Difference between revisions of "Data Screening"
(→Data Screening) |
(→Data Cleaning) |
||
| Line 14: | Line 14: | ||
== Data Cleaning == | == Data Cleaning == | ||
| + | |||
| + | == Value Cleaning == | ||
| + | |||
| + | |||
| + | == Outliers == | ||
| + | |||
| + | |||
| + | == Causes of Outliers == | ||
Revision as of 17:58, 16 November 2019
Data Screening
Once data from a research study is gathered and has been entered into SPSS, researchers must examine their data to be sure they can validly interpret their results. Valid interpretation of data is reliant on two data features:
1. The data must meet the assumptions of the analysis procedure.
2. The data in the data file are "an accurate representation or transcription of what was provided by research participants as their original responses or what was provided by archival sources as original data" (Lawrence, Gamst, & Guarino, 2017, p. 31).
Contribution by: Britany Kuslis, WCSU Cohort 8
Reference Lawrence, S., Gamst, G, & Guarino, A.J. (2017). Applied multivariate research: Design and interpretation. Thousand Oaks, CA: Sage Publications.
