Literature Review

[BlairCurrey]

assignment: https://www.coursera.org/learn/uol-cm3070-computer-science-final-project/peer/kvQpo/topic-3-literature-review referencing guidelines: https://onlinelibrary.london.ac.uk/support/referencing, https://onlinelibrary.london.ac.uk/sites/default/files/files/guides/ACMGuide.pdf

General Guidelines:

• Online sources need to have their credibility justified.
• Use images/diagrams where appropriate
• evaluate literature
• explain how it fits into my project or proves demand for my project (tie-in)
• citations should be in ACM format

Grading Criteria:

• Does the report display knowledge of the area of study, previous work and academic literature?
• Does the report critically evaluate the previous work and/or academic literature?
• D​oes the report use proper citation?

Format Criteria

• 6 pages max (not including images/citations)
• PDF
• min font: 11pt
• min margin: 2cm

TODO:

• [x] mockup latex doc with headings and example citation
• [x] Intro, etc.
• [x] find articles (4 to 6) including
  • examples of similar projects (https://github.com/python-engineer/ml-app-salaryprediction)
  • techniques/methods (dnn for regression? "Deep Learning with Python" or academic journals)
  • "R​esearch studies that show the effectiveness of the project you intend to create"
  • ACM journal - https://dl.acm.org/
  • [x] literature 1: https://github.com/python-engineer/ml-app-salaryprediction
  • [x] literature 2: glassdoor
  • [x] literature 3: deep learning with python by chollet
  • [x] literature 4: Working Knowledge: Salary Negotiation 101
  • [x] literature 5: Who Asks and Who Receives in Salary Negotiation
  • [x] literature 6: How much do others make for the same job? Here’s where employers are required by law to share salary ranges when hiring
• [x] literature 1: https://github.com/python-engineer/ml-app-salaryprediction
  • [x] justify source (because its online)
  • [x] evaluate
  • [x] tie-in
  • [x] properly cite
• [x] literature 2: glassdoor
  • [x] justify source (because its online)
  • [x] evaluate
  • [x] properly cite
• [x] literature 3: deep learning with python by chollet
  • [x] evaluate
  • [x] tie-in: hyperparameter tuning in chapter 7, one-hot encoding countries to overcome problems with loeber's app
  • [X] properly cite
• [X] literature 4: Working Knowledge: Salary Negotiation 101
  • [X] evaluate
  • [X] tie-in
  • [X] properly cite
• [X] literature 5: Who Asks and Who Receives in Salary Negotiation
  • [X] evaluate
  • [X] tie-in
  • [X] properly cite
• [x] literature 6: How much do others make for the same job? Here’s where employers are
  • [X] evaluate
  • [X] tie-in
  • [X] properly cite
• [x] comb through Latex doc and fix formatting as needed/able (dont worry too much about it while writing)
  • citation name isnt exactly right. "Michell Marks and Crystal Harold" should be "Marks, Michelle and Crystal Harold"
• [x] submit for peer review https://www.coursera.org/learn/uol-cm3070-computer-science-final-project/peer/kvQpo/topic-3-literature-review
• [x] update based on feedback

[BlairCurrey]

Working Knowledge: Salary Negotiation 101

https://www.jstor.org/stable/25649074?searchText=salary+negotiation&searchUri=%2Faction%2FdoBasicSearch%3FQuery%3Dsalary%2Bnegotiation%26so%3Drel&ab_segments=0%2Fbasic_search_gsv2%2Fcontrol&refreqid=fastly-default%3A9829a92d542de434ee9f5e01cf564918&seq=1

Summary:

This article is a questions and answer in the American Libraries journal. Eusa F. Topper answers a questions regarding salary negotiation best practices and offers additional resources related to librarian salaries and professional development.

Evaluation:

She notes that you need to know the salary range for the position and know "what you are worth in the marketplace". She advises the best way to do this is to "review salary surveys for your specific area of librarianship (see resources below) and ask colleagues what the salary range is for a similar position in their organization. You can also get advice from local recruiters, including a number who specialize in placement of information professionals.

This reinforces the initial motivation for my project. That is, to empower job seekers to know their value in the market so they can maximize their salary. Topper lists many tools of which my salary estimator would fall in the same category.

[BlairCurrey]

Who Asks and Who Receives in Salary Negotiation

https://www.jstor.org/stable/41415674?searchText=salary+negotiation&searchUri=%2Faction%2FdoBasicSearch%3FQuery%3Dsalary%2Bnegotiation%26so%3Drel&ab_segments=0%2Fbasic_search_gsv2%2Fcontrol&refreqid=fastly-default%3A6a81428396d0909e748cadfee545b8fc&seq=1

Summary

Who Asks and Who Receives in Salary Negotiation is a study conducted by Michelle Marks and Crystal Harold. They took a smaple of 149 new hires across industries and found that participants who negotiated increased their average salaries by and average of $5000. Participants who negotiated also reported higher feelings of job satisfaction and general fairness. The authors also noted that individual tendencies were large factors whether to participants negotiated with and if so, what strategies they employed.

Evaluation

Excerpts

"Pay dissatisfaction has been linked with undesirable employee outcomes and behaviors, such as performance decrements, lateness, and job seeking."

" Power was used as a covariate in this study representing the negotiating context, and was operationalized as informational power..." (They consider "informational power" a factor in negotiations)

[BlairCurrey]

might have some justifications for my motivation https://www.cnbc.com/2022/01/12/states-and-cities-where-employers-must-share-salary-ranges-when-hiring.html

basically makes the point that knowing the pay range increases salary outcomes