scholarly journals Bias in community-weighted mean analysis of plant functional traits and species indicator values

2016 ◽  
Author(s):  
David Zeleny
Keyword(s):  
Beta Diversity ◽  
Degrees Of Freedom ◽  
Permutation Test ◽  
Effect Sizes ◽  
Data Sets ◽  
Indicator Values ◽  
Weighted Mean ◽  
Community Weighted Mean ◽  
Species Indicator Values ◽  
The Relationship

One way to analyze the relationship between species attributes and sample attributes via the matrix of species composition is to calculate the community-weighted mean of species attributes (CWM) and relate it to sample attributes by correlation, regression or ANOVA. This weighted-mean approach is frequently used by vegetation ecologists to relate species attributes like plant functional traits or Ellenberg-like species indicator values to sample attributes like measured environmental variables, biotic properties, species richness or sample scores in ordination analysis. The problem with the weighted-mean approach is that, in certain cases, it yields biased results in terms of both effect size and P-values, and this bias is contingent upon the beta diversity of the species composition data. The reason is that CWM values calculated from samples of communities sharing some species are not independent of each other. This influences the number of effective degrees of freedom, which is usually lower than the actual number of samples, and the difference further increases with decreasing beta diversity of the data set. The discrepancy between the number of effective degrees of freedom and the number of samples in analysis turns into biased effect sizes and an inflated Type I error rate in those cases where the significance of the relationship is tested by standard tests, a problem which is analogous to analysis of two spatially autocorrelated variables. Consequently, results of studies using rather homogeneous (although not necessarily small) compositional data sets may be overly optimistic, and effect sizes of studies based on data sets differing by their beta diversity are not directly comparable. Here, I introduce guidelines on how to decide in which situation the bias is actually a problem when interpreting results, recognizing that there are several types of species and sample attributes with different properties and that ecological hypotheses commonly tested by the weighted-mean approach fall into one of three broad categories. I also compare available analytical solutions accounting for the bias (modified permutation test and sequential permutation test using the fourth-corner statistic) and suggest rules for their use.

scholarly journals Statistical issues about solar–climate relations

2010 ◽  
Vol 6 (5) ◽  
pp. 565-573 ◽  
Author(s):  
P. Yiou ◽  
E. Bard ◽  
P. Dandin ◽  
B. Legras ◽  
P. Naveau ◽  
...  
Keyword(s):  
Time Series ◽  
Solar Activity ◽  
Geomagnetic Field ◽  
Degrees Of Freedom ◽  
Statistical Significance ◽  
Data Sets ◽  
Statistical Issues ◽  
The Relationship ◽  
Temperature Time ◽  
Field Variability

Abstract. The relationship between solar activity and temperature variation is a frequently discussed issue in climatology. This relationships is usually hypothesized on the basis of statistical analyses of temperature time series and time series related to solar activity. Recent studies (Le Mouël et al., 2008, 2009; Courtillot et al., 2010) focus on the variabilities of temperature and solar activity records to identify their relationships. We discuss the meaning of such analyses and propose a general framework to test the statistical significance for these variability-based analyses. This approach is illustrated using European temperature data sets and geomagnetic field variations. We show that tests for significant correlation between observed temperature variability and geomagnetic field variability is hindered by a low number of degrees of freedom introduced by excessively smoothing the variability-based statistics.

scholarly journals Do Smarter People Have More Conservative Economic Attitudes? Assessing the Relationship Between Cognitive Ability and Economic Ideology

2021 ◽  
pp. 014616722110468
Author(s):  
Alexander Jedinger ◽  
Axel M. Burger
Keyword(s):  
Cognitive Ability ◽  
Survey Data ◽  
Meta Analysis ◽  
Positive Association ◽  
Effect Sizes ◽  
Weighted Mean ◽  
Economic Issues ◽  
Economic Attitudes ◽  
Economic Ideology ◽  
The Relationship

Evidence on the association of cognitive ability with economic attitudes is mixed. We conducted a meta-analysis ( k = 20, N = 46,426) to examine the relationship between objective measures of cognitive ability and economic ideology and analyzed survey data ( N = 3,375) to test theoretical explanations for the association. The meta-analysis provided evidence for a small positive association with a weighted mean effect size of r = .07 (95% CI = [0.02, 0.12]), suggesting that higher cognitive ability is associated with conservative views on economic issues, but effect sizes were extremely heterogeneous. Tests using representative survey data provided support for both a positive association of cognitive ability with economic conservatism that is mediated through income as well as for a negative association that is mediated through a higher need for certainty. Hence, multiple causal mechanisms with countervailing effects might explain the low overall association of cognitive ability with economic political attitudes.

scholarly journals KL-6 as an Immunological Biomarker Predicts the Severity, Progression, Acute Exacerbation, and Poor Outcomes of Interstitial Lung Disease: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Zhang ◽  
Ping Shen ◽  
Chunyan Duan ◽  
Lingyun Gao
Keyword(s):  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Acute Exacerbation ◽  
Adverse Outcome ◽  
Weighted Mean Difference ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Weighted Mean ◽  
Poor Outcomes ◽  
The Relationship

ObjectInterstitial lung disease (ILD) is a specific form of chronic fibrosing interstitial pneumonia with various etiology. The severity and progression of ILD usually predict the poor outcomes of ILD. Otherwise, Krebs von den Lungen-6 (KL-6) is a potential immunological biomarker reflecting the severity and progression of ILD. This meta-analysis is to clarify the predictive value of elevated KL-6 levels in ILD.MethodEBSCO, PubMed, and Cochrane were systematically searched for articles exploring the prognosis of ILD published between January 1980 and April 2021. The Weighted Mean Difference (WMD) and 95% Confidence Interval (CI) were computed as the effect sizes for comparisons between groups. For the relationship between adverse outcome and elevated KL-6 concentration, Hazard Ratio (HR), and its 95%CI were used to estimate the risk factor of ILD.ResultOur result showed that ILD patients in severe and progressive groups had higher KL-6 levels, and the KL-6 level of patients in the severe ILD was 703.41 (U/ml) than in mild ILD. The KL-6 level in progressive ILD group was 325.98 (U/ml) higher than that in the non-progressive ILD group. Secondly, the KL-6 level of patients in acute exacerbation (AE) of ILD was 545.44 (U/ml) higher than stable ILD. Lastly, the higher KL-6 level in ILD patients predicted poor outcomes. The KL-6 level in death of ILD was 383.53 (U/ml) higher than in survivors of ILD. The pooled HR (95%CI) about elevated KL-6 level predicting the mortality of ILD was 2.05 (1.50–2.78), and the HR (95%CI) for progression of ILD was 1.98 (1.07–3.67).ConclusionThe elevated KL-6 level indicated more severe, more progressive, and predicted the higher mortality and poor outcomes of ILD.

scholarly journals Which results of the standard test in community weighted mean approach are too optimistic?

2018 ◽  
Author(s):  
David Zelený
Keyword(s):  
Species Composition ◽  
Error Rate ◽  
Type I Error ◽  
Permutation Test ◽  
Standard Test ◽  
Type I ◽  
Type I Error Rate ◽  
Inflated Type ◽  
Community Weighted Mean ◽  
The Relationship

AbstractQuestionsCommunity weighted mean (CWM) approach analyses the relationship species attributes (like traits or Ellenberg-type indicator values) to sample attributes (environmental variables). Recently it has been shown to suffer from inflated Type I error rate if tested by standard parametric or (row-based) permutation test. Results of many published studies are likely influenced, reporting overly optimistic relationships that are in fact merely a numerical artefact. Can we evaluate results of which studies are likely to be influenced and how much?MethodsI suggest that hypotheses commonly tested by CWM approach are classified into three categories, which differ by assumption they make about the link of species composition to either species or sample attributes. I used a set of simulated and one simple real dataset to show how is the inflated Type I error rate influenced by data characteristics.ResultsFor hypotheses assuming the link of species composition to species attributes, CWM approach with standard test returns correct Type I error rate. However, for the other two categories (assuming link of species composition to sample attributes or not assuming any link) it returns inflated Type I error rate and requires alternative tests to control for it (column-based and max test, respectively). Inflation index is negatively related to the beta diversity of species composition and positively to the strength of species composition-sample attributes relationship and the number of samples in the dataset. Inflation index is also influenced by modifying species composition matrix (by transformation or removal of species). The relationship of CWM with intrinsic species attributes is a case of spurious correlation and can be tested by column-based (modified) permutation test.ConclusionsThe concept of three hypothesis categories offers a simple tool to evaluate whether given study reports correct or inflated Type I error rate, and how inflated the rate can be.

The Relationship Between Immorality and Cleansing

2018 ◽  
Vol 49 (5) ◽  
pp. 303-309 ◽  
Author(s):  
Jedidiah Siev ◽  
Shelby E. Zuckerman ◽  
Joseph J. Siev
Keyword(s):  
Effect Size ◽  
Meta Analysis ◽  
Weighted Mean ◽  
The Relationship

Abstract. In a widely publicized set of studies, participants who were primed to consider unethical events preferred cleansing products more than did those primed with ethical events ( Zhong & Liljenquist, 2006 ). This tendency to respond to moral threat with physical cleansing is known as the Macbeth Effect. Several subsequent efforts, however, did not replicate this relationship. The present manuscript reports the results of a meta-analysis of 15 studies testing this relationship. The weighted mean effect size was small across all studies (g = 0.17, 95% CI [0.04, 0.31]), and nonsignificant across studies conducted in independent laboratories (g = 0.07, 95% CI [−0.04, 0.19]). We conclude that there is little evidence for an overall Macbeth Effect; however, there may be a Macbeth Effect under certain conditions.

The visual arrays task…visual storage capacity or attention control?

2019 ◽  
Author(s):  
Jessie Martin ◽  
Jason S. Tsukahara ◽  
Christopher Draheim ◽  
Zach Shipstead ◽  
Cody Mashburn ◽  
...  
Keyword(s):  
Working Memory ◽  
Storage Capacity ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Attention Control ◽  
Data Sets ◽  
Independent Data ◽  
Visual Storage ◽  
Design Selection ◽  
The Relationship

**The uploaded manuscript is still in preparation** In this study, we tested the relationship between visual arrays tasks and working memory capacity and attention control. Specifically, we tested whether task design (selection or non-selection demands) impacted the relationship between visual arrays measures and constructs of working memory capacity and attention control. Using analyses from 4 independent data sets we showed that the degree to which visual arrays measures rely on selection influences the degree to which they reflect domain-general attention control.

Epidemiological Evidence that Maternal Influenza Contributes to the Aetiology of Schizophrenia

1993 ◽  
Vol 163 (4) ◽  
pp. 522-534 ◽  
Author(s):  
W. Adams ◽  
R. E. Kendell ◽  
E. H. Hare ◽  
P. Munk-Jørgensen
Keyword(s):  
Data Sets ◽  
Time Lags ◽  
Epidemiological Evidence ◽  
Intrauterine Development ◽  
Increased Risk ◽  
Schizophrenic Patients ◽  
The Relationship ◽  
Monthly Incidence ◽  
Maternal Influenza

The epidemiological evidence that the offspring of women exposed to influenza in pregnancy are at increased risk of schizophrenia is conflicting. In an attempt to clarify the issue we explored the relationship between the monthly incidence of influenza (and measles) in the general population and the distribution of birth dates of three large series of schizophrenic patients - 16 960 Scottish patients born in 1932–60; 22 021 English patients born in 1921–60; and 18 723 Danish patients born in 1911–65. Exposure to the 1957 epidemic of A2 influenza in midpregnancy was associated with an increased incidence of schizophrenia, at least in females, in all three data sets. We also confirmed the previous report of a statistically significant long-term relationship between patients' birth dates and outbreaks of influenza in the English series, with time lags of - 2 and - 3 months (the sixth and seventh months of pregnancy). Despite several other negative studies by ourselves and others we conclude that these relationships are probably both genuine and causal; and that maternal influenza during the middle third of intrauterine development, or something closely associated with it, is implicated in the aetiology of some cases of schizophrenia.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals Dampness and student-reported social climate: two multilevel mediation models

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Eerika Finell ◽  
Asko Tolvanen ◽  
Juha Pekkanen ◽  
Timo Ståhl ◽  
Pauliina Luopa
Keyword(s):  
Indoor Air ◽  
Social Climate ◽  
School Level ◽  
Data Sets ◽  
Student Relationships ◽  
Teacher Student Relationships ◽  
Teacher Student ◽  
Mediational Models ◽  
The Relationship ◽  
Perceptions Of School

Abstract Background Little previous research has analysed the relationship between schools’ indoor air problems and schools’ social climate. In this study, we analysed a) whether observed mould and dampness in a school building relates to students’ perceptions of school climate (i.e. teacher-student relationships and class spirit) and b) whether reported subjective indoor air quality (IAQ) at the school level mediates this relationship. Methods The data analysed was created by merging two nationwide data sets: survey data from students, including information on subjective IAQ (N = 25,101 students), and data from schools, including information on mould and dampness in school buildings (N = 222). The data was analysed using multilevel mediational models. Results After the background variables were adjusted, schools’ observed mould and dampness was not significantly related to neither student-perceived teacher-student relationships nor class spirit. However, our mediational models showed that there were significant indirect effects from schools’ observed mould and dampness to outcome variables via school-level subjective IAQ: a) in schools with mould and dampness, students reported significantly poorer subjective IAQ (standardised β = 0.34, p < 0.001) than in schools without; b) the worse the subjective IAQ at school level, the worse the student-reported teacher-student relationships (β = 0.31, p = 0.001) and class spirit (β = 0.25, p = 0.006). Conclusions Problems in a school’s indoor environment may impair the school’s social climate to the degree that such problems decrease the school’s perceived IAQ.

scholarly journals 45 A permutation test for validation of genomic estimated breeding values

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 8-9
Author(s):  
Zahra Karimi ◽  
Brian Sullivan ◽  
Mohsen Jafarikia
Keyword(s):  
Permutation Test ◽  
Breeding Value ◽  
Small Data ◽  
Data Sets ◽  
Type I ◽  
Future Performance ◽  
Small Data Sets ◽  
Estimated Breeding Value ◽  
Estimated Breeding Values ◽  
Top 40

Abstract Previous studies have shown that the accuracy of Genomic Estimated Breeding Value (GEBV) as a predictor of future performance is higher than the traditional Estimated Breeding Value (EBV). The purpose of this study was to estimate the potential advantage of selection on GEBV for litter size (LS) compared to selection on EBV in the Canadian swine dam line breeds. The study included 236 Landrace and 210 Yorkshire gilts born in 2017 which had their first farrowing after 2017. GEBV and EBV for LS were calculated with data that was available at the end of 2017 (GEBV2017 and EBV2017, respectively). De-regressed EBV for LS in July 2019 (dEBV2019) was used as an adjusted phenotype. The average dEBV2019 for the top 40% of sows based on GEBV2017 was compared to the average dEBV2019 for the top 40% of sows based on EBV2017. The standard error of the estimated difference for each breed was estimated by comparing the average dEBV2019 for repeated random samples of two sets of 40% of the gilts. In comparison to the top 40% ranked based on EBV2017, ranking based on GEBV2017 resulted in an extra 0.45 (±0.29) and 0.37 (±0.25) piglets born per litter in Landrace and Yorkshire replacement gilts, respectively. The estimated Type I errors of the GEBV2017 gain over EBV2017 were 6% and 7% in Landrace and Yorkshire, respectively. Considering selection of both replacement boars and replacement gilts using GEBV instead of EBV can translate into increased annual genetic gain of 0.3 extra piglets per litter, which would more than double the rate of gain observed from typical EBV based selection. The permutation test for validation used in this study appears effective with relatively small data sets and could be applied to other traits, other species and other prediction methods.

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