Analysing Qualitative and Quantitative Data

8/11/2019 Analysing Qualitative and Quantitative Data

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Anal sin ualitativeand

Quantitativedata

MohammadHaider

[email protected]


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Blind men and an elephant

-

Things arent always what we think!

.

the elephant is like a wall. One feels the tusk and thinks the elephant is

a like a spear. One touches the squirming trunk and thinks the.

like a tree. One touches the ear, and thinks the elephant is like a fan.

One grasps the tail and thinks it is like a rope. They argue long and

ou an oug eac was par y n e r g , a were n e wrong.


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Commonmyt s

Complexanalysisandbigwordsimpresspeople.

Mostpeopleappreciatepracticalandunderstandableanalyses.

Analysiscomesattheendafterallthedataarecollected.

WethinkaboutanalysisupfrontsothatweHAVEthedataweWANTtoanalyze.

Quantitativeana ysisist emostaccuratetypeo ata

analysis. Somethinknumbersaremoreaccuratethanwordsbutitisthe

qualityoftheanalysisprocessthatmatters.


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D t h v th ir wnm nin .

Datamustbeinterpreted. Numbersdonotspeakforthemselves.

Statinglimitationstotheanalysisweakensthe

Allanalyseshaveweaknesses;itismorehonestandres onsibletoacknowled ethem.

Computeranalysisisalwayseasierandbetter.

competencies. Forsmallsetsofinformation,handtabulationmaybemoreefficient.


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Pur oseofDataAnal sis

Describe

or

summarize

data

clearly

Searchforconsistentpatternsorthemesamongdata

Enableyoutoansweryourresearchquestions


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Wh UseStatistics?

Statistics

are

powerful

tools

that

help

people

.

et eryou

are

astu ent,

aresearc er,

or

ust

a

citizeninterestedinunderstandingtheworldaround,

makesenseofyourenvironment(Urdan,2001).


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O tions for Calc latin tatistical Anal sis

statisticallyanalyzingdataeasyenoughfornont ti t i i n t .

available,mosteducatorshaveeasyaccesstoMicrosoft

statisticalanalysis.


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ren s Frequency

R n fm h

rowt rates

Average

toanalysedata

Mean

Median

Mode Variance

Standard

deviation Range

Timeseriesanalysis

Scattergraphs

Correlation


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Fre uenc distributions

describe

how

data

are

distributed.

Anarran ementofthevaluesofavariableandtheirresponses.


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Relativecom arisons

describe

how

data

for

a

variable

compare

with

each

other.

Rate=frequencyofoccurrenceofaparticular

Ratio=comparisonofthefrequencyofoneresponsew ano er

Proportion=

a

ratio

to

the

total Percentage=proportionmultipliedby100


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Trends

Looking

for

patterns

in

data

collections

Frequencyandreliabilityoftrends

Impactofexternalfactors,e.g.seasonalvariation,randomevents,cyclicaltrends


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Avera esAveragesareameasureofcentraltendency themost

Median

Mode


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Avera esMean=Sumofitemsintheseries/numberofitems

X= x

Median=middlenumberinadataseries 0.5(n+1)

Mode=themostfrequentlyoccurringvalueinadata


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TheStandardDeviation

S =(xi x )

2

n


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Correlation

The

degree

to

which

there

is

a

relationship

between

e

c oser

t e

re at ons p

t e

g er

t e

egree

o

correlation

Perfectcorrelation

would

be

wherer=1


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DirectionofCorrelations

os t ve + asonevar a e ncreases ecreases ,theothervariableincreases(decreases).

egat ve

s

one

var a e

ncreases

ecreases ,

theothervariabledecreases(increases).


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TimeSeriesAnal sis

Used

to

analyse

movements

of

a

variable

over

a

meper o usua yyears,quar ers,mon s,e c.

Importanceofassessingthe: Trend

Seasonality

Ke moments Magnitude


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Important orresearc erstorecognisean accountfor ownperspective

Respon ent

va i ation Seekalternativeexplanations

Workcloselywithsamelanguagekeyinformantfamiliarwiththelanguagesandperspectivesofboth


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Principlesof

qualitative

data

analysis

on ex scr ca

i.e.physical,historical,social,political,organisational,

Dependence/interdependence

Identifyconvergence/divergenceofviewsandhow

contextualfactorsma influencethedifferences


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Roleoftheoryguidesapproachtoanalysis

Establishedconceptual

framework

predetermined

categoriesaccordingtoresearchquestions

Groun e t eory interrogatet e ata oremergentthemes


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Princi lesof

ualitative

data

anal sis

Dataanalysisisanonlinear

rephrasing,analysing,theorising,verifyingaftereach

observation,interview,orFocusGroupDiscussion


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Interrelatedratherthansequential

Duringdatacollection

Coding listentothedataforemergingthemesand

be intoattachlabelsorcodestothetextsthatrepresentthethemes


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Stagesinqualitativedataanalysis

Displaying thethemes(allinformation)

,

Reducing fromthedisplayeddataidentifythemain


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ages

nqua a ve

a a

ana ys s

a s ages searc ng orcoremean ngso thoughts,feelings,andbehavioursdescribed

Identifyhowthemesrelatetoeachother

Explainwhatthefindingsmeanbeyondthecontextof

your

study


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Processesinqualitativedataanalysis

Reading/Dataimmersion

Readforcontent

Areyou

obtaining

the

types

of

information

you

intendedtocollect

en yemergen emesan eve op en a veexplanations

Note new

/

sur risin to icsthatneedtobeexploredinfurtherfieldwork


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Processesin

qua itative

ata

ana ysis

Readnotingthequalityofthedata Have ouobtainedsu erficialorrichanddee res onses Howvividanddetailedarethedescriptionsof

observations

Problemsinthequalityofthedatarequireareviewof: Howyouareaskingquestions

evenue

Thecompositionofthegroups

Thestyle

and

characteristics

of

the

interviewer

Howsoonafterthefieldactivityarenotesrecorded

Developasystemtoidentifyproblemsinthedata(audittrail)


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Processesinqualitativedataanalysis

Rea ing/Dataimmersion

Readidentifyingpatterns

Afteridentifyingthemes,examinehowthesearepatterned

Aretheirrelationshipsbetweenthemes

Arethere

contradictory

responses

Aretheregapsinunderstanding theserequirefurtherexploration


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Coding Identifyingemergingthemes o es a e s

Emergentcodes

Closel matchthelan ua eandideasinthetextualdata Borrowedcodes

Representmoreabstractconceptsinthefieldofstudy

n erstoo yaw erau ence

Insertnotesduringthecodingprocess Ex lanator notes uestions

Giveconsiderationtothewordsthatyouwilluseascodes/labels mustcapturemeaningandleadtoexplanations

ex eco ngsc eme recor co es, e n ons,an revisions


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Coding Identifyingemergingthemes

Imposesasystematicapproach Hel stoidentif a sor uestionswhileitis ossibleto

returnformoredata

Revealsearlybiases

Helpstoredefineconcepts


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Coding Identifyingemergingthemes

Conductacodingsort Cutandpastetogetherintoonefilesimilarlycodedblocks

oftext


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Processesin

qua itative

ata

ana ysis

i.e.layingoutortakinganinventoryofwhatdatayouhave

relatedtoatheme Conductquantitativeandqualitativeanalysis

Capturethevariationorrichnessofeachtheme

ote erences etween n v ua san su groups

Returntothedataandexamineevidencethat

supportseachsubtheme Noteintensity/emphasis;firstorsecondhand

experiences;identifydifferentcontextswithinw c ep enomenonoccurs


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Processesin

qua itative

ata

ana ysis

eve op ng ypo eses,ques on ngan ver ca on

Extract

meaning

from

the

data Dothecategoriesdevelopedmakesense

Whatpiecesofinformationcontradictmyemergingeas

Whatpiecesofinformationaremissingor

Whatotheropinionsshouldbetakenintoaccount?

w y w analysisprocess?


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Processesin

qua itative

ata

ana ysis

i.e.identifyingthecoremeaningofthedata,remaining

faithfultothe ers ectivesofthestud artici antsbutwithwidersocialandtheoreticalrelevance

Credibilityofattributedmeaning

Consistentwithdatacollected

Verifiedwithrespondents

resentmu t p eperspect ves convergentan divergentviews)

Did ou obe ondwhat ouex ectedtofind?


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Processesin

qua itative

ata

ana ysis

Dependability

Multipleanalysts

Audittrail Permitsexternalreviewofanalysisdecisions

Transferability

Apply

lessons

learned

in

one

context

to

another Support,refine,limitthegeneralisability of,or

proposeanalternativemodelortheory


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Documents

Analysing Qualitative and Quantitative Data