Presenting and communicating statistics.Principles, components and assessmentFilomena MagginoUniversit degli Studi di Firenze

The study presented here is the result of a project developed by myself and

Marco Fattore Universit degli Studi di Milano-Bicocca andMarco Trapani Universit degli Studi di Firenze

1. Communication: full component of the statistical workContents2. Communicating statistics

3. Assessing the quality of communication in statistics

1. Communication: full component of the statistical workContents2. Communicating statistics

3. Assessing the quality of communication in statistics

Communication in statistics: From DATA to MESSAGE

DATA PRODUCTIONobjective observation transformed inaseptic dataDATA ANALYSIS, RESULTS AND INTERPRETATIONdatainformationPRESENTATIONinformation message

Communication in statistics: From DATA to MESSAGEnot only a technical problem

data production

data analysis

representation

communication

VAS= N*[(QSA*MF)*RS*TS*NL] Giovannini, 2008

This detailed formula, including many relevant aspects like the role of media and users numeracy, can be reconsidered by including also aspects concerning quality e incisiveness of the message:

VAS = ( N,QSA,MF,RS,TS,NL,QIP) additional component VASValue added of official statistics NSize of the audienceQSAStatistical information producedMF Role of mediaRSRelevance of the statistical informationTSTrust in official statisticsNLUsers numeracyQIPQuality and incisiveness of presentationa formula

cannot be presented in an aseptic and impartial wayby leaving interpretation to the audiencestatistics

can be accomplished through different even if correct perspectives

the glass is half-full the glass is half-empy

through a dynamic perspective

the glass is getting filled up the glass is getting empty

The message will be transmitted and interpreted by the audience without realizing the mere numeric aspect.Interpretation

Communication in statistics: from DATA to MESSAGEstatisticianfacilitator between reality and its representationCOMPLEXITY

DESIRED

OUTCOME

OUTPUT

ACHIEVED

OUTCOME

(

(

(

assessment

1. Communication: full component of the statistical workContents2. Communicating statistics

3. Assessing the quality of communication in statistics

2. Communicating statistics 1. Fundamental aspectsContents2. Main components3. The codes

1. Fundamental aspects

Aspects ofstatisticalpresentationsCorrespondingdisciplineContentEthicsAppealAestheticsPersuasionRhetoricTheory of presentation

2. Main componentsTRCODECODEMessageChannelContext - settingFEEDBACKNoise

in statistical communication

Outline telling statistics Tools depicting statistics Clothes dressing statistics3. Codes

INVENTIO

DISPOSITIO

ELOCUTIO

ACTIOA. Outline telling statisticsSTART

1- Inventio (invention)allows arguments to be arguedA. Outline telling statisticsWhoWhatWhenWhereWhythe subject of tellingthe fact the time locationthe field locationthe causes

2- Dispositio (layout)allows topics to be put in sequenceA. Outline telling statisticsdeductive inductive time-progressionproblems-relatedadvantages-disadvantagesfrom-points-of-viewtop-down approaches

2- Dispositio (layout)PremiseGeneral PrinciplesDeveloping argumentsPratical consequences/examplesDeductive approachCase / specific situationReflectionConceptsConsequences / other casesInductive approachOnce upon a timeWhy something changedYesterday TodayTomorrowTime progression approachMeaningful questionsWhy in important to talk aboutSolutions (and concepts)Conclusions and consequencesProblems approachSubjectAdvantages-disadvantages approachSubjectFrom point of view approachPont of view 1 values defectsPont of view 4 values defectsPont of view 3 values defectsPont of view 2 values defectsTop-down approachGeneralReflections Concepts ConsequencesParticularReflections Concepts ConsequencesSpecificReflections Concepts ConsequencesDetailReflections Concepts ConsequencesMicroReflections Concepts ConsequencesPremiseReflections Concepts ConsequencesA. Outline telling statistics

Point to be evaluatedAdvantageDisadvantages

3- Elocutio (expression)allows each piece of the presentation to be prepared by selecting words and constructing sentencesA. Outline telling statisticsLanguage should be appropriate to the audience consistent with the messagewordinglanguagestongues

3- Elocutio (expression)A. Outline telling statistics

Figures ofDefinitionThinkingchange in words or propositions invention and imaginative shapeMeaning (or tropes)change in words meaningDictionchange in words shapeElocutionchoice of the most suitable or convenient wordsConstructionchange in words order inside a sentenceRhythmphonic effects

4- Actio (execution)concerns the way in which the telling is managedA. Outline telling statisticsin terms of introduction developments comments time space use ending{

B. Tools Depicting statistics Refer to all instruments aimed at depicting statisticsgraphstablespictogramsThe tools should preserve the message

functionsB. Tools Depicting statistics

Supporting attentionActivating and building prior knowledgeMinimizing cognitive loadBuilding mental modelsSupporting transfer of learningSupporting motivation

Graph PrinciplesB. Tools Depicting statistics

Categories PrinciplesConnect with the audienceMessage should connect with the goals and interests of your audience.RelevanceAppropriate knowledgeDirect and hold attentionPresentation should lead the audience to pay attention to what is important.SalienceDiscriminabilityPerceptual organizationPromote understanding and memoryPresentation should be easy to follow, digest, and remember.CompatibilityInformation changesCapacity limitations

(i) Choosing a graph by taking into account number of involved variables nature of data (level of measurement) statistical information to be represented

by preferring

a simple graph with reference to the audience a clear graph instead of an attractive one a correct graph with reference to dataB. Tools Depicting statistics

(ii) Preparing a graphB. Tools Depicting statistics

Scale definitioncorrectly defining and showing scale/sDimensionalityreducing dimensionality as much as possible by showing few variables for each graph using no meaningless axisColours as statistical codes using colours consistently with statistical informationRounding off valuesrounding up and down through standard criteriaDynamics presentationdynamic perspective should reflect a dynamic phenomenonLegibilityfew elements as possible. Wise use of legends and captions

C. Clothes dressing statisticsRefer to the process of dressing statisticsWith reference to:balanceharmonyproportionelegancestyleDifferent aspects: text arrangementcharacters and fontscolours

1. Communication: full component of the statistical workContents2. Communicating statistics

3. Assessing the quality of communication in statistics

3. Assessing the quality of communication in statistics 1. The conceptual modelContents2. The application

The dimensions to evaluateThe evaluating criteria The components of the transmission process1. The conceptual model

A. The dimensions to evaluateOUTLINE telling statisticsTOOLS depicting statisticsCLOTHES dressing statistics

B. The evaluating criteriaThey refer to the transmitters ability to use the codes in terms ofEvaluating scale appropriateness pertinencecorrectness accuracyclarity

PolarityLabelsScoresBipolarNo0Yes1

Audience tourists, harvesters, minersChannel auditory, visual, .Context seminars, conferences, books, booklets,

But alsoTopicData C. The component of the transmission process message}

The assessment modelThe dimensions have to be evaluated with reference to the of the code -through the defined crieria- components of the transmission processOutlineToolsClothes

Appropriateness ( pertinence)Correctness ( accuracy) Clarity

AudienceChannelContextTopicData

The assessing tableStudy planning and data collectionData analysis2. The application

A. The assessing tableeach judge can evaluate presence (1) or absence (0).The conceptual model can be consistently assessed by developing an Assessing Tablethrough which

of the criterion(A) appropriateness (B) correctness (C) clarity..in each code 1. outline 2. tools 3. clotheswith reference to(i) audience (ii) channel (iii) context (iv) topic (v) dataA. The assessing table

Assessing Table IA. The assessing table

Assessing Table IIsynthesis of the previous oneA. The assessing table

Quality of Communication in Statistics:

ASSESSING TABLE II

EVALUATING CRITERIA

(A)

APPROPRIATENESS

(B)

CORRECTNESS

(C)

CLARITY

with reference to

(i)audience

(ii)channel

(iii)context

(iv)topic

(v)data

(i)audience

(ii)channel

(iii)context

(iv)topic

(v)data

(i)audience

(ii)channel

(iii)context

(iv)topic

(v)data

1. OUTLINE

a. Invention

b. Layout

c. Expression

d. Execution

2. TOOLS

a. Tables

b. Graphs

c. Pictograms

3. CLOTHES

B. The study planning and data collectionSelection of the judges Competence in survey methodology and statistical issuesCompetence in communication theory

B. The study planning and data collectionCentral Statistical Office (2009) Poland in the European Union, Central Statistical Office, Warsaw.Eurostat (2008) Statistical Portrait of the European Union European Year of Intercultural Dialogue, Eurostat, Statistical Books, Luxembourg.Federal Statistical Office (2009) Statistical Data on Switzerland, Federal Statistical Office, NeuChtel, Switzerland.Kazakhstan Statistics (2008) The Statistical Guidebook, Agency of the Republic of Kazakhstan on Statistics (Astana).ISTAT (2009) Italy in Figures, Rome, ItalyUnited Nations Economic Commission for Europe (2009) UNECE. Countries in Figures, United Nations, New York Geneva.Selected publications for the study (collected at the UNECE Work Session on Communication and Dissemination of Statistics held in Warsaw, Poland 13-15 May 2009):

C. Data analysisSOLUTIONPROBLEMOBJECTIVEassessing each statistical publicationthrough binary data & ordinal dimensionshow to combine the evaluationson each quality dimensioninto a final quality assessmentcomputing quality assessmentsrespecting the ordinal nature of datathrough a fuzzy approachbased on the use of partial order theoryOBJECTIVEPROBLEMSOLUTION

C. Data analysisEach publication has a sequence of [0/1] for each criterion PROFILE Best configuration 111111 Worst configuration 000000

The analysis was performed for each criterion. We show just the results concerning appropriateness and clarity.

EVALUATING CRITERIA

(A)

APPROPRIATENESS

(B)

CORRECTNESS

(C)

CLARITY

with reference to

(i)audience

(ii)channel

(iii)context

(i)audience

(ii)channel

(iii)context

(i)audience

(ii)channel

(iii)context

1. OUTLINE

0

1

0

2. TOOLS

1

1

1

3. CLOTHES

1

1

1

Hasse diagrams of quality configurations audience appropriateness (left) and audience clarity (right) for the publication outlinesLinked nodes are ordered from top to bottom.

Not linked nodes represent incomparable quality (appropriateness or clarity) configurations.C. Data analysis

Definition of thresholds (subjective choices)which element in the sequence is related with high quality configuration (quality degree = 1) s2 poor quality configuration (quality degree = 0) s1

Given such thresholds, what quality degrees do other configurations receive, in the appropriateness and clarity posets respectively?C. Data analysis

P2 and P5 are above the high quality threshold, in both posets, they receive quality degree 1 in both appropriateness and clarity

C. Data analysis

P4 is below the poor quality threshold, in appropriateness, It receives appropriateness degree = 0C. Data analysis

P6 is below the poor quality threshold, in clarity, It receives clarity degree = 0C. Data analysis

By analysing how frequently a configuration is above the high quality threshold (or below the poor quality threshold) in the set of complete orders

we can determine the degree of appropriateness and clarityof each configuration ( publication)C. Data analysis

Final ranking scatterplotC. Data analysis

PublicationAudienceappropriatenessAudienceclarityP10.60.6P21.01.0P30.90.9P40.00.2P51.01.0P60.60.0

GoalsImproving the assessing modelNew applicationsPromoting an improvement of statisticians education by proposing a training module on communicationThe way forward

Filomena Maggino, Marco Fattore, Marco TrapaniContact: filomena.maggino@unifi.it

***************(*) Vale S. (2008) Accessibility and clarity: The most neglected dimensions of quality?, presented at Conference on Data Quality for International Organizations, Rome, Italy, July 7-8, 2008, Session 3: Dissemination platforms to make data more accessible and interpretable. UNECE.