Differences

This shows you the differences between two versions of the page.

--- path_analysis [2022/11/09 21:22] – [Output] hkimscil
+++ path_analysis [2022/11/11 08:27] – hkimscil
@@ Line 1: / Line 1: @@
 ====== Path Analysis ======
 ===== Planned Behavior Modeling =====
-<code>
+<code php>
 ######################################################
 ## data file: PlannedBehavior.csv
@@ Line 27: / Line 27: @@
 # Summarize model
 summary(fitmod, fit.measures=TRUE, rsquare=TRUE)
 </code>
 ====== Output ======
-<coce>
+<code>
 ######################################################
 > ## data file: PlannedBehavior.csv
@@ Line 140: / Line 138: @@
                    Estimate
     intention         0.369
+</code>
+====== Model 2 ======
+<code>
+# Specify model
+specmod2 <- "
+intention ~ attitude + norms + control
+attitude ~~ norms + control
+norms ~~ control
+"
+# Estimate model
+fitmod2 <- sem(specmod2, data=df)
+# Summarize model
+summary(fitmod2, fit.measures=TRUE, rsquare=TRUE)
+</code>
+<code>
+fitmod <- lm(intention ~ attitude + norms + control, data=df)
+summary(fitmod)
+</code>
+<code>
+> # Specify model
+> specmod2 <- "
++ intention ~ attitude + norms + control
++ attitude ~~ norms + control
++ norms ~~ control
++ "
+> # Estimate model
+> fitmod2 <- sem(specmod2, data=df)
+>
+> # Summarize model
+> summary(fitmod2, fit.measures=TRUE, rsquare=TRUE)
+lavaan 0.6-9 ended normally after 17 iterations
+  Estimator                                         ML
+  Optimization method                           NLMINB
+  Number of model parameters                        10
+  Number of observations                           199
+Model Test User Model:
+  Test statistic                                 0.000
+  Degrees of freedom                                 0
+Model Test Baseline Model:
+  Test statistic                               136.306
+  Degrees of freedom                                 6
+  P-value                                        0.000
+User Model versus Baseline Model:
+  Comparative Fit Index (CFI)                    1.000
+  Tucker-Lewis Index (TLI)                       1.000
+Loglikelihood and Information Criteria:
+  Loglikelihood user model (H0)              -1011.828
+  Loglikelihood unrestricted model (H1)      -1011.828
+  Akaike (AIC)                                2043.656
+  Bayesian (BIC)                              2076.589
+  Sample-size adjusted Bayesian (BIC)         2044.908
+Root Mean Square Error of Approximation:
+  RMSEA                                          0.000
+Percent confidence interval - lower         0.000
+Percent confidence interval - upper         0.000
+  P-value RMSEA <= 0.05                             NA
+Standardized Root Mean Square Residual:
+  SRMR                                           0.000
+Parameter Estimates:
+  Standard errors                             Standard
+  Information                                 Expected
+  Information saturated (h1) model          Structured
+Regressions:
+                   Estimate  Std.Err  z-value  P(>|z|)
+  intention ~
+    attitude          0.352    0.058    6.068    0.000
+    norms             0.153    0.059    2.577    0.010
+    control           0.275    0.058    4.740    0.000
+Covariances:
+                   Estimate  Std.Err  z-value  P(>|z|)
+  attitude ~~
+    norms             0.200    0.064    3.128    0.002
+    control           0.334    0.070    4.748    0.000
+  norms ~~
+    control           0.220    0.065    3.411    0.001
+Variances:
+                   Estimate  Std.Err  z-value  P(>|z|)
+   .intention         0.530    0.053    9.975    0.000
+    attitude          0.928    0.093    9.975    0.000
+    norms             0.830    0.083    9.975    0.000
+    control           0.939    0.094    9.975    0.000
+R-Square:
+                   Estimate
+    intention         0.369
+>
+> fitmod <- lm(intention ~ attitude + norms + control, data=df)
+> summary(fitmod)
+Call:
+lm(formula = intention ~ attitude + norms + control, data = df)
+Residuals:
+     Min       1Q   Median       3Q      Max
+-1.80282 -0.52734 -0.06018  0.51228  1.85202
+Coefficients:
+            Estimate Std. Error t value Pr(>|t|)
+(Intercept)  0.58579    0.23963   2.445   0.0154 *
+attitude     0.35232    0.05866   6.006 9.13e-09 ***
+norms        0.15250    0.05979   2.550   0.0115 *
+control      0.27502    0.05862   4.692 5.09e-06 ***
+---
+Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+Residual standard error: 0.7356 on 195 degrees of freedom
+Multiple R-squared:  0.369,	Adjusted R-squared:  0.3593
+F-statistic: 38.01 on 3 and 195 DF,  p-value: < 2.2e-16
+>
+</code>
+====== Model 3 ======
+<code>
+# Specify model
+specmod3 <- "
+  # directional relationships
+  intention ~ attitude + norms + control
+  behavior ~ intention
+  # covariances
+  attitude ~~ norms + control
+  norms ~~ control
+"
+# Estimate model
+fitmod3 <- sem(specmod3, data=df)
+# Summarize model
+summary(fitmod3, fit.measures=TRUE, rsquare=TRUE)
+</code>
+<code>
+a <- (5*(5+1))/2
+b <- 12
+a-b
 </code>