Letting Data Speak Enunciative Modalites of Correspondence Analysis - - PowerPoint PPT Presentation

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Letting Data Speak Enunciative Modalites of Correspondence Analysis - - PowerPoint PPT Presentation

Feb 17, 2024 232 likes •458 views

Letting Data Speak Enunciative Modalites of Correspondence Analysis Email: Richard@Volpato.net Richard.Volpato.net Letting Data Speak -- the Truth Veritas Filia Temporis Truth is the Daughter of Time From: Francesco Marcolino 1536,

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Richard.Volpato.net

Letting Data Speak

Enunciative Modalites of Correspondence Analysis Email: Richard@Volpato.net

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Letting Data Speak -- the Truth

Richard.Volpato.net Veritas Filia Temporis Truth is the Daughter of Time

From: Francesco Marcolino 1536, Venetian printer

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Letting Data Speak -- the Truth

Richard.Volpato.net “Statistics is not a means of knowledge, draining into an idea the flood of facts; it is a mode of being”

From: J P Benzecri, “The Soul at the Razor’s Edge”, Les Cahiers de l’Analyse des Donnes, V(2) pp.229-242, 1980 (trans Fionn Murtagh)

Veritas Filia Temporis Truth is the Daughter of Time

From: Francesco Marcolino 1536, Venetian printer

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Letting Data Speak -- the Truth

Richard.Volpato.net “Statistics is not a means of knowledge, draining into an idea the flood of facts; it is a mode of being”

From: J P Benzecri, “The Soul at the Razor’s Edge”, Les Cahiers de l’Analyse des Donnes, V(2) pp.229-242, 1980 (trans Fionn Murtagh)

Veritas Filia Temporis Truth is the Daughter of Time

From: Francesco Marcolino 1536, Venetian printer

Data Analysis now matters as the Material carrier of Culture has changed:

  • Alphabets
  • Printing
  • Media
  • Data  Today’s Challenge
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Letting Data Speak by: asking questions

Richard.Volpato.net Look at this data table What do you see? What do you want to know?

Smoking Category Staff Row Group None Light Medium Heavy Totals Senior Managers 4 2 3 2 11 Junior Managers 4 3 7 4 18 Senior Employees 25 10 12 4 51 Junior Employees 18 24 33 13 88 Secretaries 10 6 7 2 25 Column Totals 61 45 62 25 193

Originating from Michael Greenacre, now proliferating everywhere!

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Letting Data Speak by: asking questions

Richard.Volpato.net Look at this data table What do you see? What do you want to know? Differences that might matter

  • Seniority
  • Authority
  • Who smokes? Why?

Smoking Category Staff Row Group None Light Medium Heavy Totals Senior Managers 4 2 3 2 11 Junior Managers 4 3 7 4 18 Senior Employees 25 10 12 4 51 Junior Employees 18 24 33 13 88 Secretaries 10 6 7 2 25 Column Totals 61 45 62 25 193

Originating from Michael Greenacre, now proliferating everywhere!

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Letting Data Speak by: asking questions

Richard.Volpato.net Look at this data table What do you see? What do you want to know? Differences that might matter

  • Seniority
  • Authority
  • Who smokes? Why?

Smoking Category Staff Row Group None Light Medium Heavy Totals Senior Managers 4 2 3 2 11 Junior Managers 4 3 7 4 18 Senior Employees 25 10 12 4 51 Junior Employees 18 24 33 13 88 Secretaries 10 6 7 2 25 Column Totals 61 45 62 25 193

Originating from Michael Greenacre, now proliferating everywhere!

Older Folk Managers Smokers Young, Managers Dichotomize all, focus on one interesting value

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Letting Data Speak by: asking questions

Richard.Volpato.net Look at this data table What do you see? What do you want to know? Differences that might matter

  • Seniority
  • Authority
  • Who smokes? Why?

Smoking Category Staff Row Group None Light Medium Heavy Totals Senior Managers 4 2 3 2 11 Junior Managers 4 3 7 4 18 Senior Employees 25 10 12 4 51 Junior Employees 18 24 33 13 88 Secretaries 10 6 7 2 25 Column Totals 61 45 62 25 193

Originating from Michael Greenacre, now proliferating everywhere!

Older Folk Managers Smokers Young, Managers Differences (in percentages) can be spoken: Eg Older folk take to cigarettes more often than younger people. As they age, some reach management where smoking becomes less frequent ….. Young managers mostly avoid this habit Dichotomize all, focus on one interesting value

See: James A. Davis, “Extending Rosenberg’s Technique for Standardizing Percentage Tables” Social Forces, Vol. 62, 1984

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Letting Data Speak: through visual prompts

Richard.Volpato.net Graphs convert Differences  Distances

  • Graphs play on memory
  • Graphs have Grammar
  • End with macro views
  • Pacing matters

Macro-views Micro-views http://www.worldvaluessurvey.org Maps show which relationships are likely to be most revealing

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Letting Data Speak by: writing for them

Richard.Volpato.net Five principles for writing

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Letting Data Speak by: writing for them

Richard.Volpato.net Five principles for writing

  • Blind writing – a good graph leaves a clear short

term memory – so write with the screen off.

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Letting Data Speak by: writing for them

Richard.Volpato.net Five principles for writing

  • Blind writing – a good graph leaves a clear short

term memory – so write with the screen off.

  • E-Prime writing – write without “is”.
  • Active verbs revivify data!
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Letting Data Speak by: writing for them

Richard.Volpato.net Five principles for writing

  • Blind writing – a good graph leaves a clear short

term memory – so write with the screen off.

  • E-Prime writing – write without “is”.
  • Active verbs revivify data!
  • Para-graphic ascent:
  • Encapsulate (start paragraph)
  • Exemplify (concrete examples)
  • Explicate (concepts / meanings)
  • Elaborate (links – causal or conceptual)
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Letting Data Speak by: writing for them

Richard.Volpato.net Five principles for writing

  • Blind writing – a good graph leaves a clear short

term memory – so write with the screen off.

  • E-Prime writing – write without “is”.
  • Active verbs revivify data!
  • Para-graphic ascent:
  • Encapsulate (start paragraph)
  • Exemplify (concrete examples)
  • Explicate (concepts / meanings)
  • Elaborate (links – causal or conceptual)
  • Write along dimensions
  • Inertia = topicality;
  • name them using many little contributors
  • use big variables as supplementary to validate
  • Follow order of eigenvalues
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Letting Data Speak by: writing for them

Richard.Volpato.net Five principles for writing

  • Blind writing – a good graph leaves a clear short

term memory – so write with the screen off.

  • E-Prime writing – write without “is”.
  • Active verbs revivify data!
  • Para-graphic ascent:
  • Encapsulate (start paragraph)
  • Exemplify (concrete examples)
  • Explicate (concepts / meanings)
  • Elaborate (links – causal or conceptual)
  • Write along dimensions
  • Inertia = topicality;
  • name them using many little contributors
  • use big variables as supplementary to validate
  • Follow order of eigenvalues
  • Ecological
  • Verbs imply activities that interact with other
  • Ecology = the map of destiny
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Letting Data Speak through: Benzecri’s synthesis

Fidelity As Filter (questions) Data as Disjunctive Matrix Experience Informed by Clustering (objects) Expertise Informed by BURT matrix (concepts)

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Letting Data Speak through: Benzecri’s synthesis

Fidelity As Filter (questions) Data as Disjunctive Matrix Experience Informed by Clustering (objects) Expertise Informed by BURT matrix (concepts)

Flux and Flow of Events Legacy Eventualities

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Letting Data Speak through: Benzecri’s synthesis

Fidelity As Filter (questions) Data as Disjunctive Matrix Experience Informed by Clustering (objects) Expertise Informed by BURT matrix (concepts)

Flux and Flow of Events Legacy Eventualities

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Letting Data Speak through: Benzecri’s synthesis

Fidelity As Filter (questions) Data as Disjunctive Matrix Experience Informed by Clustering (objects) Expertise Informed by BURT matrix (concepts)

Flux and Flow of Events Legacy Biblical Eventualities

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Richard.Volpato.net

Letting Data Speak: Ecstatically

Bernini’s Ecstasy of St Theresa (based on his own reworking of Veritas Filia Temporis)

  • The Golden light from heaven - now the light of a questioning quest of high fidelity on earth
  • The heavy marble floats - now scattered mute data gather to offer insights
  • St Theresa filled with ecstasy – now the reader can face the future ecstatically
  • The Logos pierces the heart – now expertise cuts into a problem appropriately contextualised