further analysis

VCG · Jul 25, 2023 · a98529c · a98529c
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diff --git a/.DS_Store b/.DS_Store
diff --git a/.gitignore b/.gitignore
@@ -1,3 +1,4 @@
 .idea/*
 /.idea/*
 /.idea/trust_in_science.iml
+node_modules/
diff --git a/README.md b/README.md
diff --git a/README.pdf b/README.pdf
diff --git a/data_testing.py b/data_testing.py
diff --git a/study_data/.DS_Store b/study_data/.DS_Store
diff --git a/study_data/full_Study/.DS_Store b/study_data/full_Study/.DS_Store
diff --git a/study_data/full_Study/affectMeasures.png b/study_data/full_Study/affectMeasures.png
diff --git a/study_data/full_Study/complexity_dataType_interaction.pdf b/study_data/full_Study/complexity_dataType_interaction.pdf
diff --git a/study_data/full_Study/complexity_interaction.pdf b/study_data/full_Study/complexity_interaction.pdf
diff --git a/study_data/full_Study/f7.pdf b/study_data/full_Study/f7.pdf
diff --git a/study_data/full_Study/provenanceResults.png b/study_data/full_Study/provenanceResults.png
diff --git a/study_data/full_Study/trustFullAnalysis_6.12.2023.Rmd b/study_data/full_Study/trustFullAnalysis_6.12.2023.Rmd
@@ -69,7 +69,7 @@ results %>%
                 group_by(complexity, isCovidData) %>%
                 summarize(n = n(), 
                            mean = mean(vis.trust_6),
-            se = sd(data.trust_6)/sqrt(n)), 
+            se = sd(vis.trust_6)/sqrt(n)), 
               aes(x = mean, xend = mean, y = -.25, yend = .25,  colour = as.factor(isCovidData)), size = 1) +
         # stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 0.5, position = position_nudge(x=0.25, y=0), alpha=0.5) +
 
@@ -118,18 +118,27 @@ results %>%
   geom_jitter(data = results, width = 0.25, height = 0.2, color = "light gray", alpha = 0.5) +
   geom_boxplot(lwd = 1, fatten = NULL, width = 0.25, alpha = 0.5, color = "salmon") +
   # labs(title = "Trust in data") + 
-  geom_vline(data = results %>% 
+
+  
+   geom_segment(data = results %>% 
                 group_by(complexity) %>%
                 summarize(n = n(), 
-                          data.trust_6 = mean(data.trust_6)), 
-              aes(xintercept = data.trust_6), size = 1,colour = "salmon") +
+                           mean = mean(data.trust_6),
+            se = sd(data.trust_6)/sqrt(n)), 
+              aes(x = mean, xend = mean, y = -.25, yend = .25,  colour ="salmon"), size = 1) +
+
+  
   geom_text( data = results %>% 
                 group_by(complexity) %>%
                 summarize(n = n(), 
                            mean = round(mean(data.trust_6),digits=2),
             se = round(sd(data.trust_6)/sqrt(n),digits=2),
-                          vis.trust_6 = mean(vis.trust_6)),
-            aes(label = paste(mean, "[",mean-se,",",mean+se,"]"), x = 6.8, y = 0.43, fontface = 3), size=3, colour = "black")+
+                          dadta.trust_6 = mean(data.trust_6)),
+            # aes(label = paste(mean, "[",mean-se,",",mean+se,"]"), x = 6.2, y = 0.43, fontface = 3), size=3, colour = "black")+
+              aes(label = paste(mean), x = mean, y = .35, fontface = 3), size=4, colour = "black")+
+  
+
+  
   facet_grid(rows = vars(complexity)) +
   xlab("Trust in Data") +
   theme_minimal() + 
@@ -146,21 +155,56 @@ ggsave(paste("complexity_interaction.pdf", sep=""))
 
 ```{r}
 results %>%
+ filter(isCovidData ==0 ) %>%
   group_by(complexity) %>%
   summarize(n = n(),
-            mean = mean(data.trust_6),
-            se = sd(data.trust_6)/sqrt(n),
+            mean = mean(vis.trust_6),
+            se = sd(vis.trust_6)/sqrt(n),
             n = n)
 ```
 
+```{r}
+model <- lm(formula = vis.trust_6 ~ complexity  * chartType + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+            data = results%>%filter(isCovidData == 1)
+ filter(isCovidData ==1 ))
+anova(model)
+```
+```{r}
+model <- lm(formula = vis.trust_6 ~ complexity  * as.factor(isCovidData) + chartType + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+            data = results%>% filter(complexity !='moderatex'))
+anova(model)
+
+
+```
+
+
+```{r}
+model <- lm(formula = vis.trust_6 ~ complexity * as.factor(isCovidData) + chartType + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+            data = results)
+anova(model)
+
+
+```
+
+
 Linear Regression Model for trust in vis as a function of 
 ```{r}
-model <- lm(formula = vis.trust_6 ~ complexity * as.factor(isCovidData) * chartType
-                                    + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+model <- lm(formula = vis.trust_6 ~ complexity * as.factor(isCovidData) * chartType  + Age + Gender + State_1 + Income + Education + Parents_education + Language + Ethnicity  + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
             data = results)
 anova(model)
 
 
+```
+```{r}
+# can change the predictor to bar.vis
+model<- manova(cbind(vis.trust_6, 
+                     vis.trust_5, 
+                     vis.trust_4, 
+                     vis.trust_3, 
+                     vis.trust_2, 
+                     vis.trust_1) ~ complexity  * chartType  + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+               data = results %>%filter(isCovidData == 1))
+summary.aov(model)
 ```
 
 ```{r}
@@ -174,14 +218,14 @@ eta_squared(aov(vis.trust_6 ~ complexity * as.factor(isCovidData) * chartType +
 ``` 
 # Colinearity of trust in vis and trust in data
 ```{r}
-colinearity_model <- lm(formula = trust.in.science_7 ~ affect.aesthetic_1 + affect.clarity_1 + affect.science_1 + vis.trust_1 + vis.trust_2 + vis.trust_3 + vis.trust_4 + vis.trust_5 + vis.trust_6 + data.trust_6 + data.trust_5 + data.trust_4 + data.trust_3 + data.trust_2 + data.trust_1,
+colinearity_model <- lm(formula = Age ~ vis.trust_1 + vis.trust_2 + vis.trust_3 + affect.science_1 + affect.clarity_1 + affect.aesthetic_1 + vis.trust_6 + data.trust_1 + data.trust_2 + data.trust_3 + data.trust_4 + data.trust_5 + data.trust_6 + interpersonal.trust_1 + trust.in.science_7 + need_for_cognition,
             data = results)
 vif(colinearity_model)
 ```
 
 vif(colinearity_model)relation of trust in vis and trust in data
 ```{r}
-data_frame = data.frame(results$vis.trust_1, results$vis.trust_2, results$vis.trust_3, results$vis.trust_4, results$vis.trust_5, results$vis.trust_6, results$data.trust_1, results$data.trust_2, results$data.trust_3, results$data.trust_4, results$data.trust_5, results$data.trust_6)
+data_frame = data.frame(results$vis.trust_1, results$vis.trust_2, results$vis.trust_3, results$affect.science_1, results$affect.clarity_1, results$affect.aesthetic_1, results$vis.trust_6, results$data.trust_1, results$data.trust_2, results$data.trust_3, results$data.trust_4, results$data.trust_5, results$data.trust_6, results$interpersonal.trust_1, results$trust.in.science_7, results$need_for_cognition)
 cor(data_frame)
 ```
 
@@ -229,9 +273,9 @@ results %>%
 ```
 
 ```{r}
-model <- lm(formula = data.trust_6 ~ complexity * as.factor(isCovidData) * chartType
+model <- lm(formula = data.trust_6 ~ complexity * chartType
                                     + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
-            data = results)
+            data = results%>% filter(isCovidData == 0))
 anova(model)
 
 
@@ -435,7 +479,7 @@ results %>%
 How does performance on VLAT questions predict trust?
 
 ```{r}
-model <- lm(formula = vis.trust_6 ~ vlat_simple * vlat_moderate * vlat_complex +
+model <- lm(formula = vis.trust_6 ~  assigned_vlat *
               Age + Gender + State_1 + Education + Parents_education + Language + 
               Ethnicity + Income + Religion + trust.in.science_7 + 
               need_for_cognition + interpersonal.trust_1,