NSCA CPSS Data Hygiene Questions and Complete Solutions Graded A+

NSCA CPSS Data Hygiene Questions and Complete Solutions Graded A+ data hygiene - Answer: practice to ensure minimal error in collection/storage of data validity - Answer: degree which test/instrument measures what it purports to measure criterion validity - Answer: when measure shows strong relationship to a gold standard (criterion) assessment technique construct validity - Answer: any measurement domain in which direct criterion measure is unavailable concurrent validity - Answer: extent of agreement between two measures or assessments taken at the same time What is the pillar to measurement precision - Answer: reliability reliability - Answer: consistency of a measure A test/instrument cannot be considered ______ if it is not also _____. - Answer: valid, reliable typical error - Answer: random error that may be observed in repeated test observations in the context of reliability, which is signal and which is noise? - Answer: signal: smallest worthwhile change noise: typical error filtering - Answer: removes unnecessary information to reduce noise from signal raw data - Answer: unprocessed data data integrity - Answer: consistency, completeness, and accuracy of data what is central to maintaining data integrity - Answer: reproducible data workflow reproducible workflow - Answer: one with enough detailed info that another person can replicate it data dictionary - Answer: contains definitions of all variables collected as well as any naming/coding conventions benefits of data dictionary - Answer: 1. removes ambiguity if multiple people are collecting data that may have different naming conventions 2. creates permanent record of what is in the data Principles of good spreadsheet design - Answer: 1. unique ID code referencing the participant 2. column names w/o spaces or special characters 3. avoid empty cells, columns, rows 4. don't apply calculations, cell merging, syntax highlighting to raw data files Data workflow - Answer: Collect data Data cleaning Data processing Data Analysis Save results Thinks to look for in data cleaning - Answer: Consistent naming conventions Non-unique ID variables Date formats Outliers