MGMT 725 Strategic HR Metrics

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1 MGMT 725 Strategic HR Metrics Instructor: E mail: Office: Office Hours: Paul D. Bliese, Ph.D. paul.bliese@moore.sc.edu 410B Mondays and Wednesday, 9:30 11:30am and by appointment. Note that setting up an appointment is the most reliable way to see me as meetings and other events often impinge upon regular office hours. Class Room: Rm. #141 Class Hours: Monday and Wednesday 8:05 9:20am. January 9 to April 24, 2017 Course Description: Organizations increasingly rely on analytics for HR decision making. To effectively make use of analytics, HR practitioners need to understand (1) the types of problems that can be addressed using analytics; (2) when findings are likely to be valid and reproducible, and (3) how to evaluate and present analytic results. In short, HR practitioners need to develop critical thinking skills about analytics. This course is designed to teach critical thinking about analytics by applying basic statistics principles to HR related data and by discussing examples and problems from a variety of HR contexts. The course will focus on topics related to metrics and the measurement of HR concepts; strengths and weaknesses of different study designs; ways to use statistics, simulations and HR specific tools to augment decision making, and understanding how data visualization tools can help communicate patterns in data. Required Texts: Caldwell, S. (2009). Statistics Unplugged (3 rd Edition) (about $15.00 used) or 4 th Edition. Cascio, W. & Boudreau (2011). Investing in People: Financial Impact of Human Resource Initiatives. (2nd Edition). Upper Saddle Ridge, NJ: Pearson Education. (about $33.00) Schmuller, J. (2013). Statistical Analysis with Excel For Dummies 3rd Edition (about $18.00). My colleague Dan Ganster recommended this book and I completely agree with his declaimer on his syllabus, Please don t be offended by the reference to dummies; I know you re not. I like this book because it is simple as statistics should be and it uses Excel, which will be the platform for this course. Although Excel is not a sophisticated statistical package, it does [almost] all the statistical analyses you need to do. Bliese, P. D., (2016). Multilevel Modeling in R (2.6): A Brief Introduction to R, the Multilevel Package and nlme Package. ( project.org/doc/contrib/bliese_multilevel.pdf) Various Readings as assigned. 1

2 Learning Objectives 1. Develop the ability to conduct basic data analyses using widely available tools (EXCEL and optionally R). 2. Develop an understanding of the importance of measurement in HR related research. 3. Develop critical thinking skills about the strengths and weaknesses of different research designs. 4. Understand how to apply statistical analytic skills to HR problems. 5. Enhance skills creating professional presentations and reports and effectively communicate results and implications of results. Analysis ToolPak Add in for Excel This course will require the use of the Analysis ToolPak Excel add in. Therefore, you will need to check your computer as soon as possible to make sure the add in is installed. To check your computer select the Data tab on Excel 2010 and look at the Analysis group of commands. If Data Analysis does not appear as one of the choices, then: 1. Click on the File Button, and then click Options. 2. Click Add Ins, and then in the Manage box, select Excel Add Ins. 3. Click Go. 4. In the Add Ins available box, select the Analysis ToolPak, check box, and then click OK. Tip: If Analysis ToolPak is not listed in the Add Ins available box, click Browse to locate it. If you get prompted that the Analysis ToolPak is not currently installed on your computer, click Yes to install it. You will need your Office Disk for this. You do not need to load the Analysis ToolPak VBA add in. 5. After you load the Analysis ToolPak, the Data Analysis command is available in the Analysis group on the Data tab MAC users: Depending upon your version, you may not have the Analysis ToolPak available. If it is not available you may be able to use the analysis toolpak for MAC ( Class Organization & Activities The day to day class activities will vary, but these are some important regular elements of class: Procedures and class business: The very beginning of each class is set aside to cover any questions or issues with regard to the syllabus, process, deliverables, and expectations. Lecture: I do not like straight lectures at least not for very long. I will actively seek your help in making these lecture sessions interactive. Please share your 2

3 ideas, questions, and experiences during or after (i.e., on line) any of our discussions. You should bring your laptop computer to class if possible. We may not use the computer each day, but on many days we will work with data and you may want to follow along when I do tasks in class. Grading: Projects will be due by the beginning of class. Projects received after the beginning of class will be considered late. Grades will be based on the following criteria: EXCEL Packet 10% Quizzes 30% Individual Projects 30% Final 20% Participation 10% EXCEL Packet. I recognize that you all completed the EXCEL packet prior to arriving at the course, but I suspect that many of you could use a refresher, so I would like you to resubmit the three sections in the packet to be graded as a pass / fail. If you complete each section on time, you will receive 100. The material was developed for other classes here at the Moore school and it reflects a bit of the feel of an in house developed resource. That said, it covers some extremely useful tips and tools so it is an excellent source for either expanding or refreshing your EXCEL skills. Quizzes. Quizzes will be conducted in class. Most classes will begin with a short quiz. The quizzes will cover material from the assigned readings. I will allow you to drop 1 quiz grade and make up 2 quiz grades (see bonus quiz section). Individual Projects. Two individual projects and one optional project (see Project 3) will consist of analyzing data and developing brief reports (2 pages) summarizing results. Note that you have a lot of discretion in how you approach these (like you will in your internship / job) so I am intentionally not providing step by step details on what you should do with these projects. I want you to show me how you would approach the problem. Project 1: Descriptive Statistics/Dashboard. Question for the report: In what areas do Executive Leadership teams seem to do well and in what areas do they seem to need improvement? In what leadership areas do CEOs seem to excel and in what areas do they need improvement? I am looking for no more than a two page, single space report with additional pages containing appendices for tables, etc. Imagine this dataset is something your boss asked you to analyze and synthesize. Analyses to run: Analyze the specific Likert type items in the ELT dynamics section and the CEO Leadership style section using descriptive statistics. Examples of descriptive statistics may include means, standard deviations, range, etc. How can a company use this information? What are the implications of your findings? Run internal consistency reliabilities for the ELT dynamics and CEO Leadership scales. Why do we care about these statistics, what do these statistics mean, what do they tell 3

4 us? Your boss is particularly interested in Firm 49. Once you calculate scale scores and estimate descriptive statistics, go back and analyze the response from Firm 49. What do the statistics say about Firm 49? Project 2: Correlation and Regression/Satisfaction vs. Engagement. What demographic characteristics are related to how CHROs spend their time in a variety of roles? How are those roles related to the leadership style and the ELT s cohesiveness? I am looking for no more than a two page, single space report with potential pages for appendices for tables, etc. Again, imagine this dataset is something your boss asked you to analyze and synthesize. Analyses to run: Run a correlations matrix of all 7 roles, the demographic characteristics of CHROs, and the ELT cohesiveness and CEO Leadership style. What does a correlation tell us? What are the limitations of correlation analysis? Run regressions using the 7 roles to predict ELT dynamics and to predict CEO style. What does a regression tell us? What are the limitations of regression analysis? What are the implications of your findings? Project 3: (Optional). My goal in this class is to do what I can to make you feel comfortable running analyses and presenting results. Therefore, you may choose to re do either Project 1 or Project 2 as a project 3. If you choose to redo Project 1 or Project 2, I will change your grade on the project you are re doing to be the average of your original grade and the Project 3 grade. Final Exam. The final exam will cover all the material from in the course. Final grades will be based on the scale below: Percent Grade % A % B % B % C % C % D % D Less than 60% F Academic Integrity Academic integrity is expected in this class. Academic dishonesty will not be tolerated in any form, with its determination as set by the university honor code. The following examples are illustrative of conduct that violates the honor code: (a) giving or receiving unauthorized assistance, or attempting to give or receive such assistance, in connection with the performance of any academic work; (b) unauthorized use of materials or information of any type; (c) use of another person s work or ideas without proper acknowledgement. 4

5 University procedures will be used to investigate any potential instances of academic dishonesty. Please visit the following website for more information on the university s code of academic integrity: Attendance I expect attendance at all class sessions. Because learning in this course occurs primarily through class lectures and demonstrations, you should make every effort to attend each class. There is no substitute for being present, prepared, and participating in the class discussion. While I recognize that at some point during the class session an absence may be unavoidable, and tradeoffs need to be made, absences necessarily limit your class contribution and hence can influence your class contribution grade. I also expect everyone to be present and ready when the class starts. Being late is disruptive to the flow of the class, so I will consider tardiness as a variable potentially influencing your class contribution grade. If you know that you will be absent prior to class for events such as interviews, etc., inform me via , and I will make arrangements for you to take a quiz. My policy is not to excuse absence requests made the day of class. Bonus Quiz Prior to the final, I will offer a bonus quiz. The bonus quiz will be short, but comprehensive. The bonus quiz is optional, but I will allow you to substitute any two quiz grades with your grade on the bonus quiz. Late Assignments Late assignments will incur, at minimum, a 10% grade reduction. I will also generally incur an additional 10% grade reduction for each additional day the assignment is late. Disability Policy If you have a documented disability or other special needs and wish to discuss appropriate academic accommodations, please contact me as soon as possible but no later than the first day of class. Course Schedule: The schedule below is tentative and likely to change to accommodate guest lectures and/or the need to spend more or less time on particular topic areas. Session Due Topic(s) Introducons Session 1 (Jan 9) None Syllabus review WSJ Arcle (blackboard) QUIZ 1 (C & B reading) Cascio & Boudreau Chapter 1 Session 2 (Jan 11) Background on Conceptual Look at / print out FA Survey Model (blackboard) No Class (Jan 16) Session 3 (Jan 18) Martin Luther King Day Cascio & Boudreau Chapter 2 (pages 19 32) 5 QUIZ 2

6 Session 4 (Jan 23) Session 5 (Jan 25) Cascio & Boudreau Chapter 6 Turn in Quiz 1 from the EXCEL Packet BusinessWeek Article (blackboard) Turn in Quiz 2 from EXCEL Packet Turn in Quiz 3 from EXCEL Packet Review R Multilevel pdf (Bliese) page 1 24 Build and Test Conceptual Model Test Conceptual Model Test Conceptual Model (in R) Session 6 (Jan 30) Caldwell Chapter 1 QUIZ 3 Basic Definions Session 7 (Feb 1) Caldwell Chapter 2 Schmuller Chapter 4 QUIZ 4 Basic Definitions II (Mean) Session 8 (Feb 6) Schwab Chapter 9 Schmuller Chapter 5 QUIZ 5 Basic Definions III (SD) Session 9 (Feb 8) Schwab Chapter 3 QUIZ 6 Measurement Session 10 (Feb 13) Schwab Chapter 3 (review) Guest Instructor Measurement (cont.) Class Group Exercise Session 11 (Feb 15) Schwab Chapter 17 Guest Instructor QUIZ 7 Measurement / Reliability Session 12 (Feb 20) Caldwell Chapter 3 Schmuller Chapter 7 QUIZ 8 Group Presentation(s) Shape of Distributions and Plots (z score, t scores) Session 13 (Feb 22) Caldwell Chapter 4 Schmuller Chapter 8 QUIZ 9 Normal Curve and Plots (zscores, t scores) Session 14 (Feb 27) None In Class Work Day Project 1 Session 15 (Mar 1) Project 1 Due (via ) Aer Acon Review of Project 1 No Class (March 6 8) SPRING BREAK Session 16 (Mar 13) Caldwell Chapter 8 Schmuller Chapter 11 Session 17 (Mar 15) Review Caldwell Chapter 8 Review Schmuller Chapter 11 Session 18 (Mar 20) Caldwell Chapter 12 (up to but not including section on Regression Analysis ) Schmuller Chapter 15 (correlation) QUIZ 10 Dependent /Independent t tests Dependent /Independent t tests QUIZ 11 Correlaon Regression I 6

7 Session 19 (Mar 22) Caldwell Chapter 12 (Regression Analysis section) Schmuller Chapter 14 (regression) Session 20 (Mar 27) Review Caldwell Chapter 12 Review Schmuller Chapters 14 and 15 Session 21 (Mar 29) Review Caldwell Chapter 12 Review Schmuller Chapters 14 and 15 Session 22 (Apr 3) Review Caldwell Chapter 12 Review Schmuller Chapters 14 and 15 QUIZ 12 Correlaon Regression II QUIZ 13 Correlaon Regression III QUIZ 14 Correlaon Regression IV Correlaon Regression V (Interactions) Session 23 (Apr 5) None In Class Work Day Project 2 Session 24 (Apr 10) Project 2 Due (via ) Aer Acon Review of Project 2 Session 25 (Apr 12) Caldwell Chapter 11 QUIZ 15 The Chi Square Test Session 26 (Apr 17) Schwab Chapter 5 Geng to the truth in workplace surveys (Morrel Samuels, 2002) QUIZ 16 Research Design: Field Studies and Surveys Session 27 (Apr 19) None BONUS QUIZ Evaluang Programs: Comprehensive Soldier Fitness Session 28 (Apr 24) Monday May 1 9:00am None Oponal Project 3 due FINAL EXAM Evaluang Programs: Soldier Safety Show and Monty Hall 7