Examination of 4,5,7,and 10 point scales in terms of extreme responding, acquiescent responding, and midpoint responding as well as a comparison of labelled and unlabeled points
Perhaps we are all speaking English but men and women do it just a little bit differently. Find out what words are used more often by men or women and see how you fit the stereotype.
Just a few years ago, social media research was hailed as the panacea of all marketing research. The ridiculous quantities of brand opinions and opinionators available in social media would mean that focus groups would die, surveys would die, and all research questions would have instant answers. Fast forward to today and surveys continue to thrive. Learn why social media research didn’t hold up to expectations and why it’s finally breaking through. Presented at #IIEXap14 #IIEX
Previous research has suggested that people who are willing to provide their telephone numbers may be more likely to provide good quality data. This study examined whether asking people for their phone number 1) at the beginning of a survey or or 2) at the end of a survey affects the results. And yes, it matters.
Every great questionnaire deserves a great analysis. Listen to Jeffrey Henning, President of Researchscape International, and Annie Pettit, Chief Research Officer at Peanut Labs, discuss important tips for analyzing your survey data.
Perhaps we are all speaking English but men and women do it just a little bit differently. Find out what words are used more often by men or women and see how you fit the stereotype.
Just a few years ago, social media research was hailed as the panacea of all marketing research. The ridiculous quantities of brand opinions and opinionators available in social media would mean that focus groups would die, surveys would die, and all research questions would have instant answers. Fast forward to today and surveys continue to thrive. Learn why social media research didn’t hold up to expectations and why it’s finally breaking through. Presented at #IIEXap14 #IIEX
Previous research has suggested that people who are willing to provide their telephone numbers may be more likely to provide good quality data. This study examined whether asking people for their phone number 1) at the beginning of a survey or or 2) at the end of a survey affects the results. And yes, it matters.
Every great questionnaire deserves a great analysis. Listen to Jeffrey Henning, President of Researchscape International, and Annie Pettit, Chief Research Officer at Peanut Labs, discuss important tips for analyzing your survey data.
Most surveys use data quality techniques such as red herrings, speeding, straightlining, and jibberish monitoring to determine which completes represent good responders or poor responders. This paper, presented at the 2013 NewMR online festival, demonstrates which techniques properly identify good and poor responders.
We like to think that everyone answering our surveys is perfectly fluent in English but let's be realistic. About 10% of Americans have difficulty reading/writing in English because it is not their native language. And when we apply standard techniques to identify which survey takers provide good data and which are simply giving random answers, we are often making the mistake of applying measurements that all require high level language skills.
Of all the datasets that could be delivered to your desk, the most difficult one to work with might be that big dataset. Besides its massive size, it’s exponential growth even as you work on it, and the variety of data types present, big data presents many issues that make it difficult to turn data into action. In this presentation, you will learn how to take thousands of variables and billions of records and turn them into useable and actionable results, just as you would with any traditional research dataset.
For the most part, people who answer marketing research surveys want and try to do a good job. However, sometimes respondents want to get through a survey as quick as possible in order to earn the incentive and move to the next task.
- Learn the various types of data quality questions you can use, beyond speeding and straightlining.
- How to fit them into your questionnaire with minimal impact on responders.
- And most importantly, how to use the data quality questions effectively so that you don't accidentally exclude data from honest respondents.
A two part presentation first showing how people talk about their customer experiences in general and with banks. Followed by a case study of how Royal Bank handles customer experience via social media.
Winner of best ESOMAR paper of the year.
By Melanie Courtright, Kartik Pashupati, Roddy Knowles, and Annie Pettit
Discussion of how different types of scales are used differently around the world
Researchers know we're supposed to sample people in the proper proportions but how do we know what those proportions are? In this webinar, I will demonstrate how to use census data to determine what your sample should really look like in terms of variables like age, gender, region and .education
Annie Pettit's AI presentation at the 2018 annual Travel and Tourism Research Association (TTRA) conference in Miami. Sharing results from a Sklar Wilton white paper on Canadian perceptions of AI, plus applications of AI in marketing research.
Links to videos I showed:
@SklarWilton #AI white paper on what Canadians think about #AI, #VoiceAssistants, and #Chatbots.
https://www.sklarwilton.com/wp-content/uploads/2017/12/Sklar-Wilton-Canadian-Artificial-Intelligence-Paper-2017.pdf
Joy Buolamwini of M.I.T.’s Media Lab shows how facial recognition technology has trouble recognizing dark faces.
https://www.youtube.com/watch?v=TWWsW1w-BVo
Google can now make #AI phone calls that are virtually indistinguishable from human beings.
https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html
#AI can write newspaper articles about anything.
http://articlecreator.fullcontentrss.com/index.php
#AI can create humour that people actually laugh at.
https://www.youtube.com/watch?v=Vhe-JOP7PCs
#AI can read your mind.
https://www.youtube.com/watch?v=RuUSc53Xpeg
mindread
#AI, #Chatbots, and #VoiceAssistants are already running #questionnaires.
https://www.messenger.com/t/202671200242510/?messaging_source=source%3Apages%3Amessage_shortlink
Examples of ten cognitive biases in the marketing world that you can apply to your own work. Presented at the AMA Houston conference in September 2016.
In the first of a two-part session, four research professionals throw caution to the wind and fight for their passionately held beliefs about the way the insight world works. This is the session for those who want to hear the uncensored, unshackled and revolutionary voice of research.
I wish I had kept track of every time a conference speaker said they didn't understand the statistics they were referring to but if anyone had a question, they could find someone to answer it.
I wish I had kept track of every presenter whose 20 slides consisted of 20 pictures.
I wish it was even possible to count the number of infographics floating around the interweebs spewing countless unsubstantiated and out-of-context percentages with multiple decimal places.
In this presentation, I will plead with the audience to reconsider how they communicate to their clients about research, and how they present that research to various audiences.
People are sometimes intimidated by big data because it seems overwhelming and they’re much more familiar with using statistics on survey data or analyzing opinions from focus group data. But here are nine examples from companies like Netflix, Ceasars Entertainment, Walmart, eBay, and UPS, that could have conducted survey or focus group research have instead used big data to accomplish big things.
Theory is nice but data is heaven. Most market researchers have heard a lot of theory about big data, but few have seen the data and worked with it themselves. And we all know that the best way to truly understand and internalise something is to see the raw data for yourself. In this presentation, we'll blast ten big data myths using stories that many researchers can actually relate to - survey panel data. With millions of panellists, millions of profiles, millions of survey clicks, and millions of incentives, market researchers have been sitting on pretty big data for nearly 20 years. See how easy it is for trained scientists like yourselves to learn some SAS, R, or SQL, and dig into that big data on your own.
Surveys have a lot of tradition and norms behind them. And a lot of templates. Many of these templates were written many years ago when formal language made a lot more sense. Today, people expect casual and friendly language everywhere including from brands and companies. This presentation shows what happens to data quality and survey results when real language, not Charles Dickens language, is used.
At CASRO digital in San Antonio, Texas: Five minutes each from four different presenter. See how the US census uses social media, and how other people use it for customer experience and market research.
Most surveys use data quality techniques such as red herrings, speeding, straightlining, and jibberish monitoring to determine which completes represent good responders or poor responders. This paper, presented at the 2013 NewMR online festival, demonstrates which techniques properly identify good and poor responders.
We like to think that everyone answering our surveys is perfectly fluent in English but let's be realistic. About 10% of Americans have difficulty reading/writing in English because it is not their native language. And when we apply standard techniques to identify which survey takers provide good data and which are simply giving random answers, we are often making the mistake of applying measurements that all require high level language skills.
Of all the datasets that could be delivered to your desk, the most difficult one to work with might be that big dataset. Besides its massive size, it’s exponential growth even as you work on it, and the variety of data types present, big data presents many issues that make it difficult to turn data into action. In this presentation, you will learn how to take thousands of variables and billions of records and turn them into useable and actionable results, just as you would with any traditional research dataset.
For the most part, people who answer marketing research surveys want and try to do a good job. However, sometimes respondents want to get through a survey as quick as possible in order to earn the incentive and move to the next task.
- Learn the various types of data quality questions you can use, beyond speeding and straightlining.
- How to fit them into your questionnaire with minimal impact on responders.
- And most importantly, how to use the data quality questions effectively so that you don't accidentally exclude data from honest respondents.
A two part presentation first showing how people talk about their customer experiences in general and with banks. Followed by a case study of how Royal Bank handles customer experience via social media.
Winner of best ESOMAR paper of the year.
By Melanie Courtright, Kartik Pashupati, Roddy Knowles, and Annie Pettit
Discussion of how different types of scales are used differently around the world
Researchers know we're supposed to sample people in the proper proportions but how do we know what those proportions are? In this webinar, I will demonstrate how to use census data to determine what your sample should really look like in terms of variables like age, gender, region and .education
Annie Pettit's AI presentation at the 2018 annual Travel and Tourism Research Association (TTRA) conference in Miami. Sharing results from a Sklar Wilton white paper on Canadian perceptions of AI, plus applications of AI in marketing research.
Links to videos I showed:
@SklarWilton #AI white paper on what Canadians think about #AI, #VoiceAssistants, and #Chatbots.
https://www.sklarwilton.com/wp-content/uploads/2017/12/Sklar-Wilton-Canadian-Artificial-Intelligence-Paper-2017.pdf
Joy Buolamwini of M.I.T.’s Media Lab shows how facial recognition technology has trouble recognizing dark faces.
https://www.youtube.com/watch?v=TWWsW1w-BVo
Google can now make #AI phone calls that are virtually indistinguishable from human beings.
https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html
#AI can write newspaper articles about anything.
http://articlecreator.fullcontentrss.com/index.php
#AI can create humour that people actually laugh at.
https://www.youtube.com/watch?v=Vhe-JOP7PCs
#AI can read your mind.
https://www.youtube.com/watch?v=RuUSc53Xpeg
mindread
#AI, #Chatbots, and #VoiceAssistants are already running #questionnaires.
https://www.messenger.com/t/202671200242510/?messaging_source=source%3Apages%3Amessage_shortlink
Examples of ten cognitive biases in the marketing world that you can apply to your own work. Presented at the AMA Houston conference in September 2016.
In the first of a two-part session, four research professionals throw caution to the wind and fight for their passionately held beliefs about the way the insight world works. This is the session for those who want to hear the uncensored, unshackled and revolutionary voice of research.
I wish I had kept track of every time a conference speaker said they didn't understand the statistics they were referring to but if anyone had a question, they could find someone to answer it.
I wish I had kept track of every presenter whose 20 slides consisted of 20 pictures.
I wish it was even possible to count the number of infographics floating around the interweebs spewing countless unsubstantiated and out-of-context percentages with multiple decimal places.
In this presentation, I will plead with the audience to reconsider how they communicate to their clients about research, and how they present that research to various audiences.
People are sometimes intimidated by big data because it seems overwhelming and they’re much more familiar with using statistics on survey data or analyzing opinions from focus group data. But here are nine examples from companies like Netflix, Ceasars Entertainment, Walmart, eBay, and UPS, that could have conducted survey or focus group research have instead used big data to accomplish big things.
Theory is nice but data is heaven. Most market researchers have heard a lot of theory about big data, but few have seen the data and worked with it themselves. And we all know that the best way to truly understand and internalise something is to see the raw data for yourself. In this presentation, we'll blast ten big data myths using stories that many researchers can actually relate to - survey panel data. With millions of panellists, millions of profiles, millions of survey clicks, and millions of incentives, market researchers have been sitting on pretty big data for nearly 20 years. See how easy it is for trained scientists like yourselves to learn some SAS, R, or SQL, and dig into that big data on your own.
Surveys have a lot of tradition and norms behind them. And a lot of templates. Many of these templates were written many years ago when formal language made a lot more sense. Today, people expect casual and friendly language everywhere including from brands and companies. This presentation shows what happens to data quality and survey results when real language, not Charles Dickens language, is used.
At CASRO digital in San Antonio, Texas: Five minutes each from four different presenter. See how the US census uses social media, and how other people use it for customer experience and market research.
Behavioral economics has exploded over the last couple of years and market researchers have to begun to explore how it can be used in their work. But is it new or just new to you? This presentation outlines the growth of BE over the last 300 years and also describes some classic psychology experiments over the last 100 years. Moral of the story? If you just heard about a new theory of research, don't conduct research from scratch. Check what's already been done over the last few centuries!
Two case studies demonstrating tracking a campaign (#TheAnswerIsColombia), brand stereotypes (coffee and the drug trade in colombia), as well as a comparison of Shakira and Sofia Vergara
Social media research has generated such hype that clients can be overwhelmed with information, lack of information, and misinformation. Annie Pettit, Vice President of Research Standards at Research Now and Chief Research Officer at Conversition, will share examples of those problems and how they can be detrimental to your research. We’ll cover topics such as brand awareness, incidence, sentiment validity, spam, and other geeked-out topics.
More from Annie Pettit, Research Methodologist (14)
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
3. Battle of the Scales
Background
Scales are a cornerstone of market research
They’re how we determine that:
• 49% of people like Coca-Cola and 42% of people
like Pepsi
• Men like watching sports more than
women do
• How citizens feel about the government
• Canadians like Shania Twain more than
Brits… or do they??
3
5. Battle of the Scales
So Many Client Questions…
Is there a difference in the reliability of
attitudinal scales when using 4-point, 5-
point, 7-point, and 10-point scales?
Does excluding a neutral point impact the
answers?
Does labeling each point vs. only the end
points produce different results?
Can we replicate and extend the results of previous research on the
impact of cultural factors on response styles?
Do scales with greater variance (e.g., 7-point and 10-point scales)
reduce Extreme Response Style (ERS) compared to lesser variance
(e.g., 4-point and 5-point scales)?
Conversely, do scales with greater variance (e.g., 7-point and 10-point
scales) produce a greater incidence of Medium Response Style (MRS)
compared to lesser variance (e.g., 4-point and 5-point scales)?
5
7. Battle of the Scales
Research Plan
7-minute attitudinal survey
Globally relevant topics
Mix of positive and negative wording
Scales with published measures of reliability
Include behavioral statements that should correlate to
attitudinal questions
Simultaneously field across ten countries
Age and gender quota sampling by country
Same sample source throughout
Research Now’s proprietary Valued Opinions Panel (VOP)
First study to
Simultaneously compare the effect of multiple response
options (4-, 5-, 7- and 10-point scales)
Using a large, census-balanced, multi-country sample
7
8. Battle of the Scales
Research Plan
Fieldwork conducted December 2012 and January 2013
All five scale options tested in each of ten countries
Sample 4 5 point 5 point 7 10
TOTAL
Sizes point LABELED UNLABELED point point
Brazil 500 250 250 500 500 2,000
China 500 250 250 500 500 2,000
France 500 250 250 500 500 2,000
Germany 500 250 250 500 500 2,000
India 500 250 250 500 500 2,000
Japan 500 250 250 500 500 2,000
Mexico 500 250 250 500 500 2,000
Russia 500 250 250 500 500 2,000
UK 500 250 250 500 500 2,000
US 500 250 250 500 500 2,000
TOTAL 5,000 2,500 2,500 5,000 5,000 19,886
Note: For ease of reading, sample sizes have been rounded up or down by no more than 11.
8
9. Battle of the Scales
Analysis Plan
Three indices:
Extreme Response Style Index (ERSI)
– Respondents who answered either extreme of scale were
assigned a score of 1. Otherwise, they were assigned zero.
Acquiescence Response Style Index (ARSI)
– Respondents who strongly agreed with an item were
assigned a score of 1. Otherwise, they were assigned zero.
Medium Response Style Index (MRSI)
– On scales with an odd number of options (i.e., 5 point, 7
point), respondents who answered exactly in the middle
were assigned a medium response score of 1. Otherwise,
they were assigned zero.
Possible summary values ranged from 0.0 to 1.
9
10. Battle of the Scales
Use of Scale Results
Hypothesis 1: There will be no meaningful differences in
ERS, MRS or ARS indices between male and female
respondents.
Male Female
ERS .43 .43
ARS .32 .33
MRS .28 .28
Result 1 Confirmed: Men and women do not differ in
their response patterns.
10
11. Battle of the Scales
Use of Scale Results
Hypothesis 2: There will be significant but not systematic
differences in ERS, ARS and MRS Indices across the
different age groups.
.50
.46
.44 .44
.45 .43 .43
.41 .41 .41 .41
ERS (Extreme)
.40
.33 .33
.35 .32 .32
.32
.31
.30
.28 .29 .29 .29
.29
.30 .28 .28 .28
.27 .27
.25
ARS (Acquiescent)
.25
MRS (Medium)
.20
15-17 18-24 25-34 35-44 45-54 55-64 65-74 75-84 85+
Results 2 partially confirmed: There were significant
differences but the differences were systematic. ERS and
MRS gradually increased with age until 55-64, and then
declined. The pattern for ARS (yea-saying) was reversed.
11
12. Battle of the Scales
Use of Scale Results
Hypothesis 3: There will be significant differences in
ERS, ARS, and MRS across the different countries.
.60
.53 .53
.50
.48 MRS (Acquiescent)
.45
.40
.41 .41 .40 .40
.37
▌ERS (Extreme)
.31
.27 .29 .28 .28 .29 .29
.27 .26 .26
.30
.33
.20
.10
.00
Results 3 confirmed: Respondents from Brazil and Mexico
have the highest tendency to give extreme responses.
Respondents from Japan have a significantly lower ERS
Index, and significantly higher MRS (and ARS).
12
13. Battle of the Scales
Use of Scale Results
Hypothesis 4: Individualism (Hofstede) will correlate positively
with ERS and negatively with MRS. Individualism will equate
to stronger, and therefore more extreme, opinions.
.55
.49
.50
ERS (Extreme)
.45 .41
.39
.38
.40
.35
.31 ARS (Acquiescent)
.29 .28
.30 .27
MRS (Medium)
.25
.20
Low Medium High
Individualism
Results 4 not confirmed: Differences could not be attributed
to individualism. Analysis actually showed a negative
correlation with ERS.
13
14. Battle of the Scales
Use of Scale Results
Hypothesis 5: Respondents in countries that are higher in
masculinity (Hofstede) would exhibit higher ERS.
.50
.46
.45 .42
.41
ERS (Extreme)
.40
.34
.35
.32
.31
.30 .28 .28
.30
ARS (Acquiescent)
.25
MRS (Medium)
Low Medium High
Masculinity
Results 5 not confirmed: While masculinity did affect the
differences, the results were not in the expected direction.
Countries with lower masculinity demonstrated higher ERS
indices.
14
16. Battle of the Scales
Number of Scale Points Results
Hypothesis 6: The number of scale points and scale labeling
will affect ERS, MRS, and ARS.
.50
.45 .45
.45 .42
.41 .41
.40 ERS (Extreme)
.34
.33 .33
.33
.35 .32
.31 .32 .31
.30 .27
ARS (Acquiescent)
.25
.20
.20 MRS (Medium)
.15
4 point 5 point 5 point 7 point 10 point
labeled unlabeled
Results 6 confirmed: 7- and 10-point scales saw fewer
medium responses. ERS, MRS, and ARS were all lower for
the 5-point labeled scale versus the unlabeled scale.
16
17. Battle of the Scales
Number of Scale Points Results
Hypothesis 7: The number of scale points and scale
labeling will have an impact on scale reliability.
Cronbach's alpha
# of # of 5 point
Total Reverse 4 5 point unlabel 7 10
Scale Items Items point labeled ed point point
Health Environment Sensitivity 8 1 0.8 0.8 0.8 0.8 0.8
Personal Health Responsibility 8 2 0.7 0.6 0.6 0.6 0.7
Motorcycle Helmet Mandate 2 1 0.7 0.7 0.7 0.7 0.7
Attitude toward helping others
4 0 0.9 0.85 0.9 0.9 0.9
(AHO)
Material Values Scale (MVS 9) 9 2 0.8 0.75 0.8 0.8 0.8
Attitude toward Advertising in
7 4 0.7 0.7 0.7 0.7 0.7
General (AAG)
Online privacy concern 2 0 0.5 0.5 0.5 0.6 0.55
Lie acceptability scale 8 4 0.8 0.8 0.8 0.8 0.8
Results 7 Not confirmed: There is no significant variation in
the reliability of scales by number nor labeling of scale
points.
Note: For ease of reading, alphas were rounded. See the paper for precise values.
17
18. Battle of the Scales
Summary Findings
ERS, MRS, and ARS do not differ by gender, but do differ
by age
Response styles vary by country
– India, Mexico, Russia and Brazil are similar
– The US and UK are similar, as are France and Germany.
– Respondents from Japan are unique in terms of lower extreme and
higher medium response styles
Reasons for country differences are not yet isolated
– Need more research and
– Need scale norms that are available on a multi-country basis.
Varying the number of response options
– Does affect MRS
– Does NOT impact scale reliability or ERS
Scale labeling did not impact scale reliability, but did
impact ERS, MRS, and ARS
18
19. Battle of the Scales
Closing Thoughts
READ THE PAPER!
19
21. Battle of the Scales
Closing Thoughts
What about mobile?
If number of
options and
labeling do
impact results
and screen space
is a luxury on
mobile devices…
21
22. Battle of the Scales
Closing Thoughts
What about social media? Hideous
Disgusting
Abhor
If number of Crap
options and Yuck
Dumb
labeling do Huh
impact results, Dunno
Whatevs
what is the Good
complementary Nice
Cool
impact on textual Awesome
data… Wicked
Bomb
22