R is a great tool to test regression models. The following is an R code for a logistic regression model. The objective of this model is to find effects of collective coping and social movement intent on the choice of one of the two hashtags related to racial discrimination (#AliveWhileBlack and #BlackLivesMatter).

######## BlackLivesMatter & AliveWhileBlack Preliminary Statistics ########

data1 <- read.csv(“/Research/…/Data/FinalDataset.csv”, header = TRUE)

data <-as.data.frame(data1)

summary(data1)

##########Logit Model, Resource: http://www.ats.ucla.edu/stat/r/dae/logit.htm############

data$hahtag <- factor(data$Hashtag)#converting Hashtag into a categorical factor

#Regression Model

logit1 <- glm(Hashtag ~ COMSTRS+COMCOP+ADVSS+BOUND+IDENT+GRPCOM+INFODIS+COORD, data = data, family = “binomial”)

summary(logit1)

## Confidence intervals (CIs) using profiled log-likelihood

confint(logit1)

## CIs using standard errors

confint.default(logit1)

## odds ratios only

exp(coef(logit1))

## odds ratios and 95% CI

exp(cbind(OR = coef(logit1), confint(logit1)))

#Model fit

with(logit1, null.deviance – deviance)#Chi-square

with(logit1, df.null – df.residual) #degree of freedom

with(logit1, pchisq(null.deviance – deviance, df.null – df.residual, lower.tail = FALSE))#p-value

#Plot the model

plot(logit1)

###################################################################

#It looks like the last variable in the above model is problematic

#Regression Model2 |||| This is a better model

logit2 <- glm(Hashtag ~ COMSTRS+COMCOP+ADVSS+BOUND+IDENT+GRPCOM+INFODIS, data = data, family = “binomial”)

summary(logit2)

## Confidence intervals (CIs) using profiled log-likelihood

confint(logit2)

## CIs using standard errors

confint.default(logit2)

## odds ratios only

exp(coef(logit2))

## odds ratios and 95% CI

exp(cbind(OR = coef(logit1), confint(logit2)))

#Model fit

with(logit2, null.deviance – deviance)#Chi-square

with(logit2, df.null – df.residual) #degree of freedom

with(logit2, pchisq(null.deviance – deviance, df.null – df.residual, lower.tail = FALSE))#p-value

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