PSY201H5 Lecture Notes - Lecture 2: Observational Error, Face Validity, Google Analytics
Document Summary
Variables: know the difference between discrete and continuous variables. Measurement: define and provide examples of the four levels of measurement; articulate why we should care about them, explain concepts of reliability and validity- how they are different but related. Frequency distributions: what are they, find scores that correspond to a percentile, find percentiles that correspond to a score. Discrete: separate categories where no values exist in between neighboring categories: eg. Days of the week, rolling a dice, number of people, etc. Continuous: infinite number of values that fall between neighboring values: unlikely to find two observations that are the same, eg. Time, length, weight, etc: values are not discrete but reflect real limits- boundaries of scores that are exactly halfway between scores, eg. 2 seconds measure in 1 second intervals: eg. 70 lbs measured at 1/2 pound intervals is actually 69. 75 to. Continuous example- michael phelps: discrete= number of medals, continuous= swim time. Round to 3 decimals in this class.
