Rangle is publishing the results of our internal #diversity and inclusion survey in hopes of furthering the conversation and #transparency in technology. This is an extension of the summary posted on our blog: https://blog.rangle.io/diversity-and-inclusion-report/
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
The document discusses arithmetic sequences and series. It begins by defining arithmetic sequences as sequences where the difference between consecutive terms is constant. It provides the general formula for the n-th term of an arithmetic sequence as Un = a + (n-1)b, where a is the first term and b is the common difference. It then defines an arithmetic series as the sum of the terms of an arithmetic sequence, providing the general formula for the sum of the first n terms as Sn = 1/2n(2a + (n-1)b). It concludes by discussing examples and problems involving arithmetic sequences and series.
This document proposes creating a data warehouse at Rivier College to address several challenges: data is locked in different systems requiring manual extraction; administrators struggle to pull consistent data for reporting from different sources; and data analysis is basic without standard processes. The goals of the data warehouse are to improve planning and decision making through timely delivery of standardized, repeatable reports from a centralized collection of integrated, nonvolatile data. It will evolve to incorporate more institutional data sources over time.
This slide contains basic understanding on the concept of program evaluation. The key learning objectives include -
- Evaluation fundamentals
- Developing a logic model
- Understanding evaluation design
- Data analysis approach
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
Theories of Planning by: Dr. Eusebio F. Miclat Jr. Development Planning & Bu...Jo Balucanag - Bitonio
This document discusses theories, concepts, and rationale of strategic planning. It describes different theories of strategic planning including philosophical synthesis, rationalism, organizational development, and empiricism. It also discusses concepts of strategic planning in the context of business, administration, and socio-economic development. Finally, it outlines various purposes of strategic planning such as orienting organizations to community needs, establishing priorities amid limited resources, and realistically forecasting events through an exercise in "futurology".
Data-Ed Webinar: Data Quality EngineeringDATAVERSITY
Organizations must realize what it means to utilize data quality management in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
Takeaways:
Understanding foundational data quality concepts based on the DAMA DMBOK
Utilizing data quality engineering in support of business strategy
Data Quality guiding principles & best practices
Steps for improving data quality at your organization
The document discusses arithmetic sequences and series. It begins by defining arithmetic sequences as sequences where the difference between consecutive terms is constant. It provides the general formula for the n-th term of an arithmetic sequence as Un = a + (n-1)b, where a is the first term and b is the common difference. It then defines an arithmetic series as the sum of the terms of an arithmetic sequence, providing the general formula for the sum of the first n terms as Sn = 1/2n(2a + (n-1)b). It concludes by discussing examples and problems involving arithmetic sequences and series.
This document proposes creating a data warehouse at Rivier College to address several challenges: data is locked in different systems requiring manual extraction; administrators struggle to pull consistent data for reporting from different sources; and data analysis is basic without standard processes. The goals of the data warehouse are to improve planning and decision making through timely delivery of standardized, repeatable reports from a centralized collection of integrated, nonvolatile data. It will evolve to incorporate more institutional data sources over time.
This slide contains basic understanding on the concept of program evaluation. The key learning objectives include -
- Evaluation fundamentals
- Developing a logic model
- Understanding evaluation design
- Data analysis approach
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
Theories of Planning by: Dr. Eusebio F. Miclat Jr. Development Planning & Bu...Jo Balucanag - Bitonio
This document discusses theories, concepts, and rationale of strategic planning. It describes different theories of strategic planning including philosophical synthesis, rationalism, organizational development, and empiricism. It also discusses concepts of strategic planning in the context of business, administration, and socio-economic development. Finally, it outlines various purposes of strategic planning such as orienting organizations to community needs, establishing priorities amid limited resources, and realistically forecasting events through an exercise in "futurology".
Master Data Management - Gartner Presentation303Computing
This document discusses Digital Realty's implementation of a master data management (MDM) system. It provides an overview of MDM and why most projects fail. Digital Realty is succeeding by taking an agile approach with flexible multi-domain solutions. They leverage data virtualization and have identified data champions to manage master data domains like customers, products, facilities and people. The MDM implementation has provided benefits like improved data quality monitoring, faster integration of acquired companies, and ensuring compliance with data governance policies. Digital Realty is working to expand their MDM to additional transactional and dimensional master data entities.
Materi Kisi Kisi Pada Pembelajaran EvaluasiPutri Viona
Evaluasi pembelajaran matematika 2 membahas tes kemampuan berpikir kreatif matematis tentang materi matriks. Tes tersebut terdiri dari empat soal yang mencakup pengoperasian matriks, sistem persamaan linear, matriks invers, dan masalah cerita yang dipecahkan menggunakan matriks.
Dokumen tersebut membahas tentang relasi setara, relasi terurut, dan representasi relasi dengan matriks dalam matematika diskret. Tujuan pembelajaran antara lain mahasiswa dapat menjelaskan konsep-konsep tersebut dan menganalisis serta merepresentasikan berbagai relasi.
The document provides information about Christmas and New Year celebrations and events at The Ritz London hotel. It details a variety of dining options taking place between December 9th and January 1st, including Christmas Eve gala dinner, Christmas lunches and dinners, New Year's Eve galas, and a Boxing Day lunch. It also mentions seasonal programming like dinner dances, champagne selections, and gift vouchers for meals, overnight stays, and spa services.
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
The Evolving Role of the Data Architect – What Does It Mean for Your Career?DATAVERSITY
If you’re a data architect, you’ve heard it all—from ‘data management is the sexiest job of the 21st century’ to ‘data management is dead’. The truth almost certainly lies somewhere in the middle of the extremes, but how can you make sense of the true future of the data architect’s role in the rapidly-changing data landscape? The Data Architect holds a unique position as the translator between business value and technical implementation.
Join this webinar to learn how you can take advantage of the uniqueness of this role to catapult your career to the next level.
Data Analyst Roles & Responsibilities | EdurekaEdureka!
YouTube: https://youtu.be/nhyomoZS30M
( ** Data Analyst Master's Program: https://www.edureka.co/masters-program/data-analyst-certification ** )
This Edureka tutorial on "Data Analyst Roles and Responsibilities" will explain, what are the Roles and Responsibilities of a Data Analyst in the Industry. It also explains who is a Data Analyst and what does it takes to become one.
Following topics are included in the PPT:
Determine Organizational Goals
Mining Data
Data Cleaning
Analyzing Data
Pinpointing Trends and Patterns
Creating Reports with Clear Visualizations
Maintaining Databases and Data Systems
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
The document discusses business intelligence and the decision making process. It defines business intelligence as using technology to gather, store, access and analyze data to help users make better decisions. This includes applications like decision support systems, reporting, online analytical processing, and data mining. It also discusses key concepts like data warehousing, OLTP vs OLAP, and the different layers of business intelligence including the presentation, data warehouse, and source layers.
Here's a starting template for anyone presenting data science topic to elementary school students. Exhibits how fun the field is and how the job market for these skills is excellent. Includes hyperlinks to various examples of interesting interactive visualizations.
Data governance Program PowerPoint Presentation Slides SlideTeam
The document discusses the need for data governance programs in companies. It outlines why companies suffer without effective data governance, such as applications being unable to communicate and inconsistencies in data leading to increased costs. The document then compares manual and automated approaches to data governance. It provides details on key aspects of building a data governance program, including establishing a framework, defining roles and responsibilities, and outlining a roadmap for improving data governance over time.
The document defines data mining and knowledge discovery in databases (KDD). It states that data mining involves sorting through large datasets to identify patterns and relationships. The goal is extraction of knowledge from data, not just extraction of data itself. Data mining is part of the KDD process. KDD discovers useful knowledge from data through preparation, cleansing, interpretation and prior knowledge. Major KDD areas include marketing, fraud detection and manufacturing. The KDD process has improved over the last 10 years using different discovery approaches like statistics and machine learning. The overall KDD process involves domain understanding, data selection, cleaning, reduction, choosing a task/algorithm, mining patterns, and interpreting results.
Numerous challenges faced by leaders require public and nonprofit organizations to think and act more strategically. Strategic planning helps organizations assess their current situation, set goals for the future, and develop plans to achieve those goals. While strategic planning provides benefits like improved decision-making and organizational effectiveness, it requires tailored implementation and is not a substitute for leadership. When done well, strategic planning is a smart management practice for navigating today's complex environment.
Based partially on Bryson (2011), this is the first class for the Siena Heights Graduate College LDR 660 Strategic Planning class I teach at Lake Michigan College.
Data and analytics strategy PUBLIC - ADHB 2021Ali Khan
The document provides an executive summary of the Data & Analytics Strategy for Auckland District Health Board (ADHB). It discusses four key areas: 1) unleashing their data through a new "DataOps" delivery model focused on self-service and automation, 2) changing their operating model, information architecture, and delivery ecosystem to embed cost-effectiveness, 3) enabling self-service data innovation for front-line staff through safe environments, and 4) leveraging their data to meet diverse business needs through a "build once, use many" approach. The strategy aims to unlock value in data and empower their "citizen workforce" to build insights.
Translating Big Raw Data Into Small Actionable InformationAlan McSweeney
This document discusses translating big raw data into small actionable information. It begins by outlining some of the challenges with big raw data, such as its wide scope and lack of common definitions. It then advocates focusing on developing approaches and solutions to extract useful insights and business value from raw data. The document describes how to define potential use cases across an organization's various external interactions and priorities. It provides a template for documenting use cases and evaluating their potential value and implementation requirements. Finally, it cautions against an illusion of being able to directly manage outcomes and stresses the importance of influencing them through appropriate use cases and activities.
This document discusses different types of evaluations conducted prior to developing new programs or policies. It describes needs assessments, which identify the needs of a target population; research reviews, which synthesize existing literature on issues; and reviews of exemplary practices, which identify best practices from other programs. The goal of these "front-end" evaluations is to inform decisions about future programs by understanding needs, lessons learned, and benchmark practices.
This chapter discusses process modeling, improvement, and ERP implementation. It describes tools for modeling business processes like flowcharts and event process chains. Process modeling helps identify areas for improvement. The chapter also covers evaluating processes for value added activities and using simulation to test improvements. ERP systems provide workflow tools to automate processes. Managing organizational change is key to a successful ERP implementation.
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Business Value Metrics for Data GovernanceDATAVERSITY
This document discusses how to quantify and communicate the business value of data governance initiatives. It begins with background on information capability and data maturity levels. It then discusses frameworks for understanding business value, such as key performance indicators and how initiatives can generate revenue, cost savings or avoidance. The document provides examples of how to calculate return on investment, net present value and payback period to quantify benefits. It also discusses how to effectively communicate a business case by aligning it with organizational objectives and knowing your audience.
The document summarizes findings from Bhutan's 2010 GNH survey in Lhuntse Dzongkhag (district). Key findings include:
- Respondents reported high levels of psychological well-being, health, and cultural engagement. Nearly all spoke their mother tongue well and felt Bhutanese traditions were important.
- Access to infrastructure like electricity and internet was limited in some areas. Respondents engaged in local governance and had trust in their legal system.
- Communities reported strong social cohesion, with high levels of trust between neighbors, family ties, and volunteerism. Safety from crime was also largely perceived as good.
- While cultural practices like use of the traditional language and games remained strong
The survey summarizes respondent demographics and responses to questions about charitable giving habits. It found that most respondents were female, aged 18-29, from East Java province, and spent between $101-200 monthly excluding major expenses. When deciding whether to donate, factors like an organization's mission, effective use of funds, and addressing urgent needs were very important. Education and health charities received the most donations, primarily through direct solicitation, fundraisers, or at the charity's location.
Master Data Management - Gartner Presentation303Computing
This document discusses Digital Realty's implementation of a master data management (MDM) system. It provides an overview of MDM and why most projects fail. Digital Realty is succeeding by taking an agile approach with flexible multi-domain solutions. They leverage data virtualization and have identified data champions to manage master data domains like customers, products, facilities and people. The MDM implementation has provided benefits like improved data quality monitoring, faster integration of acquired companies, and ensuring compliance with data governance policies. Digital Realty is working to expand their MDM to additional transactional and dimensional master data entities.
Materi Kisi Kisi Pada Pembelajaran EvaluasiPutri Viona
Evaluasi pembelajaran matematika 2 membahas tes kemampuan berpikir kreatif matematis tentang materi matriks. Tes tersebut terdiri dari empat soal yang mencakup pengoperasian matriks, sistem persamaan linear, matriks invers, dan masalah cerita yang dipecahkan menggunakan matriks.
Dokumen tersebut membahas tentang relasi setara, relasi terurut, dan representasi relasi dengan matriks dalam matematika diskret. Tujuan pembelajaran antara lain mahasiswa dapat menjelaskan konsep-konsep tersebut dan menganalisis serta merepresentasikan berbagai relasi.
The document provides information about Christmas and New Year celebrations and events at The Ritz London hotel. It details a variety of dining options taking place between December 9th and January 1st, including Christmas Eve gala dinner, Christmas lunches and dinners, New Year's Eve galas, and a Boxing Day lunch. It also mentions seasonal programming like dinner dances, champagne selections, and gift vouchers for meals, overnight stays, and spa services.
DAS Slides: Data Governance - Combining Data Management with Organizational ...DATAVERSITY
Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.
The Evolving Role of the Data Architect – What Does It Mean for Your Career?DATAVERSITY
If you’re a data architect, you’ve heard it all—from ‘data management is the sexiest job of the 21st century’ to ‘data management is dead’. The truth almost certainly lies somewhere in the middle of the extremes, but how can you make sense of the true future of the data architect’s role in the rapidly-changing data landscape? The Data Architect holds a unique position as the translator between business value and technical implementation.
Join this webinar to learn how you can take advantage of the uniqueness of this role to catapult your career to the next level.
Data Analyst Roles & Responsibilities | EdurekaEdureka!
YouTube: https://youtu.be/nhyomoZS30M
( ** Data Analyst Master's Program: https://www.edureka.co/masters-program/data-analyst-certification ** )
This Edureka tutorial on "Data Analyst Roles and Responsibilities" will explain, what are the Roles and Responsibilities of a Data Analyst in the Industry. It also explains who is a Data Analyst and what does it takes to become one.
Following topics are included in the PPT:
Determine Organizational Goals
Mining Data
Data Cleaning
Analyzing Data
Pinpointing Trends and Patterns
Creating Reports with Clear Visualizations
Maintaining Databases and Data Systems
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
The document discusses business intelligence and the decision making process. It defines business intelligence as using technology to gather, store, access and analyze data to help users make better decisions. This includes applications like decision support systems, reporting, online analytical processing, and data mining. It also discusses key concepts like data warehousing, OLTP vs OLAP, and the different layers of business intelligence including the presentation, data warehouse, and source layers.
Here's a starting template for anyone presenting data science topic to elementary school students. Exhibits how fun the field is and how the job market for these skills is excellent. Includes hyperlinks to various examples of interesting interactive visualizations.
Data governance Program PowerPoint Presentation Slides SlideTeam
The document discusses the need for data governance programs in companies. It outlines why companies suffer without effective data governance, such as applications being unable to communicate and inconsistencies in data leading to increased costs. The document then compares manual and automated approaches to data governance. It provides details on key aspects of building a data governance program, including establishing a framework, defining roles and responsibilities, and outlining a roadmap for improving data governance over time.
The document defines data mining and knowledge discovery in databases (KDD). It states that data mining involves sorting through large datasets to identify patterns and relationships. The goal is extraction of knowledge from data, not just extraction of data itself. Data mining is part of the KDD process. KDD discovers useful knowledge from data through preparation, cleansing, interpretation and prior knowledge. Major KDD areas include marketing, fraud detection and manufacturing. The KDD process has improved over the last 10 years using different discovery approaches like statistics and machine learning. The overall KDD process involves domain understanding, data selection, cleaning, reduction, choosing a task/algorithm, mining patterns, and interpreting results.
Numerous challenges faced by leaders require public and nonprofit organizations to think and act more strategically. Strategic planning helps organizations assess their current situation, set goals for the future, and develop plans to achieve those goals. While strategic planning provides benefits like improved decision-making and organizational effectiveness, it requires tailored implementation and is not a substitute for leadership. When done well, strategic planning is a smart management practice for navigating today's complex environment.
Based partially on Bryson (2011), this is the first class for the Siena Heights Graduate College LDR 660 Strategic Planning class I teach at Lake Michigan College.
Data and analytics strategy PUBLIC - ADHB 2021Ali Khan
The document provides an executive summary of the Data & Analytics Strategy for Auckland District Health Board (ADHB). It discusses four key areas: 1) unleashing their data through a new "DataOps" delivery model focused on self-service and automation, 2) changing their operating model, information architecture, and delivery ecosystem to embed cost-effectiveness, 3) enabling self-service data innovation for front-line staff through safe environments, and 4) leveraging their data to meet diverse business needs through a "build once, use many" approach. The strategy aims to unlock value in data and empower their "citizen workforce" to build insights.
Translating Big Raw Data Into Small Actionable InformationAlan McSweeney
This document discusses translating big raw data into small actionable information. It begins by outlining some of the challenges with big raw data, such as its wide scope and lack of common definitions. It then advocates focusing on developing approaches and solutions to extract useful insights and business value from raw data. The document describes how to define potential use cases across an organization's various external interactions and priorities. It provides a template for documenting use cases and evaluating their potential value and implementation requirements. Finally, it cautions against an illusion of being able to directly manage outcomes and stresses the importance of influencing them through appropriate use cases and activities.
This document discusses different types of evaluations conducted prior to developing new programs or policies. It describes needs assessments, which identify the needs of a target population; research reviews, which synthesize existing literature on issues; and reviews of exemplary practices, which identify best practices from other programs. The goal of these "front-end" evaluations is to inform decisions about future programs by understanding needs, lessons learned, and benchmark practices.
This chapter discusses process modeling, improvement, and ERP implementation. It describes tools for modeling business processes like flowcharts and event process chains. Process modeling helps identify areas for improvement. The chapter also covers evaluating processes for value added activities and using simulation to test improvements. ERP systems provide workflow tools to automate processes. Managing organizational change is key to a successful ERP implementation.
Metadata is hotter than ever, according a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
Business Value Metrics for Data GovernanceDATAVERSITY
This document discusses how to quantify and communicate the business value of data governance initiatives. It begins with background on information capability and data maturity levels. It then discusses frameworks for understanding business value, such as key performance indicators and how initiatives can generate revenue, cost savings or avoidance. The document provides examples of how to calculate return on investment, net present value and payback period to quantify benefits. It also discusses how to effectively communicate a business case by aligning it with organizational objectives and knowing your audience.
The document summarizes findings from Bhutan's 2010 GNH survey in Lhuntse Dzongkhag (district). Key findings include:
- Respondents reported high levels of psychological well-being, health, and cultural engagement. Nearly all spoke their mother tongue well and felt Bhutanese traditions were important.
- Access to infrastructure like electricity and internet was limited in some areas. Respondents engaged in local governance and had trust in their legal system.
- Communities reported strong social cohesion, with high levels of trust between neighbors, family ties, and volunteerism. Safety from crime was also largely perceived as good.
- While cultural practices like use of the traditional language and games remained strong
The survey summarizes respondent demographics and responses to questions about charitable giving habits. It found that most respondents were female, aged 18-29, from East Java province, and spent between $101-200 monthly excluding major expenses. When deciding whether to donate, factors like an organization's mission, effective use of funds, and addressing urgent needs were very important. Education and health charities received the most donations, primarily through direct solicitation, fundraisers, or at the charity's location.
This document summarizes survey findings from 2010 on GNH in Sarpang Dzongkhag, Bhutan. Key findings include:
- Respondents reported high levels of psychological well-being, health, and time balance.
- Cultural diversity was strongly maintained, with nearly universal importance placed on traditions and high levels of respect reported between children and elders.
- Governance was seen as generally free and fair, with high voter turnout and performance ratings for elected officials.
- Community vitality was strong, with high levels of trust, volunteering, and sense of belonging reported.
- Environmental stewardship was also high, with strong personal responsibility felt for conservation and high reuse and waste reduction behaviors.
This document provides a summary report of course evaluations for English composition courses at BHSU for the 2019 Fall term. It shows the number of students enrolled, responded and response rates for 4 course sections. It then provides data on students' self-reported progress on 13 learning objectives using a 5-point scale. Finally, it includes students' level of agreement with 3 statements about the course on a 5-point scale.
We all need to eat and, for a majority of consumers, that involves the chore of grocery shopping, but how and when do most consumers go about it? A survey of 1,000 U.S. adults.
A comparative assessment of sociodemographic factors associated with hiv infe...Dr Saude Ibrahim
This study compared socio-demographic factors associated with HIV infection between the Federal Capital Territory (FCT), a consistently high prevalence area, and Jigawa State, a consistently low prevalence area in Nigeria. HIV prevalence was higher in FCT (6.6%) than Jigawa (1.9%). Key predictors of HIV in FCT were having multiple sexual partners and a history of blood transfusion. In Jigawa, lack of Qur'anic education was the main predictor. Across both areas, being unaware of a partner's HIV status and having sexually transmitted infections increased HIV risk. The study concludes differences in HIV prevalence may relate to socio-demographic, cultural and religious factors between the areas.
Is there public support for policies promoting healthybrownschool
This document summarizes a study on public support for policies promoting physical activity. The study surveyed over 1,200 adults about their support for various school and community physical activity policies. It found high support for policies requiring physical education in schools. Characteristics like gender, age, and education predicted support for school policies, while race, community walkability, and education predicted support for community policies. The results provide insights into advocacy strategies and which groups to target for increasing policy support. The study is part of a larger research agenda on physical activity policy through a national research network.
The document summarizes key findings from surveys distributed to 5000 households in two rural health regions of Manitoba, Canada. The surveys gathered information on residents' preferences for accessing mental health supports and barriers to care. Key results included:
- Respondents preferred accessing help from books, websites, and individual counseling over group programs or computer-based treatment.
- Top barriers to seeking help were wanting to handle problems independently and feeling issues were not severe enough. Other barriers included cost concerns, wait times, and lack of available services.
- Factors making help-seeking more likely included being able to directly contact providers, providers having mental health training and living in the community.
The document summarizes the results of a sexual harassment survey conducted in November 2017 and October 2018. Some key findings include:
- 17.5% of all respondents reported experiencing sexual harassment at work.
- The most common harassers were colleagues (25%) and direct bosses (36%).
- 59% of those harassed did not report the incident, most commonly due to fear of retribution or believing it would not result in action.
- The majority of harassment encountered was verbal (46-50%) and emotional/psychological (28-31%).
- Most respondents believe increased media attention on harassment will help hold harassers more accountable but not necessarily reduce incidents.
Survey of Career Service Professionals: OntarioCERIC
How do Canada’s career service professionals differ across the country, in terms of their education, salary or professional development needs? CERIC has undertaken a Regional Analysis of its popular Survey of Career Service Professionals, offering a fascinating breakdown of trends in Ontario.
Stressed control of mothers in Vietnam DI Marketing
Although 41% consider themselves becoming more positive after having kids, still 46% sometimes feel more stressed
Find more at: https://www.di-onlinesurvey.com
NAAHP 2010 poster presentation on self-evaluation, external evaluation, an admissions outcomes validation of the competency-based holistic evaluation rubric implemented for GMU applicants over EY 2009 and EY 2010. All EY2011 data should be considered preliminary.
The Division of Student Affairs at the University of North Texas conducted a climate survey of its employees in Fall 2014. The survey aimed to develop a better understanding of how DSA employees relate to one another and their level of job satisfaction. Over 380 employees responded, with most having worked at UNT for 1-5 years. The survey found generally positive ratings of aspects like feeling part of a team and appreciation from supervisors. However, fewer employees agreed they have clear career paths or that their opinions count within DSA. The results will help DSA identify areas of strength and opportunities for improving inclusion, equity and job satisfaction.
The 2014 Global Translators Survey is a project we commissioned earlier this year in cooperation with academics and our friends among translators world wide. We wanted to get a better sense of where the industry is headed and what translators think about the profession. Special thanks to those of you who have helped spread the word.
The document summarizes the key findings of an online survey conducted in Thailand regarding employee satisfaction. 340 employees nationwide participated in the survey from May 8-12, 2017. Key findings include:
- 60% of respondents were satisfied with their current working environment, though low payment, lack of bonuses, and heavy workload were the top 3 reasons for dissatisfaction.
- 38% felt unfair competition occurs frequently in their workplace, and the most common solution was to speak to one's direct manager.
- 49% intended to remain with their current employer long-term, on average taking 1 month to adapt to a new workplace.
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3. Rangle.io November 2018
Diversity and inclusion has been important to Rangle since our inception.
While we have a long way to go, we’ve made great strides.
3
Diversity Guild
Our journey towards a culture of belonging
4. Rangle.io November 2018
An inclusive workplace is a non-negotiable priority for Rangle.
In order to improve, we need to understand exactly where we
are today. A survey was one of primary goals for the D&I Guild
in 2018.
This year’s survey will act as a benchmark so that we can
statistically measure our progress in the years to come.
We’re committed to furthering the dialogue around
diversity in tech. By publishing our results and committing
to improving, we hope to inspire more conversations at
Rangle and in the community.
4
Why conduct a survey?
5. 15 (6.8%)
132 (60.3%)
72 (32.9%)
Tenure
Less than 1 Year
1-2 Years
3 or More Years
Age Group
7 (3.2%)
68 (31.1%)
28 (12.8%)
20-24 Years Old
25-29 Years Old
30-34 Years Old
35-39 Years Old
40-44 Years Old
45-49 Years Old
50+ Years Old
60 (27.4%)
34 (15.5%)
13 (5.9%)
9 (4.1%)
6. 7 (3.2%)
117 (53.4%)
65 (29.7%)
30 (13.7%)
Was English your first language?
Yes, English was my first language
Yes, but English was one among other(s)
I consider as first languages
No, English was not my first language
Prefer not to answer + No Response
+ Did Not Participate
Were you born in Canada?
6 (2.7%)
110 (50.2%)
103 (47%)
Yes
No
Prefer not to answer + No Response
+ Did Not Participate
7. 11 (5.0%)
73 (33.3%)
135 (61.6%)
Do you consider yourself a
person of colour?
Yes
No
Prefer not to answer + No Response +
Did Not Participate
Do you consider yourself to be an
indigenous person of Canada?
208 (95.0%)
11 (5.0%)
No
Yes + Prefer not to answer + No Response +
Did Not Participate
8. Choose the category that best represents your heritage
120 (50.0%)
24 (10.0%)
9 (3.8%)
Black
East Asian
South Asian
Hispanic or Latinx
Middle Eastern
White
Pacific Islander + Self Describe +
Prefer not to Answer + Did Not Participate
32 (13.3%)
14 (5.8%)
30 (12.5%)
11 (4.6%)
9. 10 (4.6%)
197 (90.0%)
12 (5.5%)
Do you identify as a person with
a physical disability?
Yes
No
Not Sure + Prefer not to answer +
No Response + Did Not Participate
Do you identify as a person with a
cognitive disability?
For example, but not limited to: ADHD, dyslexia, etc.
181 (82.6%)
11 (5.0%)
14 (6.4%)
13 (5.9%)
Yes
No
Not Sure
Prefer not to answer + No Response +
Did Not Participate
10. Do you identify as a person with a mental illness?
160 (73.1%)
Yes
No
Not sure
Prefer not to answer + No Response +
Did Not Participate
29 (13.2%)
18 (8.2%)
12 (5.5%)
11. 10 (4.6%)
76 (34.7%)
133 (60.7%)
What is your current gender identity?
Woman
Man
Prefer to self-describe + Prefer not to answer + No
Response + Did Not Participate
Do you identify as LGBTQIA+
(Lesbian, Gay, Bisexual, Transgender, Queer, Questioning,
Two-spirited, Intersex or Asexual)?
6 (2.7%)
189 (86.3%)
14 (6.4%)
Yes, publicly
Yes, but privately
No (includes allies)
Prefer to self-describe + Prefer not to answer +
No Response + Did Not Participate
10 (4.6%)
12. Which of the following best fits your current relationship status?
49 (22.4%)
Single, not in a relationship
Single, in a relationship
Married / Common-law
Other + Prefer not to answer +
No Response + Did Not Participate
44 (20.1%)
110 (50.2%)
16 (7.3%)
13. Do you have dependents (able
AND/OR disabled) relying on you
for care?
Do you provide care for children up to
18 years of age OR adults (including
elderly) 19 years or over?
73 (33.3%)
134 (61.2%)
12 (5.5%)
Yes
No
Prefer not to answer + No Response +
Did Not Participate
7 (8.6%)
Children up to and including
18 years WITHOUT disabilities
Adults 19 years or older
(including elderly) WITHOUT disabilities
Children up to and including
18 years + Adults 19 years or older
(including elderly) WITH disabilities
58 (71.6%)16 (19.8%)
14. What is the highest level of education
you have fully completed?
Have you experienced economic
disadvantage in your lifetime?
124 (56.6%)
43 (19.6%)
8 (3.7%)
High School or equivalent + Technical / Vocational School
or equivalent
College Diploma or equivalent
Bachelor’s Degree or equivalent
Master’s Degree or equivalent +Ph.D. or equivalent
Other + Prefer not to answer + No Response +
Did Not Participate
11 (5.0%)
Yes, I am currently experiencing economic disadvantage in my
life (eg. juggling bills, missing payments, severely indebted)
Yes, I previously experienced economic disadvantage in my life
(eg. family was economically disadvantaged, earlier in my
career I juggled bills, missed payments, and/or was severely
indebted)
No
Prefer not to answer + No Response + Did Not Participate
85 (38.3%)108 (48.6%)
34 (15.5%)
10 (4.6%) 18 (8.1%)
15. 49.1% 50.9%
22.2% 77.8%
All Departments
Except Technology
Technology
Gender Identity by Department
Woman Man
Self Described +
Prefer not to answer +
No Response
Total
All Departments
except Technology
54 (49.1%) 56 (50.9%) 4 114
Technology 22 (22.2%) 77 (77.8%) 6 105
Total 76 133 10 219
*Please note that in order to ensure large enough group sizes for reporting, all Departments except for Technology were merged into one group.
Woman
Man
0 50 100
16. 32.2% 67.8%
38.0% 62.0%
In a Leadership Role
Not in a Leadership
Role
Gender Identity by Leadership Role
Woman Man
Self Described +
Prefer not to answer +
No Response
Total
In a Leadership
Role
19 (32.2%) 40 (67.8%) 3 62
Not in a Leadership
Role
57 (38.0%) 93 (62.0%) 7 157
Total 76 133 10 219
Woman
Man
0 50 100
17. 38.1% 61.9%
33.7% 66.3%
Less than 1 Year
More than 1 Year
Gender Identity by Tenure
Woman Man
Self Described +
Prefer not to answer +
No Response
Total
Less than 1 Year 48 (38.1%) 78 (61.9%) 6 132
More than 1 Year 28 (33.7%) 55 (66.3%) 4 87
Total 76 133 10 219
*Please note that in order to ensure large enough group sizes for reporting, 1-2 Years and 3 or More Years were merged into one group.
Woman
Man
0 50 100
18. 39.8% 60.2%
30.0% 70.0%
All Departments
except Technology
Technology
Person of Colour by Department
Yes (Consider
yourself to be a
Person of Colour)
No (Do not consider
yourself to be a
Person of Colour)
Prefer not to answer +
No Response
Total
All Departments
except Technology
43 (39.8%) 65 (60.2%) 6 114
Technology 30 (30.0%) 70 (70.0%) 5 105
Total 73 135 11 219
*Please note that in order to ensure large enough group sizes for reporting, all Departments except for Technology were merged into one group.
Yes (consider yourself to
be a Person of Colour)
No (Do not consider
yourself to be a Person of
Colour)
0 50 100
19. 28.8% 71.2%
37.6% 62.4%
In a
Leadership Role
Not in a
Leadership Role
Person of Colour by Leadership Role
Yes (Consider
yourself to be a
Person of Colour)
No (Do not consider
yourself to be a
Person of Colour)
Prefer not to answer +
No Response
Total
In a Leadership
Role
17 (28.8%) 42 (71.2%) 3 62
Not in a
Leadership Role
56 (37.6%) 93 (62.4%) 8 157
Total 73 135 11 219
Yes (consider yourself to
be a Person of Colour)
No (Do not consider
yourself to be a Person of
Colour)
0 50 100
20. 32.8% 67.2%
42.9% 57.1%
0-1
3+
Person of Colour by Tenure
Yes (Consider
yourself to be a
Person of Colour)
No (Do not consider
yourself to be a
Person of Colour)
Prefer not to answer +
No Response
Total
0-1 Year 41 (32.8%) 84 (67.2%) 7 132
1-2 Years 26 (37.7%) 43 (62.3%) 3 72
3 or More Years 6 (42.9%) 8 (57.1%) 1 15
Total 73 135 11 219
Yes (consider yourself to
be a Person of Colour)
No (Do not consider
yourself to be a Person of
Colour)
0 50 100
37.7% 62.3%1-2 Years
21. 45.2% 54.8%
52.3% 47.7%
20-29 Years Old
40+ Years Old
Age Group by Economic Disadvantage
Yes, I am currently
experiencing economic
disadvantage and/or I have
previously experienced
economic disadvantage
No
Prefer not to answer +
No Response
Total
20-29 Years Old 28 (45.2%) 34 (54.8%) 7 69
30-39 Years Old 45 (45.9%) 53 (54.1%) 4 102
40+ Years Old 23 (52.3%) 21 (47.7%) 4 48
Yes, I am currently
experiencing economic
disadvantage and/or I
have previously
experienced economic
disadvantage
No
0 50 100
45.9% 54.1%30-39 Years Old
22. 14.6% 85.4%
65.0% 35.0%
20-34 Years Old
Age Group by Leadership Role
In a Leadership Role Not in a Leadership Role Total
20-34 Years Old 20 (14.6%) 117 (85.4%) 137
35-39 Years Old 14 (41.2%) 20 (58.8%) 34
40-44 Years Old 15 (53.6%) 13 (46.4%) 28
45+ Years Old 13 (65.0%) 7 (35.0%) 20
Total 62 157 219
0 50 100
41.2% 58.8%35-39 Years Old
53.6% 46.4%40-44 Years Old
45+ Years Old
In a Leadership Role
Not in a
Leadership Role
23. 92.2%
73.3% 26.7%
20-29 Years Old
40+ Years Old
Dependents by Age Group
Yes (Have
dependents relying
on you for care)
No (Do not have
dependents relying on
you for care)
Prefer not to answer +
No Response
Total
20-29 Years Old 5 (7.8%) 59 (92.2%) 5 69
30-39 Years Old 35 (35.7%) 63 (64.3%) 4 102
40+ Years Old 33 (73.3%) 12 (26.7%) 3 48
Total 73 134 12 219
Yes (Have dependents
relying on you for care)
No (Do not have
dependents relying on
you for care)
0 50 100
35.7% 64.3%30-39 Years Old
7.8%
*Please note that in order to ensure large enough group sizes for reporting, the following age groups were merged: 20-24 and 25-29; 30-34 and 35-39; 40-44, 45-49 and 50+.
24. 56.1% 43.9%
100.0%
High School or equivalent +
Technical / Vocational School
or equivalent + College
Diploma or equivalent
Level of Education by Economic Disadvantage
0 50 100
48.7% 51.3%
Bachelor’s Degree or
equivalent
34.9% 65.1%
Master’s Degree or equivalent
+ Ph.D. or equivalent
Other + Prefer not to answer
+ No Response
Yes, I am currently
experiencing economic
disadvantage and/or I
have previously
experienced economic
disadvantage
No
Yes, I am currently
experiencing economic
disadvantage and/or I have
previously experienced
economic disadvantage
No
Prefer not to answer +
No Response
Total
High School or equivalent +
Technical/Vocational
23 (56.1%) 18 (43.9%) 3 44
Bachelor’s Degree or equivalent 58 (48.7%) 61 (51.3%) 5 124
Master’s Degree or equivalent + Ph.D.
or equivalent
15 (34.9%) 28 (65.1%) 0 43
Other + Prefer not to answer + No
Response
0 (0.0%) 1 (100.0%) 7 8
*Please note that in order to ensure large enough group sizes for reporting, the following Education groups were merged: High School or equivalent + Technical / Vocational School or equivalent
and College Diploma or equivalent. In addition, “Yes, I am currently experiencing economic disadvantage in my life (eg., juggling bills, missing payments, severely indebted)” was merged with
“Yes, I previously experienced economic disadvantage in my life (eg., family was economically disadvantaged, earlier in my career I juggled bills, missed payments, and/or was severely indebted)”
25. 51.0% 49.0%
58.3% 41.7%
Yes, I am currently
experiencing economic
disadvantage and/or I have
previously experienced
economic disadvantage
Prefer not to answer +
No Response
Economic Disadvantage by Born in Canada
Yes (Born in
Canada)
No (Not Born in
Canada)
Prefer not to answer +
No Response
Total
Yes, I am currently
experiencing economic
disadvantage and/or I
have previously
experienced economic
disadvantage
49 (51.0%) 47 (49.0%) 0 96
No 56 (51.9%) 52 (48.1%) 0 108
Prefer not to answer + No
Response
7 (58.3%) 5 (41.7%) 6 18
Yes (Born in Canada)
No (Not Born in Canada)
0 50 100
51.9% 48.1%No
26. Support for Diversity and Inclusivity (SDII) at Rangle - Overall
Average
Strongly
Agree
Agree
Somewhat
Agree
Neither Agree
Nor Disagree
Somewhat
Disagree
Disagree
Strongly
Disagree
Support for Diversity & Inclusivity Index (SDII) 88.7% 604 283 101 13 8 9 16
A. I feel that my team respects teammates with
diverse and inclusive backgrounds and values their
contributions
87.4% 110 65 21 4 2 1 4
B. I believe that my manager would respond
immediately and seriously to deal with a situation
involving discrimination.
91.1% 145 39 13 3 2 2 3
C. I believe that Rangle Senior Management would
respond immediately and seriously to deal with a
situation involving discrimination.
88.7% 125 51 20 2 3 4 2
D. I feel that Rangle respects employees with
diverse and inclusive backgrounds and values their
contributions.
88.3% 113 64 23 2 0 1 4
E. Overall, I feel that Rangle values and promotes an
environment that supports diversity and inclusion. 88.3% 111 64 24 2 1 1 3
0 50 100
Support for Diversity & Inclusivity Index (SDII)
A. I feel that my team respects teammates with diverse and inclusive
backgrounds and values their contributions
B. I believe that my manager would respond immediately and seriously to
deal with a situation involving discrimination.
C. I believe that Rangle Senior Management would respond immediately
and seriously to deal with a situation involving discrimination.
D. I feel that Rangle respects employees with diverse and inclusive
backgrounds and values their contributions.
E. Overall, I feel that Rangle values and promotes an environment that
supports diversity and inclusion.
Strongly Disagree
Disagree
Somewhat Disagree
Neither Agree Nor
Somewhat Agree
Agree
Strongly Agree
Mean
Please note, SDII is the average of the 5 index
items and is used for group comparisons in the
rest of this report.