How to Perform Independent
Sample t-test in R

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What is an Independent Samples t-test?

The independent samples t-test is a parametric statistical technique that we use to compare the mean value of a continuous variable in two different groups. Therefore, for an independent samples t-test, we need one continuous dependent variable (e.g. stress level, anxiety level, self-esteem level, etc.) and one categorical independent variable with two groups (e.g. gender (male, female), belief in love (yes, no), in a relationship (yes, no), etc.).

When Should an Independent t-test be Used?

The independent t-test is used when you have two separate groups of individuals or cases in a between-participants design.

An Example Of Independent t-tests

For example, a researcher wants to examine whether men’s and women’s stress levels differ. Therefore, we have one dependent variable – the stress level, which we measure on a scale from 1 to 10, and one independent variable, gender, with two categories (male and female).

Therefore, we test the following hypotheses:

Null hypothesis: There is no significant difference in stress levels between males and females.

Alternative hypothesis: There is a significant difference in stress levels between males and females.

R function to Compute Independent t-test

The code to run an independent-samples t-test using R is as follows:

t.test (DV~ IV, var.equal=TRUE, data = dataframe)

DV: dependent variable

IV: Independent variable

Running Independent t test in Rstudio

In this section, we will show you how to run the independent sample t-test using the r studio program and how to interpret the test results after we obtain the result of the test. In the first part, we present the r program code and function for the independent t-test. Next, you will see the outputs as a result of running the r codes. In the last section, you can find the interpretation of the independent t-test in APA format.

# LOAD DATA
library(readxl)
Data <- read_excel(“Data.xlsx“)
# VIEW DATA
View(Data)
# NAME VARIABLES
data <- Data
stress <- data$stress
gender <- data$gender
# SHOW LEVELS OF CATEGORICAL VARIABLE
levels(gender)

# SHOW MEANS AND STANDARD DEVIATIONS OF DEPENDENT VARIABLE BY CATEGORICAL VARIABLE
library(dplyr)
group_by(data, gender) %>% summarise(count = n(), mean = mean(stress, na.rm = TRUE), sd = sd(stress, na.rm = TRUE))

# PERFORM INDEPENDENT SAMPLES T-TEST
t.test (stress ~ gender, var.equal=TRUE, data = data)

> group_by(data, gender) %>% summarise(count = n(), mean = mean(stress, na.rm = TRUE), sd = sd(stress, na.rm = TRUE))

## # A tibble: 2 × 4
##   gender count  mean    sd
##   <chr>  <int> <dbl> <dbl>
## 1 female    44  5.20  2.78
## 2 male      55  5.22  2.83

> t.test (stress ~ gender, var.equal=TRUE, data = data)

## 
##  Two Sample t-test
## 
## data:  stress by gender
## t = -0.024015, df = 97, p-value = 0.9809
## alternative hypothesis: true difference in means between group female and group male is not equal to 0
## 95 percent confidence interval:
##  -1.140638  1.113365
## sample estimates:
## mean in group female   mean in group male 
##             5.204545             5.218182

Reporting Independent t-test in R

Independent samples t-test was conducted to determine whether there is a difference in stress levels between males and females. The results indicate a non-significant difference in stress level between males (M = 5.22; SD = 2.83) and females (M = 5.20; SD = 2.78), t(97) = -0.02, p = 0.981. The 95% confidence interval range from -1.14 to 1.11 and does not indicate a significant difference between the sample means. We, therefore, fail to reject the null hypothesis and conclude that there is no significant difference in stress levels between males and females.

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How to Perform Independent Sample t-test in R