Suppose we have population P that is split into large number of close size sub-populations (e.g. country is split to zips). For each sub-population a small sample was drawn and average of a variable X was calculated.

The researcher has only access to the average values and he/she needs to study a correlation between X and a random variable Y that researcher has the full access to the population data.

What is the correct way to organize the correlation calculation?

Concern #1 is that the researcher doesn't have access to the individual instances used for the sampling of Y. So if he/she take a ratio between the mean of X and mean of Y it might not be the same as the mean of the ratio X/Y.

Concern #2 is the small size of the X samples. However, since the number of sub-populations is large the same forces that lead to the CLT will be applied to the correlation.

  • 2
    $\begingroup$ (+1) Make sure you first understand the ecological fallacy. This suggests that, without additional knowledge or assumptions, this effort might be doomed. The CLT will not rescue it. $\endgroup$
    – whuber
    Oct 24, 2014 at 16:04

1 Answer 1


This isn't an answer, but a proof that, as @whuber says, the effort is doomed without more information.

The correlation is defined as:

\begin{equation} corr(X,Y) = \frac{cov(X,Y)}{\sqrt{var(X)var(Y)}} \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad (1) \end{equation}

$var(Y)$ is known, so it's only necessary to estimate $var(X)$ and $cov(X,Y)$.

Let $Z$ index sub-populations. The following equalities are proved in introductory stat theory classes.

\begin{equation} var(X) = E_Z(var(X|Z)) + var_Z(E(X|Z) \quad\quad\quad\quad\quad\quad\quad\quad (2) \end{equation}

\begin{equation} cov(X,Y) = E_Z(cov(X,Y)|Z) + cov_Z(E(X|Z), E(Y|Z)) \quad\quad\quad (3) \end{equation}

Here $E_Z$ means that expectation is taken over the distribution of sub-populations, and a similar definition applies to $var_Z$ and $cov_Z$. The $|$ indicates a within-sub-population distribution; for example, $E(X|Z)$ is the mean of X in sub=population Z.

The second term in Eqn 2 and in Eqn 3 depends on the sub-population means of $X$; so both terms can be estimated. However the first term in each equation depends on the within-sub-population distribution of X (Eqn 2) and (X,Y) (Eqn 3), so neither term can be estimated from the available data.


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