Analytical Approaches for ATAC‐seq Data Analysis

This book is an introduction to quantitative research design and data analysis presented in the context of music education scholarship. The book aims for readers to come away with a familiarity of prototypical research design possibilities as well as a fundamental understanding of data analysis techniques necessary for carrying out scientific inquiry. The book includes examples that demonstrate how the methodological and statistical concepts presented throughout can be applied to pertinent issues in music education. For the majority of Part I, the strategy is to present traditional design categories side by side with explanations of general analytical approaches for dealing with data yielded from each respective design type. Part II consists of chapters devoted to methodological and analytical approaches that have become common in related fields (e.g., psychology, sociology, general education research, educational policy) but are as yet not frequently exploited by music education researchers. Ultimately, this work is motivated by a desire to help scholars acquire the means to actualize their research curiosities and to contribute to the advancement of rigor in music education research throughout the profession at large.

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Conquery: an Open Source Application to analyze High Content Healthcare Data (Preprint)

10.2196/preprints.32745 ◽

2021 ◽

Author(s):

Fabian Kovacs ◽

Max Thonagel ◽

Marion Ludwig ◽

Alexander Albrecht ◽

Manuel Hegner ◽

...

Keyword(s):

Decision Making ◽

Big Data ◽

Data Analysis ◽

Open Source ◽

Open Source Software ◽

Medical Records ◽

Healthcare Sector ◽

Study Cohort ◽

Decision Making Processes ◽

Analytical Approaches

BACKGROUND Big data in healthcare must be exploited to achieve a substantial increase in efficiency and competitiveness. Especially the analysis of patient-related data possesses huge potential to improve decision-making processes. However, most analytical approaches used today are highly time- and resource-consuming. OBJECTIVE The presented software solution Conquery is an open-source software tool providing advanced, but intuitive data analysis without the need for specialized statistical training. Conquery aims to simplify big data analysis for novice database users in the medical sector. METHODS Conquery is a document-oriented distributed timeseries database and analysis platform. Its main application is the analysis of per-person medical records by non-technical medical professionals. Complex analyses are realized in the Conquery frontend by dragging tree nodes into the query editor. Queries are evaluated by a bespoke distributed query-engine for medical records in a column-oriented fashion. We present a custom compression scheme to facilitate low response times that uses online calculated as well as precomputed metadata and data statistics. RESULTS Conquery allows for easy navigation through the hierarchy and enables complex study cohort construction whilst reducing the demand on time and resources. The UI of Conquery and a query output is exemplified by the construction of a relevant clinical cohort. CONCLUSIONS Conquery is an efficient and intuitive open-source software for performant and secure data analysis and aims at supporting decision-making processes in the healthcare sector.

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With the Right Map, Survey Key Driver Analysis Can Help Get Organizations to the Right Destination

Industrial and Organizational Psychology ◽

10.1017/iop.2017.21 ◽

2017 ◽

Vol 10 (2) ◽

pp. 290-298

Author(s):

Charles A. Scherbaum ◽

Justin Black ◽

Sara P. Weiner

Keyword(s):

Data Analysis ◽

Best Practices ◽

Survey Data ◽

Analytical Techniques ◽

Opinion Survey ◽

Key Driver ◽

Driver Analysis ◽

Employee Opinion Survey ◽

The Right ◽

Analytical Approaches

Cucina, Walmsley, Gast, Martin, and Curtin (2017) raise an important issue in evaluating whether our current approaches for key driver analysis on employee opinion survey data are indeed best practices. As has been argued elsewhere (Putka & Oswald, 2016; Scherbaum, Putka, Naidoo, & Youssefnia, 2010), there is and can be misalignment between current and best practices. We agree with Cucina et al. that our field should engage in larger discussion of these issues. That discussion is critical, as industrial and organizational (I-O) psychologists are competing with those outside our field who have either little knowledge of best practices in data analysis (but who have been empowered by technology that automates the analysis) or little knowledge of psychology (but a great deal of knowledge in big data analytical techniques). I-O psychologists are in the vanguard of survey data analysis (Ducey et al., 2015), and we have a responsibility to maintain the standards of our field as well as to wield our influence to guide other practitioners outside our field on sound theoretical and analytical approaches.

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What Analytical Approaches Are Available for Data Analysis?

Conducting Qualitative Research of Learning in Online Spaces ◽

10.4135/9781483398617.n6 ◽

2017 ◽

pp. 110-137

Keyword(s):

Data Analysis ◽

Analytical Approaches

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Comparison of the Level of Familiarity of IT Units' Staff and students with Big Data Analyzes

10.21203/rs.2.22539/v1 ◽

2020 ◽

Author(s):

Elham Nazari ◽

Parnian Asgari ◽

hamed tabesh

Keyword(s):

Big Data ◽

Data Analysis ◽

Work Experience ◽

Rapid Development ◽

Big Data Analysis ◽

Individual Characteristics ◽

Scientific Study ◽

Level Of Knowledge ◽

And Gender ◽

Analytical Approaches

Abstract introduction The rapid development of technology in recent decades has led to the production of a huge amount of data. This type of data analysis that is called Big Data Analysis obtain Many benefits, including reducing costs. One of the challenges of these analyses is the lack of specialized expertise and knowledge in this area. The purpose of this study was to compare the familiarity of IT staff and students with big data analyzes at various universities and organizations. Materials and method This analytical study was conducted on IT units' staff and students of different organizations and universities in Mashhad, Iran. A questionnaire was designed based on reviewing the texts published in PubMed, google scholar, science direct, and EMBASE databases and using the Delphi method and the attendance of 10 specialists in different disciplines. The designed questionnaire evaluated the participants' knowledge about the Big Data analyzes in two parts. The participants were 265 IT units' staff and students of different organizations, completing the designed questionnaire. Participants' opinion was evaluated using two descriptive and analytical approaches. The relationship between knowledge scores and individual characteristics such as gender, age, work experience, Field of study, degree, the average number of hours’ scientific study and non-scientific study per week was examined. To investigate the synchronous and reciprocal effects GLM was used. Results Scores earned by students and staff were 2.66 ± 1.13 and 2.28 ± 1.21 respectively that p =. 012 represented a significant correlation between the level of knowledge of students and staff. In other words, the level of knowledge of staff about big data was more than the level of knowledge of the students.The correlation of each of the variables was not significant with the score of the Big Data Analysis Knowledge.But There was a significant correlation between experience and gender with the knowledge scores. Conclusions In general, the level of knowledge in analyzing big data in different groups of people was at a low level that implementing measures such as holding training courses in this field seems necessary.

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System-integrated technology-enabled model of care (SINEMA) to improve the health of stroke patients in rural China: Statistical analysis plan for a cluster-randomized controlled trial

International Journal of Stroke ◽

10.1177/1747493019869707 ◽

2019 ◽

Vol 15 (2) ◽

pp. 226-230

Author(s):

Enying Gong ◽

Lijing L Yan ◽

Kara McCormack ◽

John A Gallis ◽

Janet Prvu Bettger ◽

...

Keyword(s):

Data Analysis ◽

Statistical Analysis ◽

Rural China ◽

Controlled Trial ◽

Statistical Analysis Plan ◽

Outcome Data ◽

Secondary Outcome ◽

Model Of Care ◽

Integrated Technology ◽

Analytical Approaches

Background The system-integrated technology-enabled model of care (SINEMA) trial aimed to evaluate the effectiveness of a community-based multi-component intervention for secondary prevention of stroke in rural China. Objective To present the detailed statistical analysis plan for the trial prior to database locking and data analysis. Methods The detailed analysis plan outlines primary and secondary outcome measures, describes the over-arching data analysis principles to be adopted as well as more detailed descriptions of specific analytical approaches for effectiveness analyses, as well strategies to handle missing outcome data. Discussion Publication of the statistical analysis plan increases the transparency of the data analysis procedure and reduces potential bias in trial reporting. Trial registration The trial was registered with clinicaltrials.gov (NCT03185858).

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GRANADA consensus on analytical approaches to assess associations with accelerometer-determined physical behaviours (physical activity, sedentary behaviour and sleep) in epidemiological studies

British Journal of Sports Medicine ◽

10.1136/bjsports-2020-103604 ◽

2021 ◽

pp. bjsports-2020-103604

Author(s):

Jairo H Migueles ◽

Eivind Aadland ◽

Lars Bo Andersen ◽

Jan Christian Brønd ◽

Sebastien F Chastin ◽

...

Keyword(s):

Physical Activity ◽

Data Analysis ◽

Sedentary Behaviour ◽

Compositional Data ◽

Epidemiological Studies ◽

Future Research ◽

Compositional Data Analysis ◽

Accelerometer Data ◽

Physical Behaviour ◽

Analytical Approaches

The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers’ decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.

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EMERGING TRENDS IN THE SPORTING GOODS INDUSTRY IN NORTH AMERICA AND EUROPE

Acta Prosperitatis ◽

10.37804/1691-6077-2021-12-38-58 ◽

2021 ◽

Vol 12 ◽

pp. 38-58

Author(s):

Filip Lestan ◽

◽

Sajal Kabiraj ◽

Keyword(s):

Data Analysis ◽

Quantitative Research ◽

International Institutions ◽

Statistical Tests ◽

Secondary Data ◽

Data Sources ◽

Emerging Trends ◽

Sporting Goods ◽

Secondary Data Sources ◽

Analytical Approaches

Sporting goods industry is a multibillion industry including apparel, gear and equipment. Beyond that, it is currently under flux of change. Disruptive actions cause emergence of new trends on which stakeholder must stay keen. This study analyses consumers’ economic, and technological trends within the sporting goods industry among different markets and stakeholders. Stakeholders such as businesses, organizations and consumers are shifting towards advanced sporttechniques, hence the sporting goods industry faces tremendous challenges and changes. The empirical part of the research was conducted with quantitative research methodology utilizing descriptive and analytical approaches. Data was collected from credible secondary data sources such as international institutions, governments, and databases. Data analysis was elaborated with help of descriptive methods and statistical tests.

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Resolving Cytosolic Diffusive States in Bacteria by Single-Molecule Tracking

10.1101/483321 ◽

2018 ◽

Author(s):

J. Rocha ◽

J. Corbitt ◽

T. Yan ◽

C. Richardson ◽

A. Gahlmann

Keyword(s):

Data Analysis ◽

Single Molecule ◽

Time Window ◽

Analysis Framework ◽

Cellular Functions ◽

Single Molecule Tracking ◽

State Switching ◽

Single Protein ◽

Simplifying Assumptions ◽

Analytical Approaches

AbstractThe trajectory of a single protein in the cytosol of a living cell contains information about its molecular interactions in its native environment. However, it has remained challenging to accurately resolve and characterize the diffusive states that can manifest in the cytosol using analytical approaches based on simplifying assumptions. Here, we show that multiple intracellular diffusive states can be successfully resolved if sufficient single-molecule trajectory information is available to generate well-sampled distributions of experimental measurements and if experimental biases are taken into account during data analysis. To address the inherent experimental biases in camera-based and MINFLUX-based single-molecule tracking, we use an empirical data analysis framework based on Monte Carlo simulations of confined Brownian motion. This framework is general and adaptable to arbitrary cell geometries and data acquisition parameters employed in 2D or 3D single-molecule tracking. We show that, in addition to determining the diffusion coefficients and populations of prevalent diffusive states, the timescales of diffusive state switching can be determined by stepwise increasing the time window of averaging over subsequent single-molecule displacements. Time-averaged diffusion (TAD) analysis of single-molecule tracking data may thus provide quantitative insights into binding and unbinding reactions among rapidly diffusing molecules that are integral for cellular functions.

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African Primary Care Research: Qualitative data analysis and writing results

African Journal of Primary Health Care & Family Medicine ◽

10.4102/phcfm.v6i1.640 ◽

2014 ◽

Vol 6 (1) ◽

Cited By ~ 5

Author(s):

Langalibalele H. Mabuza ◽

Indiran Govender ◽

Gboyega A. Ogunbanjo ◽

Bob Mash

Keyword(s):

Primary Care ◽

Data Analysis ◽

Qualitative Data ◽

Qualitative Data Analysis ◽

Primary Care Research ◽

Care Research ◽

Key Concepts ◽

Practical Guidance ◽

Thematic Index ◽

Analytical Approaches

This article is part of a series on African primary care research and gives practical guidance on qualitative data analysis and the presentation of qualitative findings. After an overview of qualitative methods and analytical approaches, the article focuses particularly on content analysis, using the framework method as an example. The steps of familiarisation, creating a thematic index, indexing, charting, interpretation and confirmation are described. Key concepts with regard to establishing the quality and trustworthiness of data analysis are described. Finally, an approach to the presentation of qualitative findings is given.

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