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  1. #Scaling the Predictor (or Standardizing “talkrad”)
  2.  
  3. >mydata$Ztalkrad <- scale(mydata$talkrad, center = T, scale = T)
  4.  
  5. #Creating Dummies
  6. #(others as reference group)
  7. mydata$dem [mydata$party == "1"] <- "1"
  8. mydata$dem [mydata$party == "2"] <- "0"
  9. mydata$dem [mydata$party == "3"] <- "0"
  10.  
  11. mydata$rep [mydata$party == "1"] <- "0"
  12. mydata$rep [mydata$party == "2"] <- "1"
  13. mydata$rep [mydata$party == "3"] <- "0"
  14.  
  15. > model1 <- lm(pknow~ dem + rep + Ztalkrad, data = mydata)
  16. > summary(model1)
  17.  
  18. Call:
  19. lm(formula = pknow ~ dem + rep + Ztalkrad, data = mydata)
  20.  
  21. Residuals:
  22. Min 1Q Median 3Q Max
  23. -10.6713 -3.0597 0.2945 2.9453 10.7363
  24.  
  25. Coefficients:
  26. Estimate Std. Error t value Pr(>|t|)
  27. (Intercept) 9.1860 0.5681 16.170 < 2e-16 ***
  28. dem1 2.4418 0.6703 3.643 0.000312 ***
  29. rep1 2.4076 0.6680 3.604 0.000360 ***
  30. Ztalkrad 1.1844 0.2311 5.125 5e-07 ***
  31. ---
  32. Signif. codes:
  33. 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  34.  
  35. Residual standard error: 4.212 on 339 degrees of freedom
  36. Multiple R-squared: 0.1128, Adjusted R-squared: 0.105
  37. F-statistic: 14.37 on 3 and 339 DF, p-value: 7.806e-09
  38.  
  39. > model2 <- lm(pknow~ dem + rep + Ztalkrad + dem:Ztalkrad + rep:Ztalkrad, data = mydata)
  40. > summary(model2)
  41.  
  42. Call:
  43. lm(formula = pknow ~ dem + rep + Ztalkrad + dem:Ztalkrad + rep:Ztalkrad,
  44. data = mydata)
  45.  
  46. Residuals:
  47. Min 1Q Median 3Q Max
  48. -10.9693 -3.1915 0.0307 2.8176 10.8176
  49.  
  50. Coefficients:
  51. Estimate Std. Error t value Pr(>|t|)
  52. (Intercept) 9.1915 0.5630 16.325 < 2e-16 ***
  53. dem1 2.2770 0.6672 3.413 0.000722 ***
  54. rep1 2.2877 0.6628 3.452 0.000628 ***
  55. Ztalkrad 1.2959 0.5342 2.426 0.015800 *
  56. dem1:Ztalkrad -1.0128 0.6620 -1.530 0.126969
  57. rep1:Ztalkrad 0.4969 0.6292 0.790 0.430266
  58. ---
  59. Signif. codes:
  60. 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  61.  
  62. Residual standard error: 4.171 on 337 degrees of freedom
  63. Multiple R-squared: 0.1352, Adjusted R-squared: 0.1223
  64. F-statistic: 10.53 on 5 and 337 DF, p-value: 2.066e-09
  65.  
  66. > anova(model1, model2)
  67. Analysis of Variance Table
  68.  
  69. Model 1: pknow ~ dem + rep + Ztalkrad
  70. Model 2: pknow ~ dem + rep + Ztalkrad + dem:Ztalkrad + rep:Ztalkrad
  71. Res.Df RSS Df Sum of Sq F Pr(>F)
  72. 1 339 6014.4
  73. 2 337 5862.9 2 151.51 4.3545 0.01358 *
  74. ---
  75. Signif. codes:
  76. 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  77.  
  78. #probing simple slopes
  79. #republican as reference group
  80. > mydata$dem_eff [mydata$party == "1"] <- "1"
  81. > mydata$dem_eff [mydata$party == "2"] <- "0"
  82. > mydata$dem_eff [mydata$party == "3"] <- "0"
  83. >
  84. > mydata$rep_eff [mydata$party == "1"] <- "0"
  85. > mydata$rep_eff [mydata$party == "2"] <- "0"
  86. > mydata$rep_eff [mydata$party == "3"] <- "1"
  87.  
  88. > model3 <- lm(pknow~ dem_eff + rep_eff + Ztalkrad, data = mydata)
  89. > summary(model3)
  90.  
  91. Call:
  92. lm(formula = pknow ~ dem_eff + rep_eff + Ztalkrad, data = mydata)
  93.  
  94. Residuals:
  95. Min 1Q Median 3Q Max
  96. -10.6713 -3.0597 0.2945 2.9453 10.7363
  97.  
  98. Coefficients:
  99. Estimate Std. Error t value Pr(>|t|)
  100. (Intercept) 11.59360 0.35011 33.114 < 2e-16 ***
  101. dem_eff1 0.03415 0.50361 0.068 0.94597
  102. rep_eff1 -2.40764 0.66804 -3.604 0.00036 ***
  103. Ztalkrad 1.18438 0.23108 5.125 5e-07 ***
  104. ---
  105. Signif. codes:
  106. 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  107.  
  108. Residual standard error: 4.212 on 339 degrees of freedom
  109. Multiple R-squared: 0.1128, Adjusted R-squared: 0.105
  110. F-statistic: 14.37 on 3 and 339 DF, p-value: 7.806e-09
  111.  
  112. > model4 <- lm(pknow~ dem_eff + rep_eff + Ztalkrad + dem_eff:Ztalkrad + rep_eff:Ztalkrad, data = mydata)
  113. > summary(model4)
  114.  
  115. Call:
  116. lm(formula = pknow ~ dem_eff + rep_eff + Ztalkrad + dem_eff:Ztalkrad +
  117. rep_eff:Ztalkrad, data = mydata)
  118.  
  119. Residuals:
  120. Min 1Q Median 3Q Max
  121. -10.9693 -3.1915 0.0307 2.8176 10.8176
  122.  
  123. Coefficients:
  124. Estimate Std. Error t value Pr(>|t|)
  125. (Intercept) 11.47919 0.34965 32.830 < 2e-16
  126. dem_eff1 -0.01071 0.50041 -0.021 0.982935
  127. rep_eff1 -2.28770 0.66277 -3.452 0.000628
  128. Ztalkrad 1.79277 0.33239 5.394 1.3e-07
  129. dem_eff1:Ztalkrad -1.50965 0.51313 -2.942 0.003486
  130. rep_eff1:Ztalkrad -0.49686 0.62919 -0.790 0.430266
  131.  
  132. (Intercept) ***
  133. dem_eff1
  134. rep_eff1 ***
  135. Ztalkrad ***
  136. dem_eff1:Ztalkrad **
  137. rep_eff1:Ztalkrad
  138. ---
  139. Signif. codes:
  140. 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  141.  
  142. Residual standard error: 4.171 on 337 degrees of freedom
  143. Multiple R-squared: 0.1352, Adjusted R-squared: 0.1223
  144. F-statistic: 10.53 on 5 and 337 DF, p-value: 2.066e-09
  145.  
  146. > anova(model3, model4)
  147. Analysis of Variance Table
  148.  
  149. Model 1: pknow ~ dem_eff + rep_eff + Ztalkrad
  150. Model 2: pknow ~ dem_eff + rep_eff + Ztalkrad + dem_eff:Ztalkrad + rep_eff:Ztalkrad
  151. Res.Df RSS Df Sum of Sq F Pr(>F)
  152. 1 339 6014.4
  153. 2 337 5862.9 2 151.51 4.3545 0.01358 *
  154. ---
  155. Signif. codes:
  156. 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  157.  
  158. #rotate reference group to democrat
  159. mydata$dem_ro [mydata$party == "1"] <- "0"
  160. mydata$dem_ro [mydata$party == "2"] <- "1"
  161. mydata$dem_ro [mydata$party == "3"] <- "0"
  162.  
  163. mydata$rep_ro [mydata$party == "1"] <- "0"
  164. mydata$rep_ro [mydata$party == "2"] <- "0"
  165. mydata$rep_ro [mydata$party == "3"] <- "1"
  166.  
  167. > model5 <- lm(pknow~ dem_ro + rep_ro + Ztalkrad, data = mydata)
  168. > summary(model5)
  169.  
  170. Call:
  171. lm(formula = pknow ~ dem_ro + rep_ro + Ztalkrad, data = mydata)
  172.  
  173. Residuals:
  174. Min 1Q Median 3Q Max
  175. -10.6713 -3.0597 0.2945 2.9453 10.7363
  176.  
  177. Coefficients:
  178. Estimate Std. Error t value Pr(>|t|)
  179. (Intercept) 11.62775 0.35706 32.565 < 2e-16 ***
  180. dem_ro1 -0.03415 0.50361 -0.068 0.945972
  181. rep_ro1 -2.44179 0.67027 -3.643 0.000312 ***
  182. Ztalkrad 1.18438 0.23108 5.125 5e-07 ***
  183. ---
  184. Signif. codes:
  185. 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  186.  
  187. Residual standard error: 4.212 on 339 degrees of freedom
  188. Multiple R-squared: 0.1128, Adjusted R-squared: 0.105
  189. F-statistic: 14.37 on 3 and 339 DF, p-value: 7.806e-09
  190.  
  191. > model6 <- lm(pknow~ dem_ro + rep_ro + Ztalkrad + dem_ro:Ztalkrad + rep_ro:Ztalkrad, data = mydata)
  192. > summary(model6)
  193.  
  194. Call:
  195. lm(formula = pknow ~ dem_ro + rep_ro + Ztalkrad + dem_ro:Ztalkrad +
  196. rep_ro:Ztalkrad, data = mydata)
  197.  
  198. Residuals:
  199. Min 1Q Median 3Q Max
  200. -10.9693 -3.1915 0.0307 2.8176 10.8176
  201.  
  202. Coefficients:
  203. Estimate Std. Error t value Pr(>|t|)
  204. (Intercept) 11.46847 0.35799 32.036 < 2e-16
  205. dem_ro1 0.01071 0.50041 0.021 0.982935
  206. rep_ro1 -2.27698 0.66721 -3.413 0.000722
  207. Ztalkrad 0.28312 0.39092 0.724 0.469423
  208. dem_ro1:Ztalkrad 1.50965 0.51313 2.942 0.003486
  209. rep_ro1:Ztalkrad 1.01279 0.66198 1.530 0.126969
  210.  
  211. (Intercept) ***
  212. dem_ro1
  213. rep_ro1 ***
  214. Ztalkrad
  215. dem_ro1:Ztalkrad **
  216. rep_ro1:Ztalkrad
  217. ---
  218. Signif. codes:
  219. 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  220.  
  221. Residual standard error: 4.171 on 337 degrees of freedom
  222. Multiple R-squared: 0.1352, Adjusted R-squared: 0.1223
  223. F-statistic: 10.53 on 5 and 337 DF, p-value: 2.066e-09
  224.  
  225. > anova(model5, model6)
  226. Analysis of Variance Table
  227.  
  228. Model 1: pknow ~ dem_ro + rep_ro + Ztalkrad
  229. Model 2: pknow ~ dem_ro + rep_ro + Ztalkrad + dem_ro:Ztalkrad + rep_ro:Ztalkrad
  230. Res.Df RSS Df Sum of Sq F Pr(>F)
  231. 1 339 6014.4
  232. 2 337 5862.9 2 151.51 4.3545 0.01358 *
  233. ---
  234. Signif. codes:
  235. 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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