Summary of Performance Evaluation

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Do curso por University of Pennsylvania

People Analytics

classificação

University of Pennsylvania

People Analytics

classificação

Curso 3 de 5 em Specialization Análises Empresariais

People analytics is a data-driven approach to managing people at work. For the first time in history, business leaders can make decisions about their people based on deep analysis of data rather than the traditional methods of personal relationships, decision making based on experience, and risk avoidance. 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 is brought to bear on people-related issues, such as recruiting, performance evaluation, leadership, hiring and promotion, job design, compensation, and collaboration. 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. Organizations 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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Introduction to People Analytics, and Performance Evaluation

In this module, you'll meet Professors Massey, Bidwell, and Haas, cover the structore and scope of the course, and dive into the first topic: Performance Evaluation. Performance evaluation plays an influential role in our work lives, whether it is used to reward or punish and/or to gather feedback. Yet its fundamental challenge is that the measures we used to evaluate performance are imperfect: we can't infer how hard or smart an employee is working based solely on outcomes. In this module, you’ll learn the four key issues in measuring performance: regression to the mean, sample size, signal independence, and process vs. outcome, and see them at work in current companies, including an extended example from the NFL. By the end of this module, you’ll understand how to separate skill from luck and learn to read noisy performance measures, so that you can go into your next performance evaluation sensitive to the role of chance, knowing your environment, and aware of the four most common biases, so that you can make more informed data-driven decisions about your company's most valuable asset: its employees.

Introduction to People Analytics8:45

Goals for the Course1:40

Course Outline and Overview3:50

People Analytics in Practice4:55

Performance Evaluation: the Challenge of Noisy Data6:46

Chance vs. Skill: the NFL Draft22:08

Finding Persistence: Regression to the Mean11:56

Extrapolating from Small Samples5:53

The Wisdom of Crowds: Signal Independence5:56

Process vs. Outcome7:47

Summary of Performance Evaluation3:36

Conheça os instrutores

Cade Massey
Practice Professor
The Wharton School
Martine Haas
Associate Professor of Management
The Wharton School
Matthew Bidwell
Associate Professor of Management
The Wharton School

So a quick summary of the performance evaluation section.

I just wanna emphasize two main points.

Number one, understand you environment.

One way to think about this is the distinction between the two

sets of curves we showed you early in this module.

How noisy is the environment relative to the signal that you observe in

the performance evaluation.

We gave you two examples, one of which quite a bit of difference between high

effort and low effort, so it's relatively easy to draw the signal from the noise.

And conversely there's, it could be that there's not much difference between high

effort and low effort, so it's much harder to find the signal and the noise.

And a qualitative way to think about this is you might characterize an environment

as being relatively chance based, more like a lottery or more skill based?

More like a math problem.

And then ask yourself how?

Most are mixed by the way.

So you should be asking yourself how much is my environment, or

how much is the environment of the employees I'm evaluating

like a math problem and how much of it is like a lottery?

A trick here though is that you need to also understand that you're biased.

We had these biases in making these assessments.

So, we touched on them briefly but we tend to make non-regressive predictions.

We don't understand regression to the mean and so

we forecast too directly from things that have happened in the past.

We tend to believe.

The outcomes reflect the underlying quality or efforts with this Outcome bias.

We tend to believe that we knew something was gonna happen before it happened,

even though we didn't, this is Hindsight bias.

And we tend to tell narratives that make sense of all the events that we observe.

All of these things get in the way of our appreciating the role that chance plays.

And that means that they will buy us our understanding of the environment.

It will lead us to believe that our environments are more like a math problem

than they actually are.

It will lead us to underestimate the extent to which our environments

are a little bit like a lottery.

All of those are problems for the way that we infer the effort level of our

employees, the quality of our employees, from our performance measures.

The key of course, the trick is to account for chance, and

the key there is persistence.

You need to find the most fundamental performance measure possible.

That means the most skill-related.

And the most skill related,

the most fundamental will be the most persistent overtime.

So we gave you an extent of example of how we did the NFL draft.

But the key tactic is to take these split samples and

ask are the differences we observe in one period there in the second period?

That's the key test for persistence, that's the key test for true skill,

true signal as opposed to chance.

And finally, some critical questions.

You can think of this as a battery you can take to any performance evaluation

meeting, to any budget meeting, resource allocation meeting.

Anything that turns on judgements about an employee or

technology, some kind of performance evaluation, take these questions.

Are the differences persistent or random?

How do we know that this isn't just good or bad luck?

Is the sample large enough to draw strong conclusions?

How can we make the sample larger?

How many different signals are we really tapping into here?

How can we make them as independent as possible?

And then finally, what else do we care about?

Are we measuring enough?

What can we measure that's actually more fundamental than

what we're measuring right now?

The keys persistence, large samples, independent assessments, and

more fundamental measures, more process measures.

You can take these critical questions to any evaluation and

improve the decision making process.

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