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Hiring 1: Predicting Performance

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People Analytics

University of Pennsylvania

4.5 (3,078 ratings) | 68K Students Enrolled

Course 1 of 5 in the Business Analytics Specialization

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. 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 introduced you to 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.

Skills You'll Learn

Talent Management, Analytics, Performance Management, Collaboration

Reviews

4.5 (3,078 ratings)

5 stars
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4 stars
834 ratings
3 stars
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1 star
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ST

Mar 18, 2017

Real helpful course especially for individuals who are in HR field or interested in becoming a better leaders, team players. Professors incorporated data analytics into human talent management!

BC

Dec 15, 2017

Excellent course! I learned more about HR and staffing strategy from this single course than the Human Resource Management specialization from the other university program offered on Coursera.

From the lesson

Staffing

In this module, you'll learn how to use data to better analyze the key components of the staffing cycle: hiring, internal mobility and career development, and attrition. You'll explore different analytic approaches to predicting performance for hiring and for optimizing internal mobility, to understanding and reducing turnover, and to predicting attrition. You'll also learn the critical skill of understanding causality so that you can avoid using data incorrectly. By the end of this module, you'll be able to use data to improve the quality of the decisions you make in getting the right people into the right jobs and helping them stay there, to benefit not only your organization but also employee's individual careers.

Introduction to Professor Bidwell0:30

Staffing Analytics Overview2:04

Hiring 1: Predicting Performance8:50

Hiring 2: Fine-tuning Predictors9:07

Hiring 3: Using Data Analysis to Predict Performance7:44

Internal Mobility 1: Analyzing Promotibility4:55

Internal Mobility 2: Optimizing Movement within the Organization8:26

Causality 15:11

Causality 26:47

Attrition: Understanding and Reducing Turnover10:25

Turnover: Predicting Attrition7:52

Staffing Analytics Conclusion0:49

Taught By

Cade Massey
Practice Professor
Martine Haas
Associate Professor of Management
Matthew Bidwell
Associate Professor of Management

Transcript

So in this segment I want to talk about hiring,

the process of getting people into jobs.

If you think about hiring in most organizations,

it's pretty clear that sometimes we do it well and sometimes we do it badly.

If you Google online you'll see a lot of statistics,

about maybe half of all new hires fail.

It's never been clear to me where they get those statistics from, but

it's certainly the case that we can see in most organizations.

We hire a bunch of people, some of them work out, some of them don't.

A lot of people leave their jobs fairly quickly which suggest

this was not a good match.

And so, it's done fairly imperfectly pretty much everywhere.

When you think about what hiring is

there are a bunch of different ways of thinking about it.

So often we think about it as a match.

It's almost like dating.

It's finding the right chemistry.

And that's usually influential in how people actually hire.

From an analytic standpoint though,

the way that we think about hiring is about predicting performance.

We have a lot of people who are applying to the job.

What we want to know is who's going to perform best in it?

And so all of the effort goes into figuring out

what predicts high performance, so that we can then screen on those attributes.

What I've got here is a list of different things that companies use in

order to try and screen applicants, to try and understand what their performance is.

So we see things like job knowledge tests, cognitive ability tests.

use personality test, reference checks, structured interviews, unstructured

interviews, work samples, integra test, a wide variety of things that can

be used to try and get at this question, who is going to perform well in the job?

Question that we have to ask then is which of these are best?

So, if we're thinking about assessing candidates.

Which of these should we be using and which of them shouldn't we be using?

So as you look at that list.

What do you think are some of the thing that are gonna be most effective at

predicting performance?

Had a chance to look at it?

Think about it a little.

Okay, well let's see what actually works.

So what I've got here are a series of correlations.

So I don't know how much you know about statistics, and

one thing that I'm going to say repeatedly during this module, is that if you

haven't taken a course on statistics and you're interested in this topic.

I really strongly recommend that you take a couple of introductory courses in

statistics, because they're sort of the complement to what we're doing.

You want to understand the people side of analytics, and

how to apply analytics to people but statistics just

gives you an understanding of some of the ways of interpreting the results.

And for those of you who haven't done this recently,

correlation is basically a measure of how closely two variables move together.

So correlation of zero means these things are totally unrelated to one another

of correlation of one means they move together perfectly that

they could almost be the same thing.

And where they fall in that spectrum tells you how closely they're correlated.

And so what you see in this graph is a variety of correlations.

The largest correlations are the things that are doing the best job of predicting

performance.

Correlation of .5 is quite strong, it's, again, not 1.

It's not a 1 to 1 correspondence, but

it tells you this is quite good at predicting performance.

When we get down to kind of, .2, .3, that's a lot weaker.

And so, what we've done here is we've listed in order

how the various different assessment techniques actually predict performance.

So if you got it right, congratulations.

Give yourself a pat on the back as long as you're not carrying anything heavy or

something like that.

If you got it wrong, I'm sorry, but better luck next time.

Anyway, if you go through what you see, is that there are a number of things

that really do predict performance quite effectively.

So more than anything else, perhaps not surprisingly is work samples.

And so some companies, when they hire, they ask people to either produce, or

perform something like the work that they're going to do.

So for example, technical writers.

Maybe you'll give them a description of your product and

ask them to give us a sample of writing about it.

I heard companies hiring analysts,

maybe we'll send them a spreadsheet of data and ask them to analyze it.

Just getting a sense of, can they perform the job they're being asked to do.

Another thing that is surprising, I would say affective,

is congnative ability testing.

It turns out that actually, general intelligence is really quite predictive

of performance in a very wide range of jobs.

And so some organizations will actually have candidates do something that looks

like an IQ test, if intelligence is something they care about and

drives performance, it's the easiest way to measure it okay?

Third thing that we've seen is very effective is structured interviews.

What's the difference between a structured interview and

an unstructured interview that comes down at the bottom?

An unstructured interview is really when we meet somebody, and

we talk to them, and we try and get a sense of Who they are,

what they're like, what their strengths and weaknesses might be.

We all think we're good judges of character.

It turns out, we're not.

So, I don't know if you've read, it's a marvelous book.

Thinking Fast, Thinking Slow, by Daniel Kahneman.

You get a chance, you should really look at it

Carniman is a Nobel prize winning psychologist, which is quite a neat trick,

since there isn't actually a Nobel prize in Psychology.

He won it for Economics.

He has done a lot of work on biases and characteristics.

He tells this lovely story about the first job that he ever had.

So he was working with the Israeli defense forces, and

his job as a psychologist was to try and assess candidates for officer training.

And so they had this exercise where they would get these candidates

carrying this large log, and say, okay, you've got get all of you candidates, and

this log, over a six foot wall, and neither you nor

the log can touch the wall at any point, and the log can't touch the ground.

And the idea was that psychologists would stand around and watch them as they try.

And so you'd see some people would try and

organize people that come up with the ideas.

So coordinate, other people might hang back,

some people get very annoyed when their ideas got ignored.

In other cases you'd see when it was going wrong.

Some people would give up, but others would really step forward and

start trying to organize people.

They figured, based on this,

you'd get a really good sense of people's leadership ability.

And so, they would rate people, and

make recommendations about who should go to officer's training.

The interesting thing about the story is that

they actually knew how good their assessment were.

They could see the end as people went through officer training

who actually turned out to be good at this.

And they discovered there was almost no correlation between what they thought

based on kind of what seemed like a good set of test, and

how people actually performed.

What was really scary to Cornerman was that even though over time they learnt

that this test was in no was predictive of performance, nonetheless for

each individual they still felt very confident in making a prediction.

Okay we know in general this doesn't work, but we know this guy is going to make it.

This guy definitely not going to make it.

And he calls this the illusion of validity,

a sense that we think we know much more about people than we actually do.

And the challenge with on structured interviews when we sit down and

we talk to people, and try and figure out what they're like

is really giving into the illusion of validity,right,

we don't know learn very much, but whatever biases, prejudices we have.

They fill up the whole room in our mind in terms of actually judging them.

So unstructured interviews are found to be really pretty bad at predicting

what people are going to do and how they are going to perform.

Structured interviews, on the other hand, are when actually interviews are designed

to assess specific characteristics of the individual, and

get at whether or not they have the attributes needed for the job.

They're found to be fairly predictive.

On the other end of the scale, I think something that often surprises people is

how ineffective reference checks are.

We have a sense that if we ask people who know the candidate,

they should really be able to tell us how they perform.

There are a couple of problems with this.

I mean there are certainly legal issues in the U.S. now, which is companies are very

afraid of giving negative references because they might be sued.

On top of that, and certainly having read a lot of reference letters over the years.

You realize that most of what drives the quality of a reference

is actually the person writing the reference, right.

How happy they are to lie outrageously on somebody else's behalf to help them get

a job, and when you're trying to predict performance, and most of the variations in

your measure is being driven by the person writing the reference.

Rather than the person the reference is about,

is going to be quite a poor measure of their future performance.

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