1uts Cricos 00099f21887 People Analyticsassessment Task 1 Class Prepa ✓ Solved

1 UTS CRICOS 00099F 21887 People Analytics Assessment Task 1 Class Preparation Activities Activity one • Using the 'Find ArticlesLinks to an external site.’ link on the UTS Library web page, locate and read one of the following articles: Davenport, T., Harris, J., & Shapiro, J. (2010). Competing on talent analyticsLinks to an external site.. Harvard Business Review, 88(10), 52-58. Rasmussen, T., & Ulrich, D. (2015). Learning from practice: how HR analytics avoids being a management fadLinks to an external site..

Organizational Dynamics, 44(3), 236–242. • Once you have read at least one article write a review that looks at the article’s strengths and weaknesses in terms of what the article is attempting to accomplish. • Review should be approx. 200 words and include description, paraphrases, and your own analysis. Your review should also include the full reference for the chosen article using the either APA or UTS Harvard referencing system. Activity two • Watch Tricia Wang’s 13-minute TED talk, ‘The human insights missing from big data’ • Summarize the main points, and discuss why is having more data not helping us to make better decisions? • Your response should be approx. 200 words and include description, paraphrases, and your own analysis.

Activity three • Using the 'Find ArticlesLinks to an external site.’ link on the UTS Library web page, locate and read the following article: • Khilji, S., & Wang, X. (2007). New evidence in an old debate: Investigating the relationship between HR satisfaction and turnoverLinks to an external site.. International Business Review, 16(3), 377–395. • Once you have read the article answer the following questions: What methodology did the researchers employ to test their hypotheses? What variables were used to analyse the relationship between turnover intention and HRM? Explain the difference between independent variables, dependent variables and control variables.

How were each of these variables used in the current article? • Your answers should be fully reference using the either APA or UTS Harvard referencing system and in total should be contained within approximately 500 words. Activity Four • Using the 'Find ArticlesLinks to an external site.’ link on the UTS Library web page, locate and read the following article: • Lawler, E., Levenson, A., & Boudreau, J. (2004). HR metrics and analytics: use and impact. Human Resource Planning, 27(4), 27–35. • Once you have read the article explain the difference between efficiency, effectiveness, and impact metrics. • Your answer should be fully reference using the either APA or UTS Harvard referencing system and in total should be contained within approximately 200 words. • Please upload this completed before Monday 19th October at 23:59pm Activity Five • Numbers can be used in a variety of different ways in data sets, hence Stevens in 1946 developed a way of distinguishing between various uses.

Stevens details four levels of measurement, each of which gives rise to numbers which carry different amounts of information. These measurements scales are: Nominal scale Ordinal scale • Explain your understanding of each of the levels of measurement. • Your answers should be fully reference using the either APA or UTS Harvard referencing system and in total should be contained within approximately 500 words. • Stevens, S., & Stevens, S. (1946). On the Theory of Scales of Measurement. Science (New York, N.Y.), ), 677–680. • Please upload this completed before Monday 19th October at 23:59pm Interval scale Ratio scale Activity Six • Watch David McCandless’ 18-minute TED talk, The beauty of data visualization • David suggests that good design is the best way to navigate the information glut.

What does he mean by this? • Your response should be approx. 200 words and include description, paraphrases, and your own analysis. • Please upload this completed before Monday 19th October at 23:59pm Completion options • Activities 1 – 3 already completed • There are two options for the completion of the final work: Option One o Complete the remaining three activities as set out in the previous slides. o In this option each of the remaining activities are worth 10% each o Please upload this completed before Monday 19th October at 23:59pm Option Two o Complete a single activity which is a modified version of activity four or six o In you choose this option the single activity will be worth 30% o The following slide details what is required, if you chose to complete option two. o Please upload this completed before Monday 19th October at 23:59pm 5 Option Two Activity Locate and read: Lawler, E., Levenson, A., & Boudreau, J. (2004).

HR metrics and analytics: use and impact. Human Resource Planning, 27(4), 27–35. Or Watch David McCandless’ 18-minute TED talk, The beauty of data visualization • Write a 500-word review of the article or video. In writing the review you should consider the following: • Objectives: what does the article or video set out to do? • Concepts: what are the central concepts? Are they clearly defined? • Argument: what is the central argument? • Contribution: how well does the work advance our knowledge?

Note as the reader I am not interested in a general descriptive overview of the article or video. You should be providing a critical assessment of the ideas and arguments that are being presented by the author. • Please upload this completed before Monday 19th October at 23:59pm Research Critique Guidelines – Part II Use this document to organize your essay. Successful completion of this assignment requires that you provide a rationale, include examples, and reference content from the studies in your responses. Quantitative Studies Background 1. Summary of studies.

Include problem, significance to nursing, purpose, objective, and research question. How do these two articles support the nurse practice issue you chose? 1. Discuss how these two articles will be used to answer your PICOT question. 2.

Describe how the interventions and comparison groups in the articles compare to those identified in your PICOT question. Method of Study: 1. State the methods of the two articles you are comparing and describe how they are different. 2. Consider the methods you identified in your chosen articles and state one benefit and one limitation of each method.

Results of Study 1. Summarize the key findings of each study in one or two comprehensive paragraphs. 2. What are the implications of the two studies you chose in nursing practice? Outcomes Comparison 1.

What are the anticipated outcomes for your PICOT question? 2. How do the outcomes of your chosen articles compare to your anticipated outcomes? © 2019. Grand Canyon University. All Rights Reserved. 2

Paper for above instructions

Assignment Solution - People Analytics

Activity 1: Article Review

Reference:
Davenport, T., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard Business Review, 88(10), 52-58.
In their article, Davenport, Harris, and Shapiro (2010) argue that organizations striving to outperform their competitors should leverage talent analytics to optimize their human capital. The authors effectively highlight the strategic importance of data-driven HR decisions, citing successful companies that have integrated analytics into their HR functions. One of the article's strengths is its robust use of case studies, which underscores the real-world implications of analytics in decision-making processes. The authors illustrate how firms such as Google and IBM utilize data to inform their talent management strategies, thereby enhancing productivity and employee satisfaction.
However, while the authors provide compelling examples, the article's weakness lies in its lack of detailed methodologies on how other companies can implement similar analytics frameworks. Additionally, less attention is given to the potential ethical considerations and data privacy issues surrounding talent analytics, which is crucial in today's data-sensitive environment. Overall, although the article effectively champions the cause for talent analytics, it could have delved deeper into the steps necessary for broader organizational adoption and the associated risks.

Activity 2: TED Talk Summary

In her TED talk, "The Human Insights Missing from Big Data," Tricia Wang (2012) argues that despite the abundance of data available to organizations, many fail to make informed decisions due to an overreliance on this data. Wang emphasizes that quantitative data often overlooks the qualitative aspects of human experience, which can provide deeper insights into consumer behavior and needs.
Her central argument is that while big data can identify trends and patterns, it does not capture the "why" behind those trends, which is imperative for effective decision-making. She presents the case of a smartphone manufacturer that relied solely on data analytics, neglecting the cultural context and emotions of its users, leading to a product that ultimately failed in the market. This illustrates that more data does not equal better decisions; understanding the human context is crucial. Wang’s insights are a reminder for organizations to balance data with qualitative assessments in their strategies, thereby ensuring that they are not just data-driven but also human-centered (Wang, 2012).

Activity 3: Analysis of Khilji & Wang’s Article

Reference:
Khilji, S., & Wang, X. (2007). New evidence in an old debate: Investigating the relationship between HR satisfaction and turnover. International Business Review, 16(3), 377–395.
Khilji and Wang (2007) utilized a quantitative research methodology employing surveys to test their hypotheses regarding the relationship between HR satisfaction and turnover intentions among employees. They gathered data from employees in diverse organizations to derive meaningful conclusions regarding job satisfaction's impact on turnover intention.
The researchers analyzed several variables, including dependent and independent variables, as well as control variables. Dependent variables in their study included turnover intention, which reflects the likelihood of employees leaving their jobs. The independent variables were HR satisfaction levels, derived from employees' perceptions of HR practices and the support they received.
Control variables included factors such as age, tenure, and job type, which could confound the relationship between HR satisfaction and turnover intention. Each of these variables played a crucial role in isolating the impact of HR satisfaction on employees' turnover intentions. This structured approach allowed the researchers to present a robust analysis and contribute to the ongoing discussion on HR effectiveness and employee retention strategies.

Activity 4: Metrics Explanation

Reference:
Lawler, E., Levenson, A., & Boudreau, J. (2004). HR metrics and analytics: use and impact. Human Resource Planning, 27(4), 27–35.
Lawler, Levenson, and Boudreau (2004) differentiate between three primary types of HR metrics: efficiency, effectiveness, and impact metrics. Efficiency metrics assess the cost-effectiveness of HR processes, such as the cost per hire or time taken to fill a position, providing a snapshot of resource utilization. Effectiveness metrics, on the other hand, evaluate the outcomes produced by these processes, like employee retention rates and overall job satisfaction. Finally, impact metrics go a step further by measuring the overall contribution of HR to organizational goals, such as employee productivity or business profitability.
The article effectively illustrates how organizations can use these metrics to inform decision-making and drive strategic alignment within HR practices. By clarifying these distinctions, the authors provide a comprehensive framework for HR professionals to enhance their analytical capabilities and demonstrate HR's value to the broader business.

Activity 5: Levels of Measurement

Reference:
Stevens, S., & Stevens, S. (1946). On the Theory of Scales of Measurement. Science (New York, N.Y.), 103(2684), 677–680.
Stevens (1946) introduced four fundamental levels of measurement: nominal, ordinal, interval, and ratio scales. The nominal scale is the most basic, used for categorizing data without a specific order, such as gender or nationality. An example would be classifying individuals based on their favorite colors, where no color holds more significance than another.
The ordinal scale follows, allowing for ranking but not measuring the distance between ranks. For instance, customer satisfaction ratings can be ranked from "poor" to "excellent," but the difference between "good" and "excellent" is not quantitatively defined.
Next is the interval scale, which measures distances between data points but lacks a true zero point, such as temperature measured in Celsius, where no temperature reflects a complete absence of heat.
Finally, the ratio scale is the most informative, not only allowing for ranking and distance measurement but also having a meaningful zero point, allowing for the calculation of ratios. Examples include height or weight, where zero indicates an absence of length or mass.
Each level of measurement carries different implications for data analysis and interpretation, guiding researchers on how to appropriately handle data types in their studies.

Activity 6: TED Talk Analysis

In his TED Talk, "The Beauty of Data Visualization," David McCandless (2010) argues that effective design can help us navigate the overwhelming amount of data available today. He emphasizes that data, when presented creatively and effectively, can lead to better understanding and insights.
McCandless contends that the traditional ways of presenting data—often in spreadsheets or boring graphs—fail to engage people or communicate meaning powerfully. Instead, he advocates for using visualization as a storytelling tool, which brings data to life and makes it more accessible. Good design, he suggests, can expose relationships and patterns within data that might otherwise go unnoticed, effectively bridging the gap between complex data and audience comprehension.
By framing data in an aesthetically pleasing and intuitive format, organizations can transform potential information overload into clarity, enabling better decision-making processes. McCandless illustrates that effective data visualization is not just about displaying data but about enhancing our comprehension and ability to act on that information (McCandless, 2010).

References

1. Davenport, T., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard Business Review, 88(10), 52-58.
2. Wang, T. (2012). The human insights missing from big data. TED. Retrieved from https://www.ted.com/talks/tricia_wang_the_human_insights_missing_from_big_data
3. Khilji, S., & Wang, X. (2007). New evidence in an old debate: Investigating the relationship between HR satisfaction and turnover. International Business Review, 16(3), 377–395.
4. Lawler, E., Levenson, A., & Boudreau, J. (2004). HR metrics and analytics: use and impact. Human Resource Planning, 27(4), 27–35.
5. Stevens, S., & Stevens, S. (1946). On the Theory of Scales of Measurement. Science (New York, N.Y.), 103(2684), 677–680.
6. McCandless, D. (2010). The beauty of data visualization. TED. Retrieved from https://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization
7. HR Analytics: Understanding the Power of Data (2021). HR Technologist. Retrieved from https://www.hrtechnologist.com/articles/data-analytics/hr-analytics-understanding-the-power-of-data/
8. Bassi, L. J., & McMurrer, D. (2007). Maximizing your investment in people: How HR metrics can help. People and Strategy, 30(2), 12-18.
9. Houghton, J. D., & Welsh, M. A. (2019). The role of HR analytics in the development of an organization. International Journal of Human Resource Studies, 9(3), 19-28.
10. Marler, J. H., & Boudreau, J. W. (2017). An evidence-based approach to HR analytics. HR Magazine, 62(3), 1-17.