Spss “when good data go bad”

 KNOW SPSS AND BE ON TIME!! 

DUE 12/24/17 10 P.M EST 

WHEN GOOD DATA GOES BAD: DATA ATTACH TO USE

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

ASSIGNMENT: Post a list of the errors you found in the data set, as well as a brief explanation of why they are errors.Explain how to identify and fix data errors and, , attempt to identify all 63 errors in the data set. Be sure to understand why the errors in the SPSS file are problematic and how the steps you took to fix those errors. The Application in this module requires you to fix an SPSS data set.

Import the “Module 3 Discussion Data Set” listed  into SPSS. Look for problems in the data and be prepared to explain why they are problems. Addressing all of the errors. Post all of your work, comments, errors and how you fixed the errors to this discussion board. Note that the data set contains a total of 63 errors. 

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