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Idiosyncratic Rater Effect

November 27, 2016 By editor

performance review cartoon

A colleague of mine cautions that performance ratings say more about the marriage of the person doing the assessing than the performance of the person being assessed.

Turns out she may have a point.

Most people have some experience with performance appraisals. Maybe as part of an annual salary review. Or even just completing a customer satisfaction survey. It’s become a pretty ubiquitous process over the past decade or so.

Unfortunately it’s process that is flawed. Deeply flawed.

It turns out that we’re really bad at rating others. In fact, we’re so bad at it that someone has given the phenomenon a name—the Idiosyncratic Rater Effect.

And it’s well documented.

It was first shown in a study from 1998, published in Personnel Psychology. This was followed by a 2000 paper in the Journal of Applied Psychology. Then, in 2010, the results were confirmed by yet another paper in Personnel Psychology.

Each study found that the idiosyncrasies of the person doing the rating accounted for over half of the variations in ratings. The three studies put the number at 71%, 58% and 55%, respectively.

No other factor accounted for more than 20% of the score.

As performance management expert Marcus Buckingham put it in an interview

Most of what is being measured is the unique rating tendency of the rater. Thus ratings always reveal more about the rater than they do about the ratee.

Later in the same interview he highlighted the data quality problem facing organizations who rely on these flawed assessments, saying

We all need to grapple with that problem [the Idiosyncratic Rater Effect] as we move into the big data world of the future. We need to figure out how we put good data in.

Once this data ends up in the system, it’s sliced, diced and combined with other data to produce a range of derivative conclusions…all based on a false premise—i.e. that one person is able to effectively judge the performance of another.

There has been some suggestion that the Idiosyncratic Rater Effect can be minimized by averaging across many assessments. But this, amongst other things, assumes that the idiosyncrasies exhibited by raters are independent. That’s a big assumption.

Research on the Idiosyncratic Rater Effect has largely focused on HR. However, the same problems plague customer satisfaction surveys. Developers of iPhone apps have long suffered the vagaries of the App Store review process, for example. In what turns out to be a brutal demonstration of the Idiosyncratic Rater Effect a number of leading iOS app developers made a hilarious video of themselves reading one-star reviews received by their apps.

Business consultant and author Frederick Reichheld has argued for customer feedback to be boiled down to one question—or “The Ultimate Question” as he calls it in his book of the same name. This question is

How likely is it that you would recommend us to a friend or colleague?

In his research, satisfaction scores for this question, when compared with those for other questions, consistently showed the strongest correlation with repeat purchases or referrals.

Amazon is the latest company to say it will overhaul its controversial performance assessment rating system. This follows shake-ups at major organizations such as Goldman Sachs, the Pentagon, IBM and GE. Accenture has ditched ratings altogether.

Others have argued in defense of performance evaluations—looking at ways to “minimize” bias. This strikes me as an attempt to push on with something that is fundamentally flawed because no-one has a better solution.

We need to focus on linking individual performance directly and transparently to well-defined organizational goals. This isn’t easy. It’s been problematic when attempted in the US school system. But, if performance-related management is going to work, and gain credibility, it’s going to need a radical overhaul.

Filed Under: Data analysis, Data science, Decision science

Spurious correlations

November 27, 2016 By editor

chart plotting divorce rate in Maine with per capita consumption of margarine

Most people know that correlation doesn’t mean causation. Some people are fed up of hearing it.

When there are studies showing that people who have more sex earn more money you can see why people really want to make the inference.

I find that most of the much-maligned link bait articles reporting fascinating correlations don’t actually claim any causality. They leave that to the febrile mind of the reader.

In the majority of cases, such as the sex and money one, making the link is a bit of harmless entertainment. However, it’s potentially less harmless when people start to draw political or economic conclusions from such findings.

Tyler Vigen’s spurious corrlations website is a fun demonstration of the dangers of conflating correlation with causation. The r = 0.9926 correlation between per capita consumption of margarine and the divorce rate in Maine (chart above—from his site) stands out.

Of course, there’s always the danger that we are too quick to dismiss causality when it’s not immediately obvious. If my mother switched my father’s butter for margarine divorce would certainly be on the cards.

Filed Under: Data science Tagged With: causation, correlation, spurious correlation

Non-transitive dice

November 23, 2016 By editor

non-transitive dice

Just took delivery of my non-transitive dice. Adding a bit of fun to my statistics talks.

Filed Under: Data science Tagged With: non-transitive dice

The 5 most downloaded R packages

November 18, 2016 By editor

DataCamp have published an article on the five R packages with the most (direct) downloads. This is based on their leaderboard.

Packages 3-5 are currently swapping positions. As I write this (18 November 2016) the top five are

  • dplyr
  • devtools
  • ggplot2
  • cluster
  • foreign

It’s notable that the list of the most popular packages is heavily weighted towards the manipulation and display of data. This is the bulk of the work done by data scientists.

The highest ranking analytical package is for performing cluster analysis. No surprise, really, as looking for groups in data is a very common requirement.

Filed Under: Data analysis, Data science Tagged With: cluster analysis, R

Public data sources

November 10, 2016 By editor

ethernet cables

Data science requires data. Yep. Insightful.

Unless you work at a data-rich organization, data can be hard to obtain. You may want to try out a new technique or tool. Alternatively, you may need additional data to fuse with your own limited in-house data. In either case, Nathan Yau’s updated list of public data sources might help.

He lists sources for the following types of data

  • demographic
  • health
  • geographic
  • news
  • sports
  • general purpose

Filed Under: Data analysis, Data science Tagged With: data sources

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