Reference no: EM131961542

Question: Provide a substantive contribution that advances the discussion in a meaningful way by identifying strengths of the posting, challenging assumptions, and asking clarifying questions. Your response is expected to reference the assigned readings, as well as other theoretical, empirical, or professional literature to support your views and writings.

The discussion post this week asks us to discuss data screening. Data screening is a way to go through your data, looking for errors that might need to be fixed prior to running analysis. Data screening is done to look for errors in data entry, outliers, and missing data. Errors in data entry can occur during data collection or when the data is entered into the system. When data is collected from a sample group, especially with self-answer data, it is possible that data may be incorrectly written down. This could occur as simply as the individual missing a line or missing a question on the survey. Alternatively, the data received may have been erroneously entered into the system. Missing data also seems to be clear in that it is data that is literally missing from the fields. SPSS can replace missing data with reasonable estimates based on the known data (Warner, 2013). Lastly, the outliers. These are data that is extremely high or extremely low based on the average of the data collected. Some scientists use the standard deviation of the mean and accept a certain number higher and lower as permittable while considering numbers outside of this range outliers. There is a risk to doing this to frequently as the researcher can create any number set of his data that he wants to "fit" by manipulating his numbers. It is recommended that one should not simply delete marginal data as it could lead to type 2 error, or falsely identifying outliers when none exist. This can lead to bad results (Spectroscopy, 2018).

References: Spectroscopy. (2018). Outliers, Part II: Pitfalls in detecting outliers. Spectroscopy, 33(2), 1-4.

Warner, R. (2013). Applied statistics: From bivariate through multivariate techniques (2nd ed). Thousand Oaks, CA: Sage Publishing.