ID_Num 1 2 3 4 4 6 7 8 9 10 1o0 12 13 14 15 17 17 18 19 20 21 22 23 24 25 Age_BP sex 68 57 41 79 49 68 69 70 39 42 51 66 70 75 72 42 71 58 40 25y 48

Collecting valid data is an art unto itself. Ensuring that your results are valid and unbiased is a skill beyond the scope of this module. However, even quality data can be corrupted by human error and inattention to detail. Some of the most common data errors include:

• Data that are not read properly
• Dates are interpreted improperly
• Hidden characters that result in improper fields (such as a tab character in a field that looks blank but causes SPSS to classify the field as a "string")
• SPSS auto-assigning incorrect levels of measurement 
• Data that are out of range
• Impossible categories
• Calculating a nonsensical mean (if 1 = blonde, 2 = brunette, 3 = red hair, 4 = black hair, etc., then a mean hair color of 3.1 provides no useful information)
• Lower- and upper-case letters used interchangeably (such as "f" and "F" for sex)
• Inconsistency of measurement and coding

Import the "Module 3 Discussion Data Set" attached below into SPSS. Look for problems in the data and  explain why they are problems. Present solution to repair the data set by addressing all of the errors. Note that the data set contains a total of 63 errors.