The marker-variable technique of Lindell and Whitney (2001) estimates common method variance from the correlation of a marker variable that is theoretically unrelated to at least one of the substantive variables: any non-zero correlation it shows with that variable is attributed to shared method, and that amount is partialled out of the substantive correlations. When no a priori marker is available, the smallest positive correlation among the substantive items is used as a proxy, the common marker-free variant of the method. A correlation that remains statistically significant after the adjustment, by the paper's t test of the adjusted correlation with \(N - 3\) degrees of freedom (their Equation 5), is evidence that the relationship is not an artifact of method variance; the test is applied by the user, since this function works from the correlation matrix alone and does not take \(N\).
common_method_marker(R, marker_r = NULL)A data.frame (class dmar_tbl) with rows
marker_correlation, mean_abs_r_unadjusted, and
mean_abs_r_adjusted in the value column. The full adjusted
correlation matrix is the "adjusted" attribute.
A correlation matrix among the substantive items.
The marker variable's (CMV) correlation. When
NULL (default) the smallest positive off-diagonal correlation in
R is used as the proxy marker.
Ken Kelley [email protected]
Writing \(r_M\) for the marker (or proxy) correlation, each substantive
correlation is adjusted as
\(r^{A}_{ij} = (r_{ij} - r_M) / (1 - r_M)\) (Lindell & Whitney, 2001,
Equation 4). The CMV-adjusted correlation matrix is returned as the
"adjusted" attribute; the reported table summarizes the marker
correlation and the average absolute correlation before and after
adjustment.
The method presumes the variables are reflected so that their intercorrelations are positive; a negative substantive correlation is pushed further from zero by the adjustment rather than attenuated, so reverse-code as needed before adjusting.
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114--121. tools:::Rd_expr_doi("10.1037/0021-9010.86.1.114")
common_method_single_factor for the single-factor
screen.
Other multivariate and latent variable methods:
average_variance_extracted(),
bifactor_indices(),
cfa_1(),
cfa_2(),
cfa_k(),
ci_eigenvalue(),
common_method_single_factor(),
dmacs(),
ecvi(),
htmt(),
irt_grm(),
irt_information(),
measurement_alignment(),
measurement_invariance(),
procrustes_phi(),
simple_structure()
R <- matrix(c(1, .5, .4, .5, 1, .45, .4, .45, 1), 3, 3,
dimnames = list(c("a", "b", "c"), c("a", "b", "c")))
res <- common_method_marker(R, marker_r = 0.10)
res
attr(res, "adjusted")
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