People Analytics Coursera Quiz Answers – Networking Funda

All Weeks People Analytics Coursera Quiz Answers

In this brand new course, three of Wharton’s top professors, all pioneers in the field of people analytics, will explore the state-of-the-art techniques used to recruit and retain great people, and demonstrate how these techniques are used at cutting-edge companies.

They’ll explain how data and sophisticated analysis are brought to bear on people-related issues, such as recruiting, performance evaluation, leadership, hiring and promotion, job design, compensation, and collaboration.

This course is an introduction to the theory of people analytics and is not intended to prepare learners to perform complex talent management data analysis. By the end of this course, you’ll understand how and when hard data is used to make soft-skill decisions about hiring and talent development so that you can position yourself as a strategic partner in your company’s talent management decisions.

This course is intended to introduce you to Organizations that flourish when the people who work in them flourish. Analytics can help make both happen. This course in People Analytics is designed to help you flourish in your career, too.

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People Analytics Week 01 Quiz Answers

Performance Evaluation Quiz

Q1. Which of the following is an example of how people analytics are being used by organizations:

  • Employee retention
  • Hiring
  • Performance evaluation
  • Team composition
  • All are true

Q2. Which of the following is not true?

“Separating skill from chance is…”

  • …a problem because performance measures are imperfectly related to employee effort.
  • …a fundamental issue in performance evaluation.
  • …a motivation for evaluating how persistent performance is.
  • …equally difficult in all environments.

Q3. Which of the following is not true?

“If we select the best performers on a specific metric in a given period of time…”

  • Their performance on other metrics in that same period is likely closer to average.
  • In subsequent periods we would expect their performance to be average.
  • Their success is likely due to a mix of good skill and good luck.
  • Their ranks relative to each other may not reflect their true underlying abilities.

Q4. A company designs a training program for underperforming employees. They enroll in the program the employees who received the worst 10% of performance evaluations in a given year. They find that very few of the employees who go through the program are in the bottom 10% of performance evaluations the next year. What can they conclude about their new program?

  • That their performance evaluation system must be flawed.
  • Though it may not be clear why, the program is effective at improving performance.
  • On average, employees respond favorably to additional attention.
  • Nothing unless they can compare their performance to similar employees who did not receive the training.

Q5. Which of the following does not dilute the value of collecting opinions from a number of people (e.g., regarding a performance evaluation or hiring decision)?

  • They have discussed the matter with each other.
  • They have similar backgrounds.
  • They evaluated the same materials.
  • They have discussed the matter with the same people.

Q6. Which of the following is not a benefit of the “wisdom of crowds” (i.e., collecting a group of independent opinions) approach to employee-related decisions?

  • Reduces variance
  • People are sensitive to whether opinions are truly independent
  • Reduces the likelihood of extremely bad judgment
  • Idiosyncratic errors cancel each other out

Q7. There are a handful of common mistakes people make when trying to separate skill from chance. Which of the following is not one of those mistakes?

  • Loss aversion
  • Hindsight bias
  • Narrative seeking
  • Outcome bias

Q8. Which of the following mistakes is not implied by the “Law of Small Numbers”, the tendency to believe that small samples closely match the underlying conditions.

  • Believing employees are more different from each other than they actually are
  • Highly variable performance evaluations
  • Status quo bias
  • Punishing bad performance too harshly

Q9. A firm is evaluating the performance of two managers running a summer training program. One manager is in a large division with over 100 trainees, while the other is in a small division with only 15 trainees. What is one principle the firm should use in its evaluation?

  • Treat them equally, as it’s not clear ex ante whether it is an advantage or disadvantage to have a large group.
  • Expect more volatility from the small group.
  • Expect higher performance from the large group because of scale economies.
  • Expect higher performance from the smaller group because of closer supervision.

Q10. An organization contracts with software developers to produce apps for clients. The organization is paid in part by how well the app sells in the marketplace. Historically they have evaluated their developers primarily by this same outcome measure, how well the apps sell. The organization is considering adding process measures to the performance evaluation, e.g., time to completion, quality of code, coordination with other developers, client satisfaction. Which of the following is not a reason the firm should include these process measures in employee performance evaluations?

  • Processes measures are more objective.
  • Developer performance on the process measures is likely more persistent – good or bad – than it is on the outcome measures.
  • There is considerable uncertainty in app sales that is outside the control of the developer.
  • The organization has multiple objectives, not just sales.

People Analytics Week 02 Quiz Answers

Staffing Quiz

Q1. Which of these means of assessing candidates generally has the lowest correlation with subsequent performance?

  • Job knowledge tests
  • Integrity tests
  • Unstructured interviews
  • Cognitive ability tests

Q2. Which of the following is a weakness of using multivariate regression for predicting which hires will perform best?

  • You only have data on the people that you actually hired
  • It does not separate out the effects of all of the different possible influences
  • it doesn’t allow you to test the statistical significance of your result
  • Differences across cohorts may become confounded with other sources of performance

Q3. Why might we see performance improve after training, even if the training is completely ineffective?

  • Because people who get trained are more likely to have been at the company longer
  • Because training is more likely to be provided to the highest performers
  • Because people are more likely to go to training when their performance dips

Q4. When could you be confident that the effect of a given intervention (e.g. new incentive system) on performance was causal (ie the intervention caused a change in performance)?

  • When you run a multivariate analysis where you control for other influences on performance
  • When you can see that performance increased after the intervention
  • When assignment to the intervention was unrelated to any factors that might influence performance

Q5. When does including controls in multivariate regressions not solve problems of omitted variable bias?

  • When you have more than one potential omitted variable
  • When you think that there is some “noise” or error in your measure of your outcome
  • When important potential omitted variables cannot be measured

Q6. Why is there only a moderate correlation between job satisfaction and turnover?

  • Because most turnover comes from people being dismissed rather than quitting their jobs
  • Because job satisfaction needs to be assessed before you know whether workers have quit
  • Because levels of job satisfaction only appear in some of the paths that lead to turnover
  • Because it is difficult to measure job satisfaction accurately

Q7. What is NOT a weakness of conducting simple comparisons of attrition rates across groups?

  • It fails to account for differences in the content of the work that people are doing
  • It cannot take into account the way that older workers are less likely to leave than younger workers
  • Attrition rates (% of workers who leave) is a poor measure of turnover
  • Attrition rates are highly sensitive to average tenure levels within each group

Q8. Which of the following is a critical requirement for predicting who will perform well following promotion?

  • Data on the workers who enter their jobs directly by hiring versus being promoted
  • Randomization of decisions about who to promote
  • Multi-dimensional measures of performance and/or competences

Q9. What information would you want to compare different methods for filling jobs internally (e.g. posting versus sponsorship)?

  • Performance before and after moves, key demographic information, numbers of vacant positions and how the job was filled
  • Performance after moves, key demographic information, and how the job was filled
  • Performance after moves, as well as how the job was filled
  • Performance before and after moves, as well as how the job was filled

Q10. Which of the following is NOT a reason why it is useful to understand the main drivers of turnover within a company:

  • To devise training programs for managers
  • To accurately estimate the cost of turnover
  • To be able to hire workers who are more likely to stay
  • To identify those jobs where the company should focus its retention efforts

People Analytics Week 03 Quiz Answers

Collaboration Quiz

Q1. Which of the following statements is not an accurate way to describe an organizational network?

  • A map of the ties between employees
  • A map of the formal reporting relationships between employees
  • A map of the informal structure of the organization
  • A map of employees and the connections between them

Q2. Is a network that is larger in size always better?

  • No, because more ties create more opportunities for conflict between employees
  • Yes, because more ties to others mean that employees have more friendships
  • Yes, because more ties to others help employees to obtain more information
  • No, because more ties to others take more time and effort to manage

Q3. What does high network range mean?

  • An employee is connected to other employees who have a wide variety of interests
  • An employee is connected to many different kinds of employees
  • An employee is connected to many employees who are located in other countries
  • An employee is connected to many other employees

Q4. What does low network density mean?

  • An employee’s contacts are not connected to each other
  • An employee’s contacts are connected to each other
  • An employee is very central in the organizational network
  • An employee is not very central in the organizational network

Q5. Which of the following is not necessary when collecting organizational network data via surveys?

  • Ensuring that the respondents’ answers will be kept confidential
  • Ensuring that the survey sample is at least 300 people
  • Ensuring that the survey achieves a high response rate
  • Ensuring that the network is not too large

Q6. How can collaboration networks within organizations be evaluated?

  • All answers are correct
  • By comparing changes in network metrics over time
  • By identifying relationships between network metrics and important outcomes
  • By comparing network metrics (e.g. network size, range, etc) across employees

Q7. One implication of organizational network analysis is that:

  • Data must be collected and analyzed to understand what is best for a particular organization in a particular situation
  • Collaboration patterns are very difficult to analyze
  • Mapping collaboration networks can only be done using survey data
  • There is one best collaboration network for every organization

Q8. What does it mean to say that an outcome measure must be both reliable and valid?

  • Reliability means that the measure must be consistent over time and across raters; validity means that the measure must accurately capture what it is intended to capture
  • Validity means that the measure must be consistent over time and across raters; reliability means that the measure must accurately capture what it is intended to capture
  • Validity means that the measure must be available for all or most units in the dataset; reliability means that it must not be too expensive to collect
  • Reliability means that the measure must be available for all or most units in the dataset; validity means that it must not be too expensive to collect

Q9. Is more collaboration always desirable in organizations?

  • Yes, because most organizations have much too little collaboration
  • Yes, because collaboration is always valuable for getting work done
  • No, because not everyone can or should be collaborating more all the time
  • No, because collaboration is usually a waste of time

Q10. Interventions in collaboration networks can be used to:

  • Reduce employee overload
  • Make global teams more resilient
  • All answers are correct
  • Help eliminate organizational silos

People Analytics Week 04 Quiz Answers

Talent Management Quiz

Q1. All of the following are examples of contextual factors people tend to neglect when evaluating an employee except:

  • The functionality or dysfunctionality of her team.
  • Her aptitude for the type of work required.
  • The growth of her industry.
  • The ease or difficulty of her project.

Q2. All of the following are true about self-fulfilling prophecies except:

  • People tend to react consistently with our expectations of them.
  • We treat people differently depending on our expectations for them.
  • Our expectations of others tend to influence their behavior but not our own.
  • High expectations induce better performance than do low expectations.

Q3. All of the following are implications of the fact that most employees work interdependently with others except:

  • Reliable individual evaluations requires seeing them with multiple teams.
  • Performance evaluation is often best done at the group level.
  • Network analytics are a potentially valuable assessment tool.
  • Reverse causality is an especially easy mistake to make.

Q4. All of the following are benefits of testing & algorithms except:

  • Hyper-focused
  • Processing efficiency
  • Broader search
  • Unbiased

Q5. All of the following are prescriptions for wisely using testing & algorithms except:

  • Use multiple, diverse tools
  • Rigorously pre-test in relevant setting
  • Maintain safe distance between those who build them and those who use them.
  • Provide human oversight

Q6. Accurate evaluations of an employee typically require a broad sample. All of the following are ways to “broaden the sample” except:

  • Additional assignments
  • Additional performance measures
  • Additional self-reports
  • Additional supervisors

Q7. A formal rotational program makes talent assessment easier for all of the following reasons except:

  • Change assignments on a pre-determined schedule
  • Observe an employee in different environments
  • Observe multiple employees in a similar environment
  • Short-term operational efficiency

Q8. Consulting firms and law firms typically grant partnership to employees after “probation” periods that last multiple years. This is an example of:

  • Avoiding self-fulfilling prophecies
  • Granting rewards in proportion to signals
  • Emphasizing development
  • Creating exogenous variation

Q9. Which of the following statements best characterizes the role of people analytics in developing employees:

  • The most successful organizations focus primarily on selection rather than development.
  • Testing people for selection is generally more palatable than for development.
  • Testing and assessment are more effective for selection than for development.
  • Even in industries where selection is critical, many firms invest heavily in development.

Q10. Which of the following is not a well-established tactic that effective analysts use to influence organizations?

  • Formal Authority
  • Transparency
  • Sharing control
  • Embedding themselves
People Analytics Coursera Course Review:

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