setwd("/Users/levibrackman/Desktop/Adult_study")
data_pre <- read.csv("pre.csv")
data_post <- read.csv("post.csv")
table(data_pre$GROUP, dnn = "group")
## group
## 0 1
## 35 32
data <- merge(data_pre, data_post, by = "ID", all = TRUE)
library(psych)
library(lattice)
t.test(data$PERMA17.x ~ GROUP.x, data = data)
##
## Welch Two Sample t-test
##
## data: data$PERMA17.x by GROUP.x
## t = -0.1446, df = 64.81, p-value = 0.8855
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.9655 0.8351
## sample estimates:
## mean in group 0 mean in group 1
## 7.029 7.094
t.test(data$PERMA17.y ~ GROUP.x, data = data)
##
## Welch Two Sample t-test
##
## data: data$PERMA17.y by GROUP.x
## t = -1.718, df = 44.56, p-value = 0.09266
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.587 0.126
## sample estimates:
## mean in group 0 mean in group 1
## 7.269 8.000
PERMA_HAPPI_ANCOVA <- lm(data$PERMA17.y ~ as.factor(GROUP.x) + data$PERMA17.x,
data = data)
# check assumptions visually
plot(PERMA_HAPPI_ANCOVA)
# see results
summary(PERMA_HAPPI_ANCOVA)
##
## Call:
## lm(formula = data$PERMA17.y ~ as.factor(GROUP.x) + data$PERMA17.x,
## data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.368 -0.399 0.272 0.632 1.334
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.113 0.832 2.54 0.015 *
## as.factor(GROUP.x)1 0.639 0.320 2.00 0.052 .
## data$PERMA17.x 0.702 0.109 6.41 8.4e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.09 on 44 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.513, Adjusted R-squared: 0.491
## F-statistic: 23.2 on 2 and 44 DF, p-value: 1.34e-07
PERMA_HAPPI_ANCOVA2 <- lm(PERMA17.y ~ as.factor(GROUP.x) + PERMA17.x, data = data,
-15)
summary(PERMA_HAPPI_ANCOVA2)
##
## Call:
## lm(formula = PERMA17.y ~ as.factor(GROUP.x) + PERMA17.x, data = data,
## subset = -15)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.272 -0.284 0.231 0.712 1.247
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.618 0.824 4.39 7.2e-05 ***
## as.factor(GROUP.x)1 0.503 0.281 1.79 0.081 .
## PERMA17.x 0.519 0.107 4.87 1.6e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.95 on 43 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.382, Adjusted R-squared: 0.353
## F-statistic: 13.3 on 2 and 43 DF, p-value: 3.19e-05
PERMA_HAPPI_ANCOVA3 <- lm(PERMA17.y ~ as.factor(GROUP.x) + PERMA17.x, data = data[-c(15,
39), ])
summary(PERMA_HAPPI_ANCOVA3)
##
## Call:
## lm(formula = PERMA17.y ~ as.factor(GROUP.x) + PERMA17.x, data = data[-c(15,
## 39), ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.7833 -0.3319 0.0468 0.4983 1.2167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.3949 0.5934 5.72 1.0e-06 ***
## as.factor(GROUP.x)1 0.7184 0.2051 3.50 0.0011 **
## PERMA17.x 0.5486 0.0768 7.14 9.1e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.683 on 42 degrees of freedom
## (22 observations deleted due to missingness)
## Multiple R-squared: 0.596, Adjusted R-squared: 0.577
## F-statistic: 31 on 2 and 42 DF, p-value: 5.34e-09