Social Science Research/Causes of Depression

Social Science Research Causes of Depression Stigma Against Mental Illness on Social Networks Loneliness and Social Support for Older Adults The experimental study on Zenodo: https://zenodo.org/records/10821700, titled "An experimental paradigm for triggering a depressive syndrome," aimed to induce depressive symptoms in healthy individuals through a controlled social exclusion paradigm. 134 healthy, nonclinical college students were randomly assigned to an Exclusion (EX) or Inclusion (IN) group via the online ball-throwing game "Cyberball" (believing they were playing in real time with other participants). However, the game was pre-programmed: participants in the Inclusion group received an equal distribution of throws during the game; participants in the Exclusion group received significantly fewer throws. At the end of the game, the Exclusion group received negative feedback. Andrews, P. (2024). Data from: An experimental paradigm for triggering a depressive syndrome [Data set]. Zenodo. https://doi.org/10.5061/dryad.v6wwpzh2v Results indicate that excluded participants: Self-reported a significant increase in sadness and a decrease in happiness, but not in anxiety or calmness. Achieved significantly higher scores on four of the five variables related to depressive rumination. Showed significantly worse performance on a double interference task, suggesting impaired concentration. Loading libraries and data into R The data from the experimental study are attached to the project: library(dplyr) library(ggplot2) library(statsr) library(gridExtra) library(readxl) data <-read.csv("RFactorScore.csv", header = TRUE) data$Time <- as.factor(data$Time) data$Cond <- factor(data$Cond, levels = c(1, 0), labels = c("Exclusion", "Inclusion")) data2 <-read_excel("CyberMLCNF.xlsx") data2$Cond <- factor(data2$Cond, levels = c(1, 0), labels = c("Exclusion", "Inclusion")) data2$WordCond <- factor(data2$WordCond) data2$`RAPM Score` <- as.numeric(data2$`RAPM Score`) attach(data) Increase in sadness and decrease in happiness thumb|upright=1.8 We create the total sadness, happiness, anxiety, and calmness variables in the dataset by adding their respective columns and plotting the four graphs of the means of these variables by condition (Inclusion and Exclusion) and across the three time points at which the measurements were taken. When excluded, a significant increase in sadness and a decrease in happiness are noted, but not in anxiety or calm: data$TotalSadness = Sad + Low + Dep + Down data$TotalHappiness = Happy + Over + Joy data$TotalAnxiety = Anx + Nerv + Wor + Tense + Uneasy data$TotalCalmness = Calm + Peace + Cont + Satis g1<-data %>% group_by(Cond,Time) %>% summarize(mean_sadness = mean(TotalSadness, na.rm=T)) %>% ggplot(aes(Time, mean_sadness, group = Cond, colour = Cond))+ geom_line()+ geom_point(position = position_dodge(width = 0))+ ggtitle("Tristezza") g2<-data %>% group_by(Cond,Time) %>% summarize(mean_happiness = mean(TotalHappiness, na.rm=T)) %>% ggplot(aes(Time, mean_happiness, group = Cond, colour = Cond))+ geom_line()+ geom_point(position = position_dodge(width = 0))+ ggtitle("Felicità") g3<-data %>% group_by(Cond,Time) %>% summarize(mean_anxiety = mean(TotalAnxiety, na.rm=T)) %>% ggplot(aes(Time, mean_anxiety, group = Cond, colour = Cond))+ geom_line()+ geom_point(position = position_dodge(width = 0))+ ggtitle("Ansia") g4<-data %>% group_by(Cond,Time) %>% summarize(mean_calmness = mean(TotalCalmness, na.rm=T)) %>% ggplot(aes(Time, mean_calmness, group = Cond, colour = Cond))+ geom_line()+ geom_point(position = position_dodge(width = 0))+ ggtitle("Calma") grid.arrange(g1,g2,g3,g4, nrow=2, ncol=2) Depressive Rumination Depressive rumination was measured in this study primarily through the analysis of texts written by participants after their experience with the Cyberball game. Statistical analyses examined differences between the inclusion and exclusion conditions in relation to these rumination indicators. The results showed that participants in the exclusion group scored significantly higher on four of the five variables related to depressive rumination. The five variables measured are: Causal Reconstruction (CR variable): The extent to which the participant attempts to reconstruct the chain of events that led to a problem or failure, identifying possible intervention points. Raters assigned a score to this aspect based on how clearly the participant communicated the causal reconstruction. inference(x = Cond, y = CR, data = data2, statistic = "mean", type = "ht", method = "theoretical", alternative = "greater", null = 0, conf_level = 0.95) Response variable: numerical Explanatory variable: categorical (2 levels) n_Exclusion = 71, y_bar_Exclusion = 1.5634, s_Exclusion = 1.2275 n_Inclusion = 63, y_bar_Inclusion = 0.6825, s_Inclusion = 1.0599 H0: mu_Exclusion = mu_Inclusion HA: mu_Exclusion > mu_Inclusion t = 4.4573, df = 62 p_value = < 0.0001 Since p_value is < 0.05, we reject the null hypothesis (HO) and accept the alternative hypothesis (HA). Therefore, we are 95% confident that the mean reconstruction score The causal CR score is higher in excluded subjects than in included subjects. Self-blame (variable SelfB): Extent to which the participant identifies themselves as the primary cause of a problem or negative feeling. The score reflected how clearly the participant attributed blame to themselves. Similarly, since p_value <0.05, we reject the null hypothesis HO and accept the alternative hypothesis HA, meaning we are 95% confident that the mean self-blame score selfB is higher in excluded subjects than in included subjects. Low self-esteem (variable LSE): Extent to which the participant expresses a negative perception of their own value or importance. The evaluators looked for indications of this negative perception in the texts. Similarly, since p_value <0.05, we reject the null hypothesis HO and accept the alternative hypothesis HA, meaning we are 95% confident that the mean low self-esteem score (LSE) is higher in excluded participants than in included participants. Presence of a problem (PofaP variable): Although not specifically a facet of "rumination," the admission of a problem or negative feeling was assessed in the tests. Similarly, since p_value <0.05, we reject the null hypothesis HO and accept the alternative hypothesis HA, meaning we are 95% confident that the mean low self-esteem score (PofaP) is higher in excluded participants than in included participants. Counterfactual Thoughts (CT variable): Extent to which the participant reflects on past events and imagines alternatives to what happened. Again, the evaluators determined a score based on the clarity with which the counterfactual thoughts were expressed. inference(x = Cond, y = CT, data = data2, statistic = "mean", type = "ht", method = "theoretical", alternative = "twosided", null = 0, conf_level = 0.95) Response variable: numerical Explanatory variable: categorical (2 levels) n_Exclusion = 71, y_bar_Exclusion = 0.3521, s_Exclusion = 0.7191 n_Inclusion = 63, y_bar_Inclusion = 0.2222, s_Inclusion = 0.7058 H0: mu_Exclusion = mu_Inclusion HA: mu_Exclusion != mu_Inclusion t = 1.0539, df = 62 p_value = 0.296 Being p_value > 0.05, we accept the null hypothesis H0, meaning we are 95% confident that there is no significant difference in the mean CT counterfactual thinking score between those excluded and those included. Difficulty concentrating Participants were given 10 minutes to complete the RAPM, while also having to recall a list of words to assess their concentration and memorization skills. Those excluded showed significantly worse performance on this task, suggesting impaired concentration. inference(x = Cond, y = `RAPM Score`, data = data2, statistic = "mean", type = "ht", method = "theoretical", alternative = "less", null = 0, conf_level = 0.95) Response variable: numerical Explanatory variable: categorical (2 levels) n_Exclusion = 69, y_bar_Exclusion = 6.5362, s_Exclusion = 2.6711 n_Inclusion = 62, y_bar_Inclusion = 7.4677, s_Inclusion = 1.9646 H0: mu_Exclusion = mu_Inclusion HA: mu_Exclusion < mu_Inclusion t = -2.2887, df = 61 p_value = 0.0128 Since the p-value is <0.05, we reject the null hypothesis (HO) and accept the alternative hypothesis (HA). Therefore, we are 95% confident that the mean RAPM Score is lower in the excluded participants than in the included participants.