Global Opine Research is a research company with proprietary panels in 18 countries and over 1 million panellists. It manages panellists across geographies through in-house technology and experienced project managers who assist clients ranging from global corporations to research firms. The company works to deliver high quality samples while avoiding speeders and professional survey takers. It also conducts niche recruitment campaigns to improve its panel capabilities.
Toluna is a leading provider of digital consumer insights, and empowers companies to brainstorm ideas, uncover new business opportunities and answer their questions in real-time. Toluna is transforming the way marketing decisions are made by bringing consumers and brands together via the world’s largest social voting community of nine million members across 49 countries. This real-time access to consumers is coupled with its state-of-the-art, market research survey and analytics platform. Toluna has 18 offices in Europe, North America and Asia Pacific. For more information, please visit http://www.toluna-group.com/
Toluna is a leading provider of digital consumer insights, and empowers companies to brainstorm ideas, uncover new business opportunities and answer their questions in real-time. Toluna is transforming the way marketing decisions are made by bringing consumers and brands together via the world’s largest social voting community of nine million members across 49 countries. This real-time access to consumers is coupled with its state-of-the-art, market research survey and analytics platform. Toluna has 18 offices in Europe, North America and Asia Pacific. For more information, please visit http://www.toluna-group.com/
Attendees at the annual Charlotte Chamber Planning Retreat on October 13-14, 2011, were asked a series of questions via text polling. Here are the results.
Case Study on promoting a Game Art and Design course (Biswadeep Ghosh Hazra) ...Biswadeep Ghosh Hazra
The case study entailed promoting a Game Art and Design course at École Intuit Lab. I focussed on certain key areas like-
1) Industry Overview
2) Cohort Analysis
3) Segmentation
4) Marketing and Branding
California transit association workshop slideshare 13 november 2014Mark McCrindle
Leading times in changing times: Recruiting, retaining & motivating diverse generations
In a world of flat structures and consultative practices, coaching and mentoring has replaced commanding and controlling. This session delivers the latest findings on how to effectively motivate and lead teams in these 21st Century times. It provides an overview of the world’s best HR practices for today: from attracting and engaging with the globally-minded millennials to management practices that connect with an intergenerational workforce.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Attendees at the annual Charlotte Chamber Planning Retreat on October 13-14, 2011, were asked a series of questions via text polling. Here are the results.
Case Study on promoting a Game Art and Design course (Biswadeep Ghosh Hazra) ...Biswadeep Ghosh Hazra
The case study entailed promoting a Game Art and Design course at École Intuit Lab. I focussed on certain key areas like-
1) Industry Overview
2) Cohort Analysis
3) Segmentation
4) Marketing and Branding
California transit association workshop slideshare 13 november 2014Mark McCrindle
Leading times in changing times: Recruiting, retaining & motivating diverse generations
In a world of flat structures and consultative practices, coaching and mentoring has replaced commanding and controlling. This session delivers the latest findings on how to effectively motivate and lead teams in these 21st Century times. It provides an overview of the world’s best HR practices for today: from attracting and engaging with the globally-minded millennials to management practices that connect with an intergenerational workforce.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
2. Global Opine Research is a research company with proprietary panels in 18 countries and more than 1 million
panellists. Our in-house technology solutions help us in effectively managing and utilizing our panellists across
different geographies. Our experienced project managers help clients ranging from global corporations to research
firms round the clock.
We constantly work on delivering quality samples and to avoid speeders and professional survey takers from
accessing the survey. The in-depth profiling helps us in reaching niche audiences.
We improvise our research panel capabilities by running industry specific recruitment campaigns through affiliate
networks, industry websites, recruitment agencies etc.
Request a panel quote or learn more about Global Opine Research bids@globalopine.com | www.globalopine.com
Global Opine Research Panel Introduction
3. China } Panel Size: 110,000
Language: Chinese
Age Range Census (%) Panel (%)
0-14 Years 17.10 4.00
15-24 Years 14.70 15.00
25-54 Years 47.20 58.00
55-64 Years 11.30 20.00
65 years & above 9.60 3.00
Gender Census (%) Panel (%)
Male 51.00 45.00
Female 49.00 55.00
Region Panel (%)
East China 37.00
North China 16.00
North East China 11.00
South Central China 36.00
Income Range Panel (%)
Less than 20,000 CNY 20.00
20,000 to 29,999 CNY 16.00
30,000 to 49,999 CNY 14.00
50,000 to 69,999 CNY 12.00
70,000 to 99,999 CNY 10.00
100,000 to 119,999 CNY 9.00
120,000 to 129,999 CNY 7.00
130,000 to 149,999 CNY 5.00
150,000 to 179,999 CNY 4.00
180,000 to 199,999 CNY 2.00
200,000 to 219,000 CNY 1.00
4. Hong Kong } Panel Size: 9,000
Language: Chinese and English
Age Range Census (%) Panel (%)
0-14 Years 12.10 1.00
15-24 Years 11.50 24.00
25-54 Years 46.90 73.00
55-64 Years 14.80 1.00
65 years & above 14.70 1.00
Gender Census (%) Panel (%)
Male 48.00 45.00
Female 52.00 55.00
Income Range Panel (%)
Less than 125,000 HKD 22.00
125,000 to 159,999 HKD 18.00
160,000 to 189,999 HKD 9.00
190,000 to 254,999 HKD 7.00
255,000 to 319,999 HKD 8.00
320,000 to 379,999 HKD 10.00
380,000 to 444,999 HKD 5.00
445,000 to 508,499 HKD 6.00
508,500 to 569,999 HKD 4.00
570,000 to 634,999 HKD 4.00
635,000 HKD or more 7.00
Region Panel (%)
Hong Kong Island 16.00
Kowloon 25.00
New territories 55.00
Outlying Islands 4.00
5. India } Panel Size: 250,000
Language: Hindi and English
Age Range Census (%) Panel (%)
0-14 Years 28.50 3.00
15-24 Years 18.10 26.00
25-54 Years 40.60 59.00
55-64 Years 7.00 10.00
65 years & above 5.80 2.00
Gender Census (%) Panel (%)
Male 51.00 40.00
Female 49.00 60.00
Region Panel (%)
East 8.00
North 25.00
North Central 10.00
North Eastern 2.00
Southern 30.00
Western 25.00
Income Range Panel (%)
Less than Rs 30,000 23.00
Rs 30,000 to Rs 49,999 17.00
Rs 50,000 to Rs 99,999 10.00
Rs 1,00,000 to Rs 1,49,999 10.00
Rs 1,50,000 to Rs 1,99,999 8.00
Rs 2,00,000 to Rs 2,99,999 5.00
Rs 3,00,000 to Rs 4,99,999 6.00
Rs 5,00,000 to Rs 6,99,999 5.00
Rs 7,00,000 to Rs 9,99,999 5.00
Rs 10,00,000 to Rs 15,99,999 4.00
Rs 16,00,000 and above 7.00
6. Indonesia } Panel Size: 32,000
Language: Indonesian
Age Range Census (%) Panel (%)
0-14 Years 26.20 1.00
15-24 Years 17.10 19.00
25-54 Years 42.30 74.00
55-64 Years 7.90 6.00
65 years & above 6.50 0
Gender Census (%) Panel (%)
Male 50.00 49.00
Female 50.00 51.00
Income Range Panel (%)
Less than 2,500,000 IDR 35.00
2,500,001 to 5,000,000 IDR 25.00
5,000,001 to 10,000,000 IDR 21.00
10,000,001 to 15,000,000 IDR 8.00
15,000,001 to 20,000,000 IDR 7.00
20,000,001 and above 4
Region Panel (%)
Sumatera Utara 4.00
Sumatera Barat 6.00
Riau 3.00
Kepulauan Riau 4.00
Jambi 2.00
Bengkulu 2.00
Sumatera Selatan 2.00
Lampung 2.00
Ibukota Jakarta 15.00
Banten 6.00
Jawa Barat 17.00
Jawa Tengah 14.00
Jawa Timur 14.00
Lesser Sunda Islands 4.00
Borneo 3.00
Others 2.00
7. Japan } Panel Size: 25,000
Language: Japanese
Age Range Census (%) Panel (%)
0-14 Years 13.20 2.00
15-24 Years 9.70 21.00
25-54 Years 38.10 62.00
55-64 Years 13.20 14.00
65 years & above 25.80 1.00
Gender Census (%) Panel (%)
Male 49.00 56.00
Female 51.00 44.00
Region Panel (%)
Chubu 15.00
Chugoku 8.00
Hokkaido 7.00
Kanto 41.00
Kinki 19.00
Thohuku 6.00
Others 4.00
Income Range Panel (%)
1,500,000 JPY or less 9.00
1,500,001 JPY to 3,000,000 JPY 14.00
3,000,001 JPY to 4,000,000 JPY 16.00
4,000,001 JPY to 5,000,000 JPY 16.00
5,000,001 JPY to 6,000,000 JPY 7.00
6,000,001 JPY to 8,000,000 JPY 14.00
8,000,001 JPY to 10,000,000 JPY 9.00
10,000,001 JPY to 13,000,000 JPY 7.00
13,000,001 JPY to 15,000,000 JPY 4.00
15,000,001 JPY to 20,000,000 JPY 3.00
20,000,001 JPY and above 1.00
8. Malaysia } Panel Size: 15,000
Language: Malaysian and English
Age Range Census (%) Panel (%)
0-14 Years 28.80 2.00
15-24 Years 16.90 14.00
25-54 Years 41.20 56.00
55-64 Years 7.60 26.00
65 years & above 5.50 2.00
Gender Census (%) Panel (%)
Male 51.00 42.00
Female 49.00 58.00
Income Range Panel (%)
Less than 20,000 Ringgit 40.00
20,000 to 34,999 Ringgit 30.00
35,000 to 49,999 Ringgit 14.00
50,000 to 99,999 Ringgit 12.00
100,000 Ringgit or above 4.00
Region Panel (%)
Selangor 16.00
Wilayah Persekutuan K Lumpur 10.00
Johor 10.00
Sabah 9.00
Kedah 9.00
Pahang 6.00
Perak 7.00
Kelantan 8.00
Terengganu 6.00
Melaka 4.00
Sarawak 4.00
Negeri Sembilan 3.00
Pinang 4.00
Putrajaya 4.00
9. Philippines } Panel Size: 28,000
Language: Filipino
Age Range Census (%) Panel (%)
0-14 Years 33.70 2.00
15-24 Years 19.00 15.00
25-54 Years 37.00 68.00
55-64 Years 5.80 15.00
65 years & above 4.50 0
Gender Census (%) Panel (%)
Male 50.00 49.00
Female 50.00 51.00
Region Panel (%)
Metro Manila 25.00
Luzon 28.00
Visayas 26.00
Mindanao 21.00
Income Range Panel (%)
Less than 5,000 PHP 23.00
5,000 to 9,999 PHP 15.00
10,000 to 14,999 PHP 10.00
15,000 to 19,999 PHP 10.00
20,000 to 29,999 PHP 8.00
30,000 to 39,999 PHP 5.00
40,000 to 59,999 PHP 6.00
60,000 to 99,999 PHP 5.00
100,000 to 149,999 PHP 5.00
150,000 to 199,999 PHP 4.00
200,000 to 499,999 PHP 5.00
500,000 to 999,999 PHP 2.00
1,000,000 PHP or above 2.00
10. Singapore } Panel Size: 10,000
Language: English
Age Range Census (%) Panel (%)
0-14 Years 13.40 6.00
15-24 Years 17.80 19.00
25-54 Years 50.30 60.00
55-64 Years 10.00 12.00
65 years & above 8.50 3.00
Gender Census (%) Panel (%)
Male 49.00 45.00
Female 51.00 55.00
Ethnicity Panel (%)
Chinese 55.00
Malay 18.00
Indian 17.00
Other Ethnicity 10.00
Income Range Panel (%)
Less than 30,000 SGD 30.00
30,000 to 39,999 SGD 14.00
40,000 to 49,999 SGD 11.00
50,000 to 59,999 SGD 10.00
60,000 to 69,999 SGD 8.00
70,000 to 79,999 SGD 5.00
80,000 to 89,999 SGD 6.00
90,000 to 99,999 SGD 5.00
100,000 to 129,999 SGD 5.00
130,000 to 159,999 SGD 4.00
160,000 SGD or above 2.00
11. Taiwan } Panel Size: 15,000
Language: Mandarin
Age Range Census (%) Panel (%)
0-14 Years 14.00 3.00
15-24 Years 13.40 22.00
25-54 Years 47.40 67.00
55-64 Years 13.20 7.00
65 years & above 12.00 1.00
Gender Census (%) Panel (%)
Male 50.00 48.00
Female 50.00 52.00
Income Range Panel (%)
Less than 180.000 NTD 17.00
180.000 to 269.999 NTD 15.00
270.000 to 314.999 NTD 10.00
315.000 to 429.999 NTD 8.00
430.000 to 539.999 NTD 7.00
540.000 to 674.999 NTD 9.00
675.000 to 809.999 NTD 9.00
810.000 to 1.099.999 NTD 5.00
1.100.000 to 1.349.999 NTD 5.00
1.350.000 to 1.619.999 NTD 4.00
1.620.000 to 1.899.999 NTD 7.00
1.900.000 to 2.159.999 NTD 2.00
2.160.000 and above 2.00
12. Thailand } Panel Size: 22,000
Language: Thai
Age Range Census (%) Panel (%)
0-14 Years 17.60 1.00
15-24 Years 15.00 26.00
25-54 Years 46.90 71.00
55-64 Years 10.90 2.00
65 years & above 9.50 0.00
Gender Census (%) Panel (%)
Male 50.00 51.00
Female 50.00 49.00
Income Range Panel (%)
Less than 70,000 Baht 30.00
70,001 to 135,000 Baht 25.00
135,001 to 200,000 Baht 5.00
200,001 to 265,000 Baht 8.00
265,001 to 330,000 Baht 7.00
330,001 to 395,000 Baht 2.00
395,001 to 460,000 Baht 2.00
460,001 to 525,000 Baht 4.00
525,001 to 590,000 Baht 4.00
590,001 to 655,000 Baht 3.00
655,001 to 900,000 Baht 4.00
900,001 Baht or above 6.00
Region Panel (%)
Central Thailand 40.00
Eastern Thailand 15.00
Northeastern Thailand 13.00
Northern Thailand 32.00
13. Korea } Panel Size: 60,000
Language: Korean
Age Range Census (%) Panel (%)
0-14 Years 14.10 2.00
15-24 Years 13.50 20.00
25-54 Years 47.40 65.00
55-64 Years 12.40 12.00
65 years & above 12.70 1.00
Gender Census (%) Panel (%)
Male 49.00 45.00
Female 51.00 55.00
Income Range Panel (%)
Less than 20 Million Won 20.00
20 to 30 Million Won 20.00
30 to 40 Million Won 18.00
40 to 50 Million Won 13.00
50 to 60 Million Won 10.00
60 to 70 Million Won 6.00
70 to 80 Million Won 5.00
80 to 90 Million Won 4.00
90 to 1 Billion Won 3.00
1 Billion Won or more 1.00Region Panel (%)
Seoul 31.00
Gyeonggi-do 22.00
Incheon Metropolitan City 6.00
Busan Metropolitan City 7.00
Gwangju Metropolitan City 7.00
Others 27.00
14. USA } Panel Size: 210,000
Language: English
Age Range Census (%) Panel (%)
0-14 Years 19.40 1.00
15-24 Years 13.70 28.00
25-54 Years 39.90 64.00
55-64 Years 12.60 5.00
65 years & above 14.50 2.00
Gender Census (%) Panel (%)
Male 49.00 45.00
Female 51.00 55.00
Income Range Panel (%)
Less than $15,000 10.00
$15,000 to $24,999 14.00
$25,000 to $34,999 11.00
$35,000 to $44,999 10.00
$45,000 to $54,999 9.00
$55,000 to $64,999 7.00
$65,000 to $74,999 10.00
$75,000 to $84,999 6.00
$85,000 to $94,999 4.00
$95,000 to $124,999 5.00
$125,000 to $199,999 5.00
$200,000 or more 3.00
Prefer not to say 6.00
Region Panel (%)
Midwest 21.40
Northeast 15.90
South 39.90
West 22.60
15. Canada } Panel Size: 30,000
Language: English & French
Age Range Census (%) Panel (%)
0-14 Years 13.40 4.00
15-24 Years 17.80 15.00
25-54 Years 50.30 58.00
55-64 Years 10.00 20.00
65 years & above 8.50 3.00
Gender Census (%) Panel (%)
Male 51.00 56.00
Female 49.00 44.00
Income Range Panel (%)
Less than $15,000 10.00
$15,000 to $24,999 17.00
$25,000 to $34,999 10.00
$35,000 to $44,999 12.00
$45,000 to $54,999 9.00
$55,000 to $64,999 6.00
$65,000 to $74,999 6.00
$75,000 to $84,999 6.00
$85,000 to $94,999 6.00
$95,000 to $124,999 4.00
$125,000 to $199,999 4.00
$200,000 or more 1.00
Prefer not to say 9.00
Region Panel (%)
Atlantic Canada 7.0
British Colombia 8.00
Ontario 48.00
Prairie Provinces 9.00
Quebec 23.00
Territories 5.00
16. Germany } Panel Size: 20,000
Language: German
Age Range Census (%) Panel (%)
0-14 Years 13.00 5.00
15-24 Years 10.60 31.00
25-54 Years 41.70 48.00
55-64 Years 13.60 15.00
65 years & above 21.10 1.00
Gender Census (%) Panel (%)
Male 49.00 39.00
Female 51.00 61.00
Income Range Panel (%)
Less than 15,000 EUR 20.00
15,000 to 19,999 EUR 9.00
20,000 to 29,999 EUR 14.00
30,000 to 39,999 EUR 14.00
40,000 to 49,999 EUR 12.00
50,000 to 59,999 EUR 11.00
60,000 to 69,999 EUR 2.00
70,000 to 89,999 EUR 2.00
90,000 to 99,999 EUR 1.00
100,000 to 149,999 EUR 1.00
150,000 to 199,999 EUR 1.00
200,000 to 249,000 EUR 0.00
250,000 or more 1.00
Prefer not to say 12.00
Region Panel (%)
East 24.00
Middle 10.00
North 10.00
South 25.00
West 31.00
17. Italy } Panel Size: 25,000
Language: Italian
Age Range Census (%) Panel (%)
0-14 Years 13.80 8.00
15-24 Years 9.80 25.00
25-54 Years 43.00 60.00
55-64 Years 12.40 6.00
65 years & above 21.00 1.00
Gender Census (%) Panel (%)
Male 49.00 45.00
Female 51.00 55.00
Income Range Panel (%)
Less than 15,000 EUR 25.00
15,000 to 19,999 EUR 15.00
20,000 to 29,999 EUR 12.00
30,000 to 39,999 EUR 11.00
40,000 to 49,999 EUR 12.00
50,000 to 59,999 EUR 7.00
60,000 to 69,999 EUR 5.00
70,000 to 79,999 EUR 4.00
80,000 to 89,999 EUR 2.00
90,000 to 99,999 EUR 1.00
100,000 to 149,999 EUR 0.00
150,000 to 199,000 EUR 1.00
200,000 to 249,000 0.00
250,000 EUR or more 0.00
Prefer not to say 5.00
Region Panel (%)
Centre 19.00
North East 15.00
North West 28.00
South 38.00
18. Ukraine } Panel Size: 10,000
Language: Ukrainian
Age Range Census (%) Panel (%)
0-14 Years 14.00 5.00
15-24 Years 11.50 24.00
25-54 Years 45.00 70.00
55-64 Years 13.60 1.00
65 years or
above
15.90 0.00
Gender Census (%) Panel (%)
Male 46.00 56.00
Female 54.00 44.00
19. United Kingdom }
Count
Panel Size: 80,000
Language: English
Age Range Census (%) Panel (%)
0-14 Years 17.30 7.00
15-24 Years 12.60 42.00
25-54 Years 41.00 28.00
55-64 Years 11.50 20.00
65 years & above 17.50 3.00
Gender Census (%) Panel (%)
Male 51.00 58.00
Female 49.00 42.00
Region Panel (%)
Midlands and Wales 19.00
North and Scotland 25.00
Northern Ireland 7.00
South 49.00
Income Range Panel (%)
Less than £10000 20.00
£10,000 to £14,999 18.00
£15,000 to £19,999 10.00
£20,000 to £29,999 10.00
£30,000 to £39,999 12.00
£40,000 to £49,999 9.00
£50,000 to £59,999 2.00
£60,000 to £69,999 6.00
£70,000 to £79,999 5.00
£80,000 to £89,999 3.00
£90,000 to £99,999 3.00
£100,000 to £149,999 1.00
£150,000 to £199,999 1.00
£200,000 or more 0.00
20. Australia } Panel Size: 50,000
Language: English
Age Range Census (%) Panel (%)
0-14 Years 18.00 6.00
15-24 Years 13.30 21.00
25-54 Years 41.80 50.00
55-64 Years 11.80 17.00
65 years & above 15.10 6.00
Gender Census (%) Panel (%)
Male 50.00 58.00
Female 50.00 42.00
21. Global Opine Research
Flat Number 103 Second Floor, Shaheed Bhagat Singh Apartment
Pocket 3 Sector 14, Dwarka, New Delhi, India 110078
Phone +91 (0)11-4905-5625 | www.globalopine.com | info@globalopine.com