Data can provide the trigger needed for a transformational change to occur, but interpreting it in the right way can be a challenge or any HR professional. Although new data brings the possibility of fresh perspectives, it’s also easy to make presumptions, draw false conclusions and subconsciously value one set of data over another. Here we outline the interpretation pitfalls to watch out for…
Data does not having any meaning in itself
Facts and figures are just numbers unless they are properly interpreted. The temptation is to presume that the data you glean has a straightforward meaning, when it is likely to be the very opposite. Avoid the trap of choosing the simplest conclusions and instead spend time finding the accurate conclusions – only then will the data lead to transformational change.
People read and respond to questions in different ways
No matter how you define your rating scale, you will not have definitions understood in precisely the same way by everybody. If someone chooses 4 out of 5 on a rating scale where 4 is defined as ‘good’, you’d be forgiven for thinking that the individual in question is ‘good’ in that particular area. That is some respects true, but a person who has consistently scored 5 with an anomalous 4 could be indicating something very different.
Don’t take everything at face value
Consistently high and low scores could be the sign of some ingrained rating tendencies, like the ‘recency effect’ whereby a respondent marks particularly high or low due to a recent performance. Other quirks that can feed into data include playing politics, aiming to look good over giving accurate data, fear of reprisals, and the halo or horns effect (where good performance overshadows bad and visa versa). Avoid these issues by looking at each piece of data in the context of a bigger picture.
Data gathered a second time may be different
Research suggests that around 60-70% of a respondents answers would be the same a second time round. This means there could be a 30-40% margin for error or interpretation in the data you’ve gathered. Bear this in mind before jumping to any swift conclusions.
Look out for rogue ratings
Very high or very low ratings without any other context or similar rankings are interesting, but could skew results if taken at face value. Delve into these anomalous results to discover if there are any underlying events or inter-personal factors that could be playing a role.
There can be a number of reasons for low and high ratings
If an individual is scored 1 on ‘remains calm under pressure’ question, it could indicate a number of things; like they react emotionally or show stress in tense scenarios, or they get loud, aggressive and snappy with colleagues. Equally, if some is rated a 5 for ‘excellent’ in an item denoting ‘dynamic and driven’, it could mean they are perceived as focused, determined or steely, controlling and overtly ambitious. There is a positive and a negative in everything.
Don’t forget about open text and comments sections
Just because data is quantifiable and comparable does not mean you should ignore the more subjective comments respondents have added to their feedback surveys. In fact, comments are essential to add context to your data and are likely to explain particularly high, low or anomalous results. Ignore them at your peril.
Desperately squeezing meanings out of the data in front of you is not interpretation, it’s manipulation. Use the data in front of you honestly, and don’t be tempted to embellish findings for your, the process or the recipient’s sake.
For more on this and other incredibly powerful insights and advice, get your copy of 360 Feedback : A Transformational Approach today.