Download - 1 Intro to R and Quant Analysis
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Quantitative
Analysis
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Quantitative / Formal Methods
• objective measurement systems
• graphical methods
• statistical procedures
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why bother?
• description – esp. o populations
– e!" average height o people in room
• inerence – describe populations on the basis o samples
– test hypothesis about populations – estimate levels o uncertainty associated withinerential description
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#&atterning$
• patterning ' departures rom randomness
• strength o patterning ' ?
degree o departure romrandomness%
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• #is the patterning strong enough to either
re)uire or support an e!planatory
argument??$
• this is usually an anthropological
)uestion%
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basic vocabulary
• case
• variable• data matri!
• attribute
• aggregation
• stratiication• accuracy
• precision
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• case – e)uivalent to *record+
– something about which we want to ma(e/record
observations%
• variable – (inds o observations we want to ma(e/record
– measurements o variability among cases%
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cases and variables
variable 1 variable 2 variable 3 variable 4 variable 5 …
case 1
case 2case 3
case 4
case 5
…
,data matri!-
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• attribute – the intersection between cases and variables
– i.e. an observation about a speciic case with
reerence to a speciic variable – e!.
• #el($
• #strongly agree$
• #plainware$
– also called *value+ or *variable state+
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• aggregation – grouping cases usually on the basis o a shared
attribute
– spatial pro!imity temporal pro!imity
– gender o interment associated with grave lots
• stratiication – dividing cases into subgroups
– usually to carry out parallel analyses that relate
to dierent control conditions
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• accuracy – an e!pression o the closeness between a
measured ,or computed- value and the true
value
– re)uently conused with precision
• precision
– has to do with replicability – the closeness o repeated measures to the same
value ,not necessarily the true value-
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scales o measurement
• presence / absence data
– simply whether or not the case e!hibits a
speciic state
• nominal data
– contrasting groups usually mutually e!clusive
– sometimes reerred to as *discrete+ or*categorical+ data
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scales o measurement
• ordinal data
– a logical order or ran(ing e!ists among the
various categories
– no assumptions implied about the*measurement space+ occupied by categories
• ratio data
– also metric continuous – has a non-arbitrary zero
– can meaningully compare measurements as
ratios
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scales o measurement
• interval data
– distances between categories o measurement
are i!ed and even ,unli(e ordinal data-
– scale lac(s a nonarbitrary *0ero+ ,unli(e ratiodata-
• count data
– derived rom nominal data – really a (ind o ratio data created by
aggregation
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1rennan
• distinctions are inconsistent and not tooimportant%
• measurements vs. categories
– measurements" )uantities measured along ascale
– categories" 2/ e)uivalent to nominal data
– counts" discrete enumeration
• but conusion does occur% – e!. can+t use *goodness o it+ tests on nominal
data3
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data coding
• presence / absence data – can use 4 / 5 ,but analy0e with care3-
• nominal data – 67 to use integers ,5 8 9 etc.-
– but don’t subject them to arithmetic operations
– don+t assume rules o numerical distance
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data coding
• ordinal data – use integers%
• ratio / metric data – use integer or decimal notation
– don+t record spurious levels o accuracy or
precision
– note" ! ' 54.8 means 54.5: ; ! ; 54.8:
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coding #missing data$
• M1 more problematic than most reali0e%
• may want more than one code"
5. variable state is uncertain vs.
8. variable doesn+t apply vs.
9. variable state is not present ,not really M1-
•< gives you one coding option ,#=A$-
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recoding data
• can readily recode #down$ the scale ,e!.ratioordinal-
– implies a loss o inormation and a probably
wasted recording eort• reporting apparently dubious counts as
presence/absence data is not a good idea
• moving *up+ the scale means redoing labwor(%
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data management
• three main options or electronic storageo data"
– spreadsheet
– statistics pac(age – database
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• organi0ed by cells
• no restrictions oncell content
• most useul orshortterm
manipulation osmall datasets
• poor or longtermstorage ocomple!datastructures
*spreadsheet+
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• data orms oer
less versatilitythan spreadsheets
• organi0ed by case> variable
• powerulanalytical tools
• poor managementtools
*statpac+
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• best option ormanaging comple!data structures
*database+
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pottery design elements"
*reptile eye+
*obsidian (nie+
*cloud moti+
etc%.
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artifact # design elements
ax-122 reptile eye, obsidian knife, cloud
az-1 maguey t!orn, reptile eyeaa-1 "aguar pa
$ $
#multiple entry$
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#latile$ ormat
artifact # %1 %2 %3
ax-122 rep obk cld
az-1 mgt rep
aa-1 "gp$
artifact # rep obk cld mgt "gpax-122 1 1 1
az-1 1 1
aa-1 1
$
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artifacts
&% cat'um
1 ax-122
2 az-1
3 aa-1
design element link
art&% de&%
1 1
1 2
1 4
2 1
2 2
3 5
design elements
&% element abbrev
1 reptile eye rep
2 obsidian knife obk
3 maguey t!orn mgt
4 cloud cld
5 "aguar pa "gp
artifacts design element link
&% 1 ----- ∞ art&% design elementscat'um de&% ∞ ----- 1 &%
element
abbrev
relational database
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F<6M Ddesign elementsE
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cat'um abbrev
ax-22 rep
ax-22 obk
ax-22 cldaz-1 rep
az-1 obk
aa-1 "gp
#structured )uery language$ ,Q-