LikertMakeR synthesises and correlates Likert-scale and related rating-scale data.

You decide the mean and standard deviation, and the correlations among vectors, and the package will generate data with those properties, usually accurate to two decimal places.

The following panels show how to:

a. Generate Likert-scale items from Cronbach’s Alpha and predefined moments

b. Generate correlated multi-item scales

In both cases, this is a multi-step process

1. establish a correlation matrix

2. establish rating-scale properties

3. generate the data

4. validate the data

Step 1: Correlation matrix from Cronbach’s alpha

1. In the Correlation Matrix tab,

Click on the “Generate from Cronbach’s alpha” button (the default)

• Enter the Number of items in your scale
• set the Target alpha value
• for most purposes, leave “Variance” at the default (0.5)

When ready, hit the “Generate correlation matrix” button

You will see a correlation matrix that will deliver a dataframe with your desired alpha.

Step 2: Generate your data

2. hit the Data Generation tab

Most users prefer “Manual entry”

• Complete the online form to enter for each item:

  • Scale name,
  • Scale mean,
  • Scale standard deviation
  • Lower bound (e.g., 1 on a 1-5 scale)
  • Upper bound (e.g., 5 on a 1-5 scale)
  • nItems should remain at 1

• Enter the Number of Observations

Hit the Generate my data button.

You should see a dataframe with columns to match your specifications.

Step 3: Validate your data

3. Hit the Data Validation tab

A visual summary of your data will appear

Additional panels reveal data moments (means and standard deviations), calculated Cronbach’s alpha and more.

Step 1: Establish a correlation matrix

1. In the Correlation Matrix tab,

Hit the Upload CSV file radio button

Upload a correlation matrix in CSV file format.

The correlation matrix must be a square matrix with the same dimensions as the number of scales (columns) you want in your dataframe.

A template can be downloaded.

Correlation matrix example :

Big 6 personality protocol (Milojev et al., 2013)¹

var, Extraversion, Agreeableness, Conscientiousness, Neuroticism, Openness, Humility
Extraversion, 1, 0.213, 0.016, -0.088, 0.264, 0.106
Agreeableness, 0.213, 1, 0.14, -0.033, -0.233, 0.156
Conscientiousness, 0.016, 0.14, 1, -0.112, 0.0021, 0.072
Neuroticism, -0.088, -0.033, -0.112, 1, -0.01, -0.192
Openness, 0.264, -0.233, 0.0021, -0.01, 1, 0.018
Humility, 0.106, 0.156, 0.072, -0.192, 0.018, 1

Note: Technically, data points in a CSV file are separated with a “comma” only - no spaces - but I have separated them here for ease-of-reading. The program will quietly repair most deviations from strict CSV formatting.

Step 2: Establish scale properties

2. hit the Data Generation tab

Either, complete the online form, or upload a CSV file

2a: Complete the online form:

Required parameters are:

• n: number of observations. Acceptable range is ‘4’ to ‘1024’

• mean: mean value of each variable. Default = ‘3’.

• sd: standard deviation value of each variable. Default = ‘1’.

• lowerbound: a value for the lower bound of each variable (e.g., ‘1’ in a 1 to 5 rating scale). Default = ‘1’.

• upperbound: a value for the upper bound of each variable (e.g., ‘7’ in a 1 to 7 rating scale). Default = ‘5’.

• nItems: number of items in the scale. Default = ‘1’.

Default, starter, values are given in the input array.

2b: Upload a CSV file:

The Comma-separated Values (CSV) file should have a single row with column names, followed by ‘k’ rows, where ‘k’ = the number of scales you want to produce.

Column names must be labelled:

• scale (scale name)
• mean (scale mean)
• sd (scale standard deviation)
• lower (scale lowerbound (e.g., ‘1’ in a 1-5 scale))
• upper (scale upperbound (e.g., ‘5’ in a 1-5 scale))
• nItems (number of items (questions) in the scale)

Example parameters CSV file

Six constructs, each measured with four 1-7 rating-scale items (Milojev et al., 2013)

scale,             mean,  sd,   lower, upper, nItems
Extraversion,      4,     1.16, 1,     7,     4
Agreeableness,     5.27,  0.98, 1,     7,     4
Conscientiousness, 5.17,  1.05, 1,     7,     4
Neuroticism,       3.39,  1.1,  1,     7,     4
Openness,          4.76,  1.12, 1,     7,     4
Humility,          5.15,  1.32, 1,     7,     4

Step 3: Generate the dataframe

When you are satisfied with the correlations and the scale properties, then:

• Enter the desired sample size, and

• hit the Generate my data button

Your new dataframe will appear a moment later.

(This may take time if you have many variables and a large sample size)

• Hit the Download my data button to retrieve your data as a CSV file

Step 3: Data validation

3. Hit the Data Validation tab

You should see summary statistics and a graphic summary of your dataframe.