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Lecture 2

ARH312 Week 2.docx

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University of Toronto St. George
Geoffrey Mac Donald

ARH312 Week 2- September 20 th Measurement Theory  Data- observations and measurement you make on objects this data is theory laden (not independent from theory)  Measurement is comparison- either to a standard (mm etc.) or to a concept  There are measurement institutes that have standards for all weights lengths and concepts etc. Types of Measurement  Direct Measurement – directly compare an object of interest to a standard  Indirect Measurement- measuring one phenomena to derive measurement of another concept Validity  Degree of correlation between our measurement and the qualities or values we’re trying to measure  Face validity = widespread agreement by experts in the field  Content validity = appears to contain all important concepts and behaviours  Criterion validity = comparison of the measurement with a standard Scales of Measurement  What are some ways we can classify measurements?  Quantitative : anything with numbers  Length  Width  Mass  Qualitative : anything without numbers  Colour  Texture  Discrete : no grey areas between the categories  Apples, oranges and tomatoes  Continuous : between any two numbers on the scale you can find other numbers  The difference between 1cm and 2cm there can be 1.5cm there is always another number between the numbers you are looking at hence continuous  Nominal Scale : Categories  Unordered and equal weight  Enumeration is the count of object we assign to each class in the nominal scale  Often discrete  Examples are male female or the comparison of pottery (you often just count)  Dichotomous Measurement : one of the other  Male or Female (can be argued)  Present or absent  Ordinal Scale : Categories  With order  Chronological groups are ordinal (ex. Epipaleolithic, Neolithic etc.)  No information of the magnitude if difference between categories  Often discrete  Interval Scale: continuous data and zero is an arbitrary number  Quantitative scale of measurement  Ratio Scale : when there is zero nothing exists  Quantitative scale of measurement Measurement Errors  Tell us about the quality of our data and they assure us the differences seen in the data are real  Discrete data errors  Misclassification  Ambiguous categories  Source of error: perception and senses  Continuous data errors  Any two values have an infinity of other values between them  Source of error: measurement uncertainty (how certain is your ruler)  Outliers : surprising values in your data caused by inter observer error, intra observer error or contamination  Accuracy : the degree of bias  Precision : the spread of range of the results Presenting Data Honestly and Realistically  Let’s sa
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