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***** 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,MattCall: 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 15.64806Coefficients:Estimate Std. Error z value Pr(>|z|)(Intercept) 1.64796 0.40402 4.0789 4.524e-05discrep_net_prop_more.W -1.30912 0.18460 -7.0916 1.325e-12Lambda: 0.83807 LR test value: 425.13 p-value: < 2.22e-16Numerical Hessian standard error of lambda: 0.030919Log likelihood: -2680.358ML residual variance (sigma squared): 5.0308, (sigma: 2.2429)Number of observations: 1178Number of parameters estimated: 4AIC: 5368.7Call: 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 18.2720Coefficients: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 ‘ ’ 1Residual standard error: 2.823 on 1176 degrees of freedomMultiple R-squared: 0.003894, Adjusted R-squared: 0.003047F-statistic: 4.597 on 1 and 1176 DF, p-value: 0.03223--
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