# ARH312Y1 Lecture Notes - Lecture 2: Criterion Validity, Epipaleolithic, Measurement Uncertainty

by OC97118

Department

ArchaeologyCourse Code

ARH312Y1Professor

Geoffrey Mac DonaldLecture

2ARH312 Week 2- September 20th

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 say you measure the thickness of an artifact as 3.55mm. Is that reasonable?

What about 3.549mm?

This is dishonest because you are pretending you know more precisely then you do

This can affect your statistics

Significant Digits

Related to how accurately and precisely you actually measured something

NOT the number of decimal places

Independent of the scale of the units (mm, cm etc.)

3.2cm = 32mm = 0.032m = 0.000032km, in each case exactly TWO significant digits

Precision and Reliability

Using a reliable, high precision measuring instrument allows you consistently to get the same

result within a very small margin

Does this ensure that the result will be accurate?

Measuring instruments can still be biased, which is why we need to calibrate it

Instrument Precision

The precision of your measuring tool

Descriptive Statistics

Numeric summaries of interval or ratio scale data

Often showing measures of central tendencies

Mean= average

Median= divides observations in half

Mode= highest peak

Measuring Dispersion

How is the data distributed?

Range= difference between the highest and lowest value

Interquartile range= range of middle 50%

Variance= how far away are all your measurement from the mean

Square the deviation from the mean and sum them

Produces an average deviation from the mean but in square units

Standard Deviation

Square root of the variance

Average deviation from the mean

Standard Error

The standard deviation of the sampling distribution of a statistic

Accumulating Error

Generally , when you combine measurements the errors get somewhat bigger

If you add together two measurements the error on the sum is the square root of the

sum of the squared errors

Minimizing or Controlling Error

Pilot projects or test runs

Training and manuals

Double-checks (statistical quality control)

Control groups

Multiple measures of the same object

Calibrating the Balance

Hold down the ON button for 5 seconds to get a menu

Wait for CAL and press ON flash C

Wait for flashing 200

Put on 200g weight and press ON

C and then DONE

Calipers

Manual calipers measure within 0.1mm

Measure exteriors on one side and interiors on the other

Read measurement to the nearest mm on the main scale

Then look on the 0.1mm scale to see where the ticks line up

Measuring Grids

Chart length and chart width

Measuring Box

Need to be able to stance a sherd (place sherd on paper rock it back and forth till it sits

somewhat flat no light is coming through this is what it would look like after being stanced and

what the full piece would look like) Stance the sherd on the edge of the box

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###### Document Summary

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. Direct measurement directly compare an object of interest to a standard. Indirect measurement- measuring one phenomena to derive measurement of another concept. 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. Discrete : no grey areas between the categories. 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.

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