***** To join INSNA, visit http://www.insna.org ***** Dear all, My mentor has a large sociocentric network dataset (N = 1342) and I am trying to analyze the data using network autocorrelation models. When I run a model in R using the spautolm function and then I run the same model using the lm function, I get incredibly different results (the effects are significant but in different directions), and I have no idea on why these two methods would provide such vastly different results. See the models below. FYI, the predictor is dichotomous and the outcome is continuous. If you have any thoughts, please let me know, cause I do not know which results to trust. Best, Matt Call: spautolm(formula = BLQFMHEDWM.W ~ discrep_net_prop_more.W, listw = w.list.trunc, family = "SAR", zero.policy = TRUE) Residuals: Min 1Q Median 3Q Max -5.71857 -1.19085 -0.49541 0.55372 15.64806 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.64796 0.40402 4.0789 4.524e-05 discrep_net_prop_more.W -1.30912 0.18460 -7.0916 1.325e-12 Lambda: 0.83807 LR test value: 425.13 p-value: < 2.22e-16 Numerical Hessian standard error of lambda: 0.030919 Log likelihood: -2680.358 ML residual variance (sigma squared): 5.0308, (sigma: 2.2429) Number of observations: 1178 Number of parameters estimated: 4 AIC: 5368.7 Call: lm(formula = BLQFMHEDWM.W ~ discrep_net_prop_more.W, data = subject) Residuals: Min 1Q Median 3Q Max -2.2335 -1.7280 -1.7280 0.7665 18.2720 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.72799 0.08878 19.465 <2e-16 *** discrep_net_prop_more.W 0.50554 0.23578 2.144 0.0322 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 2.823 on 1176 degrees of freedom Multiple R-squared: 0.003894, Adjusted R-squared: 0.003047 F-statistic: 4.597 on 1 and 1176 DF, p-value: 0.03223 -- Matthew K. Meisel, Ph.D. Assistant Professor Center for Alcohol and Addiction Studies Department of Behavioral and Social Sciences Brown University | School of Public Health Box G-S121-4 Providence, RI 02903 Phone: (401) 863-6590 [log in to unmask] _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.