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AUTOMATE OR DIE
Why do we need to think about
automation via software?
2
The pressures facing researchers
How to set yourself apart as a researcher?
More data
Shorter turnaround
Tighter budgets
Clients can DIY
3
What automation can deliver
✓ Increase productivity
(because you do things faster)
✓ Lower costs
(saving sweat and tears)
✓ Opportunities for new analysis
(help you be a more effective)
✓ Higher quality
• Avoiding human errors
• Automating expertise
• More time to think and play
✓ Avoiding non-value-add work
4
“Software is eating the world”
Marc Andreason
5
How software can play a role in data setup, analysis and reporting
THE FOCUS
OF TODAY
6
Agenda
• Introduction
• 8 areas of opportunity for automation in market research
1. Automated data checking/cleaning/tidying/coding/updating
2. Automatic identification of tables that contain interesting results
3. Using standardized residuals to highlight interesting cells on a table
4. Automatic updating of analyses with new data
5. Automatic charting
6. Automatic writing of PowerPoint slides
7. Automatic updating of PowerPoint slides
8. Using Dashboards for self-service
• Live Q&A session
7
Key point to realise upfront: Automation is not black and white
Automation continuum
The automation continuum
Blood, sweat,
&
tears
“Turn-key”
automation
8
What degree of automation do we want from software?
Data
setup
Analysis
Reporting
Automation continuum
The automation continuum
Blood, sweat,
&
tears
“Turn-key”
automation
PowerPoint
Automation
Automated
reporting
Automatically-
updated
dashboards
that export to
PowerPoint
Analysis automation
Automated charting
*Cannot be entirely automated
The jobs to be done
Extracting the data
Data tidying/cleaning/coding/variables
New brands/options/questions*
Crosstabs/analysis
Constructed tables (e.g,. Brand health)
Updating charts
Updating tables in reports
Updating text
9
Automation continuum
The automation continuum
Blood, sweat,
&
tears
“Turn-key”
automation
The automation continuum
No automation
Checklists/
QA Processes
Scripting
Computer
programming
Self-
documenting
point-and-click
automation
N/A No software
SPSS
VBA For Excel
VB, R, and
Python
(and their
programmers)
Q
Tables in Excel.
Lots of cutting and
pasting.
Human effort
Computer code is
written to
automate how a
program works
(eg; SPSS
Syntax)
New apps are
written in
computer code
designed to work
on multiple
projects.
An app is used
that does the
heavy lifting
without the user
having to write any
code, except for
unusual things.
Code not mandatory
(can use buttons
and menu-items)
Clear, accessible,
linked record of
what was done in
the graphic user
interface (GUI)
10
No automation
Checklists/
QA Processes
Scripting
Computer
programming
Self-
documenting
point-and-click
automation
N/A No software
SPSS
VBA For Excel
VB, R, and
Python (and
their
programmers)
Q
Tables in Excel.
Lots of cutting and
pasting.
Human effort
Computer code is
written to
automate how a
program works
(eg; SPSS
Syntax)
New apps are
written in
computer code
designed to work
on multiple
projects.
An app is used
that does the
heavy lifting
without the user
having to write any
code, except for
unusual things.
Automation continuum
The automation continuum
Blood, sweat,
&
tears
“Turn-key”
automation
The automation continuum
THE FOCUS
OF TODAY
11
Automation continuum
The automation continuum
Blood, sweat,
&
tears
“Turn-key”
automation
The automation continuum
No automation
Checklists/
QA Processes
Scripting
Computer
programming
Self-
documenting
point-and-click
automation
Self-service
Artificial
intelligence
N/A No software
SPSS
VBA For Excel
VB, R, and
Python
(and their
programmers)
Q
SurveyMonkey
Qualtrics
Displayr
Nobody
Tables in Excel.
Lots of cutting and
pasting.
Human effort
Computer code is
written to
automate how a
program works
(eg; SPSS
Syntax)
New apps are
written in
computer code
designed to work
on multiple
projects.
An app is used
that does the
heavy lifting
without the user
having to write any
code, except for
unusual things.
An app that allows
the users to
completely DIY
(avoids need for
suppliers
altogether)
Magic
12
Automated data checking/cleaning/tidying
Using standardized residuals to highlight interesting cells on a table or chart
Automatic identification of tables that contain interesting results
Automatic updating of analyses with new data
Automatic charting
Automatic writing of PowerPoint Slides
Automatic updating of PowerPoint Slides
Using Dashboards for self-service
A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H
13
Extracting the data
Data tidying/cleaning/coding/variables
New brands/options/questions*
Crosstabs/analysis
Constructed tables (e.g,. Brand health)
Updating charts
Updating tables in reports
Updating text
Data
setup
Analysis
Reporting
The jobs to be done
14
You have a lot of dirty laundry to do with survey data
Outliers
Flatliners
Survey skips
HTML stuck in labels
Empty variable labels
Binary variables
Missing values
Speedsters
Survey loops
Variable type
15
Automated data setup: CHECKING SURVEY SKIPS
Survey skips
16
Checking total hole counts
% n
Coca-Cola 100% 600
Diet Coke 100% 600
Coke Zero 94% 563
Pepsi 100% 600
Diet Pepsi 78% 470
Pepsi Max 93% 559
Row % Hate Dislike Neutral Like Love Base n
Coca-Cola 4% 7% 15% 32% 42% 600
Diet Coke 14% 29% 23% 11% 24% 600
Coke Zero 13% 19% 24% 17% 26% 563
Pepsi 9% 12% 34% 8% 38% 600
Diet Pepsi 17% 24% 42% 5% 12% 470
Pepsi Max 12% 16% 30% 18% 24% 559
Question 1 - Awareness
Question 5 – Brand attitude
17
Checking – cross-tabbing questions
Example cross-tab: Brand Attitude - Diet Pepsi by Q1 - Aware (Diet Pepsi)
BrandAttitude-DietPepsi n Not Aware Aware
Hate 0 81
Dislike 0 112
Neutral 0 198
Like 0 23
Love 0 56
Column n 0 470
Q1 - Aware (Diet Pepsi)
18
Checking – using a Sankey diagram
19
Introducing the SANKEY diagram
20
How different software can create a Sankey Diagram
• Create > Charts > Visualization• Code
Computer
programming
Self-documenting
point-and-click
automation
The automation continuum
Example:: https://cran.r-project.org/web/packages/riverplot/
21
Automated data setup: CHECKING FOR FLATLINERS








0 1 2 3 4 5 6 7
I drink cola whenever I can
Cola is for people with unhealthy diets
I try to avoid cola whenever possible
Cola drinks are the best drinks around
I would live on cola if I could
Cola is destroying our kids’ health
Cola is only appropriate for parties
I need a cola when I eat pizza
Flat-liners
22
The steps required to figure out who’s flat-lining
a) Identify variables that belong to same scale question set
b) Compute who gave respondents gave flat-line answers on any scale
point
c) optional: additional computations to see if they were flat-lining elsewhere
in the survey
d) How to then delete them from the data? An additional deletion stage?
23
Example of SPSS Code to detect flat-liners
compute sd.brand = sd( vara1 to vara9 ).
compute sd.att1 = sd( varb1 to varb8 ).
compute sd.att2 = sd( varc1 to varc5).
* More batteries potentially
freq sd.brand sd.att1 sd.att2 /format=notable /statistics
/percentiles= 5 10 25 75 95 .
compute d.brand = ( sd.brand < 0.5).
compute d.att1 = ( sd.att1 < 0.5).
compute d.att2 = ( sd.att2 < 0.5).
count flatlines = d.brand to d.att2 (1).
freq flatlines.
* Remove those with 60% or more flatlined batteries.
compute clean.sample = (flatlines <= 1).
exe.
filter by clean.sample.
* Check across y.
use all.
format flatlines clean.sample (F1.0).
* split file by qcountry2.
set tnumbers = labels.
ctables /table clean.sample > flatlines by y
/CATEGORIES VARIABLES=flatlines EMPTY=EXCLUDE
/CATEGORIES VARIABLES=clean.sample [1, 0] EMPTY=INCLUDE
/table flatlines [COLPCT.COUNT] by y.
set tnumbers = both.
24
Detecting Flatliners
• QScript:
“Project Setup - Identify Questions with Straight-lining”
• Syntax
The automation continuum
Scripting
Computer
programming
Self-documenting
point-and-click
automation
25
Automated data setup…. Other opportunities?
Library of scripts build in
Existing scripts can be modified and/or new
bespoke scripts can be done DIY
DIY Code
Additional code/syntax can be found on web and
tweaked or write your custom own from scratch
Specialized for survey analysisGeneralist analytic package
The automation continuum
26
Automated data checking/cleaning/tidying
Using standardized residuals to highlight interesting cells on a table or chart
Automatic identification of tables that contain interesting results
Automatic updating of analyses with new data
Automatic charting
Automatic writing of PowerPoint Slides
Automatic updating of PowerPoint Slides
Using Dashboards for self-service
A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H
27
A striking table
Standardised residuals
help identify interesting
results!
28
Standardized residuals can be applied to any table with numbers
Column % 18 - 29 30 - 39 40 - 49 50 +
Coca-Cola 55% 53% 28% 38%
Diet Coke 3% 15% 14% 11%
Coke Zero 16% 16% 23% 17%
Pepsi 5% 8% 22% 6%
Diet Pepsi 1% 0% 3% 6%
Pepsi Max 18% 7% 11% 23%
29
Which do you prefer
Column % 18 - 29 30 - 39 40 - 49 50 +
Coca-Cola 55% ↑ 53% ↑ 28% ↓ 38%
Diet Coke 3% ↓ 15% 14% 11%
Coke Zero 16% 16% 23% 17%
Pepsi 5% 8% 22% ↑ 6%
Diet Pepsi 1% 0% 3% 6% ↑
Pepsi Max 18% 7% ↓ 11% 23% ↑
Column % 18 - 29 30 - 39 40 - 49 50+
Coca-Cola
55% 53% 28% 38%
C d C d
Diet Coke
3% 15% 14% 11%
a a a
Coke Zero
16% 16% 23% 17%
Pepsi
5% 8% 22% 6%
A b D
Diet Pepsi
1% 0% 3% 6%
a b
Pepsi Max
18% 7% 11% 23%
b B c
Column Names A B C D
30
𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙(𝑍) =
Observed % − Expected %
)Expected %(1 − Column Total % (1 − Row Total %)/N
Does the cell differ significantly from “expectation”?
For p < 0.05, then the Z-statistic for the cell will be
greater than +1.96 or
less than -1.96
31
Formatting can be applied them to the original tables.
We can then transplant that same colour coding and/or arrows back to the original column-% table.
column-% 18 - 29 30 - 39 40 - 49 50 +
Coca-Cola 55% ↑ 53% ↑ 28% ↓ 38%
Diet Coke 3% ↓ 15% 14% 11%
Coke Zero 16% 16% 23% 17%
Pepsi 5% 8% 22% ↑ 6%
Diet Pepsi 1% 0% 3% 6% ↑
Pepsi Max 18% 7% ↓ 11% 23% ↑
z-Statistic 18 - 29 30 - 39 40 - 49 50 +
Coca-Cola 3.3 ↑ 2.5 ↑ -3.7 ↓ -2.2
Diet Coke -3.2 ↓ 2.0 1.5 .0
Coke Zero -.5 -.6 1.5 -.3
Pepsi -1.9 -.4 4.9 ↑ -2.0
Diet Pepsi -1.7 -2.2 .0 3.5 ↑
Pepsi Max 1.0 -3.2 ↓ -1.7 3.3 ↑
32
The new-school table?
33
The new-school table?
34
35
The new-school table? Yes please.
36
How different software can compute/format standardized residuals
Automation continuum
The automation continuum
Blood, sweat,
&
tears
“Turn-key”
automation
Scripting
Computer
programming
Self-documenting
point-and-click
automation
Table of
standardized
residuals (via menu)
• Instant Z-statistics in table
• Auto-formatting of tables
based on the Z-statistic
• Table of standardized residuals (via code)
• Formatting with specific functions (eg: mosaic()
37
Performing the calculation in SPSS
38
Performing the calculation in SPSS
39
Formatting then with : SPSS Output Management System
Performing the calculation in R
40
Formatting in R via the mosaic() function
41
Formatting in R via the formattable() function
42
Automated data checking/cleaning/tidying
Using standardized residuals to highlight interesting cells on a table or chart
Automatic identification of tables that contain interesting results
Automatic updating of analyses with new data
Automatic charting
Automatic writing of PowerPoint Slides
Automatic updating of PowerPoint Slides
Using Dashboards for self-service
A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H
43
44
45
Screen out the “junk” tables
Tables with significant results ONLY
(& potentially ranked by significance)
ALL the tables
46
How different software can screen tables automatically (based on statistical significance)
• QScript
(remove uninteresting
tables)
• Built-in function
(Smart Tables)
Automation continuum
The automation continuum
Blood, sweat,
&
tears
“Turn-key”
automation
Scripting
Computer
programming
Self-documenting
point-and-click
automation
47
Automated data checking/cleaning/tidying
Using standardized residuals to highlight interesting cells on a table or chart
Automatic identification of tables that contain interesting results
Automatic updating of analyses with new data
Automatic charting
Automatic writing of PowerPoint Slides
Automatic updating of PowerPoint Slides
Using Dashboards for self-service
A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H
48
1. Segment reworking
2. Product reworking
3. Data revisions
4. Tracking
Data
setup
Analysis
Reporting
49
Automation continuum
The automation continuum
Blood,
sweat,
&
tears
“Turn-key”
automation
A key difference in reporting automation: Scripting vs. Self-documenting point-and-click
No automation
Checklists/QA
Processes
Scripting
Computer
programming
Self-
documenting
point-and-click
automation
Self-service
Artificial
intelligence
50
Scripting approach: Example from SPSS
Two potential problems:
➢ Opportunity for misspecification
➢ Opportunity for miscommunication
51
What if our data file updates?
No automation
Checklists/QA
Processes
Scripting
Computer
programming
Self-
documenting
point-and-click
automation
Self-service
Artificial
intelligence
Re-rerun syntax
What if we got in an updated datafile from our supplier that had an extra quarter?
Eg: “Cola Tracking – Jan to Sept”.sav -->> “Cola Tracking – Jan to Dec”.sav
Update data
52
Automated data checking/cleaning/tidying
Using standardized residuals to highlight interesting cells on a table or chart
Automatic identification of tables that contain interesting results
Automatic updating of analyses with new data
Automatic charting
Automatic writing of PowerPoint Slides
Automatic updating of PowerPoint Slides
Using Dashboards for self-service
A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H
53
4 ways to think about automatic charting
• OfficeReports,
• e-Tabs Graphique
• think-cell.
Quality
Speed
#1 Chart template
files
#2 Templating
within apps
#3 Specific
charting tools
#4 Bespoke
visualizations
• D3
54
55
An easy win in PowerPoint
Chart template files – the .crtx files
56
#1 Chart template
files
TECHNICAL ELEMENTS OF PPT: Charts in PowerPoint offer greater updatability than tables
40%
25%
15%
20%
30%
45%
15%
10%
20%
50%
20%
10%
Coke Pepsi Fanta Sprite
18-30 31-45 45+
Coke Pepsi Fanta Sprite
18-30 31-45 45+ 18-30 31-45 45+
Sprite
Fanta
Pepsi
Coke
57
Reference:
https://www.ibm.com/support/knowledgecenter/en/SSLVMB_23.0.0/spss/tutorials/
outputtut_msapps.html
58
#2 Templating
within apps
More automation with greater upfront work
Example: Plotly package
#3 Specific
charting tools
59
AUTO CHARTING #3 & 4: Programmed charts
Example: Plotly package
#3 Specific
charting tools
#4 Bespoke
visualizations
Example: “Palm Trees” (within Q)
60
AUTO CHARTING #4: New Charts
Chart template
files
Templating within
data analysis apps
Specific charting
tools
Bespoke
visualizations
Quality
Speed
61
Automated data checking/cleaning/tidying
Using standardized residuals to highlight interesting cells on a table or chart
Automatic identification of tables that contain interesting results
Automatic updating of analyses with new data
Automatic charting
Automatic writing of PowerPoint Slides
Automatic updating of PowerPoint Slides
Using Dashboards for self-service
A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H
62
Manually charting in PowerPoint is a chore
63
Some considerations about PowerPoint
o PowerPoint is not a uniform whole – composition of parts
o “Automatic” ≠ 100% automation
o PowerPoint has limitations – can only be “asked” to do stuff
o One-step or multi-step process?
o Replication and preservation of your design
o Alterable in PowerPoint after export/update?
64
Automatic writing of PowerPoint charts
Automation continuum
The automation continuum
Blood, sweat,
&
tears
“Turn-key”
automation
Scripting
Computer
programming
Self-documenting
point-and-click
automation
Syntax
(static, limited)
Code
(static, limited)
65
Point and click to use:
• PowerPoint template
(.pptx file)
• Chart template
(.crtx file)
Automatic writing of PowerPoint charts
Temporary.
Select if d1 <= 30.
FREQUENCIES VARIABLES=d2 d3 d4
/ORDER=ANALYSIS.
66
click File > Export and choosing to export to PowerPoint
File > Export and choose to export to PowerPoint
Automatic writing of PowerPoint charts
67
Automatic writing of PowerPoint charts
(reporteR)
68
69
Automation continuum
The automation continuum
Blood, sweat,
&
tears
“Turn-key”
automation
Scripting
Computer
programming
Self-
documenting
point-and-click
automation
Self-service
Computer code is
written to automate
how a program
works (eg; SPSS
Syntax)
New apps are written
in computer code
designed to work on
multiple projects.
An app is used that
does the heavy lifting
without the user
having to write any
code, except for
unusual things.
An app that allows
the users to
completely DIY
(avoids need for
suppliers altogether)
70
Automated data checking/cleaning/tidying
Using standardized residuals to highlight interesting cells on a table or chart
Automatic identification of tables that contain interesting results
Automatic updating of analyses with new data
Automatic charting
Automatic writing of PowerPoint Slides
Automatic updating of PowerPoint Slides
Using Dashboards for self-service
A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H
71
The jobs to be done: Reporting
Data
setup
Analysis
Reporting
1. Segment reworking
2. Product reworking
3. Data revisions
4. Tracking
72
Data
setup
Analysis
Reporting
73
74
Automated data checking/cleaning/tidying
Using standardized residuals to highlight interesting cells on a table or chart
Automatic identification of tables that contain interesting results
Automatic updating of analyses with new data
Automatic charting
Automatic writing of PowerPoint Slides
Automatic updating of PowerPoint Slides
Using Dashboards for self-service
A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H
75
Using Dashboards for self-service
76
A couple of concluding thoughts
77
A G E N D A | A U TO M AT E O R D I E
✓ Opportunities for new analysis
✓ Increase productivity
✓ Lower costs
✓ Higher quality
✓ Avoiding non-value-add work
Planning for retirement Surviving Transforming Dreaming
The automation continuum
Automation continuum
The automation continuum
Blood, sweat,
&
tears
“Turn-key”
automation
No automation
Checklists/
QA Processes
Scripting
Computer
programming
Self-
documenting
point-and-click
automation
Self-service
Artificial
intelligence
78

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Slides for automate or die (presentation)

  • 1. M AT T S T E E L E P R E S E N T S If you have any questions, enter them into the Questions field. Questions will be answered at the end. If we do not have time to get to your question, we will email you. We will email you a link to the video, slides, and data. Get a free one-month trial of Q from www.q-researchsoftware.com AUTOMATE OR DIE
  • 2. Why do we need to think about automation via software? 2
  • 3. The pressures facing researchers How to set yourself apart as a researcher? More data Shorter turnaround Tighter budgets Clients can DIY 3
  • 4. What automation can deliver ✓ Increase productivity (because you do things faster) ✓ Lower costs (saving sweat and tears) ✓ Opportunities for new analysis (help you be a more effective) ✓ Higher quality • Avoiding human errors • Automating expertise • More time to think and play ✓ Avoiding non-value-add work 4
  • 5. “Software is eating the world” Marc Andreason 5
  • 6. How software can play a role in data setup, analysis and reporting THE FOCUS OF TODAY 6
  • 7. Agenda • Introduction • 8 areas of opportunity for automation in market research 1. Automated data checking/cleaning/tidying/coding/updating 2. Automatic identification of tables that contain interesting results 3. Using standardized residuals to highlight interesting cells on a table 4. Automatic updating of analyses with new data 5. Automatic charting 6. Automatic writing of PowerPoint slides 7. Automatic updating of PowerPoint slides 8. Using Dashboards for self-service • Live Q&A session 7
  • 8. Key point to realise upfront: Automation is not black and white Automation continuum The automation continuum Blood, sweat, & tears “Turn-key” automation 8
  • 9. What degree of automation do we want from software? Data setup Analysis Reporting Automation continuum The automation continuum Blood, sweat, & tears “Turn-key” automation PowerPoint Automation Automated reporting Automatically- updated dashboards that export to PowerPoint Analysis automation Automated charting *Cannot be entirely automated The jobs to be done Extracting the data Data tidying/cleaning/coding/variables New brands/options/questions* Crosstabs/analysis Constructed tables (e.g,. Brand health) Updating charts Updating tables in reports Updating text 9
  • 10. Automation continuum The automation continuum Blood, sweat, & tears “Turn-key” automation The automation continuum No automation Checklists/ QA Processes Scripting Computer programming Self- documenting point-and-click automation N/A No software SPSS VBA For Excel VB, R, and Python (and their programmers) Q Tables in Excel. Lots of cutting and pasting. Human effort Computer code is written to automate how a program works (eg; SPSS Syntax) New apps are written in computer code designed to work on multiple projects. An app is used that does the heavy lifting without the user having to write any code, except for unusual things. Code not mandatory (can use buttons and menu-items) Clear, accessible, linked record of what was done in the graphic user interface (GUI) 10
  • 11. No automation Checklists/ QA Processes Scripting Computer programming Self- documenting point-and-click automation N/A No software SPSS VBA For Excel VB, R, and Python (and their programmers) Q Tables in Excel. Lots of cutting and pasting. Human effort Computer code is written to automate how a program works (eg; SPSS Syntax) New apps are written in computer code designed to work on multiple projects. An app is used that does the heavy lifting without the user having to write any code, except for unusual things. Automation continuum The automation continuum Blood, sweat, & tears “Turn-key” automation The automation continuum THE FOCUS OF TODAY 11
  • 12. Automation continuum The automation continuum Blood, sweat, & tears “Turn-key” automation The automation continuum No automation Checklists/ QA Processes Scripting Computer programming Self- documenting point-and-click automation Self-service Artificial intelligence N/A No software SPSS VBA For Excel VB, R, and Python (and their programmers) Q SurveyMonkey Qualtrics Displayr Nobody Tables in Excel. Lots of cutting and pasting. Human effort Computer code is written to automate how a program works (eg; SPSS Syntax) New apps are written in computer code designed to work on multiple projects. An app is used that does the heavy lifting without the user having to write any code, except for unusual things. An app that allows the users to completely DIY (avoids need for suppliers altogether) Magic 12
  • 13. Automated data checking/cleaning/tidying Using standardized residuals to highlight interesting cells on a table or chart Automatic identification of tables that contain interesting results Automatic updating of analyses with new data Automatic charting Automatic writing of PowerPoint Slides Automatic updating of PowerPoint Slides Using Dashboards for self-service A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H 13
  • 14. Extracting the data Data tidying/cleaning/coding/variables New brands/options/questions* Crosstabs/analysis Constructed tables (e.g,. Brand health) Updating charts Updating tables in reports Updating text Data setup Analysis Reporting The jobs to be done 14
  • 15. You have a lot of dirty laundry to do with survey data Outliers Flatliners Survey skips HTML stuck in labels Empty variable labels Binary variables Missing values Speedsters Survey loops Variable type 15
  • 16. Automated data setup: CHECKING SURVEY SKIPS Survey skips 16
  • 17. Checking total hole counts % n Coca-Cola 100% 600 Diet Coke 100% 600 Coke Zero 94% 563 Pepsi 100% 600 Diet Pepsi 78% 470 Pepsi Max 93% 559 Row % Hate Dislike Neutral Like Love Base n Coca-Cola 4% 7% 15% 32% 42% 600 Diet Coke 14% 29% 23% 11% 24% 600 Coke Zero 13% 19% 24% 17% 26% 563 Pepsi 9% 12% 34% 8% 38% 600 Diet Pepsi 17% 24% 42% 5% 12% 470 Pepsi Max 12% 16% 30% 18% 24% 559 Question 1 - Awareness Question 5 – Brand attitude 17
  • 18. Checking – cross-tabbing questions Example cross-tab: Brand Attitude - Diet Pepsi by Q1 - Aware (Diet Pepsi) BrandAttitude-DietPepsi n Not Aware Aware Hate 0 81 Dislike 0 112 Neutral 0 198 Like 0 23 Love 0 56 Column n 0 470 Q1 - Aware (Diet Pepsi) 18
  • 19. Checking – using a Sankey diagram 19
  • 21. How different software can create a Sankey Diagram • Create > Charts > Visualization• Code Computer programming Self-documenting point-and-click automation The automation continuum Example:: https://cran.r-project.org/web/packages/riverplot/ 21
  • 22. Automated data setup: CHECKING FOR FLATLINERS         0 1 2 3 4 5 6 7 I drink cola whenever I can Cola is for people with unhealthy diets I try to avoid cola whenever possible Cola drinks are the best drinks around I would live on cola if I could Cola is destroying our kids’ health Cola is only appropriate for parties I need a cola when I eat pizza Flat-liners 22
  • 23. The steps required to figure out who’s flat-lining a) Identify variables that belong to same scale question set b) Compute who gave respondents gave flat-line answers on any scale point c) optional: additional computations to see if they were flat-lining elsewhere in the survey d) How to then delete them from the data? An additional deletion stage? 23
  • 24. Example of SPSS Code to detect flat-liners compute sd.brand = sd( vara1 to vara9 ). compute sd.att1 = sd( varb1 to varb8 ). compute sd.att2 = sd( varc1 to varc5). * More batteries potentially freq sd.brand sd.att1 sd.att2 /format=notable /statistics /percentiles= 5 10 25 75 95 . compute d.brand = ( sd.brand < 0.5). compute d.att1 = ( sd.att1 < 0.5). compute d.att2 = ( sd.att2 < 0.5). count flatlines = d.brand to d.att2 (1). freq flatlines. * Remove those with 60% or more flatlined batteries. compute clean.sample = (flatlines <= 1). exe. filter by clean.sample. * Check across y. use all. format flatlines clean.sample (F1.0). * split file by qcountry2. set tnumbers = labels. ctables /table clean.sample > flatlines by y /CATEGORIES VARIABLES=flatlines EMPTY=EXCLUDE /CATEGORIES VARIABLES=clean.sample [1, 0] EMPTY=INCLUDE /table flatlines [COLPCT.COUNT] by y. set tnumbers = both. 24
  • 25. Detecting Flatliners • QScript: “Project Setup - Identify Questions with Straight-lining” • Syntax The automation continuum Scripting Computer programming Self-documenting point-and-click automation 25
  • 26. Automated data setup…. Other opportunities? Library of scripts build in Existing scripts can be modified and/or new bespoke scripts can be done DIY DIY Code Additional code/syntax can be found on web and tweaked or write your custom own from scratch Specialized for survey analysisGeneralist analytic package The automation continuum 26
  • 27. Automated data checking/cleaning/tidying Using standardized residuals to highlight interesting cells on a table or chart Automatic identification of tables that contain interesting results Automatic updating of analyses with new data Automatic charting Automatic writing of PowerPoint Slides Automatic updating of PowerPoint Slides Using Dashboards for self-service A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H 27
  • 28. A striking table Standardised residuals help identify interesting results! 28
  • 29. Standardized residuals can be applied to any table with numbers Column % 18 - 29 30 - 39 40 - 49 50 + Coca-Cola 55% 53% 28% 38% Diet Coke 3% 15% 14% 11% Coke Zero 16% 16% 23% 17% Pepsi 5% 8% 22% 6% Diet Pepsi 1% 0% 3% 6% Pepsi Max 18% 7% 11% 23% 29
  • 30. Which do you prefer Column % 18 - 29 30 - 39 40 - 49 50 + Coca-Cola 55% ↑ 53% ↑ 28% ↓ 38% Diet Coke 3% ↓ 15% 14% 11% Coke Zero 16% 16% 23% 17% Pepsi 5% 8% 22% ↑ 6% Diet Pepsi 1% 0% 3% 6% ↑ Pepsi Max 18% 7% ↓ 11% 23% ↑ Column % 18 - 29 30 - 39 40 - 49 50+ Coca-Cola 55% 53% 28% 38% C d C d Diet Coke 3% 15% 14% 11% a a a Coke Zero 16% 16% 23% 17% Pepsi 5% 8% 22% 6% A b D Diet Pepsi 1% 0% 3% 6% a b Pepsi Max 18% 7% 11% 23% b B c Column Names A B C D 30
  • 31. 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑𝑖𝑧𝑒𝑑 𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙(𝑍) = Observed % − Expected % )Expected %(1 − Column Total % (1 − Row Total %)/N Does the cell differ significantly from “expectation”? For p < 0.05, then the Z-statistic for the cell will be greater than +1.96 or less than -1.96 31
  • 32. Formatting can be applied them to the original tables. We can then transplant that same colour coding and/or arrows back to the original column-% table. column-% 18 - 29 30 - 39 40 - 49 50 + Coca-Cola 55% ↑ 53% ↑ 28% ↓ 38% Diet Coke 3% ↓ 15% 14% 11% Coke Zero 16% 16% 23% 17% Pepsi 5% 8% 22% ↑ 6% Diet Pepsi 1% 0% 3% 6% ↑ Pepsi Max 18% 7% ↓ 11% 23% ↑ z-Statistic 18 - 29 30 - 39 40 - 49 50 + Coca-Cola 3.3 ↑ 2.5 ↑ -3.7 ↓ -2.2 Diet Coke -3.2 ↓ 2.0 1.5 .0 Coke Zero -.5 -.6 1.5 -.3 Pepsi -1.9 -.4 4.9 ↑ -2.0 Diet Pepsi -1.7 -2.2 .0 3.5 ↑ Pepsi Max 1.0 -3.2 ↓ -1.7 3.3 ↑ 32
  • 35. 35
  • 36. The new-school table? Yes please. 36
  • 37. How different software can compute/format standardized residuals Automation continuum The automation continuum Blood, sweat, & tears “Turn-key” automation Scripting Computer programming Self-documenting point-and-click automation Table of standardized residuals (via menu) • Instant Z-statistics in table • Auto-formatting of tables based on the Z-statistic • Table of standardized residuals (via code) • Formatting with specific functions (eg: mosaic() 37
  • 39. Performing the calculation in SPSS 39 Formatting then with : SPSS Output Management System
  • 41. Formatting in R via the mosaic() function 41
  • 42. Formatting in R via the formattable() function 42
  • 43. Automated data checking/cleaning/tidying Using standardized residuals to highlight interesting cells on a table or chart Automatic identification of tables that contain interesting results Automatic updating of analyses with new data Automatic charting Automatic writing of PowerPoint Slides Automatic updating of PowerPoint Slides Using Dashboards for self-service A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H 43
  • 44. 44
  • 45. 45
  • 46. Screen out the “junk” tables Tables with significant results ONLY (& potentially ranked by significance) ALL the tables 46
  • 47. How different software can screen tables automatically (based on statistical significance) • QScript (remove uninteresting tables) • Built-in function (Smart Tables) Automation continuum The automation continuum Blood, sweat, & tears “Turn-key” automation Scripting Computer programming Self-documenting point-and-click automation 47
  • 48. Automated data checking/cleaning/tidying Using standardized residuals to highlight interesting cells on a table or chart Automatic identification of tables that contain interesting results Automatic updating of analyses with new data Automatic charting Automatic writing of PowerPoint Slides Automatic updating of PowerPoint Slides Using Dashboards for self-service A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H 48
  • 49. 1. Segment reworking 2. Product reworking 3. Data revisions 4. Tracking Data setup Analysis Reporting 49
  • 50. Automation continuum The automation continuum Blood, sweat, & tears “Turn-key” automation A key difference in reporting automation: Scripting vs. Self-documenting point-and-click No automation Checklists/QA Processes Scripting Computer programming Self- documenting point-and-click automation Self-service Artificial intelligence 50
  • 51. Scripting approach: Example from SPSS Two potential problems: ➢ Opportunity for misspecification ➢ Opportunity for miscommunication 51
  • 52. What if our data file updates? No automation Checklists/QA Processes Scripting Computer programming Self- documenting point-and-click automation Self-service Artificial intelligence Re-rerun syntax What if we got in an updated datafile from our supplier that had an extra quarter? Eg: “Cola Tracking – Jan to Sept”.sav -->> “Cola Tracking – Jan to Dec”.sav Update data 52
  • 53. Automated data checking/cleaning/tidying Using standardized residuals to highlight interesting cells on a table or chart Automatic identification of tables that contain interesting results Automatic updating of analyses with new data Automatic charting Automatic writing of PowerPoint Slides Automatic updating of PowerPoint Slides Using Dashboards for self-service A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H 53
  • 54. 4 ways to think about automatic charting • OfficeReports, • e-Tabs Graphique • think-cell. Quality Speed #1 Chart template files #2 Templating within apps #3 Specific charting tools #4 Bespoke visualizations • D3 54
  • 55. 55
  • 56. An easy win in PowerPoint Chart template files – the .crtx files 56 #1 Chart template files
  • 57. TECHNICAL ELEMENTS OF PPT: Charts in PowerPoint offer greater updatability than tables 40% 25% 15% 20% 30% 45% 15% 10% 20% 50% 20% 10% Coke Pepsi Fanta Sprite 18-30 31-45 45+ Coke Pepsi Fanta Sprite 18-30 31-45 45+ 18-30 31-45 45+ Sprite Fanta Pepsi Coke 57
  • 59. More automation with greater upfront work Example: Plotly package #3 Specific charting tools 59
  • 60. AUTO CHARTING #3 & 4: Programmed charts Example: Plotly package #3 Specific charting tools #4 Bespoke visualizations Example: “Palm Trees” (within Q) 60
  • 61. AUTO CHARTING #4: New Charts Chart template files Templating within data analysis apps Specific charting tools Bespoke visualizations Quality Speed 61
  • 62. Automated data checking/cleaning/tidying Using standardized residuals to highlight interesting cells on a table or chart Automatic identification of tables that contain interesting results Automatic updating of analyses with new data Automatic charting Automatic writing of PowerPoint Slides Automatic updating of PowerPoint Slides Using Dashboards for self-service A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H 62
  • 63. Manually charting in PowerPoint is a chore 63
  • 64. Some considerations about PowerPoint o PowerPoint is not a uniform whole – composition of parts o “Automatic” ≠ 100% automation o PowerPoint has limitations – can only be “asked” to do stuff o One-step or multi-step process? o Replication and preservation of your design o Alterable in PowerPoint after export/update? 64
  • 65. Automatic writing of PowerPoint charts Automation continuum The automation continuum Blood, sweat, & tears “Turn-key” automation Scripting Computer programming Self-documenting point-and-click automation Syntax (static, limited) Code (static, limited) 65 Point and click to use: • PowerPoint template (.pptx file) • Chart template (.crtx file)
  • 66. Automatic writing of PowerPoint charts Temporary. Select if d1 <= 30. FREQUENCIES VARIABLES=d2 d3 d4 /ORDER=ANALYSIS. 66 click File > Export and choosing to export to PowerPoint File > Export and choose to export to PowerPoint
  • 67. Automatic writing of PowerPoint charts 67
  • 68. Automatic writing of PowerPoint charts (reporteR) 68
  • 69. 69
  • 70. Automation continuum The automation continuum Blood, sweat, & tears “Turn-key” automation Scripting Computer programming Self- documenting point-and-click automation Self-service Computer code is written to automate how a program works (eg; SPSS Syntax) New apps are written in computer code designed to work on multiple projects. An app is used that does the heavy lifting without the user having to write any code, except for unusual things. An app that allows the users to completely DIY (avoids need for suppliers altogether) 70
  • 71. Automated data checking/cleaning/tidying Using standardized residuals to highlight interesting cells on a table or chart Automatic identification of tables that contain interesting results Automatic updating of analyses with new data Automatic charting Automatic writing of PowerPoint Slides Automatic updating of PowerPoint Slides Using Dashboards for self-service A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H 71
  • 72. The jobs to be done: Reporting Data setup Analysis Reporting 1. Segment reworking 2. Product reworking 3. Data revisions 4. Tracking 72
  • 74. 74
  • 75. Automated data checking/cleaning/tidying Using standardized residuals to highlight interesting cells on a table or chart Automatic identification of tables that contain interesting results Automatic updating of analyses with new data Automatic charting Automatic writing of PowerPoint Slides Automatic updating of PowerPoint Slides Using Dashboards for self-service A U T O M AT E O R D I E : 8 W AY S TO A U TO M AT E Q U A N T R E S E A R C H 75
  • 76. Using Dashboards for self-service 76
  • 77. A couple of concluding thoughts 77 A G E N D A | A U TO M AT E O R D I E
  • 78. ✓ Opportunities for new analysis ✓ Increase productivity ✓ Lower costs ✓ Higher quality ✓ Avoiding non-value-add work Planning for retirement Surviving Transforming Dreaming The automation continuum Automation continuum The automation continuum Blood, sweat, & tears “Turn-key” automation No automation Checklists/ QA Processes Scripting Computer programming Self- documenting point-and-click automation Self-service Artificial intelligence 78