presi finetune

Scripts/create_tables.R

@@ -238,7 +238,7 @@ texreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])),
        custom.model.names = c("Mean", "SD", "Treated", "Vol. Treated", "No Info", "NR-Index"), custom.note = "%stars (one-sided). Robust standard errors in parentheses.",
        stars = c(0.01, 0.05, 0.1), float.pos="tb",
        label = "tab:mxl_C_NR",
-       caption = "Results of mixed logit model with treatment interactions for Case B.",
+       caption = "Results of mixed logit model with treatment interactions for Case B including NR-index.",
        file="Tables/mxl/case_B_rent_INT_NR.tex")
 
 

Scripts/interaction_plots_presi.R

@@ -35,20 +35,20 @@ case_D_labels <- c("No Info", "Treated", "Vol. Treated")
 case_D_labels_re <- c("Treated", "Vol. Treated", "No Info")
 
 plot_interview_A <- create_interaction_term_plot(ols_time_spent_control_A, case_A_labels, case_A_labels,
-                                                 "Interview Time in Seconds", -380, 380)
+                                                 "Interview Time in Seconds", -150, 150)
 plot_interview_C <- create_interaction_term_plot(ols_time_spent_control_C, case_C_labels, case_C_labels_re,
-                                                 "Interview Time in Seconds", -380, 380)
+                                                 "Interview Time in Seconds", -150, 150)
 
 plot_interview_D <- create_interaction_term_plot(ols_time_spent_control_D, case_D_labels, case_D_labels_re,
-                                                 "Interview Time in Seconds", -380, 380)
+                                                 "Interview Time in Seconds", -150, 150)
 
 plot_cc_A <- create_interaction_term_plot(ols_time_cc_control_A, case_A_labels, case_A_labels,
-                                          "Mean Choice Card Time in Seconds", -5, 5)
+                                          "Mean Choice Card Time in Seconds", -3, 3)
 plot_cc_C <- create_interaction_term_plot(ols_time_cc_control_C, case_C_labels, case_C_labels_re,
-                                          "Mean Choice Card Time in Seconds", -5, 5)
+                                          "Mean Choice Card Time in Seconds", -3, 3)
 
 plot_cc_D <- create_interaction_term_plot(ols_time_cc_control_D, case_D_labels, case_D_labels_re,
-                                          "Mean Choice Card Time in Seconds", -5, 5)
+                                          "Mean Choice Card Time in Seconds", -3, 3)
 
 plot_mani_A <- create_interaction_term_plot(ols_percentage_correct_control_A, case_A_labels, case_A_labels,
                                             "Percentage of Correct Quiz Statements", -15, 15)

project_start.qmd

@@ -6,7 +6,9 @@ title-slide-attributes:
   data-background-size: contain
   data-background-opacity: "0.2"
 author: "Nino Cavallaro, Fabian Marder, Julian Sagebiel"
-institute: German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
+institute: 
+- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig
+- Leipzig University 
 date: today
 date-format: long
 format: 
@@ -170,7 +172,7 @@ To what extent do you agree or disagree with the following statements?
     \label{simple_logit}
 \end{equation}
 ```
--   OLS regression (survey engagement,information recall, consequentiality, status quo choices):
+-   OLS regression (survey engagement, information recall, consequentiality):
 
 ```{=tex}
 \begin{equation}
@@ -313,6 +315,22 @@ ggpubr::ggarrange(plot_mani_D, plot_cons_D)
 
 ## MXL: Case B
 
+::: {style="font-size: 60%;"}
+```{r, results='asis'}
+htmlreg(c(case_B_cols[1], remGOF(case_B_cols[2:5])),
+       custom.coef.map = list("natural" = "Naturalness", "walking" = "Walking Distance", "rent" = "Rent",
+                              "ASC_sq" = "ASC SQ", "_natural" = "Naturalness", "nat" = "Naturalness",
+                              "wd" = "Walking Distance", "asc" = "ASC SQ",
+                              "ASC_sq_info" = "ASC SQ", "rent_info" = "Rent", "nat_info" = "Naturalness", "walking_info" = "Walking Distance"),
+       custom.model.names = c("Mean", "SD", "Treated", "Vol. Treated", "No Info"), custom.note = "%stars (one-sided). Robust standard errors in parentheses.",
+       stars = c(0.01, 0.05, 0.1), float.pos="tb",
+       label = "tab:mxl_C",
+       caption = "Results of mixed logit model with treatment interactions for Case B including NR-index.")
+```
+:::
+
+## MXL: Case B with NR-index interaction
+
 ::: {style="font-size: 60%;"}
 ```{r, results='asis'}
 htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])),
@@ -327,8 +345,6 @@ htmlreg(c(case_B_cols_NR[1], remGOF(case_B_cols_NR[2:6])),
 ```
 :::
 
-
-
 ## Discussion (1)
 
 1. How do obligatory and optional information provision affect survey engagement, information recall, consequentiality, and stated preferences?