Analyze Likert scale survey data with comprehensive statistics and visualizations. Perfect for questionnaires, attitude scales, and opinion surveys used in psychology, education, market research, and social sciences.
Ready to analyze your Likert scale data? to see a customer satisfaction survey analysis in action, or upload your own survey data to get detailed insights into your respondents' attitudes and opinions.
Select the columns containing Likert scale responses. Each column should represent one survey item/question with numerical responses.
Select the type of Likert scale used in your survey, or let the calculator auto-detect the scale range from your data.
A Likert scale is a psychometric scale commonly used in questionnaires to measure attitudes, opinions, and perceptions. Named after psychologist Rensis Likert, it typically presents respondents with a statement and asks them to indicate their level of agreement or frequency on an ordered scale (e.g., 1 = Strongly Disagree to 5 = Strongly Agree).
Mean (Average): The average response for each item. Higher means indicate stronger agreement or frequency. Useful for comparing items and ranking their importance.
Median: The middle value when responses are ordered. Less affected by extreme values than the mean, providing a robust measure of central tendency.
Mode: The most frequently selected response. Indicates the most common answer among participants.
Standard Deviation (SD): Measures response variability. Low SD indicates consensus among respondents; high SD suggests diverse opinions.
Floor & Ceiling Effects: Occur when a large proportion of responses cluster at the minimum (floor) or maximum (ceiling) values, indicating limited scale sensitivity or poor item design.
For 5-point scales (1-5):
Note: Adjust thresholds proportionally for different scale lengths (e.g., 7-point or 10-point scales).
Customer Satisfaction Survey
A restaurant surveys customers using a 5-point scale (1 = Very Dissatisfied to 5 = Very Satisfied) across four items:
Interpretation: Staff friendliness scores highest with low variability (consensus), while service speed scores lower with higher variability (mixed opinions), suggesting an area for improvement.
Calculate Likert scale statistics using R:
# Load required library
library(dplyr)
# Sample Likert scale data
data <- data.frame(
item1 = c(5, 4, 5, 3, 4, 5, 4, 3, 5, 4),
item2 = c(4, 3, 4, 2, 3, 4, 3, 2, 4, 3),
item3 = c(5, 5, 4, 4, 5, 5, 4, 3, 5, 5),
item4 = c(3, 4, 3, 2, 3, 4, 3, 2, 4, 3)
)
# Calculate statistics for each item
results <- data %>%
summarise(across(everything(), list(
mean = ~mean(., na.rm = TRUE),
median = ~median(., na.rm = TRUE),
sd = ~sd(., na.rm = TRUE),
mode = ~as.numeric(names(sort(table(.), decreasing = TRUE)[1]))
)))
print(results)
# Calculate response distribution percentages
for (col in names(data)) {
cat("\n", col, "Distribution:\n")
freq <- table(data[[col]])
pct <- prop.table(freq) * 100
print(round(pct, 2))
}
# Item ranking by mean
means <- colMeans(data, na.rm = TRUE)
ranked <- sort(means, decreasing = TRUE)
cat("\nItems ranked by mean score:\n")
print(ranked)Calculate Likert scale statistics using Python:
import pandas as pd
import numpy as np
# Sample Likert scale data
data = pd.DataFrame({
'item1': [5, 4, 5, 3, 4, 5, 4, 3, 5, 4],
'item2': [4, 3, 4, 2, 3, 4, 3, 2, 4, 3],
'item3': [5, 5, 4, 4, 5, 5, 4, 3, 5, 5],
'item4': [3, 4, 3, 2, 3, 4, 3, 2, 4, 3]
})
# Calculate statistics for each item
results = data.describe().T[['mean', '50%', 'std']]
results.columns = ['Mean', 'Median', 'Std Dev']
results['Mode'] = data.mode().iloc[0]
print("Item Statistics:")
print(results)
# Calculate response distribution percentages
print("\nResponse Distributions:")
for col in data.columns:
freq = data[col].value_counts(normalize=True) * 100
print(f"\n{col}:")
print(freq.sort_index())
# Item ranking by mean
ranked = data.mean().sort_values(ascending=False)
print("\nItems ranked by mean score:")
print(ranked)Calculate Likert scale statistics using SPSS:
* Calculate descriptive statistics for all items.
DESCRIPTIVES VARIABLES=item1 item2 item3 item4
/STATISTICS=MEAN STDDEV MIN MAX.
* Calculate frequencies and percentages.
FREQUENCIES VARIABLES=item1 item2 item3 item4
/FORMAT=NOTABLE
/STATISTICS=MEAN MEDIAN MODE STDDEV
/BARCHART PERCENT.
* Create frequency tables with percentages.
FREQUENCIES VARIABLES=item1 item2 item3 item4
/ORDER=ANALYSIS.
* Calculate median for each item.
FREQUENCIES VARIABLES=item1 item2 item3 item4
/PERCENTILES=50.Analyze Likert scale survey data with comprehensive statistics and visualizations. Perfect for questionnaires, attitude scales, and opinion surveys used in psychology, education, market research, and social sciences.
Ready to analyze your Likert scale data? to see a customer satisfaction survey analysis in action, or upload your own survey data to get detailed insights into your respondents' attitudes and opinions.
Select the columns containing Likert scale responses. Each column should represent one survey item/question with numerical responses.
Select the type of Likert scale used in your survey, or let the calculator auto-detect the scale range from your data.
A Likert scale is a psychometric scale commonly used in questionnaires to measure attitudes, opinions, and perceptions. Named after psychologist Rensis Likert, it typically presents respondents with a statement and asks them to indicate their level of agreement or frequency on an ordered scale (e.g., 1 = Strongly Disagree to 5 = Strongly Agree).
Mean (Average): The average response for each item. Higher means indicate stronger agreement or frequency. Useful for comparing items and ranking their importance.
Median: The middle value when responses are ordered. Less affected by extreme values than the mean, providing a robust measure of central tendency.
Mode: The most frequently selected response. Indicates the most common answer among participants.
Standard Deviation (SD): Measures response variability. Low SD indicates consensus among respondents; high SD suggests diverse opinions.
Floor & Ceiling Effects: Occur when a large proportion of responses cluster at the minimum (floor) or maximum (ceiling) values, indicating limited scale sensitivity or poor item design.
For 5-point scales (1-5):
Note: Adjust thresholds proportionally for different scale lengths (e.g., 7-point or 10-point scales).
Customer Satisfaction Survey
A restaurant surveys customers using a 5-point scale (1 = Very Dissatisfied to 5 = Very Satisfied) across four items:
Interpretation: Staff friendliness scores highest with low variability (consensus), while service speed scores lower with higher variability (mixed opinions), suggesting an area for improvement.
Calculate Likert scale statistics using R:
# Load required library
library(dplyr)
# Sample Likert scale data
data <- data.frame(
item1 = c(5, 4, 5, 3, 4, 5, 4, 3, 5, 4),
item2 = c(4, 3, 4, 2, 3, 4, 3, 2, 4, 3),
item3 = c(5, 5, 4, 4, 5, 5, 4, 3, 5, 5),
item4 = c(3, 4, 3, 2, 3, 4, 3, 2, 4, 3)
)
# Calculate statistics for each item
results <- data %>%
summarise(across(everything(), list(
mean = ~mean(., na.rm = TRUE),
median = ~median(., na.rm = TRUE),
sd = ~sd(., na.rm = TRUE),
mode = ~as.numeric(names(sort(table(.), decreasing = TRUE)[1]))
)))
print(results)
# Calculate response distribution percentages
for (col in names(data)) {
cat("\n", col, "Distribution:\n")
freq <- table(data[[col]])
pct <- prop.table(freq) * 100
print(round(pct, 2))
}
# Item ranking by mean
means <- colMeans(data, na.rm = TRUE)
ranked <- sort(means, decreasing = TRUE)
cat("\nItems ranked by mean score:\n")
print(ranked)Calculate Likert scale statistics using Python:
import pandas as pd
import numpy as np
# Sample Likert scale data
data = pd.DataFrame({
'item1': [5, 4, 5, 3, 4, 5, 4, 3, 5, 4],
'item2': [4, 3, 4, 2, 3, 4, 3, 2, 4, 3],
'item3': [5, 5, 4, 4, 5, 5, 4, 3, 5, 5],
'item4': [3, 4, 3, 2, 3, 4, 3, 2, 4, 3]
})
# Calculate statistics for each item
results = data.describe().T[['mean', '50%', 'std']]
results.columns = ['Mean', 'Median', 'Std Dev']
results['Mode'] = data.mode().iloc[0]
print("Item Statistics:")
print(results)
# Calculate response distribution percentages
print("\nResponse Distributions:")
for col in data.columns:
freq = data[col].value_counts(normalize=True) * 100
print(f"\n{col}:")
print(freq.sort_index())
# Item ranking by mean
ranked = data.mean().sort_values(ascending=False)
print("\nItems ranked by mean score:")
print(ranked)Calculate Likert scale statistics using SPSS:
* Calculate descriptive statistics for all items.
DESCRIPTIVES VARIABLES=item1 item2 item3 item4
/STATISTICS=MEAN STDDEV MIN MAX.
* Calculate frequencies and percentages.
FREQUENCIES VARIABLES=item1 item2 item3 item4
/FORMAT=NOTABLE
/STATISTICS=MEAN MEDIAN MODE STDDEV
/BARCHART PERCENT.
* Create frequency tables with percentages.
FREQUENCIES VARIABLES=item1 item2 item3 item4
/ORDER=ANALYSIS.
* Calculate median for each item.
FREQUENCIES VARIABLES=item1 item2 item3 item4
/PERCENTILES=50.