1 intro to r and quant analysis

8/12/2019 1 Intro to R and Quant Analysis

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Quantitative

Analysis



Quantitative / Formal Methods

• objective measurement systems

• graphical methods

• statistical procedures



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



#&atterning$

• patterning ' departures rom randomness

• strength o patterning ' ?

degree o departure romrandomness%



• #is the patterning strong enough to either

re)uire or support an e!planatory

argument??$

• this is usually an anthropological

)uestion%



basic vocabulary

• case

• variable• data matri!

• attribute

• aggregation

• stratiication• accuracy

• precision



• 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%



cases and variables

variable 1 variable 2 variable 3 variable 4 variable 5 …

case 1

case 2case 3

case 4

case 5

…

,data matri!-



• 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+



• 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



• 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-



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




• 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




• 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



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



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



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:



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$-



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(%



data management

• three main options or electronic storageo data"

– spreadsheet

– statistics pac(age – database



• organi0ed by cells

• no restrictions oncell content

• most useul orshortterm

manipulation osmall datasets

• poor or longtermstorage ocomple!datastructures

*spreadsheet+



• data orms oer

less versatilitythan spreadsheets

• organi0ed by case> variable

• powerulanalytical tools

• poor managementtools

*statpac+



• best option ormanaging comple!data structures

*database+



pottery design elements"

*reptile eye+

*obsidian (nie+

*cloud moti+

etc%.



artifact # design elements

ax-122 reptile eye, obsidian knife, cloud

az-1 maguey t!orn, reptile eyeaa-1 "aguar pa

$ $

#multiple entry$



#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

$



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



@@BC artiacts.cat=um Ddesign elementsE.abbrev

F<6M Ddesign elementsE

==@< G6= ,artiacts ==@< G6= Ddesign element

lin(E

6= artiacts.1 ' Ddesign element lin(E.art1-

6= Ddesign elementsE.1 ' Ddesign element lin(E.de1H

cat'um abbrev

ax-22 rep

ax-22 obk

ax-22 cldaz-1 rep

az-1 obk

aa-1 "gp

#structured )uery language$ ,Q-

1 intro to r and quant analysis

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