Development ◽  
Time Span ◽  
Number Of Factors ◽  
Meta Regression ◽  
Meta Regression Analysis ◽  
The Relationship ◽  
Growth Hypothesis

Numerous studies have focused on delineating the relationship between tourism and economic growth. In this article, we present the results of a rigorous meta-regression analysis based on 545 estimates drawn from 113 studies that empirically tested the tourism-led growth hypothesis (TLGH). The results suggest the presence of publication bias in the literature on this topic, where the majority of studies report positive and statistically significant estimates. Findings provide support for the TLGH, but they also suggest that the estimates are sensitive to a number of factors that are related to country data, specification, and estimation characteristics, and time span. Such sensitivities suggest that greater emphasis should be placed on reporting estimates of the relationship between tourism and economic growth across a variety of methodological characteristics and specification and estimation choices. The implications of the results for theory development are also discussed.

Meta-Regression in Auditing Research: Evaluating the Evidence on the Big N Audit Firm Premium

2016 ◽  
Vol 36 (2) ◽  
pp. 133-159 ◽  
Author(s):  
David C. Hay ◽  
W. Robert Knechel
Keyword(s):  
Regression Analysis ◽  
Publication Bias ◽  
Audit Firm ◽  
Audit Firms ◽  
Meta Regression ◽  
Meta Regression Analysis ◽  
A New Technique ◽  
Significant Publication Bias ◽  
The U.S ◽  
Jel Classifications

SUMMARY The issue of whether the Big N audit firms charge higher fees is one of the most extensively researched areas in the auditing literature. This issue is also important to regulators. This paper applies a new technique—meta-regression analysis—to extract generalizable results from the research examining the premium charged by the Big N audit firms. The method first allows us to examine whether publication bias explains the inconsistent results of previous research on this issue. We are then able to examine contextual differences across studies that can explain when a Big N premium may or may not arise. The findings show that a statistically significant publication bias is present in research about the Big N audit firm premium, and the premium is somewhat overstated. Although this is a concern, the extent of publication bias is not so strong as to eliminate evidence of a remaining premium. Breaking down the publication bias shows evidence consistent with researchers with Big N audit firm affiliations being subject to greater publication bias, while researchers at more highly ranked business schools are subject to less. In terms of the context of the mixed results, we find that a positive premium is most likely to be observed in studies of the U.S. market and in the private sector. JEL Classifications: L13; L84; M40; M42.

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