{"id":19967,"date":"2016-03-21T14:15:26","date_gmt":"2016-03-21T21:15:26","guid":{"rendered":"http:\/\/www.questionpro.com\/?p=19967"},"modified":"2023-08-22T04:39:48","modified_gmt":"2023-08-22T04:39:48","slug":"mean-scores-tricks-and-traps","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/mean-scores-tricks-and-traps\/","title":{"rendered":"Mean Scores – tricks and traps"},"content":{"rendered":"\n

In recent posts, we examined the nature of the data types available to consumer or B2B market researchers including; nominal, ordinal, interval and ratio<\/a>. The latter two categories allow the user to generate mean score or averages as part of their survey data analysis<\/a>. Working with means gives the researcher access to a wealth of multivariate statistics, but mean scores are not without their issues. Let\u2019s look at a few of them.<\/p>\n\n\n\n

First, means are heavily influenced by extreme scores, also known as outliers. These high and\/or low scores can pull the mean up or down depending upon their location. This is why variables that focus on wealth, income or prices are typically reported using the median, e.g. the median housing price or median monthly income. Variables with numerical categories such as customer satisfaction<\/a> or brand awareness tend to be less influenced by outliers. Options for dealing with outliers include removing them from analysis or recoding them with the median value. Either option is not without considerations that need to be addressed. These are beyond the scope of this post.<\/p>\n\n\n\n\t

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