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Tag: reverse scoring

Reverse scoring using R statistical environment

This post is my first on R and it will describe a method to reverse scores using R.

Reverse scoring

In many instruments (i.e., questionnaires) some items are phrased so that a strong agreement indicates something negative (e.g., “When there is music in the room I find it hard to concentrate on reading”). For more information on reverse scoring, please see my earlier post: Reverse scoring in Python.  Since I was more  familiar with Python  compared to R, and I had no a clue on how to do this in SPSS, I wrote a Python script. The Python script used a function that used Pandas DataFrame and it reversed the scores nice and quickly.

However, in R was pretty much as simple to do reverse scoring as in Python. In the following script a data frame is generated with column names (i.e., columnNames,’Q1′ to ‘Q6’) and some data is generated using replicate and sample (100 responses, on the 6 questions). After that you will find two methods, that are pretty much the same, for reversing the can be found. The methods only differ in how the columns are selected. The first are select based on the index of the column and the second select based on the column names to be reversed (might be preferable if you know the names of columns but not the indices).

R Script

Reverse Scoring in Python

When working with questionnaires that use a Likert scale (e.g., strongly disagree, disagree, neutral, agree, strongly agree) we sometimes want to reverse the scoring.  For instance, in a questionnaire subjective experiences of cognition some of the questions can be positively worded (e.g., “I find it easy to read while talking in the telephone at the same time”). However, we often have questions that are negatively worded (e.g., “When there is music in the room I find it hard to concentrate on reading”).

In the case of positively worded questions we can score them like this; strongly disagree with a score of 1, disagree= 2, neutral =3, agree = 4 and strongly disagree =5. However, the same scoring can’t be applied for the negatively worded questions. We now need to reverse the score of these questions.  In the python example below, score 5 becomes 1, 4 becomes 2, 2 becomes 4 and 1 becomes 5. Three, on the other hand, (neutral) stays the same.

The following Python example uses Pandas DataFrame. If you need to learn more about Pandas check out my dataframe tutorial.