Using the bootstrap in correlation analysis, with application to a longitudinal data set

Although a substantial body of work has investigated drivers of tie formation, there is growing interest in understanding why relationships decay or dissolve altogether. The networks literature has tended to conceptualize tie decay as driven by processes similar to those underlying tie formation. Yet information that is revealed through ongoing interactions can exert different effects on tie formation and tie decay. This paper investigates how tie decay and tie formation processes differ by focusing on contentious practices. To the extent that information about dissimilarities in contentious practices is learned through ongoing interactions, it can exert diverging effects on tie formation and tie decay. Using a longitudinal data set of 141,543 physician dyads, we find that differences in contentious prescribing led ties to weaken or dissolve altogether but did not affect tie formation. The more contentious the practice and the more information available about the practice, the stronger the effect on tie decay and dissolution. Collectively, these findings contribute to a more nuanced understanding of relationship evolution as an unfolding process through which deeper-level differences are revealed and shape the outcome of the tie.

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Correlation analysis for longitudinal data: applications to HIV and psychosocial research

Statistics in Medicine ◽

10.1002/sim.2857 ◽

2007 ◽

Vol 26 (22) ◽

pp. 4116-4138 ◽

Cited By ~ 10

Author(s):

X. M. Tu ◽

C. Feng ◽

J. Kowalski ◽

W. Tang ◽

H. Wang ◽

...

Keyword(s):

Correlation Analysis ◽

Longitudinal Data

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Sexism and Gender Inequality Across 57 Societies

Psychological Science ◽

10.1177/0956797611420445 ◽

2011 ◽

Vol 22 (11) ◽

pp. 1413-1418 ◽

Cited By ~ 76

Author(s):

Mark J. Brandt

Keyword(s):

Longitudinal Data ◽

Multilevel Modeling ◽

Gender Inequality ◽

Past Research ◽

Status Quo ◽

Data Set ◽

Representative Data ◽

Gender Hierarchy ◽

And Gender

Theory predicts that individuals’ sexism serves to exacerbate inequality in their society’s gender hierarchy. Past research, however, has provided only correlational evidence to support this hypothesis. In this study, I analyzed a large longitudinal data set that included representative data from 57 societies. Multilevel modeling showed that sexism directly predicted increases in gender inequality. This study provides the first evidence that sexist ideologies can create gender inequality within societies, and this finding suggests that sexism not only legitimizes the societal status quo, but also actively enhances the severity of the gender hierarchy. Three potential mechanisms for this effect are discussed briefly.

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The normative evolution of corporate governance in the UK: an empirical analysis (1995-2014)

Corporate Governance ◽

10.1108/cg-07-2018-0239 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1015-1041 ◽

Cited By ~ 1

Author(s):

Stefanie Pletz ◽

Joan Upson

Keyword(s):

Corporate Governance ◽

Longitudinal Data ◽

Empirical Evidence ◽

Financial Market ◽

Data Set ◽

Content Type ◽

Market Growth ◽

Implementation Model ◽

Quantitative And Qualitative Research ◽

The Uk

Purpose This paper aims to analyse normative corporate governance evolution in the UK between 1995 and 2014 against the benchmark of Organisation for Economic Co-Operation and Development (OECD) regulatory principles. Design/methodology/approach Methodologically, the authors conduct an empirical, longitudinal data set analysis of the formative years of UK normative corporate governance development between 1995 and 2014. We provide a qualitative discussion of the empirical evidence that links the type of UK regulatory corporate governance development to financial market growth thereby adopting a mixed approach based on quantitative and qualitative research methods. Findings The authors find that compared to the OECD model of corporate governance, the UK model is less rigid following a more self-regulatory approach based upon a “comply or explain” paradigm. Thus it is scored below corporate governance systems that follow a compulsory implementation model. However, even with such “low” tilt towards formal shareholder primacy norms, the UK has the best performing financial market. As a quasi-empirical study, the authors suggest that there are several historical and economic reasons for this, which together with a robust rule of law in the UK contribute to this performance – and the law especially the type or tilt is less relevant. Originality/value This is the first of its kind empirical, longitudinal data set analysis with qualitative elements that links empirical evidence to regulatory developments in the wider context of UK corporate governance evolution.

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The Effect of Chain Length on Mid-Infrared and Near-Infrared Spectra of Aliphatic 1-Alcohols

Applied Spectroscopy ◽

10.1177/0003702817732253 ◽

2017 ◽

Vol 72 (2) ◽

pp. 288-296 ◽

Cited By ~ 7

Author(s):

Michał Kwaśniewicz ◽

Mirosław A. Czarnecki

Keyword(s):

Correlation Analysis ◽

Chain Length ◽

Near Infrared ◽

Hydrophobic Interactions ◽

Principal Component ◽

Butyl Alcohol ◽

Two Dimensional ◽

Data Set ◽

Mid Infrared ◽

Spectral Changes

Effect of the chain length on mid-infrared (MIR) and near-infrared (NIR) spectra of aliphatic 1-alcohols from methanol to 1-decanol was examined in detail. Of particular interest were the spectra-structure correlations in the NIR region and the correlation between MIR and NIR spectra of 1-alcohols. An application of two-dimensional correlation analysis (2D-COS) and chemometric methods provided comprehensive information on spectral changes in the data set. Principal component analysis (PCA) and cluster analysis evidenced that the spectra of methanol, ethanol, and 1-propanol are noticeably different from the spectra of higher 1-alcohols. The similarity between the spectra increases with an increase in the chain length. Hence, the most similar are the spectra of 1-nonanol and 1-decanol. Two-dimensional hetero-correlation analysis is very helpful for identification of the origin of bands and may guide selection of the best spectral ranges for the chemometric analysis. As shown, normalization of the spectra pronounces the intensity changes in various spectral regions and provides information not accessible from the raw data. The spectra of alcohols cannot be represented as a sum of the CH3, CH2, and OH group spectra since the OH group is involved in the hydrogen bonding. As a result, the spectral changes of this group are nonlinear and its spectral profile cannot be properly resolved. Finally, this work provides a lot of evidence that the degree of self-association of 1-alcohols decreases with the increase in chain length because of the growing meaning of the hydrophobic interactions. For butyl alcohol and higher 1-alcohols the hydrophobic interactions are more important than the OH OH interactions. Therefore, methanol, ethanol, and 1-propanol have unlimited miscibility with water, whereas 1-butanol and higher 1-alcohols have limited miscibility with water.

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Individual analysis of EEG background‐activity within school age: impact of age and sex within a longitudinal data set

International Journal of Developmental Neuroscience ◽

10.1016/j.ijdevneu.2010.11.005 ◽

2010 ◽

Vol 29 (2) ◽

pp. 163-170 ◽

Cited By ~ 13

Author(s):

Dennis Gmehlin ◽

Christine Thomas ◽

Matthias Weisbrod ◽

Stephan Walther ◽

Ute Pfüller ◽

...

Keyword(s):

Longitudinal Data ◽

Background Activity ◽

School Age ◽

Data Set ◽

Individual Analysis ◽

Age And Sex

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Teacher Pension Incentives, Retirement Behavior, and Potential for Reform in Arkansas

Education Finance and Policy ◽

10.1162/edfp_a_00013 ◽

2010 ◽

Vol 5 (4) ◽

pp. 492-518 ◽

Cited By ~ 28

Author(s):

Robert M. Costrell ◽

Josh B. McGee

Keyword(s):

Longitudinal Data ◽

Behavioral Response ◽

Early Retirement ◽

Pension Plan ◽

Data Set ◽

Service Requirement ◽

Retirement Plan ◽

Retirement Behavior ◽

Pension Wealth

The authors analyze the Arkansas teacher pension plan and empirically gauge the behavioral response to incentives embedded in that plan and to possible reforms. The pattern of pension wealth accrual creates sharp incentives to work until eligible for early or normal retirement, often in one's early fifties, and to separate shortly thereafter. We estimate the effect of pension wealth accrual on teacher separation decisions using a new longitudinal data set of Arkansas teachers and find a significant impact. We then simulate the response to eliminating early retirement and raising the service requirement for normal retirement. We also simulate a shift to a constant accrual retirement plan. The response to both reforms is complex, as some would leave earlier and others stay longer. A constant accrual plan smoothes the pattern of retirement behavior as individuals tailor decisions to their own preferences instead of those built into the pension formula.

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Correlation analysis for spread-spectrum induced-polarization signal processing in electromagnetically noisy environments

Geophysics ◽

10.1190/geo2016-0109.1 ◽

2017 ◽

Vol 82 (5) ◽

pp. E243-E256 ◽

Cited By ~ 8

Author(s):

Weiqiang Liu ◽

Rujun Chen ◽

Hongzhu Cai ◽

Weibin Luo ◽

André Revil

Keyword(s):

Signal Processing ◽

Correlation Analysis ◽

Correlation Coefficient ◽

Spread Spectrum ◽

Threshold Value ◽

Induced Polarization ◽

Gansu Province ◽

Data Set ◽

Complex Resistivity ◽

Primary Current

In induced-polarization (IP) surveys, the raw data are usually distorted significantly by the presence of electromagnetic (EM) interferences, including cultural noise. Several methods have been proposed to improve the signal-to-noise ratio of these data. However, signal processing in an electromagnetically noisy environment is still a challenging problem. We have determined a new and simple technique based on the analysis of the correlation between the measured potential and the injected primary current signals. This processing is applied to the data acquired using a new frequency-domain IP method called the spread-spectrum induced-polarization (SSIP) approach. In this approach, we use a pseudorandom m-sequence (also called the maximum length sequence) for the injected primary current. One of the advantages of this sequence is to be essentially spectrally flat in a given frequency range. Therefore, complex resistivity can be determined simultaneously at various frequencies. A new SSIP data set is acquired in the vicinity of Baiyin mine, Gansu Province, China. The correlation between potential difference and transmitting current signals for each period can be used to assess data quality. Only when the correlation coefficient between the two signals is greater than 0.5 can the SSIP data be used for subsequent processing and tomography. We determine what threshold value should be used for the correlation coefficient to extract high-quality apparent complex resistivity data and eliminate EM-contaminated data. We then compare the pseudosections with and without using the correlation analysis. When the correlation analysis is used, the noisy data are filtered out, and the target anomaly obtained through tomography is clearly enhanced. The inversion results of the apparent complex resistivity (amplitude and phase) for the survey area are consistent with some independent geologic and drilling information regarding the position of the ore body demonstrating the effectiveness of the approach.

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Construction of Condition-Specific Gene Regulatory Network Using Kernel Canonical Correlation Analysis

Frontiers in Genetics ◽

10.3389/fgene.2021.652623 ◽

2021 ◽

Vol 12 ◽

Author(s):

Dabin Jeong ◽

Sangsoo Lim ◽

Sangseon Lee ◽

Minsik Oh ◽

Changyun Cho ◽

...

Keyword(s):

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Computational Method ◽

Transcriptional Networks ◽

Specific Gene ◽

Transcriptome Data ◽

Data Set ◽

Kernel Canonical Correlation Analysis ◽

Kernel Cca

Gene expression profile or transcriptome can represent cellular states, thus understanding gene regulation mechanisms can help understand how cells respond to external stress. Interaction between transcription factor (TF) and target gene (TG) is one of the representative regulatory mechanisms in cells. In this paper, we present a novel computational method to construct condition-specific transcriptional networks from transcriptome data. Regulatory interaction between TFs and TGs is very complex, specifically multiple-to-multiple relations. Experimental data from TF Chromatin Immunoprecipitation sequencing is useful but produces one-to-multiple relations between TF and TGs. On the other hand, co-expression networks of genes can be useful for constructing condition transcriptional networks, but there are many false positive relations in co-expression networks. In this paper, we propose a novel method to construct a condition-specific and combinatorial transcriptional network, applying kernel canonical correlation analysis (kernel CCA) to identify multiple-to-multiple TF–TG relations in certain biological condition. Kernel CCA is a well-established statistical method for computing the correlation of a group of features vs. another group of features. We, therefore, employed kernel CCA to embed TFs and TGs into a new space where the correlation of TFs and TGs are reflected. To demonstrate the usefulness of our network construction method, we used the blood transcriptome data for the investigation on the response to high fat diet in a human and an arabidopsis data set for the investigation on the response to cold/heat stress. Our method detected not only important regulatory interactions reported in previous studies but also novel TF–TG relations where a module of TF is regulating a module of TGs upon specific stress.

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