Visualizations for Values and Norms Predicting Conformity or Deviance from Unpopular Norms
Sara Lieber
07/06/2021
Project Background
This project was a follow-up to a previous study in which we analyzed common unpopular norms among college undergraduates. Unpopular norms are rules about behavior that people widely follow in public yet disagree with privately. In a previous study, when college undergraduates were asked to describe a behavior that they perceived as being widely followed or endorsed but that they did not personally agree with, the following three common behaviors emerged: 1) Wearing fashionable/trendy clothing, 2) Constantly using technology/social media, and 3) Limiting the self-expression of one’s true ideas in conversations with others. The purpose of this follow-up study was to examine the relative importance of personal values versus normative constructs as predictors of conformity with or deviance from these unpopular norms.
Import data
data <- import("mlm_setup_follow_up_survey_values_norms_GS_Fall19.xlsx")
glimpse(data)## Rows: 960
## Columns: 79
## $ ID <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, ~
## $ Q1.1 <chr> "20", "21", "20", "18", "18", "19", "21", "21",~
## $ Q328 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,~
## $ Q328_4_TEXT <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,~
## $ Q1.3 <dbl> 16, 16, 16, 16, 8, 10, 16, 8, 17, 16, 16, 10, 1~
## $ Q1.3_17_TEXT <chr> NA, NA, NA, NA, NA, NA, NA, NA, "White", NA, NA~
## $ Behavior <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
## $ Values1 <dbl> 5, 2, 2, 3, 3, 3, 4, 4, 4, 4, 4, 3, 5, 4, 4, 4,~
## $ Values2 <dbl> 5, 2, 3, 2, 4, 3, 4, 4, 5, 4, 3, 4, 5, 4, 4, 3,~
## $ Values3 <dbl> 4, 2, 2, 1, 2, 3, 4, 4, 5, 2, 1, 3, 5, 2, 4, 2,~
## $ Norm1 <dbl> 4, 3, 2, 3, 3, 3, 3, 4, 5, 2, 1, 2, 5, 2, 2, 1,~
## $ Norm2 <dbl> 4, 2, 2, 2, 2, 3, 2, 4, 2, 1, 1, 2, 5, 1, 4, 2,~
## $ Norm3 <dbl> 4, 4, 4, 5, 4, 4, 4, 4, 5, 4, 5, 4, 5, 4, 4, 4,~
## $ Norm4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, ~
## $ Conform <dbl> 4, 1, 4, 3, 3, 4, 4, 3, 5, 4, 4, 3, 5, 5, 4, 4,~
## $ Dont_Conform <dbl> 1, 5, 1, 2, 3, 3, 1, 3, 1, 2, 2, 2, 1, 1, 2, 2,~
## $ Deviate <dbl> 1, 2, 3, 1, 2, 3, 1, 2, 1, 2, 3, 3, 2, 1, 1, 2,~
## $ Stopping <dbl> 1, 1, 4, 2, 3, 3, 4, 2, 2, 4, 1, 2, 4, 4, 4, 3,~
## $ Is_Norm <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,~
## $ Self_College_Community <dbl> 5, 5, 4, 4, 4, 2, 5, 2, 3, 3, 2, 4, 4, 6, 4, 3,~
## $ IAF_1 <dbl> 5, 4, 3, 3, 4, 5, 4, 3, 4, 4, 5, 4, 5, 4, 4, 4,~
## $ IAF_2 <dbl> 4, 3, 2, 2, 3, 1, 4, 3, 3, 4, 3, 4, 5, 4, 4, 4,~
## $ IAF_3 <dbl> 4, 3, 2, 4, 3, 3, 5, 3, 4, 5, 5, 5, 5, 5, 5, 5,~
## $ IAF_4 <dbl> 4, 4, 4, 3, 4, 3, 4, 4, 3, 3, 5, 5, 5, 4, 4, 3,~
## $ IAF_5 <dbl> 4, 4, 3, 5, 3, 1, 4, 4, 5, 4, 5, 5, 5, 3, 4, 5,~
## $ IAF_6 <dbl> 2, 3, 3, 5, 4, 1, 4, 4, 4, 5, 1, 4, 5, 4, 3, 4,~
## $ IAF_7 <dbl> 2, 2, 2, 4, 3, 1, 2, 4, 4, 3, 1, 4, 5, 4, 4, 2,~
## $ IAF_8 <dbl> 4, 2, 4, 3, 3, 2, 4, 4, 3, 3, 5, 4, 5, 5, 4, 3,~
## $ IAF_9 <dbl> 4, 5, 2, 5, 4, 3, 5, 5, 5, 4, 5, 5, 5, 5, 4, 5,~
## $ IAF_10 <dbl> 4, 4, 4, 4, 4, 4, 5, 4, 3, 2, 5, 4, 5, 4, 4, 2,~
## $ IAF_11 <dbl> 2, 2, 3, 2, 4, 3, 2, 3, 1, 1, 1, 2, 5, 2, 5, 2,~
## $ IAF_12 <dbl> 4, 4, 3, 4, 4, 5, 5, 4, 5, 4, 5, 4, 5, 5, 4, 5,~
## $ IAF_13 <dbl> 4, 5, 2, 5, 4, 4, 5, 5, 4, 5, 4, 4, 5, 5, 4, 5,~
## $ IAF_14 <dbl> 3, 3, 2, 4, 4, 3, 3, 5, 3, 2, 3, 5, 5, 4, 2, 4,~
## $ IAF_15 <dbl> 4, 3, 4, 5, 4, 5, 4, 4, 3, 4, 5, 4, 5, 5, 4, 3,~
## $ BFI_1 <dbl> 4, 4, 5, 4, 1, 5, 4, 4, 5, 4, 5, 4, 5, 5, 2, 5,~
## $ BFI_2 <dbl> 4, 2, 3, 2, 2, 1, 2, 4, 4, 4, 1, 4, 2, 1, 4, 3,~
## $ BFI_3 <dbl> 4, 5, 5, 5, 4, 5, 3, 4, 4, 5, 5, 4, 5, 4, 4, 4,~
## $ BFI_4 <dbl> 2, 1, 1, 5, 2, 1, 3, 4, 4, 5, 1, 4, 1, 2, 2, 4,~
## $ BFI_5 <dbl> 2, 2, 2, 3, 2, 2, 4, 4, 4, 5, 5, 4, 5, 3, 4, 3,~
## $ BFI_6 <dbl> 2, 2, 3, 3, 4, 1, 4, 4, 2, 5, 2, 2, 3, 2, 4, 2,~
## $ BFI_7 <dbl> 4, 4, 3, 5, 5, 3, 4, 3, 3, 4, 5, 4, 5, 3, 5, 3,~
## $ BFI_8 <dbl> 2, 2, 4, 4, 4, 2, 4, 3, 4, 4, 1, 2, 1, 4, 2, 2,~
## $ BFI_9 <dbl> 4, 3, 4, 2, 2, 4, 2, 2, 4, 2, 3, 1, 5, 4, 2, 2,~
## $ BFI_10 <dbl> 4, 3, 4, 4, 3, 5, 4, 4, 5, 4, 5, 5, 5, 4, 4, 5,~
## $ BFI_11 <dbl> 4, 3, 3, 2, 3, 4, 3, 3, 3, 1, 5, 2, 5, 4, 1, 5,~
## $ BFI_12 <dbl> 2, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 4, 1, 2,~
## $ BFI_13 <dbl> 4, 4, 4, 5, 5, 4, 4, 3, 4, 5, 5, 5, 5, 4, 5, 4,~
## $ BFI_14 <dbl> 4, 2, 3, 5, 5, 4, 5, 3, 5, 4, 4, 5, 1, 4, 4, 4,~
## $ BFI_15 <dbl> 2, 2, 1, 1, 2, 3, 4, 4, 5, 4, 5, 5, 5, 4, 4, 5,~
## $ BFI_16 <dbl> 4, 3, 4, 2, 2, 4, 4, 3, 4, 2, 5, 2, 5, 4, 2, 4,~
## $ BFI_17 <dbl> 4, 4, 4, 5, 3, 4, 4, 3, 4, 4, 5, 1, 1, 4, 4, 3,~
## $ BFI_18 <dbl> 4, 2, 2, 4, 1, 1, 2, 3, 2, 4, 1, 1, 1, 3, 2, 2,~
## $ BFI_19 <dbl> 4, 3, 2, 4, 5, 3, 4, 4, 4, 4, 4, 5, 2, 2, 4, 5,~
## $ BFI_20 <dbl> 2, 2, 2, 4, 4, 5, 4, 4, 5, 4, 4, 5, 5, 3, 2, 4,~
## $ BFI_21 <dbl> 2, 2, 2, 2, 5, 3, 2, 4, 1, 4, 1, 2, 1, 1, 4, 1,~
## $ BFI_22 <dbl> 4, 1, 5, 5, 4, 5, 4, 4, 1, 5, 5, 5, 5, 4, 4, 3,~
## $ BFI_23 <dbl> 2, 2, 3, 4, 3, 3, 4, 4, 4, 3, 1, 3, 1, 2, 2, 2,~
## $ BFI_24 <dbl> 4, 4, 4, 1, 3, 5, 2, 4, 2, 2, 5, 2, 5, 3, 2, 1,~
## $ BFI_25 <dbl> 2, 3, 2, 3, 2, 3, 2, 3, 4, 2, 3, 5, 5, 4, 4, 2,~
## $ BFI_26 <dbl> 4, 4, 2, 3, 1, 4, 2, 3, 5, 5, 2, 5, 5, 4, 2, 4,~
## $ BFI_27 <dbl> 2, 3, 3, 3, 1, 4, 4, 3, 5, 2, 1, 4, 1, 4, 4, 2,~
## $ BFI_28 <dbl> 4, 4, 4, 4, 5, NA, 4, 4, 4, 4, 5, 4, 5, 2, 4, 4~
## $ BFI_29 <dbl> 4, 2, 3, 4, 4, 5, 4, 4, 5, 5, 3, 5, 1, 4, 2, 5,~
## $ BFI_30 <dbl> 2, 3, 1, 4, 2, 5, 4, 3, 5, 5, 5, 4, 3, 4, 4, 4,~
## $ BFI_31 <dbl> 2, 2, 3, 4, 4, 4, 4, 5, 2, 4, 1, 3, 2, 2, 4, 1,~
## $ BFI_32 <dbl> 4, 4, 3, 5, 5, 5, 5, 4, 3, 4, 5, 4, 5, 4, 5, 3,~
## $ BFI_33 <dbl> 4, 4, 4, 4, 4, 4, 3, 4, 4, 2, 5, 4, 5, 4, 5, 4,~
## $ BFI_34 <dbl> 4, 4, 4, 2, 3, 4, 2, 4, 5, 2, 3, 4, 5, 4, 4, 1,~
## $ BFI_35 <dbl> 4, 4, 4, 5, 5, 5, 4, 4, 1, 4, 5, 4, 5, 4, 5, 3,~
## $ BFI_36 <dbl> 4, 4, 4, 4, 4, 5, 4, 2, 5, 2, 5, 3, 5, 4, 2, 5,~
## $ BFI_37 <dbl> 2, 2, 3, 1, 4, 4, 2, 4, 2, 2, 1, 5, 2, 4, 1, 2,~
## $ BFI_38 <dbl> 4, 4, 4, 4, 5, 5, 4, 4, 5, 4, 4, 5, 5, 2, 5, 4,~
## $ BFI_39 <dbl> 2, 4, 2, 4, 5, 3, 4, 5, 2, 4, 4, 5, 5, 2, 4, 5,~
## $ BFI_40 <dbl> 2, 3, 2, 3, 2, 5, 4, 3, 5, 4, 4, 5, 5, 4, 4, 5,~
## $ BFI_41 <dbl> 4, 3, 4, 1, 4, 5, 3, 3, 5, 1, 1, 1, 3, 2, 2, 2,~
## $ BFI_42 <dbl> 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 5, 3, 5, 4, 4, 4,~
## $ BFI_43 <dbl> 2, 3, 4, 4, 4, 3, 3, 2, 4, 5, 3, 5, 5, 3, 2, 4,~
## $ BFI_44 <dbl> 2, 2, 1, 5, 2, 4, 2, 2, 4, 4, 5, 3, 1, 4, 4, 2,~Cleaning Data
Renaming variables
data_clean <- data %>%
rename(Age = Q1.1,
Gender = Q328,
Gender_Text = Q328_4_TEXT,
Ethnicity = Q1.3,
Ethnicity_Text = Q1.3_17_TEXT)Cleaning variable types
# str(data_clean)
data_clean <- data_clean %>%
mutate(Age = as.numeric(Age),
Gender = as.factor(Gender),
Ethnicity = as.factor(Ethnicity),
Behavior = as.factor(Behavior),
Is_Norm = as.factor(Is_Norm))
levels(data_clean$Is_Norm) <- c("Yes", "No")
levels(data_clean$Is_Norm)## [1] "Yes" "No"n_distinct(data_clean$ID) # 320 participants## [1] 320Labeling levels of nominal variables
levels(data_clean$Gender) <- c("Female", "Male", "Non-binary", "Prefer to self-describe", "Prefer not to say")
levels(data_clean$Ethnicity) <- c("American Indian or Alaska Native", "Asian", "Black or African American", "Hispanic, Latinx or Spanish Origin", "Middle Eastern or North African", "Native Hawaiian or Other Pacific Islander", "White", "Some other ethnicity or origin", "I prefer not to answer")
levels(data_clean$Behavior) <- c("Wearing fashionable clothing", "Constantly using technology", "Limiting self-expression")Computing average of the values items
data_clean <- data_clean %>%
mutate(values_avg = ((Values1 + Values2 + Values3)/3))Univariate Visualizations
Investigating whether the following three behaviors previously identified as unpopular norms followed by college undergraduates are seen as norms that college students follow in new sample
- Behavior 1: Wearing fashionable/trendy clothing
- Behavior 2: Constantly using technology/social media
- Behavior 3: Limiting self-expression of one’s true ideas
data_clean %>%
filter(Is_Norm != "NA") %>%
ggplot(aes(x = Is_Norm, fill = Behavior)) +
geom_bar(position = "dodge") +
labs(x = "Is this a norm college students follow?", y = "Count")
Personal values across the three unpopular behaviors
data_clean %>%
ggplot(aes(x = values_avg, y = Behavior, fill = Behavior, color = Behavior)) +
geom_density_ridges(alpha = 0.5) +
labs(x = "Personal Valuing of the Normative Behavior", y = NULL, fill = "Behavior", color = "Behavior")## Picking joint bandwidth of 0.246
Perceived descriptive norm across the three unpopular behaviors
- Norm3: I think most other students engage in this behavior.
data_clean %>%
ggplot(aes(x = Norm3, y = Behavior, fill = Behavior, color = Behavior)) +
geom_density_ridges(alpha = 0.5) +
labs(x = "Perceived Descriptive Norm", y = NULL, fill = "Behavior", color = "Behavior")## Picking joint bandwidth of 0.242
Perceived injunctive norm across the three unpopular behaviors
- Norm4: I think most other students believe college students should engage in this behavior.
data_clean %>%
ggplot(aes(x = Norm4, y = Behavior, fill = Behavior, color = Behavior)) +
geom_density_ridges(alpha = 0.5) +
labs(x = "Perceived Inunctive Norm", y = NULL, fill = "Behavior", color = "Behavior")## Picking joint bandwidth of 0.281
Bivariate Visualizations
The relationship between personal values and conformity with three unpopular behaviors among college undergraduates
Scatterplot: Color by group, single graph
ggplot(data_clean, aes(x = values_avg, y = Conform, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
labs(title = "The Relationship Between Values and Norm Conformity", x = "Personal Valuing of the Normative Behavior", y = "Frequency of Following the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
Scatterplot: Add facet wrap by group, separate graphs per group
ggplot(data_clean, aes(x = values_avg, y = Conform, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
facet_wrap(~Behavior) +
labs(title = "The Relationship Between Values and Norm Conformity", x = "Personal Valuing of the Normative Behavior", y = "Frequency of Following the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
The relationship between perceived descriptive norm and conformity with three unpopular behaviors among college undergraduates
Scatterplot: Color by group, single graph
ggplot(data_clean, aes(x = Norm3, y = Conform, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
labs(title = "The Relationship Between Descriptive Norms and Norm Conformity", x = "Perceived Descriptive Norm", y = "Frequency of Following the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
Scatterplot: Add facet wrap by group, separate graphs per group
ggplot(data_clean, aes(x = Norm3, y = Conform, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
facet_wrap(~Behavior) +
labs(title = "The Relationship Between Descriptive Norms and Norm Conformity", x = "Perceived Descriptive Norm", y = "Frequency of Following the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
The relationship between perceived injunctive norm and conformity with three unpopular behaviors among college undergraduates
Scatterplot: Color by group, single graph
ggplot(data_clean, aes(x = Norm4, y = Conform, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
labs(title = "The Relationship Between Injunctive Norms and Norm Conformity", x = "Perceived Injunctive Norm", y = "Frequency of Following the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
Scatterplot: Add facet wrap by group, separate graphs per group
ggplot(data_clean, aes(x = Norm4, y = Conform, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
facet_wrap(~Behavior) +
labs(title = "The Relationship Between Injunctive Norms and Norm Conformity", x = "Perceived Injunctive Norm", y = "Frequency of Following the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
The relationship between personal values and deviance from three unpopular behaviors among college undergraduates
Scatterplot: Color by group, single graph
ggplot(data_clean, aes(x = values_avg, y = Deviate, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
labs(title = "The Relationship Between Values and Norm Deviance", x = "Personal Valuing of the Normative Behavior", y = "Frequency of Deviating from the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
Scatterplot: Add facet wrap by group, separate graphs per group
ggplot(data_clean, aes(x = values_avg, y = Deviate, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
facet_wrap(~Behavior) +
labs(title = "The Relationship Between Values and Norm Deviance", x = "Personal Valuing of the Normative Behavior", y = "Frequency of Deviating from the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
The relationship between perceived descriptive norm and deviance from three unpopular behaviors among college undergraduates
Scatterplot: Color by group, single graph
ggplot(data_clean, aes(x = Norm3, y = Deviate, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
labs(title = "The Relationship Between Descriptive Norms and Norm Conformity", x = "Perceived Descriptive Norm", y = "Frequency of Deviating from the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
Scatterplot: Add facet wrap by group, separate graphs per group
ggplot(data_clean, aes(x = Norm3, y = Deviate, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
facet_wrap(~Behavior) +
labs(title = "The Relationship Between Descriptive Norms and Norm Conformity", x = "Perceived Descriptive Norm", y = "Frequency of Deviating from the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
The relationship between perceived injunctive norm and deviance from three unpopular behaviors among college undergraduates
Scatterplot: Color by group, single graph
ggplot(data_clean, aes(x = Norm4, y = Deviate, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
labs(title = "The Relationship Between Injunctive Norms and Norm Conformity", x = "Perceived Injunctive Norm", y = "Frequency of Deviating from the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
Scatterplot: Add facet wrap by group, separate graphs per group
ggplot(data_clean, aes(x = Norm4, y = Deviate, color = Behavior)) +
geom_point(alpha = 0.25) +
geom_smooth(method = "lm", se = F) +
facet_wrap(~Behavior) +
labs(title = "The Relationship Between Injunctive Norms and Norm Conformity", x = "Perceived Injunctive Norm", y = "Frequency of Deviating from the Norm") +
theme_minimal() +
theme(text = element_text(size = 12)) +
theme(plot.title = element_text(size = 13)) +
theme(plot.title = element_text(hjust = 0.5))## `geom_smooth()` using formula 'y ~ x'
Repeated measures analysis
Values, perceived descriptive norm, and perceived injunctive norm predicting norm conformity with three different unpopular behaviors
mlm1 <- lmer(Conform ~ 1 + values_avg*Norm3*Norm4 + (1 | Behavior),
data = data_clean)
anova(mlm1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## values_avg 6.5805 6.5805 1 892.15 8.3181 0.00402 **
## Norm3 4.1400 4.1400 1 892.06 5.2332 0.02239 *
## Norm4 0.1617 0.1617 1 892.17 0.2045 0.65126
## values_avg:Norm3 0.3229 0.3229 1 892.26 0.4081 0.52308
## values_avg:Norm4 0.0813 0.0813 1 892.06 0.1028 0.74863
## Norm3:Norm4 0.3011 0.3011 1 892.43 0.3806 0.53744
## values_avg:Norm3:Norm4 0.1234 0.1234 1 892.23 0.1560 0.69296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(mlm1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Conform ~ 1 + values_avg * Norm3 * Norm4 + (1 | Behavior)
## Data: data_clean
##
## REML criterion at convergence: 2390.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2543 -0.6307 -0.0050 0.6501 2.9165
##
## Random effects:
## Groups Name Variance Std.Dev.
## Behavior (Intercept) 0.1109 0.3330
## Residual 0.7911 0.8894
## Number of obs: 902, groups: Behavior, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.469526 0.614501 167.448551 0.764 0.44590
## values_avg 0.725133 0.251424 892.152706 2.884 0.00402 **
## Norm3 0.369487 0.161516 892.060053 2.288 0.02239 *
## Norm4 -0.099209 0.219407 892.169980 -0.452 0.65126
## values_avg:Norm3 -0.042848 0.067070 892.257981 -0.639 0.52308
## values_avg:Norm4 0.026714 0.083339 892.055749 0.321 0.74863
## Norm3:Norm4 0.034440 0.055825 892.432780 0.617 0.53744
## values_avg:Norm3:Norm4 -0.008182 0.020716 892.227179 -0.395 0.69296
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vls_vg Norm3 Norm4 vl_:N3 vl_:N4 Nr3:N4
## values_avg -0.834
## Norm3 -0.877 0.804
## Norm4 -0.813 0.715 0.761
## vls_vg:Nrm3 0.791 -0.938 -0.885 -0.656
## vls_vg:Nrm4 0.781 -0.891 -0.723 -0.886 0.807
## Norm3:Norm4 0.796 -0.693 -0.862 -0.944 0.730 0.828
## vls_v:N3:N4 -0.776 0.876 0.825 0.851 -0.894 -0.950 -0.890Values, perceived descriptive norm, and perceived injunctive norm predicting norm deviance with three different unpopular behaviors
mlm2 <- lmer(Deviate ~ 1 + values_avg*Norm3*Norm4 + (1 | Behavior),
data = data_clean)
anova(mlm2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## values_avg 1.75284 1.75284 1 894.31 1.7016 0.1924
## Norm3 1.54781 1.54781 1 894.12 1.5026 0.2206
## Norm4 1.47750 1.47750 1 894.38 1.4343 0.2314
## values_avg:Norm3 0.40615 0.40615 1 894.62 0.3943 0.5302
## values_avg:Norm4 0.02067 0.02067 1 894.08 0.0201 0.8874
## Norm3:Norm4 2.59110 2.59110 1 894.99 2.5154 0.1131
## values_avg:Norm3:Norm4 0.31755 0.31755 1 894.53 0.3083 0.5789summary(mlm2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Deviate ~ 1 + values_avg * Norm3 * Norm4 + (1 | Behavior)
## Data: data_clean
##
## REML criterion at convergence: 2630.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6428 -0.7108 -0.0558 0.7224 3.1826
##
## Random effects:
## Groups Name Variance Std.Dev.
## Behavior (Intercept) 0.05257 0.2293
## Residual 1.03010 1.0149
## Number of obs: 904, groups: Behavior, 3
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.98458 0.67884 524.86861 4.397 1.33e-05 ***
## values_avg -0.37413 0.28681 894.31272 -1.304 0.192
## Norm3 0.22605 0.18441 894.11812 1.226 0.221
## Norm4 0.29980 0.25033 894.37995 1.198 0.231
## values_avg:Norm3 -0.04806 0.07655 894.61653 -0.628 0.530
## values_avg:Norm4 0.01347 0.09507 894.08491 0.142 0.887
## Norm3:Norm4 -0.10103 0.06370 894.99388 -1.586 0.113
## values_avg:Norm3:Norm4 0.01312 0.02364 894.53491 0.555 0.579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) vls_vg Norm3 Norm4 vl_:N3 vl_:N4 Nr3:N4
## values_avg -0.861
## Norm3 -0.906 0.804
## Norm4 -0.839 0.715 0.761
## vls_vg:Nrm3 0.817 -0.938 -0.885 -0.655
## vls_vg:Nrm4 0.806 -0.891 -0.723 -0.887 0.807
## Norm3:Norm4 0.822 -0.694 -0.862 -0.944 0.730 0.828
## vls_v:N3:N4 -0.802 0.876 0.825 0.851 -0.894 -0.950 -0.890