Sampling variance of Pearson r in a two-level design

Consider Pearson’s correlation coefficient, \(r\), calculated from two variables \(X\) and \(Y\) with population correlation \(\rho\). If one calculates \(r\) from a simple random sample of \(N\) observations, then its sampling variance will be approximately

\[ \text{Var}(r) \approx \frac{1}{N}\left(1 - \rho^2\right)^2. \]

But what if the observations are drawn from a multi-stage sample? If one uses the raw correlation between the observations (ignoring the multi-level structure), then the \(r\) will actually be a weighted average of within-cluster and between-cluster correlations (see Snijders & Bosker, 2012). Intuitively, I would expect that the sampling variance of the between-cluster correlation will be a function of the number of clusters (regardless of the number of observations per cluster), so the variance of \(r\) from a multi-stage sample would not necessarily be the same as that from a simple random sample. What is the sampling variance of \(r\) in this design?

Let me be more precise here by formalizing the sampling process. Suppose that we have a sample with \(m\) clusters, \(n_j\) observations in cluster \(j\), and total sample size \(N = \sum_{j=1}^m n_j\). Assume that

\[ \begin{aligned} X_{ij} &= \mu_x + v^x_j + e^x_{ij} \\ Y_{ij} &= \mu_y + v^y_j + e^y_{ij}, \end{aligned} \]

for \(i=1,...,n_j\) and \(j=1,...,m\), where

\[ \begin{aligned} \left[\begin{array}{c} v^x_j \\ v^y_j \end{array}\right] &\sim N\left(\left[\begin{array}{c}0 \\ 0 \end{array}\right], \left[\begin{array}{cc}\omega_x^2 & \phi \omega_x \omega_y \\ \phi \omega_x \omega_y & \omega_y^2\end{array}\right]\right) \\ \left[\begin{array}{c} e^x_{ij} \\ e^y_{ij} \end{array}\right] &\sim N\left(\left[\begin{array}{c}0 \\ 0 \end{array}\right], \left[\begin{array}{cc}\sigma_x^2 & \rho \sigma_x \sigma_y \\ \rho \sigma_x \sigma_y & \sigma_y^2\end{array}\right]\right) \end{aligned} \]

and the error terms are mutually independent unless otherwise noted. The raw Pearson’s \(r\) is calculated using the total sums of squares and cross-products:

\[ r = \frac{SS_{xy}}{\sqrt{SS_{xx} SS_{yy}}}, \]

where

\[ \begin{aligned} SS_{xx} &= \sum_{j=1}^m \sum_{i=1}^{n_j} \left(X_{ij} - \bar{\bar{x}}\right)^2, \qquad \bar{\bar{x}} = \frac{1}{N} \sum_{j=1}^m \sum_{i=1}^{n_j} X_{ij} \\ SS_{xy} &= \sum_{j=1}^m \sum_{i=1}^{n_j} \left(Y_{ij} - \bar{\bar{y}}\right)^2, \qquad \bar{\bar{y}} = \frac{1}{N} \sum_{j=1}^m \sum_{i=1}^{n_j} Y_{ij} \\ SS_{xy} &= \sum_{j=1}^m \sum_{i=1}^{n_j} \left(X_{ij} - \bar{\bar{x}}\right) \left(Y_{ij} - \bar{\bar{y}}\right). \end{aligned} \]

Common correlation and ICC

The distribution of the total correlation seems to be pretty complicated. So far, I’ve been able to obtain the variance of \(r\) for a special case that makes some further, fairly restrictive assumptions. Specifically, assume that the correlation is constant across the two levels, so that \(\phi = \rho\), and that the intra-class correlation of \(X\) is the same as that of \(Y\). Let \(k = \omega_x^2 / \sigma_x^2 = \omega_y^2 / \sigma_y^2\) and \(\psi = k / (k + 1) = \omega_x^2 / (\omega_x^2 + \sigma_x^2)\). Then

\[ \text{Var}(r) \approx \frac{(1 - \rho^2)^2}{\tilde{N}}, \]

where

\[ \tilde{N} = \frac{N[g_1 k + 1]^2}{g_2 k^2 + 2 g_1 k + 1} \approx \frac{N}{1 + (g_2 - g_1^2)\psi^2}, \]

with \(\displaystyle{g_1 = 1 - \frac{1}{N^2}\sum_{j=1}^m n_j^2}\), and \(\displaystyle{g_2 = \frac{1}{N}\sum_{j=1}^m n_j^2 - \frac{2}{N^2}\sum_{j=1}^m n_j^3 + \frac{1}{N^3} \left(\sum_{j=1}^m n_j^2 \right)^2}\).

If the clusters are all of equal size \(n\), then

\[ \tilde{N} = \frac{nm[k(m - 1) / m + 1]^2}{k^2 n (m - 1)/m + 2 k (m - 1) / m + 1} \approx \frac{N}{1 + (n - 1) \psi^2}, \]

The right-hand expression is a further approximation that will be very close to right so long as \(m\) is not too too small.

Z-transformation

Under the (restrictive) assumptions of common correlation and equal ICCs, Fisher’s z transformation is variance-stabilizing (as it is under simple random sampling), so it seems reasonable to use

\[ \text{Var}\left(z(r)\right) \approx \frac{1}{\tilde{N} - 3}. \]

Design effect

The design effect (\(DEF\)) is the ratio of the actual sampling variance of \(r\) to the sampling variance in a simple random sample of the same size. For the special case that I’ve described,

\[ DEF = \frac{N}{\tilde{N}} = 1 + (g_2 - g_1^2) \psi^2, \]

or with equal cluster-sizes, \(DEF = 1 + (n - 1)\psi^2\). These expressions make it clear that the design effect for the correlation is not equivalent to the well-known design effect for means or mean differences in cluster-randomized designs, which is \(1 + (n - 1)\psi\). We need to take the square of the ICC here, which will make the design effect for \(r\) smaller than the design effect for a mean (or difference in means) based on the same sample.

Other special cases

There are some further special cases that are not to hard to work out and could be useful as rough approximations at least. One is if the within-cluster correlation is zero \((\rho = 0)\) and we’re interested in the between-cluster correlation \(\phi\). Then the total correlation can be corrected for what is essentially measurement error using formulas from Hunter and Schmidt (2004). A further specialization is if \(X\) is a cluster-level measure, so that \(\sigma_x^2 = 0\). I’ll consider these in a later post, perhaps.

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