Madness, Mores, and Multivariate Methods

1978 ◽  
Vol 23 (11) ◽  
pp. 878-879
Author(s):  
LUIZ NATALICIO
Keyword(s):  
Multivariate Methods

Do New Chiefs Mean Fresh Perspectives? The Institutionalization of the Police Executive Environment

2021 ◽  
pp. 009385482110067
Author(s):  
Matthew C. Matusiak
Keyword(s):  
Law Enforcement ◽  
Regression Models ◽  
Ordinary Least Squares ◽  
Limited Attention ◽  
Control Group ◽  
Multivariate Methods ◽  
Environmental Perceptions ◽  
Police Organizations ◽  
Law Enforcement Agency ◽  
Time Periods

Research suggests policing is a highly institutionalized field. Limited attention has been paid, however, to the institutionalization of leaders’ views. Assessing turnover in 71 Texas police organizations between October, 2011, and July, 2015, this research evaluates whether there is consistency (i.e., institutional homogenization) after turnover in chiefs’ perceptions of their environments and agency priorities. The research is unique in that it assesses two chiefs’ perceptions that have both led the same law enforcement agency in successive time periods. Assessments of environment and priorities from former chiefs and those replacing them are evaluated utilizing descriptive, bivariate, and multivariate methods. These assessments are also compared with a control group of chiefs from agencies not experiencing turnover. Bivariate results suggest little variation across current and former chiefs, whereas ordinary least squares (OLS) regression models suggest differing relationships across chiefs groups between environmental perceptions and agency priorities. Discussion of the findings is framed by institutional theory.

Further comments on multivariate methods in behavioral research

1978 ◽  
Vol 9 (3) ◽  
pp. 474-476
Author(s):  
Robert M. Kaplan ◽  
Alan J. Litrownik
Keyword(s):  
Behavioral Research ◽  
Multivariate Methods

Hydro-meteorological drought risk assessment using linear and nonlinear multivariate methods

2021 ◽  
pp. 103046
Author(s):  
Zahra Azhdari ◽  
Ommolbanin Bazrafshan ◽  
Hossein Zamani ◽  
Marzieh Shekari ◽  
Vijay P. Singh
Keyword(s):  
Risk Assessment ◽  
Meteorological Drought ◽  
Drought Risk ◽  
Multivariate Methods ◽  
Linear And Nonlinear

Multivariate Methods in Ecological Work.

1982 ◽  
Vol 145 (1) ◽  
pp. 142
Author(s):  
R. M. Cormack ◽  
L. Orloci ◽  
C. R. Rao ◽  
W. M. Stiteler
Keyword(s):  
Multivariate Methods

Traditional Versus Multivariate Methods for Identifying Jaguar, Puma, and Large Canid Tracks

2010 ◽  
Vol 74 (5) ◽  
pp. 1141-1153 ◽  
Author(s):  
Carlos De Angelo ◽  
Agustín Paviolo ◽  
Mario S. Di Bitetti
Keyword(s):  
Multivariate Methods

scholarly journals Technical note: Multiple wavelet coherence for untangling scale-specific and localized multivariate relationships in geosciences

2016 ◽  
Vol 20 (8) ◽  
pp. 3183-3191 ◽  
Author(s):  
Wei Hu ◽  
Bing Cheng Si
Keyword(s):  
Multiple Scales ◽  
Free Water ◽  
Wavelet Coherence ◽  
Predictor Variables ◽  
Water Evaporation ◽  
Multivariate Methods ◽  
Spectral Coherence ◽  
Data Set ◽  
Multivariate Relationships ◽  
Multiple Wavelet Coherence

Abstract. The scale-specific and localized bivariate relationships in geosciences can be revealed using bivariate wavelet coherence. The objective of this study was to develop a multiple wavelet coherence method for examining scale-specific and localized multivariate relationships. Stationary and non-stationary artificial data sets, generated with the response variable as the summation of five predictor variables (cosine waves) with different scales, were used to test the new method. Comparisons were also conducted using existing multivariate methods, including multiple spectral coherence and multivariate empirical mode decomposition (MEMD). Results show that multiple spectral coherence is unable to identify localized multivariate relationships, and underestimates the scale-specific multivariate relationships for non-stationary processes. The MEMD method was able to separate all variables into components at the same set of scales, revealing scale-specific relationships when combined with multiple correlation coefficients, but has the same weakness as multiple spectral coherence. However, multiple wavelet coherences are able to identify scale-specific and localized multivariate relationships, as they are close to 1 at multiple scales and locations corresponding to those of predictor variables. Therefore, multiple wavelet coherence outperforms other common multivariate methods. Multiple wavelet coherence was applied to a real data set and revealed the optimal combination of factors for explaining temporal variation of free water evaporation at the Changwu site in China at multiple scale-location domains. Matlab codes for multiple wavelet coherence were developed and are provided in the Supplement.

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