scholarly journals The effect analysis of teacher and child variables on young children's emotional regulation strategies: With a multi-level data analysis

2012 ◽  
Vol 32 (2) ◽  
pp. 287-305
Author(s):  
이정수 ◽  
Lee, Kyung-Ok
Keyword(s):  
Data Analysis ◽  
Emotional Regulation ◽  
Effect Analysis ◽  
Level Data ◽  
Regulation Strategies ◽  
Multi Level

scholarly journals Determinants of Consistent Condom Use Vary by Partner Type among Young Men in Kisumu, Kenya: A Multi-level Data Analysis

2008 ◽  
Vol 14 (4) ◽  
pp. 949-959 ◽  
Author(s):  
Nelli Westercamp ◽  
Christine L. Mattson ◽  
Michelle Madonia ◽  
Stephen Moses ◽  
Kawango Agot ◽  
...  
Keyword(s):  
Data Analysis ◽  
Condom Use ◽  
Young Men ◽  
Consistent Condom ◽  
Consistent Condom Use ◽  
Partner Type ◽  
Level Data ◽  
Multi Level

scholarly journals Emotional Regulation in Adolescent Girls when Experiencing Menstrual Pain or Dysmenorrhea

2021 ◽  
Vol 2 ◽  
pp. 39-42
Author(s):  
Lestari Lestari ◽  
Dyah Siti Septiningsih
Keyword(s):  
Data Analysis ◽  
Young Women ◽  
Emotional Regulation ◽  
Severe Pain ◽  
Psychological Symptoms ◽  
Emotional Responses ◽  
Data Retrieval ◽  
Structured Interviews ◽  
Study Approach ◽  
Regulation Strategies

The biological pride of young women is characterized by a standard physical mechanism of menstruation or menstruation followed by pain in the lower abdomen. The pain results in psychological symptoms such as anxiety, tension, anger, or emotion. Young women who cannot regulate their feelings properly are more likely to experience severe pain or so-called dysmenorrhea. Emotional regulation is the achievement of emotional balance performed by a person either from his attitude or behavior. This study aims to examine the regulation of emotions in young women who experience dysmenorrhea during menstruation. Assessment through aspects of emotional regulation, namely emotional regulation strategies, behaviors to achieve goals, control emotional responses, and acceptance of emotional responses. This research uses qualitative research methods with a case study approach. Data retrieval was conducted through semi-structured interviews of two primary informants and four secondary informants. Credibility using triangulation of sources and methods. Data analysis using interactive model data analysis. This study's findings are that both young women's ability to regulate their emotions during menstruation can relieve severe pain or dysmenorrhea during menstruation.

scholarly journals Knowledge guided multi-level network inference

2020 ◽  
Author(s):  
Christoph Ogris ◽  
Yue Hu ◽  
Janine Arloth ◽  
Nikola S. Müller
Keyword(s):  
Data Analysis ◽  
Quantitative Trait ◽  
Network Inference ◽  
Integrated Analysis ◽  
Full Potential ◽  
Omics Data ◽  
Lasso Regression ◽  
Data Set ◽  
Level Data ◽  
Multi Level

AbstractConstantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of so-called multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Few exceptions exist, for example the pairwise integration for quantitative trait analysis. However, the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. Here we propose a versatile approach, to perform a multi-level integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo. KiMONo performs network inference using statistical modeling on top of a powerful knowledge-guided strategy exploiting prior information from biological sources. Within the resulting network, nodes represent features of all input types and edges refer to associations between them, e.g. underlying a disease. Our method infers the network by combining sparse grouped-LASSO regression with a genomic position-confined Biogrid protein-protein interaction prior. In a comprehensive evaluation, we demonstrate that our method is robust to noise and still performs on low-sample size data. Applied to the five-level data set of the publicly available Pan-cancer collection, KiMONO integrated mutation, epigenetics, transcriptomics, proteomics and clinical information, detecting cancer specific omic features. Moreover, we analysed a four-level data set from a major depressive disorder cohort, including genetic, epigenetic, transcriptional and clinical data. Here we demonstrated KiMONo’s analytical power to identify expression quantitative trait methylation sites and loci and show it’s advantage to state-of-the-art methods. Our results show the general applicability to the full spectrum multi-omics data and demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets. The method is freely available as an R package (https://github.com/cellmapslab/kimono).

Integrative Data Analysis from a Unifying Research Synthesis Perspective

2018 ◽  
Author(s):  
Eun-Young Mun ◽  
Anne E. Ray
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Research Synthesis ◽  
Alcohol Intervention ◽  
Data Set ◽  
Integrative Data Analysis ◽  
Level Data ◽  
Model Complex ◽  
Wide Range ◽  
Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

387 The moderating effect of emotion regulation strategies in the relationship between insomnia severity and bedtime procrastination

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A153-A154
Author(s):  
Huisu Jeon ◽  
Sonhye Jeoung ◽  
Goeun Kim ◽  
Hyeyoung An ◽  
Hyojin Nam ◽  
...  
Keyword(s):  
Negative Correlation ◽  
Emotional Regulation ◽  
Significant Negative Correlation ◽  
Moderating Effect ◽  
Active Regulation ◽  
Support Seeking ◽  
Regulation Strategies ◽  
Bedtime Procrastination ◽  
The Relationship ◽  
Insomnia Severity

Abstract Introduction Bedtime Procrastination (BP) is defined as the behavior of going to bed later than intended, despite the absence of external factors. Bedtime procrastination is also prevalent among insomnia patients, and is associated with various sleep problems. Recent studies suggest emotional regulation as a mechanism of the procrastination behavior that is the conceptual foundation of bedtime procrastination. Emotional regulation difficulties are also associated with insomnia, but there is still a lack of research on the relationship between insomnia, emotional regulation strategies and bedtime procrastination. Thus, the study assumed that severity of insomnia would affect bedtime procrastination, and examined the moderating effect of the emotional regulation strategies in this relationship. Methods This study was conducted in 376 adults (mean age 23.73 ±2.14 years, 84.6% females). Participants were asked to answer Bedtime procrastination scale (BPS), an emotional regulation strategy checklist, and the Insomnia severity scale (ISI). Results As a result, a significant positive correlation was found between insomnia severity and bedtime procrastination (r=.286, p<.01), and avoidant/distractive regulation style (r=.101, p<.05). active regulation style (r=-.172, p<.01) and support seeking regulation style (r=-.102, p<.01) showed a significant negative correlation with the severity of insomnia. Bedtime procrastination behavior showed significant negative correlation only with active regulation style (r=-.151, p<.01). Support seeking regulation style moderated the relationship between insomnia and bedtime procrastination behavior (B=.0165, 95%, CI=.0014, .0316). The interaction effect between insomnia and support seeking regulation style was also significant (∆R^2=.0112, p<.05), indicating that the effect of insomnia on bedtime procrastination depends on the level of use of the support seeking regulation style. Conclusion These findings suggest that the level of support seeking regulation style is meaningful in terms of how insomnia affects bedtime procrastination. Support (if any) This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2018S1A5A8026807)

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