277 Swine Producer Identities: Understanding Perceptions of a Good Producer

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 149-149
Author(s):  
Chris Gambino ◽  
Elizabeth M Brownawell ◽  
Elizabeth A Hines
Keyword(s):  
Reliability Analysis ◽  
Principal Components ◽  
Social Psychological ◽  
Water Policies ◽  
Good Producer ◽  
The Social ◽  
The Us ◽  
Components Analysis ◽  
On Farm ◽  
First Time

Abstract Studies on the social-psychological framework known as farmer identity have been conducted in the US, EU, and Australia. The focus of these studies is on understanding how farmer beliefs (i.e. Identities) translate into on-farm practices. For example, in 2015, Iowa farmers were surveyed and four identities emerged when asked which items were important to being a “Good Farmer.” Those identities were used to predict the support of soil and/or water policies in the state. Here, for the first time, we explore the identities of livestock producers. Specifically, Pennsylvania swine producer identities. Data were collected as part of a state-wide biosecurity survey. Eighty-four respondents answered some part of the survey, while 50 respondents completed all 31 items of the “Good Producer” question. Principal components analysis (PCA) was used to explore producer perception of a good producer (i.e. producer identities). Using the Kaiser-Meyer-Okin Measure (KMO=0.532) and Barrtlett’s Test of Sphericity (sig. = 0.000) we determined PCA results to be acceptable for exploratory purposes. Five producer identities (friendly conservationist, civically-savvy, willing naturalist, productivist, and appearance-minded) were identified, explaining 58.15% of the variance in these data. Reliability analysis confirmed the strength of items defining each component (i.e. identity). These identities are being used in discussion with PA swine producers to understand how producers self-identify. These identities will be used to predict whether a producer has a formal biosecurity plan written.

scholarly journals Measuring the Systematic Risk of Sectors within the US Market Via Principal Components Analysis: Before and during the COVID-19 Pandemic

2022 ◽  
Author(s):  
Jaime González Maiz Jiménez ◽  
Adán Reyes Santiago
Keyword(s):  
Principal Component Analysis ◽  
Stock Market ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Systematic Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Market Capitalization ◽  
The Us ◽  
Components Analysis

This research measures the systematic risk of 10 sectors in the American Stock Market, discerning the COVID-19 pandemic period. The novelty of this study is the use of the Principal Component Analysis (PCA) technique to measure the systematic risk of each sector, selecting five stocks per sector with the greatest market capitalization. The results show that the sectors that have the greatest increase in exposure to systematic risk during the pandemic are restaurants, clothing, and insurance, whereas the sectors that show the greatest decrease in terms of exposure to systematic risk are automakers and tobacco. Due to the results of this study, it seems advisable for practitioners to select stocks that belong to either the automakers or tobacco sector to get protection from health crises, such as COVID-19.

Examining social, physical, and environmental dimensions of tornado vulnerability in Texa

2016 ◽  
Vol 14 (2) ◽  
pp. 139
Author(s):  
Laura Siebeneck, PhD
Keyword(s):  
Vulnerability Index ◽  
Census Bureau ◽  
Source Information ◽  
Principle Components Analysis ◽  
Emergency Managers ◽  
The Social ◽  
Vulnerability Model ◽  
The Us ◽  
Components Analysis ◽  
Us Census

Objective: To develop a vulnerability model that captures the social, physical, and environmental dimensions of tornado vulnerability of Texas counties. Design: Guided by previous research and methodologies proposed in the hazards and emergency management literature, a principle components analysis is used to create a tornado vulnerability index. Data were gathered from open source information available through the US Census Bureau, American Community Surveys, and the Texas Natural Resources Information System.Setting: Texas counties.Results: The results of the model yielded three indices that highlight geographic variability of social vulnerability, built environment vulnerability, and tornado hazard throughout Texas. Further analyses suggest that counties with the highest tornado vulnerability include those with high population densities and high tornado risk.Conclusions: This article demonstrates one method for assessing statewide tornado vulnerability and presents how the results of this type of analysis can be applied by emergency managers towards the reduction of tornado vulnerability in their communities.

scholarly journals Pathologies of Between-Groups Principal Components Analysis in Geometric Morphometrics

2019 ◽  
Author(s):  
Fred L. Bookstein
Keyword(s):  
Geometric Morphometrics ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Null Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Wide Range ◽  
Pls Analysis ◽  
Components Analysis ◽  
First Time

AbstractGood empirical applications of geometric morphometrics (GMM) typically involve several times more variables than specimens, a situation the statistician refers to as “highp/n,” wherepis the count of variables andnthe count of specimens. This note calls your attention to two predictable catastrophic failures of one particular multivariate statistical technique, between-groups principal components analysis (bgPCA), in this high-p/nsetting. The more obvious pathology is this: when applied to the patternless (null) model ofpidentically distributed Gaussians over groups of the same size, both bgPCA and its algebraic equivalent, partial least squares (PLS) analysis against group, necessarily generate the appearance of huge equilateral group separations that are actually fictitious (absent from the statistical model). When specimen counts by group vary greatly or when any group includes fewer than about ten specimens, an even worse failure of the technique obtains: the smaller the group, the more likely a bgPCA is to fictitiously identify that group as the end-member of one of its derived axes. For these two reasons, when used in GMM and other high-p/nsettings the bgPCA method very often leads to invalid or insecure bioscientific inferences. This paper demonstrates and quantifies these and other pathological outcomes both for patternless models and for models with one or two valid factors, then offers suggestions for how GMM practitioners should protect themselves against the consequences for inference of these lamentably predictable misrepresentations. The bgPCA method should never be used unskeptically — it is never authoritative — and whenever it appears in partial support of any biological inference it must be accompanied by a wide range of diagnostic plots and other challenges, many of which are presented here for the first time.

scholarly journals Principal Components Analysis of Urine Caffeine and Caffeine Metabolites in the US Population

2015 ◽  
Vol 29 (S1) ◽  
Author(s):  
Ching‐I Pao ◽  
Michael Rybak ◽  
Maya Sternberg ◽  
Namanjeet Ahluwalia ◽  
Christine Pfeiffer
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
The Us ◽  
Components Analysis ◽  
Analysis Of Urine

Gli interventi di "mitigazione" del "disagio sociale" nel quadro della programmazione della Regione Autonoma della Sardegna

2012 ◽  
pp. 143-177
Author(s):  
Paolo Mattana
Keyword(s):  
Principal Components Analysis ◽  
Social Exclusion ◽  
Critical Points ◽  
Principal Components ◽  
Scientific Community ◽  
Two Stage ◽  
Regional Administration ◽  
The Social ◽  
Components Analysis

This contribution brings to the attention of the scientific community some of the results developed by the Evaluation Unit of the Regional Administration of Sardinia, regarding plausibility and effectiveness profiles of the actions undertaken in the social exclusion area based on the 2000-2006 European Funding. After obtaining a picture of the phenomena by means of a Principal Components Analysis on available data, we observe many critical points regarding the devising and effectiveness of the policy. First of all, there appears no matching between the municipalities benefitting of the program and the intensity of the phenomena. Furthermore, perhaps because of the scarcity of available funds, we find that the Heckman (1979) two-stage procedure does not signal the policy as effective in affecting the performance of the municipalities in the control of the social exclusion phenomena.

scholarly journals Social organisation of the Cape Mountain Zebra Equus Z. Zebra in the Mountain Zebra National Park*

Koedoe ◽  
1979 ◽  
Vol 22 (1) ◽  
Author(s):  
B. L Penzhorn
Keyword(s):  
Social Structure ◽  
Principal Components Analysis ◽  
Principal Components ◽  
National Park ◽  
Dominance Hierarchy ◽  
Social Organisation ◽  
Family Ties ◽  
The Social ◽  
Components Analysis

The social structure of Cape mountain zebras con- sists of breeding herds of one stallion, one to five mares and their offspring, as well as bachelor groups. Breeding herds remain stable over many years and when the stallion is displaced by another, the mares remain together. A dominance hierarchy exists, but leadership is random. Foals leave their maternal herds at a mean age of 22,3 months. The herd stallion tries to prevent the foals from leaving the herd. Bachelor groups are not as well defined as breeding herds, but core groups could be identified through a principal components analysis ordination. Family ties may be important in the establishment of core groups. Bachelors succeed in becoming herd stallions when about five years old. Aspects of the possible evolution of the social structure are discussed.

Girls' Talk

1984 ◽  
Vol 11 (2) ◽  
pp. 139-156 ◽  
Author(s):  
ANNE CAMPBELL
Keyword(s):  
New York ◽  
Principal Components ◽  
Physical Aggression ◽  
Social Acceptability ◽  
Street Gangs ◽  
Frequency Data ◽  
The Social ◽  
Components Analysis ◽  
Joint Consideration ◽  
Behavior Frequency

The present study analyzes taperecorded accounts of fights given by female members of New York street gangs to fellow members. While such data may not be veridical, they can reveal much about the way aggression is socially represented to peers which in turn is likely to be constrained by gang norms about the propriety of aggressive behavior. Frequency data indicate that fights are not restricted to specifically female or gang member opponents, that the majority are one-on-one encounters and are a result of domestic and romantic disputes and matters of individual integrity rather than gangrelated issues. A principal components analysis reveals three interpretable factors; group—personal, weapon—no weapon and victim—nonvictim. The five major reasons for the physical aggression are most clearly differentiated by a joint consideration of Factors I and III. The importance of these factors is discussed with reference to the social acceptability of limiting the extent and seriousness of the encounter.

A FAMILY OF MODELS EXPLAINING THE LEVEL-SLOPE-CURVATURE EFFECT

2003 ◽  
Vol 06 (03) ◽  
pp. 239-255 ◽  
Author(s):  
LILIANA FORZANI ◽  
CARLOS TOLMASKY
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Correlation Matrix ◽  
Yield Curve ◽  
Correlation Structure ◽  
Spectral Structure ◽  
Curvature Effect ◽  
Correlation Matrices ◽  
The Us ◽  
Components Analysis

One of the most widely used methods to build yield curve models is to use principal components analysis on the correlation matrix of the innovations. R. Litterman and J. Scheinkman found that three factors are enough to explain most of the moves in the case of the US treasury curve. These factors are level, steepness and curvature. Working in the context of commodity futures, G. Cortazar and E. Schwartz found that the spectral structure of the correlation matrices is strikingly similar to those found by R. Litterman and J. Scheinkman. We observe that in both cases the correlation between two different contracts maturing at times t and s is roughly of the form ρ|t-s|, for a certain (fixed) 0 ≤ ρ ≤ 1. Assuming this correlation structure we prove that the observed factors are perturbations of cosine waves and we extend the analysis to multiple curves.

scholarly journals Pathologies of Between-Groups Principal Components Analysis in Geometric Morphometrics

2019 ◽  
Vol 46 (4) ◽  
pp. 271-302 ◽  
Author(s):  
Fred L. Bookstein
Keyword(s):  
Geometric Morphometrics ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Null Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Wide Range ◽  
Pls Analysis ◽  
Components Analysis ◽  
First Time

Abstract Good empirical applications of geometric morphometrics (GMM) typically involve several times more variables than specimens, a situation the statistician refers to as “high p/n,” where p is the count of variables and n the count of specimens. This note calls your attention to two predictable catastrophic failures of one particular multivariate statistical technique, between-groups principal components analysis (bgPCA), in this high-p/n setting. The more obvious pathology is this: when applied to the patternless (null) model of p identically distributed Gaussians over groups of the same size, both bgPCA and its algebraic equivalent, partial least squares (PLS) analysis against group, necessarily generate the appearance of huge equilateral group separations that are fictitious (absent from the statistical model). When specimen counts by group vary greatly or when any group includes fewer than about ten specimens, an even worse failure of the technique obtains: the smaller the group, the more likely a bgPCA is to fictitiously identify that group as the end-member of one of its derived axes. For these two reasons, when used in GMM and other high-p/n settings the bgPCA method very often leads to invalid or insecure biological inferences. This paper demonstrates and quantifies these and other pathological outcomes both for patternless models and for models with one or two valid factors, then offers suggestions for how GMM practitioners should protect themselves against the consequences for inference of these lamentably predictable misrepresentations. The bgPCA method should never be used unskeptically—it is always untrustworthy, never authoritative—and whenever it appears in partial support of any biological inference it must be accompanied by a wide range of diagnostic plots and other challenges, many of which are presented here for the first time.

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