+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Trust in Science Analysis Notebook
+ +
+Attaching package: ‘MASS’
+
+The following object is masked from ‘package:rstatix’:
+
+ select
+
+The following object is masked from ‘package:patchwork’:
+
+ area
+
+The following object is masked from ‘package:dplyr’:
+
+ select
+
+Warning: namespace ‘ez’ is not available and has been replaced
+by .GlobalEnv when processing object ‘anova_result’Load Data
+ + + +results <- read.csv("data_clean_june30.csv")
+
+# trust in data is the column: bar-data_6
+# trust in vis is the column: bar-vis_6Converting covariates to factors
+ + + +results$Gender <- as.factor(results$Gender)
+results$Education <- as.factor(results$Education)
+results$Parents_education <- as.factor(results$Parents_education)
+results$Language <- as.factor(results$Language)
+results$Ethnicity <- as.factor(results$Ethnicity)
+results$Income <- as.factor(results$Income)
+results$Religion <- as.factor(results$Religion)Trust in Vis
+ + + +# results$isCovidData <- factor(results$isCovidData, levels = c(0, 1),
+# labels = c("Crop Data", "Covid Data"))
+MinMeanSEMMax <- function(x) {
+ v <- c(min(x), mean(x) - sd(x)/sqrt(length(x)), mean(x), mean(x) + sd(x)/sqrt(length(x)), max(x))
+ names(v) <- c("ymin", "lower", "middle", "upper", "ymax")
+ v
+}
+
+results %>%
+ # group_by(complexity, isCovidData) %>%
+ ggplot(aes( x = vis.trust_6, y = 0, cex=1.5, colour = as.factor(isCovidData))) +
+ scale_color_manual(values = c("purple", "orange")) +
+ ylim(-0.5, 0.5) +
+ geom_jitter(data = results, width = 0.3, height = 0.2, color = "light gray", alpha = 0.5) +
+ #stat_summary(fun.data=MinMeanSEMMax, geom="boxplot", colour="red") +
+
+ geom_boxplot(lwd = 1, fatten = NULL, width = 0.25, alpha = 0.5) +
+
+ geom_segment(data = results %>%
+ group_by(complexity, isCovidData) %>%
+ summarize(n = n(),
+ mean = mean(vis.trust_6),
+ 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) +
+
+ geom_text( data = results %>%
+ group_by(complexity, isCovidData) %>%
+ summarize(n = n(),
+ mean = round(mean(vis.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 = 6.2, 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(complexity ~ isCovidData) +
+ xlab("Trust in Visualization") +
+ theme_minimal() +
+ theme(panel.spacing = unit(2, "lines"),
+ legend.position = "none",
+ axis.text.y = element_blank(),
+ axis.title.y = element_blank(),
+ axis.ticks.y = element_blank())`summarise()` has grouped output by 'complexity'. You can override using the `.groups` argument.`summarise()` has grouped output by 'complexity'. You can override using the `.groups` argument.
#ggsave(paste("complexity_dataType_interaction.pdf", sep=""))
+
+MinMeanSEMMax <- function(x) {
+ v <- c(min(x), mean(x) - sd(x)/sqrt(length(x)), mean(x), mean(x) + sd(x)/sqrt(length(x)), max(x))
+ names(v) <- c("ymin", "lower", "middle", "upper", "ymax")
+ v
+}
+
+results %>%
+ # group_by(complexity, isCovidData) %>%
+ ggplot(aes( x = vis.trust_6, y = 0, cex=1.5, colour = chartType)) +
+ scale_color_manual(values = c("purple", "orange")) +
+ ylim(-0.5, 0.5) +
+ geom_jitter(data = results, width = 0.3, height = 0.2, color = "light gray", alpha = 0.5) +
+ #stat_summary(fun.data=MinMeanSEMMax, geom="boxplot", colour="red") +
+
+ geom_boxplot(lwd = 1, fatten = NULL, width = 0.25, alpha = 0.5) +
+
+ geom_segment(data = results %>%
+ group_by(complexity, chartType) %>%
+ summarize(n = n(),
+ mean = mean(vis.trust_6),
+ se = sd(vis.trust_6)/sqrt(n)),
+ aes(x = mean, xend = mean, y = -.25, yend = .25, colour = chartType), 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) +
+
+ geom_text( data = results %>%
+ group_by(complexity, chartType) %>%
+ summarize(n = n(),
+ mean = round(mean(vis.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 = 6.2, 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(complexity ~ chartType) +
+ xlab("Trust in Visualization") +
+ theme_minimal() +
+ theme(panel.spacing = unit(2, "lines"),
+ legend.position = "none",
+ axis.text.y = element_blank(),
+ axis.title.y = element_blank(),
+ axis.ticks.y = element_blank())`summarise()` has grouped output by 'complexity'. You can override using the `.groups` argument.`summarise()` has grouped output by 'complexity'. You can override using the `.groups` argument.ggsave(paste("complexity_chartType_interaction.pdf", sep=""))Saving 7.29 x 4.51 in image
results %>%
+ group_by(complexity, isCovidData) %>%
+ summarize(n = n(),
+ mean = mean(vis.trust_6),
+ se = sd(vis.trust_6)/sqrt(n),
+ n = n)# results$isCovidData <- factor(results$isCovidData, levels = c(0, 1),
+# labels = c("Crop Data", "Covid Data"))
+
+results %>%
+ ggplot(aes(x = data.trust_6, y = 0)) +
+ # scale_color_manual(values = c("purple", "orange")) +
+ ylim(-0.5, 0.5) +
+ 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_segment(data = results %>%
+ group_by(complexity) %>%
+ summarize(n = n(),
+ 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),
+ 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() +
+ theme(panel.spacing = unit(2, "lines"),
+ legend.position = "none",
+ axis.text.y = element_blank(),
+ axis.title.y = element_blank(),
+ axis.ticks.y = element_blank())
+
+#ggsave(paste("complexity_interaction.pdf", sep=""))
+results %>%
+ filter(isCovidData ==0 ) %>%
+ group_by(complexity) %>%
+ summarize(n = n(),
+ mean = mean(vis.trust_6),
+ se = sd(vis.trust_6)/sqrt(n),
+ n = n)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)
+anova(model)Analysis of Variance Table
+
+Response: vis.trust_6
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 4.45 2.226 1.7237 0.179624
+as.factor(isCovidData) 1 2.74 2.736 2.1181 0.146282
+chartType 1 0.02 0.020 0.0153 0.901530
+Age 1 9.31 9.308 7.2072 0.007537 **
+Gender 3 7.63 2.544 1.9699 0.117759
+State_1 46 127.87 2.780 2.1523 4.266e-05 ***
+Education 9 33.27 3.697 2.8621 0.002729 **
+Parents_education 2 1.57 0.787 0.6095 0.544099
+Language 3 3.04 1.013 0.7841 0.503252
+Ethnicity 8 16.99 2.124 1.6446 0.110101
+Income 18 29.11 1.617 1.2521 0.215752
+Religion 4 14.44 3.610 2.7951 0.025844 *
+trust.in.science_7 1 138.52 138.515 107.2476 < 2.2e-16 ***
+need_for_cognition 1 6.54 6.541 5.0644 0.024919 *
+interpersonal.trust_1 1 6.90 6.900 5.3428 0.021273 *
+complexity:as.factor(isCovidData) 2 3.13 1.564 1.2108 0.298943
+complexity:chartType 2 3.36 1.680 1.3006 0.273429
+as.factor(isCovidData):chartType 1 0.64 0.639 0.4945 0.482305
+complexity:as.factor(isCovidData):chartType 2 0.35 0.175 0.1353 0.873503
+Residuals 437 564.41 1.292
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1model <- 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)
+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
+ + + +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)Analysis of Variance Table
+
+Response: vis.trust_6
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 4.45 2.226 1.7237 0.179624
+as.factor(isCovidData) 1 2.74 2.736 2.1181 0.146282
+chartType 1 0.02 0.020 0.0153 0.901530
+Age 1 9.31 9.308 7.2072 0.007537 **
+Gender 3 7.63 2.544 1.9699 0.117759
+State_1 46 127.87 2.780 2.1523 4.266e-05 ***
+Income 18 36.70 2.039 1.5788 0.061567 .
+Education 9 27.94 3.105 2.4040 0.011443 *
+Parents_education 2 0.91 0.455 0.3526 0.703060
+Language 3 2.25 0.749 0.5797 0.628627
+Ethnicity 8 16.18 2.022 1.5659 0.132820
+Religion 4 14.44 3.610 2.7951 0.025844 *
+trust.in.science_7 1 138.52 138.515 107.2476 < 2.2e-16 ***
+need_for_cognition 1 6.54 6.541 5.0644 0.024919 *
+interpersonal.trust_1 1 6.90 6.900 5.3428 0.021273 *
+complexity:as.factor(isCovidData) 2 3.13 1.564 1.2108 0.298943
+complexity:chartType 2 3.36 1.680 1.3006 0.273429
+as.factor(isCovidData):chartType 1 0.64 0.639 0.4945 0.482305
+complexity:as.factor(isCovidData):chartType 2 0.35 0.175 0.1353 0.873503
+Residuals 437 564.41 1.292
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+model<- manova( cbind(vis.trust_6,vis.trust_1,vis.trust_2,vis.trust_3,vis.trust_4,vis.trust_5,affect.science_1,affect.clarity_1,affect.aesthetic_1) ~ 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)
+
+summary(model) Df Pillai approx F num Df den Df Pr(>F)
+complexity 2 0.15735 4.0798 18 860 2.919e-08 ***
+as.factor(isCovidData) 1 0.14918 8.3574 9 429 1.621e-11 ***
+chartType 1 0.03225 1.5884 9 429 0.116163
+Age 1 0.03849 1.9079 9 429 0.049143 *
+Gender 3 0.05602 0.9113 27 1293 0.596349
+State_1 46 0.97283 1.1513 414 3933 0.023586 *
+Education 9 0.23390 1.2956 81 3933 0.039962 *
+Parents_education 2 0.03526 0.8574 18 860 0.631723
+Language 3 0.04049 0.6553 27 1293 0.911225
+Ethnicity 8 0.15874 0.9807 72 3488 0.524493
+Income 18 0.37955 1.0689 162 3933 0.264814
+Religion 4 0.10146 1.2492 36 1728 0.148400
+trust.in.science_7 1 0.21604 13.1355 9 429 < 2.2e-16 ***
+need_for_cognition 1 0.05685 2.8732 9 429 0.002644 **
+interpersonal.trust_1 1 0.04369 2.1778 9 429 0.022599 *
+complexity:as.factor(isCovidData) 2 0.03055 0.7410 18 860 0.769808
+complexity:chartType 2 0.07113 1.7620 18 860 0.025638 *
+as.factor(isCovidData):chartType 1 0.01981 0.9633 9 429 0.469901
+complexity:as.factor(isCovidData):chartType 2 0.02939 0.7127 18 860 0.800338
+Residuals 437
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1eta_squared(model)# Effect Size for ANOVA (Type I)
+
+Parameter | Eta2 (partial) | 95% CI
+---------------------------------------------------------------------------
+complexity | 0.08 | [0.04, 1.00]
+as.factor(isCovidData) | 0.15 | [0.09, 1.00]
+chartType | 0.03 | [0.00, 1.00]
+Age | 0.04 | [0.00, 1.00]
+Gender | 0.02 | [0.00, 1.00]
+State_1 | 0.11 | [0.12, 1.00]
+Education | 0.03 | [0.00, 1.00]
+Parents_education | 0.02 | [0.00, 1.00]
+Language | 0.01 | [0.00, 1.00]
+Ethnicity | 0.02 | [0.00, 1.00]
+Income | 0.04 | [0.00, 1.00]
+Religion | 0.03 | [0.00, 1.00]
+trust.in.science_7 | 0.22 | [0.15, 1.00]
+need_for_cognition | 0.06 | [0.01, 1.00]
+interpersonal.trust_1 | 0.04 | [0.00, 1.00]
+complexity:as.factor(isCovidData) | 0.02 | [0.00, 1.00]
+complexity:chartType | 0.04 | [0.00, 1.00]
+as.factor(isCovidData):chartType | 0.02 | [0.00, 1.00]
+complexity:as.factor(isCovidData):chartType | 0.01 | [0.00, 1.00]
+
+- One-sided CIs: upper bound fixed at [1.00].summary.aov(model) Response vis.trust_6 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 4.45 2.226 1.7237 0.179624
+as.factor(isCovidData) 1 2.74 2.736 2.1181 0.146282
+chartType 1 0.02 0.020 0.0153 0.901530
+Age 1 9.31 9.308 7.2072 0.007537 **
+Gender 3 7.63 2.544 1.9699 0.117759
+State_1 46 127.87 2.780 2.1523 4.266e-05 ***
+Education 9 33.27 3.697 2.8621 0.002729 **
+Parents_education 2 1.57 0.787 0.6095 0.544099
+Language 3 3.04 1.013 0.7841 0.503252
+Ethnicity 8 16.99 2.124 1.6446 0.110101
+Income 18 29.11 1.617 1.2521 0.215752
+Religion 4 14.44 3.610 2.7951 0.025844 *
+trust.in.science_7 1 138.52 138.515 107.2476 < 2.2e-16 ***
+need_for_cognition 1 6.54 6.541 5.0644 0.024919 *
+interpersonal.trust_1 1 6.90 6.900 5.3428 0.021273 *
+complexity:as.factor(isCovidData) 2 3.13 1.564 1.2108 0.298943
+complexity:chartType 2 3.36 1.680 1.3006 0.273429
+as.factor(isCovidData):chartType 1 0.64 0.639 0.4945 0.482305
+complexity:as.factor(isCovidData):chartType 2 0.35 0.175 0.1353 0.873503
+Residuals 437 564.41 1.292
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response vis.trust_1 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 3.28 1.639 0.9045 0.405508
+as.factor(isCovidData) 1 6.62 6.616 3.6503 0.056715 .
+chartType 1 0.43 0.425 0.2346 0.628354
+Age 1 7.62 7.623 4.2062 0.040873 *
+Gender 3 3.43 1.142 0.6302 0.595862
+State_1 46 124.61 2.709 1.4946 0.023238 *
+Education 9 4.70 0.522 0.2879 0.977939
+Parents_education 2 3.90 1.949 1.0755 0.342034
+Language 3 4.53 1.509 0.8327 0.476399
+Ethnicity 8 15.84 1.980 1.0922 0.367304
+Income 18 26.77 1.487 0.8206 0.676327
+Religion 4 5.83 1.458 0.8047 0.522603
+trust.in.science_7 1 54.39 54.386 30.0075 7.279e-08 ***
+need_for_cognition 1 15.99 15.991 8.8228 0.003139 **
+interpersonal.trust_1 1 6.91 6.906 3.8107 0.051566 .
+complexity:as.factor(isCovidData) 2 1.32 0.658 0.3632 0.695639
+complexity:chartType 2 4.70 2.348 1.2955 0.274821
+as.factor(isCovidData):chartType 1 0.11 0.106 0.0587 0.808737
+complexity:as.factor(isCovidData):chartType 2 0.39 0.195 0.1075 0.898074
+Residuals 437 792.03 1.812
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response vis.trust_2 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 54.96 27.478 17.4340 5.193e-08 ***
+as.factor(isCovidData) 1 2.11 2.114 1.3412 0.2474470
+chartType 1 8.65 8.647 5.4865 0.0196120 *
+Age 1 0.31 0.309 0.1960 0.6581567
+Gender 3 10.41 3.469 2.2010 0.0872807 .
+State_1 46 87.56 1.903 1.2077 0.1734564
+Education 9 10.71 1.190 0.7548 0.6584856
+Parents_education 2 0.73 0.367 0.2330 0.7922448
+Language 3 1.22 0.405 0.2571 0.8562876
+Ethnicity 8 25.97 3.246 2.0597 0.0384731 *
+Income 18 27.58 1.532 0.9720 0.4912949
+Religion 4 9.84 2.459 1.5603 0.1839096
+trust.in.science_7 1 20.69 20.688 13.1259 0.0003255 ***
+need_for_cognition 1 32.78 32.777 20.7958 6.644e-06 ***
+interpersonal.trust_1 1 4.47 4.473 2.8381 0.0927655 .
+complexity:as.factor(isCovidData) 2 5.09 2.544 1.6139 0.2002891
+complexity:chartType 2 0.40 0.202 0.1283 0.8796074
+as.factor(isCovidData):chartType 1 0.19 0.186 0.1178 0.7315929
+complexity:as.factor(isCovidData):chartType 2 0.74 0.372 0.2358 0.7900198
+Residuals 437 688.77 1.576
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response vis.trust_3 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 10.44 5.219 2.7523 0.06489 .
+as.factor(isCovidData) 1 0.28 0.278 0.1467 0.70189
+chartType 1 8.54 8.544 4.5060 0.03434 *
+Age 1 3.53 3.526 1.8598 0.17335
+Gender 3 10.05 3.351 1.7676 0.15262
+State_1 46 115.10 2.502 1.3196 0.08530 .
+Education 9 29.25 3.250 1.7138 0.08355 .
+Parents_education 2 4.41 2.207 1.1641 0.31318
+Language 3 1.70 0.565 0.2981 0.82675
+Ethnicity 8 19.02 2.377 1.2536 0.26619
+Income 18 35.88 1.993 1.0513 0.40035
+Religion 4 8.49 2.123 1.1195 0.34667
+trust.in.science_7 1 36.58 36.583 19.2940 1.408e-05 ***
+need_for_cognition 1 30.24 30.235 15.9461 7.645e-05 ***
+interpersonal.trust_1 1 8.63 8.626 4.5495 0.03349 *
+complexity:as.factor(isCovidData) 2 3.41 1.707 0.9002 0.40724
+complexity:chartType 2 2.45 1.223 0.6448 0.52529
+as.factor(isCovidData):chartType 1 0.04 0.037 0.0196 0.88881
+complexity:as.factor(isCovidData):chartType 2 2.34 1.170 0.6169 0.54010
+Residuals 437 828.60 1.896
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response vis.trust_4 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 19.58 9.789 3.1170 0.0452781 *
+as.factor(isCovidData) 1 71.17 71.173 22.6624 2.63e-06 ***
+chartType 1 1.12 1.116 0.3553 0.5514583
+Age 1 2.94 2.942 0.9366 0.3336768
+Gender 3 4.23 1.410 0.4489 0.7182037
+State_1 46 155.61 3.383 1.0771 0.3442044
+Education 9 33.36 3.706 1.1801 0.3057658
+Parents_education 2 6.18 3.089 0.9836 0.3747885
+Language 3 0.21 0.069 0.0221 0.9955472
+Ethnicity 8 38.07 4.759 1.5154 0.1494293
+Income 18 48.03 2.668 0.8497 0.6408697
+Religion 4 32.60 8.151 2.5954 0.0359118 *
+trust.in.science_7 1 41.77 41.769 13.2997 0.0002974 ***
+need_for_cognition 1 20.90 20.903 6.6557 0.0102096 *
+interpersonal.trust_1 1 2.26 2.263 0.7204 0.3964732
+complexity:as.factor(isCovidData) 2 3.99 1.995 0.6351 0.5303587
+complexity:chartType 2 6.77 3.384 1.0776 0.3413175
+as.factor(isCovidData):chartType 1 8.37 8.372 2.6657 0.1032552
+complexity:as.factor(isCovidData):chartType 2 5.15 2.575 0.8199 0.4411534
+Residuals 437 1372.43 3.141
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response vis.trust_5 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 30.85 15.426 4.7810 0.008831 **
+as.factor(isCovidData) 1 126.78 126.778 39.2920 8.764e-10 ***
+chartType 1 3.35 3.355 1.0398 0.308433
+Age 1 1.64 1.640 0.5083 0.476253
+Gender 3 7.04 2.348 0.7276 0.535912
+State_1 46 156.29 3.398 1.0530 0.383542
+Education 9 30.49 3.388 1.0500 0.399095
+Parents_education 2 16.09 8.045 2.4933 0.083816 .
+Language 3 8.58 2.860 0.8865 0.448019
+Ethnicity 8 21.68 2.710 0.8399 0.567804
+Income 18 35.62 1.979 0.6133 0.890062
+Religion 4 7.99 1.997 0.6190 0.649177
+trust.in.science_7 1 29.72 29.719 9.2107 0.002550 **
+need_for_cognition 1 11.07 11.068 3.4304 0.064683 .
+interpersonal.trust_1 1 4.82 4.818 1.4932 0.222381
+complexity:as.factor(isCovidData) 2 2.99 1.494 0.4630 0.629724
+complexity:chartType 2 5.86 2.931 0.9083 0.403955
+as.factor(isCovidData):chartType 1 0.23 0.227 0.0704 0.790890
+complexity:as.factor(isCovidData):chartType 2 12.02 6.010 1.8626 0.156499
+Residuals 437 1410.00 3.227
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response affect.science_1 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 600 300.1 0.9126 0.402250
+as.factor(isCovidData) 1 441 441.5 1.3426 0.247210
+chartType 1 486 486.3 1.4790 0.224584
+Age 1 8 8.2 0.0249 0.874616
+Gender 3 280 93.4 0.2841 0.836919
+State_1 46 14891 323.7 0.9845 0.504662
+Education 9 3868 429.7 1.3069 0.230805
+Parents_education 2 87 43.5 0.1322 0.876181
+Language 3 408 136.0 0.4137 0.743230
+Ethnicity 8 2402 300.2 0.9130 0.505341
+Income 18 13622 756.8 2.3016 0.001901 **
+Religion 4 3059 764.7 2.3256 0.055699 .
+trust.in.science_7 1 7677 7676.8 23.3469 1.875e-06 ***
+need_for_cognition 1 137 136.7 0.4157 0.519448
+interpersonal.trust_1 1 52 51.7 0.1571 0.692014
+complexity:as.factor(isCovidData) 2 271 135.3 0.4113 0.663016
+complexity:chartType 2 313 156.6 0.4762 0.621483
+as.factor(isCovidData):chartType 1 409 408.9 1.2436 0.265382
+complexity:as.factor(isCovidData):chartType 2 870 434.9 1.3225 0.267525
+Residuals 437 143691 328.8
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response affect.clarity_1 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 11686 5842.8 8.5265 0.000233 ***
+as.factor(isCovidData) 1 1143 1142.7 1.6676 0.197264
+chartType 1 2808 2808.0 4.0977 0.043549 *
+Age 1 560 560.2 0.8175 0.366424
+Gender 3 545 181.5 0.2649 0.850670
+State_1 46 34327 746.2 1.0890 0.325669
+Education 9 7375 819.4 1.1958 0.295633
+Parents_education 2 1404 702.2 1.0247 0.359759
+Language 3 1190 396.8 0.5791 0.629017
+Ethnicity 8 3973 496.6 0.7248 0.669695
+Income 18 11055 614.2 0.8963 0.583430
+Religion 4 531 132.7 0.1936 0.941689
+trust.in.science_7 1 1535 1535.2 2.2404 0.135170
+need_for_cognition 1 5023 5023.0 7.3301 0.007047 **
+interpersonal.trust_1 1 8 7.6 0.0111 0.916232
+complexity:as.factor(isCovidData) 2 272 136.1 0.1987 0.819888
+complexity:chartType 2 2974 1487.0 2.1700 0.115405
+as.factor(isCovidData):chartType 1 463 463.0 0.6756 0.411541
+complexity:as.factor(isCovidData):chartType 2 2726 1363.1 1.9892 0.138035
+Residuals 437 299454 685.2
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response affect.aesthetic_1 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 283 141.6 0.2689 0.76437
+as.factor(isCovidData) 1 144 143.7 0.2727 0.60178
+chartType 1 222 221.8 0.4211 0.51672
+Age 1 2106 2105.7 3.9970 0.04620 *
+Gender 3 3179 1059.8 2.0117 0.11158
+State_1 46 18038 392.1 0.7443 0.89121
+Education 9 3237 359.7 0.6827 0.72480
+Parents_education 2 1766 882.9 1.6760 0.18833
+Language 3 263 87.7 0.1665 0.91896
+Ethnicity 8 3551 443.9 0.8426 0.56546
+Income 18 4902 272.3 0.5170 0.95040
+Religion 4 1972 493.0 0.9358 0.44295
+trust.in.science_7 1 3167 3167.1 6.0117 0.01460 *
+need_for_cognition 1 2194 2194.0 4.1647 0.04188 *
+interpersonal.trust_1 1 2577 2577.2 4.8921 0.02750 *
+complexity:as.factor(isCovidData) 2 645 322.6 0.6123 0.54256
+complexity:chartType 2 2044 1022.2 1.9404 0.14488
+as.factor(isCovidData):chartType 1 1504 1504.5 2.8558 0.09176 .
+complexity:as.factor(isCovidData):chartType 2 50 25.1 0.0476 0.95355
+Residuals 437 230219 526.8
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+3 observations deleted due to missingness
+model<- manova( cbind(data.trust_6,data.trust_1,data.trust_2,data.trust_3,data.trust_4,data.trust_5) ~ 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)
+
+anova(model)Analysis of Variance Table
+
+ Df Pillai approx F num Df den Df Pr(>F)
+(Intercept) 1 0.97922 3392.6 6 432 < 2.2e-16 ***
+complexity 2 0.06719 2.5 12 866 0.003029 **
+as.factor(isCovidData) 1 0.12758 10.5 6 432 6.446e-11 ***
+chartType 1 0.01608 1.2 6 432 0.317789
+Age 1 0.03575 2.7 6 432 0.014868 *
+Gender 3 0.06592 1.6 18 1302 0.047127 *
+State_1 46 0.69976 1.3 276 2622 0.004228 **
+Education 9 0.15077 1.3 54 2622 0.104194
+Parents_education 2 0.02989 1.1 12 866 0.360602
+Language 3 0.05927 1.5 18 1302 0.096618 .
+Ethnicity 8 0.09311 0.9 48 2622 0.739601
+Income 18 0.21734 0.9 108 2622 0.728266
+Religion 4 0.06342 1.2 24 1740 0.260746
+trust.in.science_7 1 0.21058 19.2 6 432 < 2.2e-16 ***
+need_for_cognition 1 0.04322 3.3 6 432 0.003876 **
+interpersonal.trust_1 1 0.03096 2.3 6 432 0.033816 *
+complexity:as.factor(isCovidData) 2 0.02812 1.0 12 866 0.419272
+complexity:chartType 2 0.03333 1.2 12 866 0.261815
+as.factor(isCovidData):chartType 1 0.02040 1.5 6 432 0.176782
+complexity:as.factor(isCovidData):chartType 2 0.01151 0.4 12 866 0.957108
+Residuals 437
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1eta_squared(model)# Effect Size for ANOVA (Type I)
+
+Parameter | Eta2 (partial) | 95% CI
+---------------------------------------------------------------------------
+complexity | 0.03 | [0.01, 1.00]
+as.factor(isCovidData) | 0.13 | [0.07, 1.00]
+chartType | 0.02 | [0.00, 1.00]
+Age | 0.04 | [0.00, 1.00]
+Gender | 0.02 | [0.00, 1.00]
+State_1 | 0.12 | [0.13, 1.00]
+Education | 0.03 | [0.00, 1.00]
+Parents_education | 0.01 | [0.00, 1.00]
+Language | 0.02 | [0.00, 1.00]
+Ethnicity | 0.02 | [0.00, 1.00]
+Income | 0.04 | [0.00, 1.00]
+Religion | 0.02 | [0.00, 1.00]
+trust.in.science_7 | 0.21 | [0.15, 1.00]
+need_for_cognition | 0.04 | [0.01, 1.00]
+interpersonal.trust_1 | 0.03 | [0.00, 1.00]
+complexity:as.factor(isCovidData) | 0.01 | [0.00, 1.00]
+complexity:chartType | 0.02 | [0.00, 1.00]
+as.factor(isCovidData):chartType | 0.02 | [0.00, 1.00]
+complexity:as.factor(isCovidData):chartType | 5.75e-03 | [0.00, 1.00]
+
+- One-sided CIs: upper bound fixed at [1.00].summary.aov(model) Response data.trust_6 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 5.73 2.864 2.0456 0.1305443
+as.factor(isCovidData) 1 1.59 1.589 1.1346 0.2873781
+chartType 1 0.01 0.005 0.0039 0.9504631
+Age 1 12.15 12.148 8.6757 0.0033971 **
+Gender 3 12.04 4.012 2.8656 0.0363516 *
+State_1 46 154.41 3.357 2.3973 2.857e-06 ***
+Education 9 37.77 4.197 2.9975 0.0017662 **
+Parents_education 2 3.49 1.743 1.2450 0.2889520
+Language 3 2.88 0.960 0.6855 0.5612446
+Ethnicity 8 14.32 1.790 1.2783 0.2527983
+Income 18 37.39 2.077 1.4836 0.0911636 .
+Religion 4 27.36 6.841 4.8855 0.0007307 ***
+trust.in.science_7 1 152.44 152.445 108.8744 < 2.2e-16 ***
+need_for_cognition 1 4.99 4.994 3.5665 0.0596190 .
+interpersonal.trust_1 1 13.33 13.333 9.5226 0.0021586 **
+complexity:as.factor(isCovidData) 2 0.43 0.215 0.1536 0.8576451
+complexity:chartType 2 3.03 1.514 1.0816 0.3399609
+as.factor(isCovidData):chartType 1 9.40 9.401 6.7144 0.0098839 **
+complexity:as.factor(isCovidData):chartType 2 2.71 1.356 0.9683 0.3805454
+Residuals 437 611.88 1.400
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response data.trust_1 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 1.39 0.696 0.5744 0.563471
+as.factor(isCovidData) 1 0.41 0.408 0.3369 0.561918
+chartType 1 1.31 1.307 1.0789 0.299510
+Age 1 7.46 7.461 6.1609 0.013435 *
+Gender 3 8.36 2.786 2.3007 0.076631 .
+State_1 46 95.69 2.080 1.7179 0.003367 **
+Education 9 18.83 2.092 1.7279 0.080452 .
+Parents_education 2 3.89 1.945 1.6061 0.201859
+Language 3 0.96 0.322 0.2656 0.850176
+Ethnicity 8 7.95 0.994 0.8209 0.584405
+Income 18 34.06 1.892 1.5624 0.065976 .
+Religion 4 19.70 4.925 4.0669 0.003008 **
+trust.in.science_7 1 103.53 103.532 85.4937 < 2.2e-16 ***
+need_for_cognition 1 0.33 0.326 0.2689 0.604363
+interpersonal.trust_1 1 9.93 9.927 8.1974 0.004397 **
+complexity:as.factor(isCovidData) 2 2.28 1.138 0.9399 0.391443
+complexity:chartType 2 4.55 2.276 1.8793 0.153935
+as.factor(isCovidData):chartType 1 1.80 1.797 1.4843 0.223763
+complexity:as.factor(isCovidData):chartType 2 2.21 1.104 0.9120 0.402474
+Residuals 437 529.20 1.211
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response data.trust_2 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 16.74 8.371 4.2992 0.014159 *
+as.factor(isCovidData) 1 6.62 6.617 3.3984 0.065935 .
+chartType 1 1.48 1.482 0.7611 0.383473
+Age 1 16.87 16.869 8.6633 0.003420 **
+Gender 3 8.02 2.673 1.3729 0.250454
+State_1 46 154.03 3.348 1.7196 0.003313 **
+Education 9 16.88 1.876 0.9634 0.469804
+Parents_education 2 9.40 4.698 2.4128 0.090758 .
+Language 3 4.08 1.358 0.6976 0.553888
+Ethnicity 8 10.19 1.274 0.6544 0.731677
+Income 18 46.17 2.565 1.3172 0.171826
+Religion 4 13.27 3.318 1.7041 0.148030
+trust.in.science_7 1 74.25 74.248 38.1304 1.514e-09 ***
+need_for_cognition 1 0.02 0.023 0.0118 0.913518
+interpersonal.trust_1 1 0.47 0.475 0.2437 0.621791
+complexity:as.factor(isCovidData) 2 10.89 5.446 2.7970 0.062082 .
+complexity:chartType 2 4.19 2.097 1.0767 0.341618
+as.factor(isCovidData):chartType 1 1.85 1.848 0.9493 0.330438
+complexity:as.factor(isCovidData):chartType 2 0.87 0.434 0.2231 0.800152
+Residuals 437 850.93 1.947
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response data.trust_3 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 7.23 3.616 2.0203 0.133847
+as.factor(isCovidData) 1 3.37 3.375 1.8857 0.170394
+chartType 1 1.42 1.417 0.7916 0.374093
+Age 1 2.48 2.481 1.3863 0.239668
+Gender 3 22.48 7.493 4.1866 0.006140 **
+State_1 46 132.72 2.885 1.6122 0.008686 **
+Education 9 28.11 3.124 1.7454 0.076748 .
+Parents_education 2 0.75 0.375 0.2097 0.810887
+Language 3 9.42 3.138 1.7537 0.155335
+Ethnicity 8 7.68 0.960 0.5366 0.828963
+Income 18 22.42 1.245 0.6959 0.816170
+Religion 4 17.68 4.421 2.4702 0.044065 *
+trust.in.science_7 1 112.21 112.213 62.7007 2.007e-14 ***
+need_for_cognition 1 0.04 0.038 0.0214 0.883870
+interpersonal.trust_1 1 6.25 6.247 3.4904 0.062396 .
+complexity:as.factor(isCovidData) 2 2.98 1.490 0.8327 0.435555
+complexity:chartType 2 3.35 1.674 0.9354 0.393205
+as.factor(isCovidData):chartType 1 7.22 7.217 4.0327 0.045241 *
+complexity:as.factor(isCovidData):chartType 2 0.08 0.040 0.0226 0.977657
+Residuals 437 782.08 1.790
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response data.trust_4 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 15.85 7.927 5.7999 0.003266 **
+as.factor(isCovidData) 1 37.97 37.971 27.7817 2.138e-07 ***
+chartType 1 3.12 3.122 2.2844 0.131404
+Age 1 5.53 5.529 4.0452 0.044910 *
+Gender 3 12.80 4.267 3.1219 0.025823 *
+State_1 46 102.15 2.221 1.6248 0.007781 **
+Education 9 10.74 1.193 0.8729 0.549473
+Parents_education 2 3.02 1.508 1.1030 0.332792
+Language 3 2.31 0.769 0.5630 0.639686
+Ethnicity 8 10.37 1.296 0.9480 0.476545
+Income 18 25.25 1.403 1.0262 0.428231
+Religion 4 1.49 0.372 0.2720 0.896022
+trust.in.science_7 1 14.18 14.176 10.3719 0.001375 **
+need_for_cognition 1 16.49 16.485 12.0615 0.000566 ***
+interpersonal.trust_1 1 2.60 2.605 1.9058 0.168138
+complexity:as.factor(isCovidData) 2 0.22 0.111 0.0813 0.921956
+complexity:chartType 2 2.63 1.316 0.9630 0.382562
+as.factor(isCovidData):chartType 1 0.39 0.394 0.2882 0.591620
+complexity:as.factor(isCovidData):chartType 2 2.09 1.046 0.7652 0.465879
+Residuals 437 597.28 1.367
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+ Response data.trust_5 :
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 12.30 6.1489 3.4900 0.031354 *
+as.factor(isCovidData) 1 3.62 3.6229 2.0563 0.152295
+chartType 1 0.28 0.2761 0.1567 0.692390
+Age 1 0.00 0.0030 0.0017 0.966865
+Gender 3 11.85 3.9512 2.2426 0.082671 .
+State_1 46 113.15 2.4599 1.3962 0.049583 *
+Education 9 19.92 2.2133 1.2562 0.258877
+Parents_education 2 0.46 0.2289 0.1299 0.878209
+Language 3 3.73 1.2417 0.7048 0.549556
+Ethnicity 8 17.49 2.1860 1.2407 0.273403
+Income 18 48.06 2.6702 1.5155 0.080086 .
+Religion 4 4.06 1.0155 0.5764 0.679915
+trust.in.science_7 1 22.33 22.3339 12.6763 0.000411 ***
+need_for_cognition 1 6.99 6.9884 3.9665 0.047037 *
+interpersonal.trust_1 1 6.27 6.2669 3.5570 0.059958 .
+complexity:as.factor(isCovidData) 2 3.30 1.6514 0.9373 0.392477
+complexity:chartType 2 1.07 0.5358 0.3041 0.737947
+as.factor(isCovidData):chartType 1 0.07 0.0671 0.0381 0.845357
+complexity:as.factor(isCovidData):chartType 2 1.21 0.6071 0.3446 0.708723
+Residuals 437 769.93 1.7619
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+3 observations deleted due to missingness# post-hoc tests
+aov(vis.trust_6 ~ complexity * as.factor(isCovidData), data = results) %>% tukey_hsd()
+# effect sizes
+eta_squared(aov(vis.trust_6 ~ complexity * as.factor(isCovidData) * chartType +
+ + Age + as.factor(Gender) + State_1 + as.factor(Education) + as.factor(Parents_education) + as.factor(Language) + as.factor(Ethnicity) + as.factor(Income) + as.factor(Religion) + trust.in.science_7 + need_for_cognition +
+ interpersonal.trust_1,
+ data = results))
+
+Colinearity of trust in vis and trust in data
+ + + +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
+ + + +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)
+
+Trust in science, need for cognition, and interpersonal trust on +trust in Vis
+ + + +results %>%
+ gather(key = variables, value = values,
+ trust.in.science_7, need_for_cognition, interpersonal.trust_1) %>%
+ ggplot(aes(x = values, y = vis.trust_6, color = variables)) +
+
+ facet_grid(cols = vars(variables)) +
+ geom_jitter() +
+ geom_smooth(color = "black") +
+ geom_blank() +
+ theme_minimal() +
+ theme(panel.spacing = unit(2, "lines"))Trust in Data
+ + + +results %>%
+ ggplot(aes(x = data.trust_6, y = isCovidData, colour = as.factor(isCovidData))) +
+ geom_jitter(data = results, width = 0.5) +
+ # labs(title = "Trust in data") +
+ geom_vline(data = results %>%
+ group_by(complexity, isCovidData, chartType) %>%
+ summarize(n = n(),
+ data.trust_6 = mean(data.trust_6)),
+ aes(xintercept = data.trust_6, colour = as.factor(isCovidData))) +
+ facet_grid(rows = vars(complexity), cols = vars(chartType)) +
+ theme_minimal()
+
+results %>%
+ group_by(complexity, isCovidData) %>%
+ summarize(n = n(),
+ mean = mean(data.trust_6),
+ se = sd(data.trust_6)/sqrt(n),
+ n = n)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%>% filter(isCovidData == 0))
+anova(model)
+# post-hoc tests
+aov(data.trust_6 ~ complexity * as.factor(isCovidData), data = results) %>% tukey_hsd()
+aov(data.trust_6 ~ chartType * as.factor(isCovidData), data = results) %>% tukey_hsd()
+# effect sizes
+eta_squared(aov(data.trust_6 ~ complexity * as.factor(isCovidData) * chartType +
+ Age + as.factor(Gender) + State_1 + as.factor(Education) + as.factor(Parents_education) + as.factor(Language) +
+ as.factor(Ethnicity) + as.factor(Income) + as.factor(Religion) + trust.in.science_7 + need_for_cognition +
+ interpersonal.trust_1,
+ data = results))
+emmeans(aov(data.trust_6 ~ complexity , data = results) , ~ complexity)Trust in science, need for cognition, and interpersonal trust on +trust in Data
+ + + +results %>%
+ gather(key = variables, value = values,
+ trust.in.science_7, need_for_cognition, interpersonal.trust_1) %>%
+ ggplot(aes(x = values, y = data.trust_6, color = variables)) +
+
+ facet_grid(cols = vars(variables)) +
+ geom_jitter() +
+ geom_smooth(color = "black") +
+ geom_blank() +
+ theme_minimal() +
+ theme(panel.spacing = unit(2, "lines"))results_long_data <- results %>%
+ select(data.trust_1, data.trust_2, data.trust_3,
+ data.trust_4, data.trust_5, data.trust_6,
+ ResponseId, complexity,
+ vlat_simple, vlat_moderate, vlat_complex) %>%
+ gather(key = trustItemData, value = trustRatingData,
+ data.trust_1, data.trust_2, data.trust_3, data.trust_4, data.trust_5, data.trust_6)
+
+results_long_data %>%
+ ggplot(aes(x = trustRatingData, y = 1, color = complexity)) +
+ geom_jitter() +
+ ylim(0, 2) +
+ labs(title = "Trust in data (All)") +
+ geom_vline(data = results_long_data %>%
+ group_by(complexity, trustItemData) %>%
+ summarize(n = n(),
+ average = mean(trustRatingData)),
+ aes(xintercept = average, color = complexity)) +
+ facet_grid(vars(trustItemData)) +
+ theme_minimal()
+
+Relationship between trust in data and vis, across complexity
+ + + +results_long_data <- results %>%
+ select(data.trust_1, data.trust_2, data.trust_3,
+ data.trust_4, data.trust_5, data.trust_6,
+ ResponseId, complexity,
+ chartType, isCovidData,
+ vlat_simple, vlat_moderate, vlat_complex) %>%
+ gather(key = trustItemData, value = trustRatingData,
+ # data.trust_1, data.trust_2, data.trust_3, data.trust_4, data.trust_5,
+ data.trust_6)
+
+results_long_vis <- results %>%
+ gather(key = trustItemVis, value = trustRatingVis,
+ # vis.trust_1, vis.trust_2, vis.trust_3, vis.trust_4, vis.trust_5,
+ vis.trust_6)
+
+results_long_all <- merge(results_long_vis, results_long_data,
+ by = c("ResponseId", "complexity", "chartType", "isCovidData"))
+
+model<- glm(trustRatingVis ~ trustRatingData * complexity +
+ trustRatingData * chartType +
+ trustRatingData * as.factor(isCovidData),
+ data = results_long_all)
+Anova(model)results_long_all %>%
+ # filter(trustItemVis == "bar.vis_2") %>%
+ ggplot(aes(x = trustRatingVis, y = trustRatingData, color = as.factor(isCovidData))) +
+ geom_jitter(alpha = 0.25) +
+ stat_smooth(method = "lm",
+ formula = y ~ x,
+ geom = "smooth", color = "black") +
+ labs(title = "Relationship bewteen trust in Vis and Data") +
+ facet_grid(rows = vars(chartType), cols = vars(complexity)) +
+ theme_minimal()
+
+Do the trust items predict trust?
+ + + +model <- lm(formula = vis.trust_6 ~ vis.trust_1 +
+ vis.trust_2 +
+ vis.trust_3 +
+ vis.trust_4 +
+ vis.trust_5 +
+ affect.science_1 +
+ affect.clarity_1 +
+ affect.aesthetic_1 +
+ Age + Gender + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+summary(model)model <- lm(formula = data.trust_6 ~ data.trust_1 +
+ data.trust_2 +
+ data.trust_3 +
+ data.trust_4 +
+ data.trust_5 +
+ Age + Gender + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+summary(model)
+
+Which trust measurements predict behavioral outcomes?
+ + + +model <- lm(formula = vis.trust_4 ~ vis.trust_1 +
+ vis.trust_2 +
+ vis.trust_3 +
+ vis.trust_6 +
+ affect.science_1 +
+ affect.clarity_1 +
+ affect.aesthetic_1 +
+ Age + Gender + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+summary(model)model <- lm(formula = vis.trust_5 ~ vis.trust_1 +
+ vis.trust_2 +
+ vis.trust_3 +
+ vis.trust_6 +
+ affect.science_1 +
+ affect.clarity_1 +
+ affect.aesthetic_1 +
+ Age + Gender + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+summary(model)
+Call:
+lm(formula = vis.trust_5 ~ vis.trust_1 + vis.trust_2 + vis.trust_3 +
+ vis.trust_6 + affect.science_1 + affect.clarity_1 + affect.aesthetic_1 +
+ Age + Gender + State_1 + Education + Parents_education +
+ Language + Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1, data = results)
+
+Residuals:
+ Min 1Q Median 3Q Max
+-3.6137 -1.0005 0.1875 1.0599 3.7954
+
+Coefficients: (1 not defined because of singularities)
+ Estimate Std. Error t value Pr(>|t|)
+(Intercept) 6.8983258 11.1541131 0.618 0.5366
+vis.trust_1 0.1654944 0.0723770 2.287 0.0227 *
+vis.trust_2 -0.0700186 0.0895431 -0.782 0.4347
+vis.trust_3 0.5055452 0.0877894 5.759 1.59e-08 ***
+vis.trust_6 0.1940812 0.0877665 2.211 0.0275 *
+affect.science_1 -0.0040897 0.0047826 -0.855 0.3929
+affect.clarity_1 0.0049858 0.0036282 1.374 0.1701
+affect.aesthetic_1 -0.0061383 0.0037793 -1.624 0.1050
+Age -0.0047859 0.0054796 -0.873 0.3829
+Gender2 -0.0002455 0.1585950 -0.002 0.9988
+Gender3 -0.8574544 0.7533807 -1.138 0.2557
+Gender5 -0.0676304 2.5614059 -0.026 0.9789
+State_1Alaska 0.9130358 3.2211060 0.283 0.7770
+State_1Arizona 0.0796834 0.7122489 0.112 0.9110
+State_1Arkansas 0.6925107 0.9857672 0.703 0.4827
+State_1California 0.2323355 0.4981027 0.466 0.6411
+State_1Colorado 0.5483217 0.6982153 0.785 0.4327
+State_1Connecticut -0.2859602 0.8865255 -0.323 0.7472
+State_1Delaware 1.3516004 1.2833472 1.053 0.2928
+State_1Florida 0.0292903 0.5338977 0.055 0.9563
+State_1Georgia 0.6537663 0.5646337 1.158 0.2475
+State_1Hawaii 0.4632148 1.1156216 0.415 0.6782
+State_1Illinois 0.4834355 0.5964777 0.810 0.4181
+State_1Indiana 0.0100316 0.7392443 0.014 0.9892
+State_1Iowa 1.5602400 0.9856159 1.583 0.1141
+State_1Kansas 0.8557160 0.7804126 1.096 0.2735
+State_1Kentucky 0.1959218 0.7272128 0.269 0.7877
+State_1Louisiana 0.3960415 0.8282551 0.478 0.6328
+State_1Maine -0.1060877 1.0852291 -0.098 0.9222
+State_1Maryland 0.8736085 0.6371119 1.371 0.1710
+State_1Massachusetts 0.3204814 0.6525378 0.491 0.6236
+State_1Michigan 0.0112196 0.6136382 0.018 0.9854
+State_1Minnesota 0.0044142 0.8862272 0.005 0.9960
+State_1Mississippi 0.7426988 0.9694226 0.766 0.4440
+State_1Missouri 0.7406180 0.7239769 1.023 0.3069
+State_1Montana -0.0802522 1.3122480 -0.061 0.9513
+State_1Nebraska 0.9540497 1.0855424 0.879 0.3800
+State_1Nevada 1.3905433 0.9460415 1.470 0.1423
+State_1New Hampshire 0.1348267 1.7640649 0.076 0.9391
+State_1New Jersey 1.4178615 0.7577706 1.871 0.0620 .
+State_1New York 0.2218299 0.5525909 0.401 0.6883
+State_1North Carolina 1.0624815 0.5886474 1.805 0.0718 .
+State_1Ohio -0.9995362 0.6267236 -1.595 0.1115
+State_1Oklahoma 0.4119686 0.8445519 0.488 0.6259
+State_1Oregon 0.7491734 0.7610634 0.984 0.3255
+State_1Pennsylvania 0.2677601 0.5497123 0.487 0.6264
+State_1Rhode Island 0.8835446 1.0897997 0.811 0.4180
+State_1South Carolina 0.1481524 0.8881443 0.167 0.8676
+State_1South Dakota 0.1598751 1.7995366 0.089 0.9292
+State_1Tennessee 0.8371711 0.6700815 1.249 0.2122
+State_1Texas 0.4906562 0.5312451 0.924 0.3562
+State_1Utah -0.1454604 0.8292123 -0.175 0.8608
+State_1Vermont 1.0352761 1.2922610 0.801 0.4235
+State_1Virginia 0.4694817 0.7552982 0.622 0.5345
+State_1Washington 0.1183486 0.5971784 0.198 0.8430
+State_1West Virginia -1.6715323 1.0709885 -1.561 0.1193
+State_1Wisconsin 1.0559749 0.6055320 1.744 0.0819 .
+State_1Wyoming 2.9971987 1.7754711 1.688 0.0921 .
+Education2 1.4889138 0.7316559 2.035 0.0424 *
+Education3 1.4182630 0.7104594 1.996 0.0465 *
+Education4 1.2258351 0.7622315 1.608 0.1085
+Education5 1.2073315 0.7706229 1.567 0.1179
+Education6 1.2551205 0.7003148 1.792 0.0738 .
+Education7 0.9924632 0.7251797 1.369 0.1718
+Education8 0.8444186 0.8620245 0.980 0.3278
+Education9 0.4324445 1.1741843 0.368 0.7128
+Education10 1.9737670 1.8700926 1.055 0.2918
+Parents_education2 -0.0090787 0.1936329 -0.047 0.9626
+Parents_education3 0.3070623 0.2177521 1.410 0.1592
+Language2 -0.5711739 0.9824173 -0.581 0.5613
+Language3 0.3583621 0.9504647 0.377 0.7063
+Language4 2.2197398 1.5882656 1.398 0.1629
+Ethnicity2 0.4442625 1.9182422 0.232 0.8170
+Ethnicity3 -0.2833989 1.8843595 -0.150 0.8805
+Ethnicity4 0.4728969 1.9433916 0.243 0.8079
+Ethnicity5 -0.6884299 2.2207802 -0.310 0.7567
+Ethnicity6 1.7937527 2.5277792 0.710 0.4783
+Ethnicity7 -0.1177346 1.8826834 -0.063 0.9502
+Ethnicity8 -0.4296101 1.8964107 -0.227 0.8209
+Ethnicity9 0.0976298 1.8892332 0.052 0.9588
+Ethnicity10 NA NA NA NA
+Income2 0.6168627 0.7924509 0.778 0.4367
+Income3 1.4647738 0.6499642 2.254 0.0247 *
+Income4 1.2214829 0.6225380 1.962 0.0504 .
+Income5 1.1764970 0.6253967 1.881 0.0606 .
+Income6 1.4282342 0.6240546 2.289 0.0226 *
+Income7 1.3419889 0.6277638 2.138 0.0331 *
+Income8 1.5060124 0.6588454 2.286 0.0227 *
+Income9 1.1814688 0.6568584 1.799 0.0728 .
+Income10 1.4512076 0.6619806 2.192 0.0289 *
+Income11 0.9909869 0.6645514 1.491 0.1366
+Income12 1.2522790 0.6845209 1.829 0.0680 .
+Income13 0.8336257 0.7052663 1.182 0.2378
+Income14 1.3716421 0.6573623 2.087 0.0375 *
+Income15 0.6717645 0.8799697 0.763 0.4456
+Income16 1.4445849 0.6902114 2.093 0.0369 *
+Income17 1.1246862 0.6333101 1.776 0.0764 .
+Income18 0.7149495 1.3455013 0.531 0.5954
+Income19 1.6020486 0.7440956 2.153 0.0319 *
+Religion2 -0.1447428 0.3506155 -0.413 0.6799
+Religion3 -0.2389463 0.3452509 -0.692 0.4892
+Religion4 -0.1774818 0.3336779 -0.532 0.5951
+Religion5 -0.0544771 0.7458376 -0.073 0.9418
+trust.in.science_7 0.0111162 0.0439041 0.253 0.8002
+need_for_cognition 0.0484377 0.1325712 0.365 0.7150
+interpersonal.trust_1 -0.1579809 0.0625460 -2.526 0.0119 *
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+Residual standard error: 1.635 on 441 degrees of freedom
+ (3 observations deleted due to missingness)
+Multiple R-squared: 0.3869, Adjusted R-squared: 0.2423
+F-statistic: 2.676 on 104 and 441 DF, p-value: 1.169e-12results %>%
+ ggplot(aes(x = data.trust_1, y = data.trust_6, color = as.factor(isCovidData))) +
+ geom_jitter() +
+ geom_smooth(method="lm") +
+ facet_wrap(~isCovidData) +
+ theme_minimal()How does performance on VLAT questions predict trust?
+ + + +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,
+ data = results)
+anova(model)Analysis of Variance Table
+
+Response: vis.trust_6
+ Df Sum Sq Mean Sq F value Pr(>F)
+assigned_vlat 1 3.03 3.031 2.3361 0.127115
+Age 1 9.80 9.797 7.5518 0.006238 **
+Gender 3 8.98 2.992 2.3066 0.076008 .
+State_1 46 129.02 2.805 2.1619 3.734e-05 ***
+Education 9 31.47 3.496 2.6950 0.004623 **
+Parents_education 2 1.22 0.612 0.4714 0.624447
+Language 3 3.96 1.320 1.0172 0.384779
+Ethnicity 8 17.01 2.126 1.6386 0.111622
+Income 18 28.11 1.562 1.2038 0.253242
+Religion 4 14.82 3.705 2.8560 0.023337 *
+trust.in.science_7 1 133.56 133.562 102.9516 < 2.2e-16 ***
+need_for_cognition 1 6.58 6.576 5.0687 0.024847 *
+interpersonal.trust_1 1 6.82 6.823 5.2591 0.022296 *
+Residuals 447 579.91 1.297
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1emmeans(aov(vis.trust_6 ~ vlat_simple * vlat_complex , data = results) , ~ vlat_simple | vlat_complex)vlat_complex = 0:
+ vlat_simple emmean SE df lower.CL upper.CL
+ 0.77 5.24 0.0956 545 5.05 5.43
+
+vlat_complex = 1:
+ vlat_simple emmean SE df lower.CL upper.CL
+ 0.77 5.47 0.0721 545 5.33 5.62
+
+Confidence level used: 0.95 results %>%
+ gather(key = vlat_level, value = vlat_performance, vlat_simple, vlat_moderate, vlat_complex) %>%
+ ggplot(aes(x = vis.trust_6, y = vlat_performance, colour = as.factor(assigned_vlat))) +
+ geom_jitter(width = 0.5) +
+ # y(0, 2) +
+ # labs(title = "Trust in data") +
+ # geom_vline(data = results %>%
+ # group_by(complexity, isCovidData) %>%
+ # summarize(n = n(),
+ # vis.trust_6 = mean(vis.trust_6)),
+ # aes(xintercept = vis.trust_6, colour = as.factor(isCovidData))) +
+ facet_grid(rows = vars(vlat_level), cols = vars(chartType)) +
+ theme_minimal() +
+ theme(panel.spacing = unit(2, "lines"))
+ # legend.position = "none")Overall distribution of VLAT performance
+Trust in Vis
+ + + +# vlat_long <- results %>%
+# gather(key = vlat_level, value = vlat_performance, vlat_simple, vlat_moderate, vlat_complex)
+#
+# model <- lmer(formula = vlat_performance ~ vlat_level * isCovidData * chartType +
+# Age + Gender + State_1 + Education + Parents_education + Language +
+# Ethnicity + Income + Religion + trust.in.science_7 +
+# need_for_cognition + interpersonal.trust_1 + (1|ResponseId),
+# data = vlat_long)
+# Anova(model)
+#
+# emmeans(aov(vlat_performance ~ vlat_level , data = results) , ~ vlat_simple | vlat_complex)
+#
+# # post-hoc tests
+# aov(vlat_performance ~ vlat_level * as.factor(isCovidData), data = vlat_long) %>% tukey_hsd()results %>%
+ gather(key = vlat_level, value = vlat_performance, vlat_simple, vlat_moderate, vlat_complex) %>%
+ group_by(vlat_level, chartType, isCovidData) %>%
+ summarize(n = n(),
+ mean_vlat_performance = mean(vlat_performance),
+ se = sd(vlat_performance)/sqrt(n),
+ n = n()) %>%
+ ggplot(aes(x = vlat_level, y = mean_vlat_performance,
+ ymax = mean_vlat_performance + se, ymin = mean_vlat_performance - se,
+ colour = vlat_level)) +
+ geom_point() +
+ geom_errorbar() +
+ # labs(title = "Trust in data") +
+ # geom_vline(data = results %>%
+ # group_by(complexity, isCovidData) %>%
+ # summarize(n = n(),
+ # vis.trust_6 = mean(vis.trust_6)),
+ # aes(xintercept = vis.trust_6, colour = as.factor(isCovidData))) +
+ facet_grid(rows = vars(isCovidData)) +
+ theme_minimal() +
+ theme(panel.spacing = unit(2, "lines"))
+ # legend.position = "none")Trust in Data
+ + + +model <- lm(formula = data.trust_6 ~ vlat_simple * vlat_moderate * vlat_complex +
+ Age + Gender + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+anova(model)
+
+emmeans(aov(data.trust_6 ~ vlat_simple * vlat_complex , data = results) , ~ vlat_simple | vlat_complex)
+
+Which antecedent drives the interaction effect for trust in +visualization for covid data?
+ + + +model <- lm(formula = vis.trust_1 ~ complexity
+ + 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))
+anova(model)model <- lm(formula = vis.trust_2 ~ complexity
+ + 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))
+anova(model)model <- lm(formula = vis.trust_3 ~ complexity
+ + 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))
+anova(model)model <- lm(formula = affect.science_1 ~ complexity
+ + 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))
+anova(model)model <- lm(formula = affect.clarity_1 ~ complexity
+ + 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))
+anova(model)model <- lm(formula = affect.aesthetic_1 ~ complexity
+ + 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))
+anova(model)
+
+Which antecedent drives the main effect for trust in data?
+ + + +model <- lm(formula = data.trust_1 ~ complexity
+ + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+ data = results)
+anova(model)model <- lm(formula = data.trust_2 ~ complexity
+ + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+ data = results)
+anova(model)model <- lm(formula = data.trust_3 ~ complexity
+ + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+ data = results)
+anova(model)model <- lm(formula = data.trust_4 ~ complexity
+ + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+ data = results)
+anova(model)model <- lm(formula = data.trust_5 ~ complexity
+ + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
+ data = results)
+anova(model)Analysis of Variance Table
+
+Response: data.trust_5
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 12.30 6.1489 3.5134 0.0306236 *
+Age 1 0.01 0.0150 0.0085 0.9263927
+Gender 3 11.20 3.7322 2.1325 0.0953809 .
+State_1 46 112.98 2.4562 1.4034 0.0467885 *
+Education 9 20.72 2.3020 1.3153 0.2263077
+Parents_education 2 0.62 0.3113 0.1778 0.8371296
+Language 3 3.64 1.2146 0.6940 0.5560659
+Ethnicity 8 17.35 2.1691 1.2394 0.2740997
+Income 18 47.44 2.6356 1.5059 0.0831539 .
+Religion 4 3.37 0.8419 0.4811 0.7496600
+trust.in.science_7 1 22.29 22.2913 12.7369 0.0003974 ***
+need_for_cognition 1 7.34 7.3354 4.1913 0.0412161 *
+interpersonal.trust_1 1 6.27 6.2738 3.5847 0.0589601 .
+Residuals 446 780.56 1.7501
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+
+How does provenance data predict trust?
+Overall
+ + + +brushed <- results %>%
+ gather(key = provenance_type, value = provenance_value, brushed, explore_interactions, explore_time) %>%
+ group_by(provenance_type, chartType, isCovidData, complexity) %>%
+ summarize(n = n(),
+ mean = mean(provenance_value),
+ se = sd(provenance_value)/sqrt(n),
+ n = n) %>%
+ filter(provenance_type == "brushed") %>%
+ ggplot(aes(x = provenance_type, y = mean, ymax = mean + se, ymin = mean - se, color = as.factor(isCovidData))) +
+ geom_point(position = position_dodge2(width = 0.5)) +
+ geom_errorbar(width = 0.5, size = 0.66, position = "dodge") +
+ facet_grid(rows = vars(complexity), cols = vars(isCovidData)) +
+ theme_minimal() +
+ theme(panel.spacing = unit(2, "lines"))
+brushed
+
+interactions <- results %>%
+ gather(key = provenance_type, value = provenance_value, brushed, explore_interactions, explore_time) %>%
+ group_by(provenance_type, chartType, isCovidData, complexity) %>%
+ summarize(n = n(),
+ mean = mean(provenance_value),
+ se = sd(provenance_value)/sqrt(n),
+ n = n) %>%
+ filter(provenance_type == "explore_interactions") %>%
+ ggplot(aes(x = provenance_type, y = mean, ymax = mean + se, ymin = mean - se, color = as.factor(isCovidData))) +
+ geom_point(position = position_dodge2(width = 0.5)) +
+ geom_errorbar(width = 0.5, size = 0.66, position = "dodge") +
+ facet_grid(rows = vars(complexity), cols = vars(chartType)) +
+ theme_minimal() +
+ theme(panel.spacing = unit(2, "lines"))
+interactions
+
+exploreTime <- results %>%
+ gather(key = provenance_type, value = provenance_value, brushed, explore_interactions, explore_time) %>%
+ group_by(provenance_type, chartType, isCovidData, complexity) %>%
+ summarize(n = n(),
+ mean = mean(provenance_value),
+ se = sd(provenance_value)/sqrt(n),
+ n = n) %>%
+ filter(provenance_type == "explore_time") %>%
+ ggplot(aes(x = provenance_type, y = mean, ymax = mean + se, ymin = mean - se, color = as.factor(isCovidData))) +
+ geom_point(position = position_dodge2(width = 0.5)) +
+ geom_errorbar(width = 0.5, size = 0.66, position = "dodge") +
+ facet_grid(rows = vars(chartType), cols = vars(complexity)) +
+ theme_minimal() +
+ theme(panel.spacing = unit(2, "lines"))
+exploreTime
+
+exploreTime + interactions + brushed
+ggsave("provenanceResults.png", width = 26, height = 14)explore_interactions
+ + + +model <- lm(formula = explore_interactions ~ complexity * chartType * isCovidData +
+ Age + Gender + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+anova(model)Analysis of Variance Table
+
+Response: explore_interactions
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 3.62 1.8108 1.3216 0.26778
+chartType 1 0.00 0.0043 0.0031 0.95556
+isCovidData 1 5.29 5.2914 3.8617 0.05003 .
+Age 1 21.87 21.8733 15.9634 7.578e-05 ***
+Gender 3 21.89 7.2978 5.3260 0.00130 **
+State_1 46 59.25 1.2881 0.9401 0.58698
+Education 9 10.41 1.1563 0.8439 0.57595
+Parents_education 2 8.55 4.2738 3.1191 0.04518 *
+Language 3 1.17 0.3914 0.2856 0.83578
+Ethnicity 8 17.46 2.1826 1.5928 0.12461
+Income 18 22.08 1.2267 0.8953 0.58471
+Religion 4 7.11 1.7787 1.2981 0.26994
+trust.in.science_7 1 6.70 6.7007 4.8903 0.02753 *
+need_for_cognition 1 5.23 5.2339 3.8198 0.05129 .
+interpersonal.trust_1 1 3.71 3.7112 2.7084 0.10054
+complexity:chartType 2 0.42 0.2120 0.1547 0.85670
+complexity:isCovidData 2 2.65 1.3246 0.9667 0.38116
+chartType:isCovidData 1 0.00 0.0001 0.0001 0.99386
+complexity:chartType:isCovidData 2 0.76 0.3781 0.2759 0.75898
+Residuals 437 598.79 1.3702
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1explore_time
+ + + +model <- lm(formula = explore_time ~ complexity * chartType * isCovidData +
+ Age + Gender + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+anova(model)Analysis of Variance Table
+
+Response: explore_time
+ Df Sum Sq Mean Sq F value Pr(>F)
+complexity 2 11546601 5773301 0.8285 0.437410
+chartType 1 9225464 9225464 1.3238 0.250536
+isCovidData 1 16042464 16042464 2.3020 0.129927
+Age 1 62648946 62648946 8.9899 0.002870 **
+Gender 3 44564779 14854926 2.1316 0.095526 .
+State_1 46 287423601 6248339 0.8966 0.666542
+Education 9 88627287 9847476 1.4131 0.179773
+Parents_education 2 41616277 20808139 2.9859 0.051525 .
+Language 3 5762167 1920722 0.2756 0.842995
+Ethnicity 8 158088313 19761039 2.8356 0.004445 **
+Income 18 123385347 6854741 0.9836 0.477474
+Religion 4 106522939 26630735 3.8214 0.004581 **
+trust.in.science_7 1 25492061 25492061 3.6580 0.056453 .
+need_for_cognition 1 3078093 3078093 0.4417 0.506655
+interpersonal.trust_1 1 16434195 16434195 2.3583 0.125345
+complexity:chartType 2 5438215 2719108 0.3902 0.677168
+complexity:isCovidData 2 5813922 2906961 0.4171 0.659191
+chartType:isCovidData 1 133966 133966 0.0192 0.889791
+complexity:chartType:isCovidData 2 5347737 2673868 0.3837 0.681571
+Residuals 437 3045364538 6968798
+---
+Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Hypothesis: for the complex condition, we expect people who brushed +more to have higher trust
+ + + +complexCondition <- results %>%
+ filter(complexity == "complex")
+
+# can change the predictor to bar.vis
+model<- manova(cbind(data.trust_1,
+ data.trust_2,
+ data.trust_3,
+ data.trust_4,
+ data.trust_5,
+ data.trust_6) ~ brushed + explore_interactions + explore_time,
+ data = complexCondition)
+summary.aov(model)Hypothesis: for all the conditions, we expect people who hovered more +to have higher trust
+ + + +# 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) ~ brushed + explore_interactions + explore_time,
+ data = results)
+summary.aov(model)
+
+Affect on Trust {it’s own section}
+ + + +results %>%
+ gather(key = affects, value = affectRatings, affect.science_1, affect.clarity_1, affect.aesthetic_1) %>%
+ group_by(affects, chartType, isCovidData, complexity) %>%
+ summarize(n = n(),
+ mean = mean(affectRatings),
+ se = sd(affectRatings)/sqrt(n),
+ n = n()) %>%
+ ggplot(aes(x = affects, y = mean,
+ ymax = mean + se, ymin = mean - se,
+ colour = as.factor(isCovidData))) +
+ geom_point(position = position_dodge2(width = 0.5)) +
+ geom_errorbar(width = 0.5, size = 0.66, position = "dodge") +
+ # labs(title = "Trust in data") +
+ # geom_vline(data = results %>%
+ # group_by(complexity, isCovidData) %>%
+ # summarize(n = n(),
+ # vis.trust_6 = mean(vis.trust_6)),
+ # aes(xintercept = vis.trust_6, colour = as.factor(isCovidData))) +
+ facet_grid(rows = vars(chartType), cols = vars(complexity)) +
+ theme_minimal() +
+ theme(panel.spacing = unit(2, "lines"))
+ # legend.position = "none")
+ggsave("affectMeasures.png", width = 17, height = 9)model <- lm(formula = affect.science_1 ~ complexity * chartType * isCovidData +
+ Age + as.factor(Gender) + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+anova(model)model <- lm(formula = affect.clarity_1 ~ complexity * chartType * isCovidData +
+ Age + as.factor(Gender) + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+anova(model)
+summary(model)model <- lm(formula = affect.aesthetic_1 ~ complexity * chartType * isCovidData +
+ Age + Gender + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+anova(model)
+summary(model)Trust in Vis
+ + + +model <- lm(formula = vis.trust_6 ~ affect.science_1 * affect.clarity_1 * affect.aesthetic_1 +
+ Age + Gender + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+anova(model)
+summary(model)
+
+emmeans(aov(vis.trust_6 ~ affect.science_1 * affect.clarity_1 , data = results) , ~ affect.clarity_1)Trust in Data
+ + + +model <- lm(formula = data.trust_6 ~ affect.science_1 * affect.clarity_1 * affect.aesthetic_1 +
+ Age + Gender + State_1 + Education + Parents_education + Language +
+ Ethnicity + Income + Religion + trust.in.science_7 +
+ need_for_cognition + interpersonal.trust_1,
+ data = results)
+anova(model)
+
+emmeans(aov(data.trust_6 ~ affect.science_1 * affect.clarity_1 , data = results) , ~ affect.clarity_1)
+
+
+Factor Analysis
+ + + +factorAnalysis <- results %>%
+ select(data.trust_1, data.trust_2, data.trust_3, data.trust_4, data.trust_5, data.trust_6,
+ vis.trust_1, vis.trust_2, vis.trust_3, vis.trust_4, vis.trust_5, vis.trust_6,
+ trust.in.science_1, trust.in.science_2, trust.in.science_3, trust.in.science_4, trust.in.science_5,
+ trust.in.science_6, trust.in.science_7, trust.in.science_8,
+ cognition_1, cognition_2, cognition_3, cognition_4, cognition_5, cognition_6,
+ # brushed, explore_interactions, # explore_time,
+ interpersonal.trust_1,
+ vlat_simple, vlat_moderate, vlat_complex,
+ # initial impression
+ affect.science_1, affect.clarity_1, affect.aesthetic_1)
+
+nfactors(factorAnalysis)Factor 4 seem to have minimum compelxity, BIC is pretty low, and big +jump for root mean 5 seems meh becuase of the big jump from 4-5 on +complexity.
+ + + +f7 <- fa(factorAnalysis, 7)
+pdf(file = "f7.pdf", # The directory you want to save the file in
+ width = 15, # The width of the plot in inches
+ height = 23) # The height of the plot in inches
+fa.diagram(f7)
+dev.off()
+# based on the factor analysis, it looks like not all the vis Qs go together and not all the data Qs go together. ---
title: "Trust in Science Analysis Notebook"
output:
  html_notebook: default
  pdf_document: default
---
```{r echo = FALSE}
require(tidyverse)
library(psych)
library(ggplot2)
library(car)
library(lme4)
library(emmeans)
library(pwr)
library(patchwork)
library(rstatix)
library(effectsize)
library(GPArotation)
library(MASS)
# environment to include the ss_psych450_rws3 platform
load("ss_psych450_rws3")
```

Load Data

```{r}
results <- read.csv("data_clean_june30.csv")

# trust in data is the column: bar-data_6
# trust in vis is the column: bar-vis_6
```

Converting covariates to factors
```{r}
results$Gender <- as.factor(results$Gender)
results$Education <- as.factor(results$Education)
results$Parents_education <- as.factor(results$Parents_education)
results$Language <- as.factor(results$Language)
results$Ethnicity <- as.factor(results$Ethnicity)
results$Income <- as.factor(results$Income)
results$Religion <- as.factor(results$Religion)
```

Trust in Vis

```{r}
# results$isCovidData <- factor(results$isCovidData, levels = c(0, 1),
#                   labels = c("Crop Data", "Covid Data"))
```

```{r}

MinMeanSEMMax <- function(x) {
  v <- c(min(x), mean(x) - sd(x)/sqrt(length(x)), mean(x), mean(x) + sd(x)/sqrt(length(x)), max(x))
  names(v) <- c("ymin", "lower", "middle", "upper", "ymax")
  v
}

results %>%
  # group_by(complexity, isCovidData) %>%
  ggplot(aes( x = vis.trust_6, y = 0, cex=1.5, colour = as.factor(isCovidData))) +
  scale_color_manual(values = c("purple", "orange")) +
  ylim(-0.5, 0.5) +
  geom_jitter(data = results, width = 0.3, height = 0.2, color = "light gray", alpha = 0.5) +
  #stat_summary(fun.data=MinMeanSEMMax, geom="boxplot", colour="red") +

  geom_boxplot(lwd = 1, fatten = NULL, width = 0.25, alpha = 0.5) +

  geom_segment(data = results %>% 
                group_by(complexity, isCovidData) %>%
                summarize(n = n(), 
                           mean = mean(vis.trust_6),
            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) +

  geom_text( data = results %>% 
                group_by(complexity, isCovidData) %>%
                summarize(n = n(), 
                           mean = round(mean(vis.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 = 6.2, 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(complexity ~ isCovidData) +
  xlab("Trust in Visualization") +
  theme_minimal() + 
  theme(panel.spacing = unit(2, "lines"),
        legend.position = "none",
        axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        axis.ticks.y = element_blank())



#ggsave(paste("complexity_dataType_interaction.pdf", sep=""))

```


```{r}

MinMeanSEMMax <- function(x) {
  v <- c(min(x), mean(x) - sd(x)/sqrt(length(x)), mean(x), mean(x) + sd(x)/sqrt(length(x)), max(x))
  names(v) <- c("ymin", "lower", "middle", "upper", "ymax")
  v
}

results %>%
  # group_by(complexity, isCovidData) %>%
  ggplot(aes( x = vis.trust_6, y = 0, cex=1.5, colour = chartType)) +
  scale_color_manual(values = c("purple", "orange")) +
  ylim(-0.5, 0.5) +
  geom_jitter(data = results, width = 0.3, height = 0.2, color = "light gray", alpha = 0.5) +
  #stat_summary(fun.data=MinMeanSEMMax, geom="boxplot", colour="red") +

  geom_boxplot(lwd = 1, fatten = NULL, width = 0.25, alpha = 0.5) +

  geom_segment(data = results %>% 
                group_by(complexity, chartType) %>%
                summarize(n = n(), 
                           mean = mean(vis.trust_6),
            se = sd(vis.trust_6)/sqrt(n)), 
              aes(x = mean, xend = mean, y = -.25, yend = .25,  colour = chartType), 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) +

  geom_text( data = results %>% 
                group_by(complexity, chartType) %>%
                summarize(n = n(), 
                           mean = round(mean(vis.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 = 6.2, 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(complexity ~ chartType) +
  xlab("Trust in Visualization") +
  theme_minimal() + 
  theme(panel.spacing = unit(2, "lines"),
        legend.position = "none",
        axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        axis.ticks.y = element_blank())



ggsave(paste("complexity_chartType_interaction.pdf", sep=""))

```



```{r}
results %>%
  group_by(complexity, isCovidData) %>%
  summarize(n = n(),
            mean = mean(vis.trust_6),
            se = sd(vis.trust_6)/sqrt(n),
            n = n)
```

```{r}
# results$isCovidData <- factor(results$isCovidData, levels = c(0, 1),
#                   labels = c("Crop Data", "Covid Data"))

results %>%
  ggplot(aes(x = data.trust_6, y = 0)) +
  # scale_color_manual(values = c("purple", "orange")) +
  ylim(-0.5, 0.5) +
  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_segment(data = results %>% 
                group_by(complexity) %>%
                summarize(n = n(), 
                           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),
                          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() + 
  theme(panel.spacing = unit(2, "lines"),
        legend.position = "none",
        axis.text.y = element_blank(),
        axis.title.y = element_blank(),
        axis.ticks.y = element_blank())

#ggsave(paste("complexity_interaction.pdf", sep=""))

```


```{r}
results %>%
 filter(isCovidData ==0 ) %>%
  group_by(complexity) %>%
  summarize(n = n(),
            mean = mean(vis.trust_6),
            se = sd(vis.trust_6)/sqrt(n),
            n = n)
```

```{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)
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 + Income + Education + Parents_education + Language + Ethnicity  + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
            data = results)
anova(model)


```
```{r}

model<- manova( cbind(vis.trust_6,vis.trust_1,vis.trust_2,vis.trust_3,vis.trust_4,vis.trust_5,affect.science_1,affect.clarity_1,affect.aesthetic_1) ~ 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)

summary(model)
eta_squared(model)

summary.aov(model)



```


```{r}

model<- manova( cbind(data.trust_6,data.trust_1,data.trust_2,data.trust_3,data.trust_4,data.trust_5) ~ 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)

anova(model)
eta_squared(model)

summary.aov(model)



```

```{r}
# post-hoc tests
aov(vis.trust_6 ~ complexity * as.factor(isCovidData), data = results) %>% tukey_hsd()
# effect sizes
eta_squared(aov(vis.trust_6 ~ complexity * as.factor(isCovidData) * chartType + 
                  + Age + as.factor(Gender) + State_1 + as.factor(Education) + as.factor(Parents_education) + as.factor(Language) + as.factor(Ethnicity) + as.factor(Income) + as.factor(Religion) + trust.in.science_7 + need_for_cognition + 
                  interpersonal.trust_1,
             data = results))
``` 
# Colinearity of trust in vis and trust in data
```{r}
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$vis.trust_3, results$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)
```

# Trust in science, need for cognition, and interpersonal trust on trust in Vis
```{r}
results %>%
  gather(key = variables, value = values, 
         trust.in.science_7, need_for_cognition, interpersonal.trust_1) %>%
  ggplot(aes(x = values, y = vis.trust_6, color = variables)) +

  facet_grid(cols = vars(variables)) +
    geom_jitter() +
  geom_smooth(color = "black") +
  geom_blank() + 
   theme_minimal() +
  theme(panel.spacing = unit(2, "lines"))
```






Trust in Data

```{r}
results %>%
  ggplot(aes(x = data.trust_6, y = isCovidData, colour = as.factor(isCovidData))) +
  geom_jitter(data = results, width = 0.5) +
  # labs(title = "Trust in data") + 
  geom_vline(data = results %>% 
                group_by(complexity, isCovidData, chartType) %>%
                summarize(n = n(), 
                          data.trust_6 = mean(data.trust_6)), 
              aes(xintercept = data.trust_6, colour = as.factor(isCovidData))) +
  facet_grid(rows = vars(complexity), cols = vars(chartType)) +
  theme_minimal()

results %>% 
  group_by(complexity, isCovidData) %>%
  summarize(n = n(), 
            mean = mean(data.trust_6),
            se = sd(data.trust_6)/sqrt(n),
            n = n)
```

```{r}
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%>% filter(isCovidData == 0))
anova(model)


```

```{r}
# post-hoc tests
aov(data.trust_6 ~ complexity * as.factor(isCovidData), data = results) %>% tukey_hsd()
aov(data.trust_6 ~ chartType * as.factor(isCovidData), data = results) %>% tukey_hsd()
# effect sizes
eta_squared(aov(data.trust_6 ~ complexity * as.factor(isCovidData) * chartType + 
                  Age + as.factor(Gender) + State_1 + as.factor(Education) + as.factor(Parents_education) + as.factor(Language) + 
                  as.factor(Ethnicity) + as.factor(Income) + as.factor(Religion) + trust.in.science_7 + need_for_cognition + 
                  interpersonal.trust_1,
             data = results))
emmeans(aov(data.trust_6 ~ complexity , data = results) , ~ complexity)
```

Trust in science, need for cognition, and interpersonal trust on trust in Data
```{r}
results %>%
  gather(key = variables, value = values, 
         trust.in.science_7, need_for_cognition, interpersonal.trust_1) %>%
  ggplot(aes(x = values, y = data.trust_6, color = variables)) +

  facet_grid(cols = vars(variables)) +
    geom_jitter() +
  geom_smooth(color = "black") +
  geom_blank() + 
   theme_minimal() +
  theme(panel.spacing = unit(2, "lines"))
```


















```{r}
results_long_data <- results %>%
  select(data.trust_1, data.trust_2, data.trust_3,
         data.trust_4, data.trust_5, data.trust_6,
         ResponseId, complexity,
         vlat_simple, vlat_moderate, vlat_complex) %>%
  gather(key = trustItemData, value = trustRatingData, 
         data.trust_1, data.trust_2, data.trust_3, data.trust_4, data.trust_5, data.trust_6)

results_long_data %>%
  ggplot(aes(x = trustRatingData, y = 1, color = complexity)) +
  geom_jitter() +
  ylim(0, 2) + 
  labs(title = "Trust in data (All)") + 
  geom_vline(data = results_long_data %>% 
               group_by(complexity, trustItemData) %>%
               summarize(n = n(), 
                         average = mean(trustRatingData)), 
             aes(xintercept = average, color = complexity)) +
  facet_grid(vars(trustItemData)) +
  theme_minimal()
```


# Relationship between trust in data and vis, across complexity

```{r}
results_long_data <- results %>%
  select(data.trust_1, data.trust_2, data.trust_3,
         data.trust_4, data.trust_5, data.trust_6,
         ResponseId, complexity,
         chartType, isCovidData, 
         vlat_simple, vlat_moderate, vlat_complex) %>%
  gather(key = trustItemData, value = trustRatingData, 
         # data.trust_1, data.trust_2, data.trust_3, data.trust_4, data.trust_5, 
         data.trust_6)

results_long_vis <- results %>%
  gather(key = trustItemVis, value = trustRatingVis, 
         # vis.trust_1, vis.trust_2, vis.trust_3, vis.trust_4, vis.trust_5, 
         vis.trust_6)

results_long_all <- merge(results_long_vis, results_long_data, 
                          by = c("ResponseId", "complexity", "chartType", "isCovidData"))

model<- glm(trustRatingVis ~  trustRatingData * complexity + 
              trustRatingData *  chartType + 
              trustRatingData *  as.factor(isCovidData),
                      data = results_long_all)
Anova(model)
```

```{r}
results_long_all %>%
  # filter(trustItemVis == "bar.vis_2") %>%
  ggplot(aes(x = trustRatingVis, y = trustRatingData, color = as.factor(isCovidData))) +
  geom_jitter(alpha = 0.25) +
  stat_smooth(method = "lm",
              formula = y ~ x,
              geom = "smooth", color = "black") +
  labs(title = "Relationship bewteen trust in Vis and Data") + 
  facet_grid(rows = vars(chartType), cols = vars(complexity)) +
  theme_minimal()
```



# Do the trust items predict trust?


```{r}
model <- lm(formula = vis.trust_6 ~ vis.trust_1 + 
              vis.trust_2 + 
              vis.trust_3 + 
              vis.trust_4 + 
              vis.trust_5 + 
              affect.science_1 +
              affect.clarity_1 + 
              affect.aesthetic_1 +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
summary(model)
```


```{r}
model <- lm(formula = data.trust_6 ~ data.trust_1 + 
              data.trust_2 + 
              data.trust_3 + 
              data.trust_4 + 
              data.trust_5 +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
summary(model)
```

# Which trust measurements predict behavioral outcomes?

```{r}
model <- lm(formula = vis.trust_4 ~ vis.trust_1 + 
              vis.trust_2 + 
              vis.trust_3 +
              vis.trust_6 +
              affect.science_1 +
              affect.clarity_1 + 
              affect.aesthetic_1 +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
summary(model)
```

```{r}
model <- lm(formula = vis.trust_5 ~ vis.trust_1 + 
              vis.trust_2 + 
              vis.trust_3 +
              vis.trust_6 +
              affect.science_1 +
              affect.clarity_1 + 
              affect.aesthetic_1 +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
summary(model)
```


```{r}
results %>%
  ggplot(aes(x = data.trust_1, y = data.trust_6, color = as.factor(isCovidData))) +
  geom_jitter() +
  geom_smooth(method="lm") +
  facet_wrap(~isCovidData) +
  theme_minimal()
```









How does performance on VLAT questions predict trust?

```{r}
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,
            data = results)
anova(model)

emmeans(aov(vis.trust_6 ~ vlat_simple * vlat_complex , data = results) , ~ vlat_simple | vlat_complex)
```

```{r}
results %>%
  gather(key = vlat_level, value = vlat_performance, vlat_simple,  vlat_moderate, vlat_complex) %>%
  ggplot(aes(x = vis.trust_6, y = vlat_performance, colour = as.factor(assigned_vlat))) +
  geom_jitter(width = 0.5) +
  # y(0, 2) + 
  # labs(title = "Trust in data") + 
  # geom_vline(data = results %>% 
  #              group_by(complexity, isCovidData) %>%
  #              summarize(n = n(), 
  #                        vis.trust_6 = mean(vis.trust_6)), 
  #            aes(xintercept = vis.trust_6, colour = as.factor(isCovidData))) +
  facet_grid(rows = vars(vlat_level), cols = vars(chartType)) +
  theme_minimal() + 
  theme(panel.spacing = unit(2, "lines"))
        # legend.position = "none")
```


Overall distribution of VLAT performance

Trust in Vis 
```{r}
# vlat_long <- results %>%
#   gather(key = vlat_level, value = vlat_performance, vlat_simple,  vlat_moderate, vlat_complex) 
# 
# model <- lmer(formula = vlat_performance ~ vlat_level * isCovidData * chartType +
#               Age + Gender + State_1 + Education + Parents_education + Language + 
#               Ethnicity + Income + Religion + trust.in.science_7 + 
#               need_for_cognition + interpersonal.trust_1 + (1|ResponseId),
#             data = vlat_long)
# Anova(model)
# 
# emmeans(aov(vlat_performance ~ vlat_level , data = results) , ~ vlat_simple | vlat_complex)
# 
# # post-hoc tests
# aov(vlat_performance ~ vlat_level * as.factor(isCovidData), data = vlat_long) %>% tukey_hsd()
```

```{r}
results %>%
  gather(key = vlat_level, value = vlat_performance, vlat_simple,  vlat_moderate, vlat_complex) %>%
  group_by(vlat_level, chartType, isCovidData) %>%
  summarize(n = n(),
            mean_vlat_performance = mean(vlat_performance),
            se = sd(vlat_performance)/sqrt(n),
            n = n()) %>%
  ggplot(aes(x = vlat_level, y = mean_vlat_performance, 
             ymax = mean_vlat_performance + se, ymin = mean_vlat_performance - se,
             colour = vlat_level)) +
  geom_point() +
  geom_errorbar() +
  # labs(title = "Trust in data") + 
  # geom_vline(data = results %>% 
  #              group_by(complexity, isCovidData) %>%
  #              summarize(n = n(), 
  #                        vis.trust_6 = mean(vis.trust_6)), 
  #            aes(xintercept = vis.trust_6, colour = as.factor(isCovidData))) +
  facet_grid(rows = vars(isCovidData)) +
  theme_minimal() + 
  theme(panel.spacing = unit(2, "lines"))
        # legend.position = "none")
```


Trust in Data


```{r}
model <- lm(formula = data.trust_6 ~ vlat_simple * vlat_moderate * vlat_complex +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)

emmeans(aov(data.trust_6 ~ vlat_simple * vlat_complex , data = results) , ~ vlat_simple | vlat_complex)
```










# Which antecedent drives the interaction effect for trust in visualization for covid data?

```{r}
model <- lm(formula = vis.trust_1 ~ complexity
                                    + 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))
anova(model)
```

```{r}
model <- lm(formula = vis.trust_2 ~ complexity
                                    + 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))
anova(model)
```

```{r}
model <- lm(formula = vis.trust_3 ~ complexity
                                    + 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))
anova(model)
```

```{r}
model <- lm(formula = affect.science_1 ~ complexity
                                    + 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))
anova(model)
```

```{r}
model <- lm(formula = affect.clarity_1 ~ complexity
                                    + 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))
anova(model)
```

```{r}
model <- lm(formula = affect.aesthetic_1 ~ complexity
                                    + 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))
anova(model)
```

# Which antecedent drives the main effect for trust in data?

```{r}
model <- lm(formula = data.trust_1 ~ complexity
                                    + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
            data = results)
anova(model)
```

```{r}
model <- lm(formula = data.trust_2 ~ complexity
                                    + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
            data = results)
anova(model)
```

```{r}
model <- lm(formula = data.trust_3 ~ complexity
                                    + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
            data = results)
anova(model)
```

```{r}
model <- lm(formula = data.trust_4 ~ complexity
                                    + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
            data = results)
anova(model)
```

```{r}
model <- lm(formula = data.trust_5 ~ complexity
                                    + Age + Gender + State_1 + Education + Parents_education + Language + Ethnicity + Income + Religion + trust.in.science_7 + need_for_cognition + interpersonal.trust_1 ,
            data = results)
anova(model)
```

# How does provenance data predict trust?

Overall

```{r}
brushed <- results %>%
  gather(key = provenance_type, value = provenance_value, brushed, explore_interactions, explore_time) %>%
  group_by(provenance_type, chartType, isCovidData, complexity) %>%
  summarize(n = n(), 
            mean = mean(provenance_value), 
            se = sd(provenance_value)/sqrt(n),
            n = n) %>%
  filter(provenance_type == "brushed") %>%
  ggplot(aes(x = provenance_type, y = mean, ymax = mean + se, ymin = mean - se, color = as.factor(isCovidData))) +
  geom_point(position = position_dodge2(width = 0.5)) +
  geom_errorbar(width = 0.5, size = 0.66, position = "dodge") +
  facet_grid(rows = vars(complexity), cols = vars(isCovidData)) + 
  theme_minimal() +
  theme(panel.spacing = unit(2, "lines"))
brushed

interactions <- results %>%
  gather(key = provenance_type, value = provenance_value, brushed, explore_interactions, explore_time) %>%
  group_by(provenance_type, chartType, isCovidData, complexity) %>%
  summarize(n = n(), 
            mean = mean(provenance_value), 
            se = sd(provenance_value)/sqrt(n),
            n = n) %>%
  filter(provenance_type == "explore_interactions") %>%
  ggplot(aes(x = provenance_type, y = mean, ymax = mean + se, ymin = mean - se, color = as.factor(isCovidData))) +
  geom_point(position = position_dodge2(width = 0.5)) +
  geom_errorbar(width = 0.5, size = 0.66, position = "dodge") +
  facet_grid(rows = vars(complexity), cols = vars(chartType)) + 
  theme_minimal() +
  theme(panel.spacing = unit(2, "lines"))
interactions

exploreTime <- results %>%
  gather(key = provenance_type, value = provenance_value, brushed, explore_interactions, explore_time) %>%
  group_by(provenance_type, chartType, isCovidData, complexity) %>%
  summarize(n = n(), 
            mean = mean(provenance_value), 
            se = sd(provenance_value)/sqrt(n),
            n = n) %>%
  filter(provenance_type == "explore_time") %>%
  ggplot(aes(x = provenance_type, y = mean, ymax = mean + se, ymin = mean - se, color = as.factor(isCovidData))) +
  geom_point(position = position_dodge2(width = 0.5)) +
  geom_errorbar(width = 0.5, size = 0.66, position = "dodge") +
  facet_grid(rows = vars(chartType), cols = vars(complexity)) + 
  theme_minimal() +
  theme(panel.spacing = unit(2, "lines"))
exploreTime

exploreTime + interactions + brushed
ggsave("provenanceResults.png", width = 26, height = 14)
```
explore_interactions

```{r}
model <- lm(formula = explore_interactions ~ complexity * chartType * isCovidData +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)
```

explore_time

```{r}
model <- lm(formula = explore_time ~ complexity * chartType * isCovidData +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)
```



Hypothesis:
for the complex condition, we expect people who brushed more to have higher trust

```{r}
complexCondition <- results %>%
  filter(complexity == "complex")

# can change the predictor to bar.vis
model<- manova(cbind(data.trust_1, 
                     data.trust_2, 
                     data.trust_3, 
                     data.trust_4, 
                     data.trust_5, 
                     data.trust_6) ~ brushed + explore_interactions + explore_time, 
               data = complexCondition)
summary.aov(model)
```

Hypothesis:
for all the conditions, we expect people who hovered more to have higher trust


```{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) ~ brushed + explore_interactions + explore_time, 
               data = results)
summary.aov(model)
```



# Affect on Trust {it's own section}

```{r}
results %>%
  gather(key = affects, value = affectRatings, affect.science_1,  affect.clarity_1, affect.aesthetic_1) %>%
  group_by(affects, chartType, isCovidData, complexity) %>%
  summarize(n = n(),
            mean = mean(affectRatings),
            se = sd(affectRatings)/sqrt(n),
            n = n()) %>%
  ggplot(aes(x = affects, y = mean, 
             ymax = mean + se, ymin = mean - se,
             colour = as.factor(isCovidData))) +
  geom_point(position = position_dodge2(width = 0.5)) +
  geom_errorbar(width = 0.5, size = 0.66, position = "dodge") +
  # labs(title = "Trust in data") + 
  # geom_vline(data = results %>% 
  #              group_by(complexity, isCovidData) %>%
  #              summarize(n = n(), 
  #                        vis.trust_6 = mean(vis.trust_6)), 
  #            aes(xintercept = vis.trust_6, colour = as.factor(isCovidData))) +
  facet_grid(rows = vars(chartType), cols = vars(complexity)) +
  theme_minimal() + 
  theme(panel.spacing = unit(2, "lines"))
        # legend.position = "none")
ggsave("affectMeasures.png", width = 17, height = 9)
```


```{r}
model <- lm(formula = affect.science_1 ~ complexity * chartType * isCovidData +
              Age + as.factor(Gender) + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)
```

```{r}
model <- lm(formula = affect.clarity_1 ~ complexity * chartType * isCovidData +
              Age + as.factor(Gender) + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)
summary(model)
```
```{r}
model <- lm(formula = affect.aesthetic_1 ~ complexity * chartType * isCovidData +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)
summary(model)
```

Trust in Vis


```{r}
model <- lm(formula = vis.trust_6 ~ affect.science_1 * affect.clarity_1 * affect.aesthetic_1 +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)
summary(model)

emmeans(aov(vis.trust_6 ~ affect.science_1 * affect.clarity_1 , data = results) , ~ affect.clarity_1)
```


Trust in Data

```{r}
model <- lm(formula = data.trust_6 ~ affect.science_1 * affect.clarity_1 * affect.aesthetic_1 +
              Age + Gender + State_1 + Education + Parents_education + Language + 
              Ethnicity + Income + Religion + trust.in.science_7 + 
              need_for_cognition + interpersonal.trust_1,
            data = results)
anova(model)

emmeans(aov(data.trust_6 ~ affect.science_1 * affect.clarity_1 , data = results) , ~ affect.clarity_1)
```










# Factor Analysis 


```{r}
factorAnalysis <- results %>%
  select(data.trust_1, data.trust_2, data.trust_3, data.trust_4, data.trust_5, data.trust_6,
         vis.trust_1, vis.trust_2, vis.trust_3, vis.trust_4, vis.trust_5, vis.trust_6,
         trust.in.science_1, trust.in.science_2, trust.in.science_3, trust.in.science_4, trust.in.science_5,
         trust.in.science_6, trust.in.science_7, trust.in.science_8, 
         cognition_1, cognition_2, cognition_3, cognition_4, cognition_5, cognition_6,
         # brushed, explore_interactions, # explore_time, 
         interpersonal.trust_1,
         vlat_simple, vlat_moderate, vlat_complex,
         # initial impression
         affect.science_1, affect.clarity_1, affect.aesthetic_1)

nfactors(factorAnalysis)
```

Factor 4 seem to have minimum compelxity, BIC is pretty low, and big jump for root mean
5 seems meh becuase of the big jump from 4-5 on complexity. 

```{r}
f7 <- fa(factorAnalysis, 7)
pdf(file = "f7.pdf",   # The directory you want to save the file in
    width = 15, # The width of the plot in inches
    height = 23) # The height of the plot in inches
fa.diagram(f7)
dev.off()
# based on the factor analysis, it looks like not all the vis Qs go together and not all the data Qs go together. 
```




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