# Why Reverse Coding Is Done In Spss

The aim of reverse scoring is to re-code the responses so that a high score is transformed into the corresponding low score on the scale. For example, in a 5-point scale, a 4 is transformed into a 2, and vice-versa.

Rather than specifying each individual recoded value–a 1 to 7, 2 to 6, and so on, just subtract the values from a constant one value higher than the highest value on the scale.

The value from which you subtract your old variable will always be one value higher than the highest value you have. So I subtracted my old variable from 8 because I have a 1 to 7 scale. If I had a 1 to 5 scale, I would subtract my old variable from 6.

For example if OldVariable is reverse coded and on a 1 to 7 scale, in SPSS, do this:

So for example let’s say you have 20 items each on a 1 to 7 scale. For most items, a 7 may indicate a positive attitude toward some issue, but for a few items, a 1 indicates a positive attitude.

COMPUTE NewVariable = 8 – OldVariable. (You can also do it in the menus in Transform–>Compute).

See what that’s doing? It is COMPUTE-ing three new variables: bss02r, bss03r, and bss07r. These new variables are based on their original values, but are each subtracted from 8 first. Make sure you have changed both sides of each equation in the syntax window: bss03r = 8-bss03, not 8-bss02. To run this syntax, click the Run menu and select All.

Most of the items in the BSS are phrased so that strong agreement indicates a belief that men should protect women, that men need women, or that women have positive qualities that men lack. However, three of the items are phrased in the reverse: #2, #3, and #7. Take a look at those items in the scale below (with rows highlighted in pink) and youll see what I mean. People who score high on items 1, 4-6, and 8-11 are high in Benevolent Sexism. They will tend to score LOW on items 2, 3, and 7. If you average 2, 3, and 7 with the other items, you will bring down the average for high-Benevolent-Sexism people and pull up the average for people who are low in Benevolent Sexism. Instead, what we want is an average that just keeps getting bigger if people endorse benevolent sexism and smaller if people disagree with it. In order to make the reversed items compatible with the other items (so that we can average them together), we will need to reverse score them.

Why are we looking at this? Because you have 2 more variables to reverse-score, and we can do that more quickly by modifying this code (or “syntax”, as SPSS calls it). First, select (highlight with the mouse) line 3, which begins with “COMPUTE bss02r…”. Press Ctrl+C to copy that line, then press Enter to add a blank line, and press Ctrl+V to paste that line twice:

Where max(x) is the maximum possible value for x. In our case, max(x) is 7 because the Likert scale only went up to 7. To reverse score, we take 7 + 1 = 8, and subtract our scores from that. 8 – 7 = 1, 8 – 1 = 7. Voila.

In this questionnaire, participants responded to the items using a 7-point Likert scale ranging from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”). When we reverse-score an item, we want 1s to turn into 7s, 7s to turn into 1s, and all the scores in between to become their appropriate opposite (6s into 2s, 5s into 3s, etc.). Fortunately, there is a simple mathematical rule for reverse-scoring:

## FAQ

Why is reverse scoring important?

What is Reverse Coding? One common validation technique for survey items is to rephrase a “positive” item in a “negative” way. When done properly, this can be used to check if respondents are giving consistent answers.

How do you reverse data in SPSS?

Reverse scoring is necessary when research instrument developers have purposefully written a group of items with some items in a different direction than others.

How do you know when to reverse a code?

Before you run a Cronbach’s alpha or factor analysis on scale items, it’s generally a good idea to reverse code items that are negatively worded so that a high value indicates the same type of response on every item.