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Ravi is an armchair futurist and an aspiring mad scientist. His mission is to create simplicity out of complexity and order out of chaos.

Monday, August 1, 2011

Evaluating interviewers - Part 1

This post mathematically answers the question - "how to determine how good of an interviewer someone is".

We evaluate interviewers in 2 complementary ways:
  1. With respect to other interviewers (covered in this blog post)
  2. With respect to interview candidate's actual job performance after being hired (covered in the next blog post).
Each method has its strengths. For example, we usually have a lot of data for method 1 (since there are more candidates interviewed than hired), making it easier to evaluate an interviewer relative to other interviewers. However, the true test of any interviewer is the consistency with which they can predict a candidate's job performance should they be hired. This data may be hard to come by (or integrate, with HRM systems). But each method can be used independently, or collectively.

(Note: subscript $i$ is used for candidates and $j$ for interviewers)

$C_i$ $i^{th}$ candidate
$R_j$ $j^{th}$ interviewer
$s_{ij}$ score for the $i^{th}$ candidate by the $j^{th}$ interviewer (this is the grade, usually between 1 and 5, given by the interviewer to the candidate based on the interview)
$m_i$ number of interviewers in the interview panel for candidate $i$ (the number of interviewers, usually between 4 and 8, that the candidate faces during the course of the interview process)
$n_j$ number of candidates interviewed by interviewer $j$ (can be large, in tens or hundreds, especially for popular interviewers)
$\hat{n_j}$ number of candidates interviewed by interviewer $j$ that joined the company/group
$p_i$ job performance of $i^{th}$ candidate after joining the company/group (usually between 1 and 5, captured in a company-internal HRM system)
$s_i$ average score given by the interview panel for the $i^{th}$ candidate, $s_i=\sum_{j}s_{ij}/{m_i}$ (usually between 1 and 5)

Evaluating an interviewer w.r.t. other interviewers
Consider the random variable $X_j$ defined below, which is the difference between the score given by the $j^{th}$ interviewer and the average score given by the interview panel, for any candidate interviewed by $R_j$:
\[X_j=\{s_{ij}-s_i | R_j ~ \text{interviewed} ~ C_i\}\] We need to answer the following questions:
  • What does a probability distribution of $X_j$ look like?
  • What does the probability distribution of $X_j$ tell us about the interviewer $R_j$?
Before answering the above question, consider the following random variable, $X$: \[X=\sum_j{X_j}\]
This is the random variable for the difference between the score given by an interviewer and the mean of the score received by the candidate. Clearly, expectation of $X$, $E(X)=0$. Moreover, we expect $X$ to have a normal distribution. So we expect some $X_j$ to be centered to the left of 0, some to the right of 0, but most others around 0.

So the answers to the above questions are:
  • We expect the probability distribution of $X_j$ to be normal, centered around 0, on an average.
  • $E(X_j)$ tells us about the type of interviewer $R_j$ is:
    • $E(X_j)=0$ or more accurately, $|E(X_j)| < a\sigma$ (where $\sigma$ is the standard deviation of $X$, and $a>0$ is appropriately chosen) implies that the interviewer $R_j$ is a normal interviewer.
    • $E(X_j)\geq a\sigma$ implies that interviewer $R_j$ generally gives higher scores than average.
    • $E(X_j)\leq -a\sigma$ implies that interviewer $R_j$ generally gives lower scores than average.

Categorization of interviewers
From the above answers regarding $X_j$, we can categorize interviewer $R_j$ into a few distinct types:

The "regular" interviewer

Most interviewers' distribution of $X_j$ would look like the above. (Perhaps not as sharp a drop off as shown above around 0 - I just couldn't draw a good graph!) This indicates that most interviewers' scores would be close to the average score given by the interview panel.

The "easy" interviewer

The easy interviewer tends to give, on an average, higher scores than the rest of the interview panel. This causes the bulk of the graph to shift beyond 0. The farther this hump from 0, the easier the interviewer. If you are an interviewee, this is the kind of interviewer you want!

The "hard-ass" interviewer

The hard interviewer tends to give lower scores than the rest of the interview panel. This causes hump of the graph to go below zero. The farther below zero, the harder the interviewer. If you are an interviewee, this is the kind of interviewer you want to avoid!

The "activist/extremist" interviewer

Some interviewers tend to artificially influence the outcome of the interview process by giving extreme scores - like "definitely hire" or "definitely no hire". The graph would not be as sharp as the one depicted above, but the idea is that there would be an above-average frequency of extreme values.

The "clueless" interviewer

Some interviewers cannot correctly judge the potential of a candidate. Their scores would be all over the range, instead of a well-defined hump.

In closing
We presented a mathematical basis to compare interviewers. This yields a categorization of interviewers. In the next blog post, Evaluating Interviewers - Part II, we analyze how to evaluate an interviewer with respect to a hired candidate's job performance.

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