Data Analytics in Tax Research: Analyzing Worker Agreements and Compensation Data to Distinguish Between Independent Contractors and Employees Using IRS Factors

2020 ◽  
Vol 35 (3) ◽  
pp. 1-23
Author(s):  
A. Faye Borthick ◽  
Lucia N. Smeal
Keyword(s):  
Data Analysis ◽  
Data Analytics ◽  
Internal Revenue Service ◽  
Database Query ◽  
Employment Practices ◽  
Accounting Data ◽  
Independent Contractors ◽  
Client Data ◽  
Tax Research ◽  
Tax Practice

ABSTRACT This case prompts learners to analyze compensation data and worker agreements to assess a company's likely compliance with requirements for classifying workers as independent contractors rather than employees based on the factors the Internal Revenue Service (IRS) uses for compliance with IRS Rev. Rul. 87-41 and Treas. Reg. § 31.3401(c)-1. Students combine tax research and data analysis to identify risky employment practices, recommend corrective action to bring the company into compliance, and estimate potential penalties if the IRS were to declare the company not in compliance. Students complete a data analysis report as a basis for preparing a research memorandum. Students electing tax practice will need to be able to perform similar analyses of client data in advance of IRS audits given that the IRS analyzes accounting data when auditing taxpayers. Given the guidance in the Teaching Notes, no database query experience is necessary on the part of instructors.

scholarly journals A multilevel approach to big data analysis using analytic tools and actor network theory

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Tiko Iyamu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Network Theory ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Actor Network Theory ◽  
Big Data Analysis ◽  
Data Sets ◽  
Multilevel Approach ◽  
Actor Network

Background: Over the years, big data analytics has been statically carried out in a programmed way, which does not allow for translation of data sets from a subjective perspective. This approach affects an understanding of why and how data sets manifest themselves into various forms in the way that they do. This has a negative impact on the accuracy, redundancy and usefulness of data sets, which in turn affects the value of operations and the competitive effectiveness of an organisation. Also, the current single approach lacks a detailed examination of data sets, which big data deserve in order to improve purposefulness and usefulness.Objective: The purpose of this study was to propose a multilevel approach to big data analysis. This includes examining how a sociotechnical theory, the actor network theory (ANT), can be complementarily used with analytic tools for big data analysis.Method: In the study, the qualitative methods were employed from the interpretivist approach perspective.Results: From the findings, a framework that offers big data analytics at two levels, micro- (strategic) and macro- (operational) levels, was developed. Based on the framework, a model was developed, which can be used to guide the analysis of heterogeneous data sets that exist within networks.Conclusion: The multilevel approach ensures a fully detailed analysis, which is intended to increase accuracy, reduce redundancy and put the manipulation and manifestation of data sets into perspectives for improved organisations’ competitiveness.

scholarly journals Hubungan Antara Kualitas Guru dengan Prestasi Belajar Siswa pada Mata Pelajaran Housekeeping

Media Wisata ◽  
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sri Larasati
Keyword(s):  
Student Achievement ◽  
Data Analysis ◽  
Teacher Quality ◽  
Test Data ◽  
Moment Analysis ◽  
Analysis Method ◽  
Improve Performance ◽  
Employment Practices ◽  
The Relationship

Teacher Quality is one factor that determines student achievement, the research to find out the relationship and contribution to the quality of teachers to student achievement in subjects Housekeeping. This Reseach is expected to expective to be useful for teachers to improve performance. To measure student achievement are used Pearson Product Moment analysis method. Of test data analysis can be seen that there is asignificant relationship with the teacher quality anatara student achiement, which toount (7.09423) is greater than ttable (2.021). Whereas the contribution of teacher quality on student achiement is the amount of KP 46.64% while the remaining 53.36% is determinedby other variables is one of the largest employment practices in the industry.

Big Data Analytics in Action

2019 ◽  
pp. 266-294 ◽  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Insight Into

Have you ever wondered how companies that adopt big data and analytics have generated value? Which algorithm are they using for which situation? And what was the result? These points will be discussed in this chapter in order to highlight the importance of big data analytics. To this end, and in order to give a quick introduction to what is being done in data analytics applications and to trigger the reader's interest, the author introduces some applications examples. This will allow you, in more detail, to gain more insight into the types and uses of algorithms for data analysis. So, enjoy the examples.

Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

2019 ◽  
pp. 161-194
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analysis ◽  
Customer Segmentation ◽  
Learning Context ◽  
Feature Vectors

Data analytics has grown in a machine learning context. Whatever the reason data is used or exploited, customer segmentation or marketing targeting, it must be processed first and represented on feature vectors. Many algorithms, such as clustering, regression, classification, and others, need to be represented and clarified in order to facilitate processing and statistical analysis. If we have seen, through the previous chapters, the importance of big data analysis (the Why?), as with every major innovation, the biggest confusion lies in the exact scope (What?) and its implementation (How?). In this chapter, we will take a look at the different algorithms and techniques analytics that we can use in order to exploit the large amounts of data.

Knowledge-Driven, Data-Assisted Integrative Pathway Analytics

2011 ◽  
pp. 225-247
Author(s):  
Padmalatha S. Reddy ◽  
Stuart Murray ◽  
Wei Liu
Keyword(s):  
Data Analysis ◽  
Drug Discovery ◽  
Text Mining ◽  
Network Architecture ◽  
Data Analytics ◽  
Efficient Manner ◽  
Omics Technologies ◽  
Functional Process ◽  
Pathway Databases ◽  
Biomarker Selection

Target and biomarker selection in drug discovery relies extensively on the use of various genomics platforms. These technologies generate large amounts of data that can be used to gain novel insights in biology. There is a strong need to mine these information-rich datasets in an effective and efficient manner. Pathway and network based approaches have become an increasingly important methodology to mine bioinformatics datasets derived from ‘omics’ technologies. These approaches also find use in exploring the unknown biology of a disease or functional process. This chapter provides an overview of pathway databases and network tools, network architecture, text mining and existing methods used in knowledge-driven data analysis. It shows examples of how these databases and tools can be used integratively to apply existing knowledge and network-based approach in data analytics.

scholarly journals Hidden big data analytics issues in the healthcare industry

2019 ◽  
Vol 26 (2) ◽  
pp. 981-998 ◽  
Author(s):  
Kenneth David Strang ◽  
Zhaohao Sun
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Research Method ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Healthcare Industry ◽  
Statistical Control ◽  
Subject Matter Experts ◽  
Journal Articles

The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniques were summarized along with strengths and weaknesses. A panel of subject matter experts was interviewed to validate the intermediate results and synthesize the key problems that would likely impact researchers conducting quantitative big data analysis in healthcare studies. The systems thinking action research method was applied to identify and describe the hidden issues. The findings were similar to the extant literature but three hidden fatal issues were detected. Methodical and statistical control solutions were proposed to overcome the three fatal healthcare big data analysis issues.

Introduction

2021 ◽  
pp. 1-8
Author(s):  
Eric A. Posner
Keyword(s):  
Economic Growth ◽  
Labor Markets ◽  
Empirical Research ◽  
Bargaining Power ◽  
Labor Law ◽  
Antitrust Law ◽  
Employment Practices ◽  
Independent Contractors ◽  
Covenants Not To Compete ◽  
The Impact

Antitrust law has very rarely been used by workers to challenge anticompetitive employment practices. Yet recent empirical research shows that labor markets are highly concentrated and that employers engage in practices that harm competition and suppress wages. These practices include no-poaching agreements, wage-fixing, mergers, covenants not to compete, and misclassification of gig workers as independent contractors. This failure of antitrust is due to a range of other failures—intellectual, political, moral, and economic. Until recently, economists assumed that labor markets are usually competitive when in fact recent studies reveal that they are usually not competitive. Commentators and politicians also seems to have assumed—falsely—that employment and labor law adequately addresses inequality of bargaining power and the resulting risk of wage suppression. The impact of this failure has been profound for wage levels, economic growth, and inequality.

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