scholarly journals Classifying ecosystem stressor interactions: Theory highlights the data limitations of the additive null model and the difficulty in revealing ecological surprises

2021 ◽  
Author(s):  
Benjamin J. Burgess ◽  
Drew Purves ◽  
Georgina Mace ◽  
David J. Murrell
Keyword(s):  
Null Model ◽  
Data Limitations

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Null Model ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Fit Indices ◽  
Cautionary Note ◽  
Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

Does the Russian Government Pay a "Fair" Wage: Review of Studies

2015 ◽  
pp. 62-85 ◽  
Author(s):  
T. Zhuravleva
Keyword(s):  
Labor Market ◽  
Public Sector ◽  
Private Sector ◽  
Wage Differentials ◽  
Omitted Variables ◽  
Russian Government ◽  
Correct Evaluation ◽  
Self Selection ◽  
Data Limitations ◽  
Fair Wage

This paper surveys the literature on public-private sector wage differentials for Russian labor market. We give an overview of the main results and problems of the existing research. The authors unanimously confirm that in Russia private sector workers receive higher wages relative to their public sector counterparts. According to different estimates the "premium" varies between 7 and 40%. A correct evaluation of this "premium" is subject to debate and is a particular case of a more general econometric problem of wage differentials estimation. The main difficulties are related to data limitations, self-selection and omitted variables. Reasons for the existence of a stable private sector "premium" in Russia are not fully investigated.

Counter culture: Causes, extent and solutions of systematic bias in the analysis of behavioural counts

2019 ◽  
Author(s):  
Joel L Pick ◽  
Nyil Khwaja ◽  
Michael A. Spence ◽  
Malika Ihle ◽  
Shinichi Nakagawa
Keyword(s):  
Recent Literature ◽  
Null Model ◽  
Mechanistic Explanation ◽  
Effect Sizes ◽  
Systematic Bias ◽  
Intra Class Correlation ◽  
Counter Culture ◽  
Provisioning Rate ◽  
Parental Provisioning ◽  
Observation Period

We often quantify a behaviour by counting the number of times it occurs within a specific, short observation period. Measuring behaviour in such a way is typically unavoidable but induces error. This error acts to systematically reduce effect sizes, including metrics of particular interest to behavioural and evolutionary ecologists such as R2, repeatability (intra-class correlation, ICC) and heritability. Through introducing a null model, the Poisson process, for modelling the frequency of behaviour, we give a mechanistic explanation of how this problem arises and demonstrate how it makes comparisons between studies and species problematic, because the magnitude of the error depends on how frequently the behaviour has been observed (e.g. as a function of the observation period) as well as how biologically variable the behaviour is. Importantly, the degree of error is predictable and so can be corrected for. Using the example of parental provisioning rate in birds, we assess the applicability of our null model for modelling the frequency of behaviour. We then review recent literature and demonstrate that the error is rarely accounted for in current analyses. We highlight the problems that arise from this and provide solutions. We further discuss the biological implications of deviations from our null model, and highlight the new avenues of research that they may provide. Adopting our recommendations into analyses of behavioural counts will improve the accuracy of estimated effect sizes and allow meaningful comparisons to be made between studies.

Uneven Decoupling

2019 ◽  
Vol 5 (4) ◽  
pp. 410-427 ◽  
Author(s):  
Ryan P. Thombs ◽  
Xiaorui Huang
Keyword(s):  
Economic Growth ◽  
Co2 Emissions ◽  
Ecological Modernization ◽  
Climate Mitigation ◽  
Data Limitations ◽  
Treadmill Of Production ◽  
Global North ◽  
Organization Of Production ◽  
Changes Over Time ◽  
Over Time

The macro-comparative decoupling literature has often sought to test the arguments made by the treadmill of production (TP) and ecological modernization (EM) theories. However, due to data limitations, these studies have been limited to analyzing the years after 1960. Given that both theories discuss historical processes operating before 1960, analyzing pre-1960 data is warranted to more comprehensively test the propositions made by both theories. We assess the long-term relationship between economic growth and CO2 emissions from 1870 to 2014 using a sample of global North nations. We use Prais-Winsten regression models with time interactions to assess whether, when, and how much CO2 emissions have decoupled from economic growth over time. We find that significant relative decoupling has occurred twice since 1870: during the last 30 years of the nineteenth century, the timing of which is contrary to what both the EM and TP theories might expect, and after 1970. We also observe that the relationship remained relatively stable from the turn of the twentieth century to approximately 1970, which aligns with the arguments made by the classical TP work. We conclude that shifts in the global organization of production have shaped the magnitude of the economic growth–CO2 emissions relationship and its changes over time, which has implications for climate mitigation policy.

Niche Sharing in Intertidal Mollusks and Decapods in Rocky Shore of Easter Island

2019 ◽  
Vol 53 (5) ◽  
pp. 417-422
Author(s):  
P. De los Ríos ◽  
E. Ibáñez Arancibia
Keyword(s):  
Spatial Distribution ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Rocky Shore ◽  
Negative Binomial ◽  
Null Model ◽  
Easter Island ◽  
Published Data ◽  
Distribution Analysis ◽  
Field Works

Abstract The coastal marine ecosystems in Easter Island have been poorly studied, and the main studies were isolated species records based on scientific expeditions. The aim of the present study is to apply a spatial distribution analysis and niche sharing null model in published data on intertidal marine gastropods and decapods in rocky shore in Easter Island based in field works in 2010, and published information from CIMAR cruiser in 2004. The field data revealed the presence of decapods Planes minutus (Linnaeus, 1758) and Leptograpsus variegatus (Fabricius, 1793), whereas it was observed the gastropods Nodilittorina pyramidalis pascua Rosewater, 1970 and Nerita morio (G. B. Sowerby I., 1833). The available information revealed the presence of more species in data collected in 2004 in comparison to data collected in 2010, with one species markedly dominant in comparison to the other species. The spatial distribution of species reported in field works revealed that P. minutus and N. morio have aggregated pattern and negative binomial distribution, L. variegatus had uniform pattern with binomial distribution, and finally N. pyramidalis pascua, in spite of aggregated distribution pattern, had not negative binomial distribution. Finally, the results of null model revealed that the species reported did not share ecological niche due to competition absence. The results would agree with other similar information about littoral and sub-littoral fauna for Easter Island.

GRAM: A True Null Model for Relative Binding Affinity Predictions

2019 ◽  
Author(s):  
Guanglei Cui ◽  
Alan P. Graves ◽  
Eric S. Manas
Keyword(s):  
Binding Affinity ◽  
Performance Metrics ◽  
Null Model ◽  
Performance Measure ◽  
Interval Estimate ◽  
Empirical Observation ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Relative Binding Affinity ◽  
Relative Binding

Relative binding affinity prediction is a critical component in computer aided drug design. Significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of random in an objective manner. Although many performance metrics, such as correlation coefficient (r2), mean unsigned error (MUE), and room mean square error (RMSE), are frequently used in the literature, a true and non-trivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are 1) it provides the uncertainty range in the predicted activities, which is important in prospective applications; 2) a true null model with well-defined PI can be established. We provide one such example termed Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, s). Having an analytically defined PI that only depends on the variation in the activities, GRAM should in principle allow us to compare the performance of relative binding affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.<br>

scholarly journals Continuous exposure to ambient air pollution and chronic diseases: prevalence, burden, and economic costs

2020 ◽  
Vol 35 (4) ◽  
pp. 379-399
Author(s):  
Seyed M. Karimi ◽  
Ali Maziyaki ◽  
Samaneh Ahmadian Moghadam ◽  
Mahtab Jafarkhani ◽  
Hamid Zarei ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Air Pollution ◽  
Chronic Diseases ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Economic Losses ◽  
Continuous Exposure ◽  
Economic Costs ◽  
Unit Change ◽  
Data Limitations

AbstractStudies that assess the connection between the prevalence of chronic diseases and continuous exposure to air pollution are scarce in developing countries, mainly due to data limitations. Largely overcoming data limitations, this study aimed to investigate the association between the likelihood of reporting a set of chronic diseases (diabetes, cancer, stroke and myocardial infarction, asthma, and hypertension) and continuous exposure to carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and coarse particulate matter (PM10). Using the estimated associations, the disease burden and economic costs of continuous exposure to air pollutants were also approximated. A 2011 Health Equity Assessment and Response Tool survey from Tehran, Iran, was used in the main analyses. A sample of 67,049 individuals who had not changed their place of residence for at least 2 years before the survey and reported all relevant socioeconomic information was selected. The individuals were assigned with the average monthly air pollutant levels of the nearest of 16 air quality monitors during the 2 years leading to the survey. Both single- and multi-pollutant analyses were conducted. The country’s annual household surveys from 2002 to 2011 were used to calculate the associated economic losses. The single-pollutant analysis showed that a one-unit increase in monthly CO (ppm), NO2 (ppb), O3 (ppb), and PM10 (μg/m3) during the 2 years was associated with 751 [confidence interval (CI): 512–990], 18 (CI: 12–24), 46 (CI: −27–120), and 24 (CI: 13–35) more reported chronic diseases in 100,000, respectively. The disease-specific analyses showed that a unit change in average monthly CO was associated with 329, 321, 232, and 129 more reported cases of diabetes, hypertension, stroke and myocardial infarction, and asthma in 100,000, respectively. The measured associations were greater in samples with older individuals. Also, a unit change in average monthly O3 was associated with 21 (in 100,000) more reported cases of asthma. The multi-pollutant analyses confirmed the results from single-pollutant analyses. The supplementary analyses showed that a one-unit decrease in monthly CO level could have been associated with about 208 (CI: 147–275) years of life gained or 15.195 (CI: 10.296–20.094) thousand US dollars (USD) in life-time labor market income gained per 100,000 30-plus-year-old Tehranis.

scholarly journals Detecting anomalous citation groups in journal networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sadamori Kojaku ◽  
Giacomo Livan ◽  
Naoki Masuda
Keyword(s):  
Null Model ◽  
Impact Factors ◽  
Academic Publishing ◽  
Scientific Communities ◽  
Citation Behavior ◽  
Journal Editors ◽  
Journal Citation Reports ◽  
Academic Climate ◽  
The Impact ◽  
Publication Standards

AbstractThe ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher publication standards. However, the fixation on higher impact factors leads some journals to artificially boost impact factors through the coordinated effort of a “citation cartel” of journals. “Citation cartel” behavior has become increasingly common in recent years, with several instances being reported. Here, we propose an algorithm—named CIDRE—to detect anomalous groups of journals that exchange citations at excessively high rates when compared against a null model that accounts for scientific communities and journal size. CIDRE detects more than half of the journals suspended from Journal Citation Reports due to anomalous citation behavior in the year of suspension or in advance. Furthermore, CIDRE detects many new anomalous groups, where the impact factors of the member journals are lifted substantially higher by the citations from other member journals. We describe a number of such examples in detail and discuss the implications of our findings with regard to the current academic climate.

Sign in / Sign up

Export Citation Format

Share Document