StudyProcedure/Statistics.md

@@ -8,7 +8,88 @@ All of the scripts below are for RStudio, but for example also 🔴*add the prog
 
 # Demographics
 
-If you gathered demogrpahic data, e.g., with SoSciSurvey, you should report who participated, by giving their number ``n=...`` participants, their gender count and at least age with mean (M) and standard deviation (SD)
+If you gathered demogrpahic data, e.g., with SoSciSurvey, you should report who participated, by giving their number ``n=...`` participants, their gender count and at least age with mean (M) and standard deviation (SD).
+
+``SoSciSurvey data`` ca be easily loaded into RStudio with the script provided alongside the data on ScoSciSurvey.
+
+*For all the following examples you abviously have to adapt the names of the data frames (here, e.g., soscisurveyData and columns, here, e.g., D001)*
+
+<details><summary>This is a script for counting occurences</summary>
+
+```r
+add_descriptive_statistics <- function(data, name){
+  print(name)
+  print(table(data))
+}
+
+#can be used, e.g., for:
+add_descriptive_statistics(soscisurveyData$D001, "Gender")
+add_descriptive_statistics(soscisurveyData$D004, "VR Frequency")
+
+
+```
+
+</details>
+
+<details><summary>Here is a script for quantitative statistics</summary>
+
+```r
+add_qualitative_statistics <- function(data, name, demographics_stats){
+  old_names = rownames(demographics_stats)
+  demographics_stats= rbind(demographics_stats, data.frame("Mean"=mean(data), 
+                                                           "SD"=sd(data), 
+                                                           "Min"=min(data), 
+                                                           "Max"=max(data)))
+  rownames(demographics_stats) = append(old_names, name)
+  demographics_stats
+}
+
+
+#and applied to age data from a SoSciSurvey data frame
+soscisurveyData$D002_01 <- as.numeric(soscisurveyData$D002_01)
+demographics_stats = data.frame()
+demographics_stats = add_qualitative_statistics(soscisurveyData$D002_01, "Age",demographics_stats)
+
+
+```
+
+</details>
+
+# Data Preparation
+
+The data gathered by the study framework should already be in an easily digestable format and can be loaded by:
+```
+ActData <- read.csv(file = 'Phase_Act.csv')
+```
+
+<details><summary>If you want to exclude, e.g., single participants this snippet can be helpful (and some more potential cleanup)</summary>
+
+```r
+# maybe participant 20 dropped out during the study
+excludedParticipants = c('20')
+#excludedParticipants = c('20', '7', '11' ) #11 and 7 also did not understand task 2 correctly, exclude?
+
+#sometimes data is misclassified as numerical or simple character, so tell R: this is a factor!
+ActData$TurnTaking <- as.factor(ActData$TurnTaking)
+ActData$ParticipantID <- as.factor(ActData$ParticipantID)
+
+#remove excluded participants
+library(dplyr)
+ActData <- filter(ActData, ! ParticipantID %in% excludedParticipants)
+
+```
+
+
+</details>
+
+
+# Hypothesis Testing
+
+Normally you want to use the gathered data to proof that a factor you evaluated has a significant effect on the outcome.
+
+For this I recommend the following article on how to apply repeated-measures ANOVAs in R, 
+
+