Short presentation delivered to UX Australia attendees on 26-28 August, 2009. The presentation runs through a breakdown of the main analysis techniques used in design research.
This document provides an overview of data analysis techniques. It defines analysis as breaking down a complex topic into smaller parts to gain understanding. Data analysis is described as inspecting, cleaning, transforming, and modeling data to highlight useful information and support decision making. Descriptive statistics are used to quantitatively summarize a data set rather than make inferences about a population. Common descriptive analysis techniques include measures of central tendency, dispersion, frequency distributions, cross tabulation, and charts/graphs.
Anthony Bak, Principal Data Scientist at Ayasdi at MLconf SEA - 5/01/15MLconf
The document discusses using topological data analysis (TDA) and shape as an organizing principle for data. It describes TDA as providing topological summaries that are invariant to coordinate changes and deformations, representing data in a compressed way while maintaining essential topological features. The document provides examples of using TDA for applications like disease state analysis, gene expression, predictive maintenance, customer churn analysis, and transaction fraud detection.
Insights provide additional value in Necto by highlighting exceptional or unexpected trends in workboard data. The session teaches how to set up Insights by ensuring the workboard contains measures, a date dimension, and allows drilling down on specific components. Insights can then be displayed, customized, and filtered according to date range, dimension, ranking method, and condition to further analyze trends and make predictions.
The document discusses the cause and effect diagram, also known as an Ishikawa or fishbone diagram, which is a tool used to analyze potential causes of problems. It describes how to construct a cause and effect diagram by identifying an effect or problem and then branching off major and minor potential causes into categories. The document also provides an example cause and effect diagram for why a car would not start and describes the steps to evaluate and rank the potential causes.
The document provides an overview of the New Seven Quality Control tools. It describes the seven tools - Affinity Diagrams, Relations Diagrams, Tree Diagrams, Matrix Diagrams, Arrow Diagrams, Process Decision Program Charts, and Matrix Data Analysis. It explains the history and development of the tools, their purposes and advantages. Examples are given of how to construct Affinity Diagrams, Relations Diagrams, Tree Diagrams, and Matrix Diagrams. The tools are intended to organize verbal data, generate ideas, improve planning, and increase the effectiveness of total quality management.
This document discusses concept maps, which are visual representations of relationships between concepts in a subject area. Concept maps use labeled nodes for concepts and labeled directional lines to show relationships between concepts. When building a concept map, students choose a topic, brainstorm related concepts, arrange concepts hierarchically from general to specific, and connect concepts with labeled lines to form propositions. The document provides tips for using concept maps, such as connecting them to course readings, and discusses assignments where students created concept maps over the course of a semester.
This document discusses two styles of concept mapping: Novakian mapping using Cmap Tools software and Hunter's infostructure mapping using PowerPoint. It addresses when each style is best used for instructional purposes and how to go beyond simple mapping by using constraints and techniques to lead learners to use specific language forms and patterns. The session will demonstrate both styles of mapping and discuss their applications in education.
This document provides an overview of data analysis techniques. It defines analysis as breaking down a complex topic into smaller parts to gain understanding. Data analysis is described as inspecting, cleaning, transforming, and modeling data to highlight useful information and support decision making. Descriptive statistics are used to quantitatively summarize a data set rather than make inferences about a population. Common descriptive analysis techniques include measures of central tendency, dispersion, frequency distributions, cross tabulation, and charts/graphs.
Anthony Bak, Principal Data Scientist at Ayasdi at MLconf SEA - 5/01/15MLconf
The document discusses using topological data analysis (TDA) and shape as an organizing principle for data. It describes TDA as providing topological summaries that are invariant to coordinate changes and deformations, representing data in a compressed way while maintaining essential topological features. The document provides examples of using TDA for applications like disease state analysis, gene expression, predictive maintenance, customer churn analysis, and transaction fraud detection.
Insights provide additional value in Necto by highlighting exceptional or unexpected trends in workboard data. The session teaches how to set up Insights by ensuring the workboard contains measures, a date dimension, and allows drilling down on specific components. Insights can then be displayed, customized, and filtered according to date range, dimension, ranking method, and condition to further analyze trends and make predictions.
The document discusses the cause and effect diagram, also known as an Ishikawa or fishbone diagram, which is a tool used to analyze potential causes of problems. It describes how to construct a cause and effect diagram by identifying an effect or problem and then branching off major and minor potential causes into categories. The document also provides an example cause and effect diagram for why a car would not start and describes the steps to evaluate and rank the potential causes.
The document provides an overview of the New Seven Quality Control tools. It describes the seven tools - Affinity Diagrams, Relations Diagrams, Tree Diagrams, Matrix Diagrams, Arrow Diagrams, Process Decision Program Charts, and Matrix Data Analysis. It explains the history and development of the tools, their purposes and advantages. Examples are given of how to construct Affinity Diagrams, Relations Diagrams, Tree Diagrams, and Matrix Diagrams. The tools are intended to organize verbal data, generate ideas, improve planning, and increase the effectiveness of total quality management.
This document discusses concept maps, which are visual representations of relationships between concepts in a subject area. Concept maps use labeled nodes for concepts and labeled directional lines to show relationships between concepts. When building a concept map, students choose a topic, brainstorm related concepts, arrange concepts hierarchically from general to specific, and connect concepts with labeled lines to form propositions. The document provides tips for using concept maps, such as connecting them to course readings, and discusses assignments where students created concept maps over the course of a semester.
This document discusses two styles of concept mapping: Novakian mapping using Cmap Tools software and Hunter's infostructure mapping using PowerPoint. It addresses when each style is best used for instructional purposes and how to go beyond simple mapping by using constraints and techniques to lead learners to use specific language forms and patterns. The session will demonstrate both styles of mapping and discuss their applications in education.
Concept maps are diagrams that show relationships between concepts through labeled connections. They were developed by Joseph Novak in the 1970s to represent science knowledge. Concept maps are based on constructivist learning theory and show how new concepts relate to prior knowledge. They organize information visually with core concepts in circles/boxes and relationships shown through labeled lines. Concept maps have various uses including assessing understanding, collaboration, and research analysis. They must include core concepts and labeled relationships to be considered true concept maps.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
The document summarizes seven new management and planning tools:
1. Affinity diagram organizes ideas into relationships and taps team creativity.
2. Relations diagram shows cause-and-effect links between complex issues.
3. Tree diagram branches items into subgroups for analyzing processes.
4. Matrix diagram relates two or more groups of information.
5. Arrow diagram shows task order and scheduling for complex projects.
6. Process decision program chart identifies and prevents problems in plans.
The document discusses the seven basic quality tools: histograms, scatter diagrams, Pareto charts, cause-and-effect diagrams, control charts, run charts, and flowcharts. These tools were emphasized by Kaoru Ishikawa and can be used to solve problems and improve quality in organizations. Histograms provide easy evaluation of data distribution. Pareto charts order data by frequency and guide corrective actions. Cause-and-effect diagrams show relationships between causes and effects. Control and run charts monitor process output over time. Scatter diagrams study relationships between two variables. Flowcharts depict how elements interact.
The document discusses diagrams and posters. It defines a diagram as a symbolic visual representation of information using shapes connected by lines or arrows. Diagrams have been used since ancient times but became more prevalent during the Enlightenment. The document then describes different types of diagrams like process diagrams, technical diagrams, and area diagrams. It also discusses guidelines for creating effective posters, such as using brief text, dramatic simplicity, and appropriate design and color. Posters are meant to quickly catch attention and implant an important idea in the viewer's mind.
The document discusses key steps in data analysis and interpretation for action research:
1) Data analysis involves summarizing collected data in an accurate manner depending on the type of data collected, such as using qualitative analysis for narrative data or quantitative analysis for numerical data.
2) Data interpretation finds meaning in the data by answering "So what?" and explaining trends, patterns, and relationships that emerge from the analysis.
3) Critical steps in analysis and interpretation include making data summaries, developing categories and coding, writing theoretical notes, quantification, and shaping metaphors to understand the data from different perspectives.
The document discusses qualitative data analysis and provides suggestions for researchers. It recommends narrowing the study focus, developing analytical questions, reviewing notes between data collection sessions, writing observer comments, memos, and trying ideas on participants. The document also discusses coding data, constructing categories, managing category schemes, developing theories from data, using models/diagrams to visualize relationships, and computer assistance for analysis. Different types of qualitative analysis are also outlined.
The document discusses business analytics and data visualization. It defines business analytics as the iterative and methodical exploration of an organization's data using statistical analysis to support data-driven decision making. It describes the main areas of business analytics techniques as business intelligence and statistical analysis. It also outlines the four main types of business analytics: descriptive, predictive, prescriptive, and diagnostic. The document further discusses data visualization, consumption of analytics, tools for data visualization, examples of data visualizations, and characteristics of effective graphical displays.
This document provides an introduction to research, outlining key concepts and processes. It discusses what research is, the different types of research (applied and basic), and why organizations conduct research. The main stages of the research process are identified as observation, problem identification, theoretical framework development, hypothesis formulation, research design, data collection and analysis, interpretation, and implementation. Key research design concepts like sampling, measurement, descriptive and inferential statistics are also overviewed. The document emphasizes that research is important for problem-solving, decision making, managing competition and risk, and should utilize available information technologies.
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding texts, identifying patterns, and reducing qualitative data into quantitative codes. It also outlines several stages of qualitative analysis including familiarization with data, transcription, organization, coding, identifying themes, recoding, developing categories, exploring relationships between categories, and developing theories. Finally, it discusses challenges of qualitative analysis including placing raw data into logical categories and communicating interpretations to others.
We are the world’s largest and most established provider of training courses globally, with extensive experience of providing quality-infused learning solutions - with the capability to deliver over 30,000 courses, in 1000+ locations, across 190 countries. As market leaders, we have successfully trained over 1 million delegates - demonstrating our internationally-renowned trust and unrivalled premium quality, to all of our aspiring learners.
Here are some potential responses to your questions:
1. A potential research topic could be "Determining student and faculty demand for a coffee shop near the university and local schools."
2. A problem statement could be: "There is currently no coffee shop located near the university and local schools to serve the needs of students, faculty and staff during the day. It is unknown whether there is sufficient demand to support a new coffee shop business."
3. Objectives may include:
- Assess the size of potential customer base from the university and schools
- Understand customer preferences for types of coffee, food offerings, atmosphere etc.
- Determine average spending amounts and frequency of visits to a coffee shop
- Identify
Design and Data Processes Unified - 3rd Corner ViewJulian Jordan
In this presentation (given in early 2020) I explain that to build digital products, data analysts/scientists and designers need to leverage each other’s processes and work as a unit.
I introduce the problem solving approach of data analysts/scientists and designers as well as how to combine these approaches. Additionally, I explain how mental models and algorithms, while associated with design and data science, respectively, are similar ways to represent phenomena and questions about them.
Here are potential responses to your questions:
1. My research topic would be "Assessing the viability and potential success of opening a coffee shop near the university and schools."
2. The problem statement could be: "There is uncertainty around whether there is sufficient demand and customer base to support a new coffee shop near the university and schools."
3. Objectives:
- Determine customer preferences and spending habits related to coffee shops
- Assess size and characteristics of potential customer base from university and schools
- Evaluate competitive landscape and identify gaps a new coffee shop could fill
4. Hypotheses:
H1: There is sufficient demand from university students and school families to support a new coffee shop.
Business research involves systematically studying a problem to find solutions. It is important for managers to make informed decisions. There are two main types of business research: applied research solves current problems, while basic research builds general knowledge. The research process involves defining a problem, developing a theoretical framework, generating hypotheses, collecting and analyzing data, and reporting findings. Proper research design, sampling, measurement, and statistical analysis are crucial for obtaining meaningful results that can help managers address issues and remain competitive.
This document provides an overview of grounded theory, including its definition, uses, methodology, and key steps. Grounded theory is a systematic qualitative research method for developing theories about phenomena grounded in data. It involves collecting and analyzing data to generate concepts and theories, rather than testing a predetermined hypothesis. The methodology includes open, axial, and selective coding of data to group concepts into categories and identify core themes from which to build an explanatory theory.
The document provides an overview of business research, including defining what research is, the importance of research for managers, and the typical research process. It discusses key concepts like the different types of business research, problem formulation, research design, data collection and analysis, and how to structure a research report. Conducting thorough research is important for managers to solve problems, make informed decisions, understand competition and risks, and invest resources effectively. The scientific process typically involves observation, problem identification, developing a theoretical framework, generating hypotheses, research design, data collection and analysis, interpretation, and implementation.
This document provides an overview of big data analytics and data visualization. It discusses key concepts like data wrangling, exploring patterns, drawing conclusions, and communicating findings. Common techniques are also summarized, including classification, clustering, association rules, and predictive analytics. Specific algorithms like decision trees, k-means clustering, and hierarchical clustering are explained. The CRISP-DM process model and applications of analytics in areas like customer understanding and process optimization are also covered at a high level. Visualization is presented as an important part of the overall analytics process.
Research design decisions and be competent in the process of reliable data co...Stats Statswork
Research Design may be described as the researchers scheme of outlining the flow of his project. It is based on research design, that the researcher goes about gathering data to answer his research question. It enables the researcher to prioritize his work, create better questionnaires and arrive at conclusions with greater clarity. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following – Always on Time, outstanding customer support, and High-quality Subject Matter Experts.
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Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics Across Methodologies | Wide Range Of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
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Estudios Clinicos Epidemiologia OdontologiaSergio Uribe
This document describes the development of a visual research package using multidimensional scaling and other techniques to help designers understand users' emotional and social responses to designed objects. The package allows flexible online research through methods like free sorting, grouping, and semantic differentials. It presents results visually through plots, networks and other interactive displays. Case studies show how the package could be used by furniture, cutlery and bathroom companies to gain insights for new product development and marketing. The goal is a tool that provides quick, visual and easily manipulated exploratory research to support designers.
Concept maps are diagrams that show relationships between concepts through labeled connections. They were developed by Joseph Novak in the 1970s to represent science knowledge. Concept maps are based on constructivist learning theory and show how new concepts relate to prior knowledge. They organize information visually with core concepts in circles/boxes and relationships shown through labeled lines. Concept maps have various uses including assessing understanding, collaboration, and research analysis. They must include core concepts and labeled relationships to be considered true concept maps.
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
The document summarizes seven new management and planning tools:
1. Affinity diagram organizes ideas into relationships and taps team creativity.
2. Relations diagram shows cause-and-effect links between complex issues.
3. Tree diagram branches items into subgroups for analyzing processes.
4. Matrix diagram relates two or more groups of information.
5. Arrow diagram shows task order and scheduling for complex projects.
6. Process decision program chart identifies and prevents problems in plans.
The document discusses the seven basic quality tools: histograms, scatter diagrams, Pareto charts, cause-and-effect diagrams, control charts, run charts, and flowcharts. These tools were emphasized by Kaoru Ishikawa and can be used to solve problems and improve quality in organizations. Histograms provide easy evaluation of data distribution. Pareto charts order data by frequency and guide corrective actions. Cause-and-effect diagrams show relationships between causes and effects. Control and run charts monitor process output over time. Scatter diagrams study relationships between two variables. Flowcharts depict how elements interact.
The document discusses diagrams and posters. It defines a diagram as a symbolic visual representation of information using shapes connected by lines or arrows. Diagrams have been used since ancient times but became more prevalent during the Enlightenment. The document then describes different types of diagrams like process diagrams, technical diagrams, and area diagrams. It also discusses guidelines for creating effective posters, such as using brief text, dramatic simplicity, and appropriate design and color. Posters are meant to quickly catch attention and implant an important idea in the viewer's mind.
The document discusses key steps in data analysis and interpretation for action research:
1) Data analysis involves summarizing collected data in an accurate manner depending on the type of data collected, such as using qualitative analysis for narrative data or quantitative analysis for numerical data.
2) Data interpretation finds meaning in the data by answering "So what?" and explaining trends, patterns, and relationships that emerge from the analysis.
3) Critical steps in analysis and interpretation include making data summaries, developing categories and coding, writing theoretical notes, quantification, and shaping metaphors to understand the data from different perspectives.
The document discusses qualitative data analysis and provides suggestions for researchers. It recommends narrowing the study focus, developing analytical questions, reviewing notes between data collection sessions, writing observer comments, memos, and trying ideas on participants. The document also discusses coding data, constructing categories, managing category schemes, developing theories from data, using models/diagrams to visualize relationships, and computer assistance for analysis. Different types of qualitative analysis are also outlined.
The document discusses business analytics and data visualization. It defines business analytics as the iterative and methodical exploration of an organization's data using statistical analysis to support data-driven decision making. It describes the main areas of business analytics techniques as business intelligence and statistical analysis. It also outlines the four main types of business analytics: descriptive, predictive, prescriptive, and diagnostic. The document further discusses data visualization, consumption of analytics, tools for data visualization, examples of data visualizations, and characteristics of effective graphical displays.
This document provides an introduction to research, outlining key concepts and processes. It discusses what research is, the different types of research (applied and basic), and why organizations conduct research. The main stages of the research process are identified as observation, problem identification, theoretical framework development, hypothesis formulation, research design, data collection and analysis, interpretation, and implementation. Key research design concepts like sampling, measurement, descriptive and inferential statistics are also overviewed. The document emphasizes that research is important for problem-solving, decision making, managing competition and risk, and should utilize available information technologies.
This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding texts, identifying patterns, and reducing qualitative data into quantitative codes. It also outlines several stages of qualitative analysis including familiarization with data, transcription, organization, coding, identifying themes, recoding, developing categories, exploring relationships between categories, and developing theories. Finally, it discusses challenges of qualitative analysis including placing raw data into logical categories and communicating interpretations to others.
We are the world’s largest and most established provider of training courses globally, with extensive experience of providing quality-infused learning solutions - with the capability to deliver over 30,000 courses, in 1000+ locations, across 190 countries. As market leaders, we have successfully trained over 1 million delegates - demonstrating our internationally-renowned trust and unrivalled premium quality, to all of our aspiring learners.
Here are some potential responses to your questions:
1. A potential research topic could be "Determining student and faculty demand for a coffee shop near the university and local schools."
2. A problem statement could be: "There is currently no coffee shop located near the university and local schools to serve the needs of students, faculty and staff during the day. It is unknown whether there is sufficient demand to support a new coffee shop business."
3. Objectives may include:
- Assess the size of potential customer base from the university and schools
- Understand customer preferences for types of coffee, food offerings, atmosphere etc.
- Determine average spending amounts and frequency of visits to a coffee shop
- Identify
Design and Data Processes Unified - 3rd Corner ViewJulian Jordan
In this presentation (given in early 2020) I explain that to build digital products, data analysts/scientists and designers need to leverage each other’s processes and work as a unit.
I introduce the problem solving approach of data analysts/scientists and designers as well as how to combine these approaches. Additionally, I explain how mental models and algorithms, while associated with design and data science, respectively, are similar ways to represent phenomena and questions about them.
Here are potential responses to your questions:
1. My research topic would be "Assessing the viability and potential success of opening a coffee shop near the university and schools."
2. The problem statement could be: "There is uncertainty around whether there is sufficient demand and customer base to support a new coffee shop near the university and schools."
3. Objectives:
- Determine customer preferences and spending habits related to coffee shops
- Assess size and characteristics of potential customer base from university and schools
- Evaluate competitive landscape and identify gaps a new coffee shop could fill
4. Hypotheses:
H1: There is sufficient demand from university students and school families to support a new coffee shop.
Business research involves systematically studying a problem to find solutions. It is important for managers to make informed decisions. There are two main types of business research: applied research solves current problems, while basic research builds general knowledge. The research process involves defining a problem, developing a theoretical framework, generating hypotheses, collecting and analyzing data, and reporting findings. Proper research design, sampling, measurement, and statistical analysis are crucial for obtaining meaningful results that can help managers address issues and remain competitive.
This document provides an overview of grounded theory, including its definition, uses, methodology, and key steps. Grounded theory is a systematic qualitative research method for developing theories about phenomena grounded in data. It involves collecting and analyzing data to generate concepts and theories, rather than testing a predetermined hypothesis. The methodology includes open, axial, and selective coding of data to group concepts into categories and identify core themes from which to build an explanatory theory.
The document provides an overview of business research, including defining what research is, the importance of research for managers, and the typical research process. It discusses key concepts like the different types of business research, problem formulation, research design, data collection and analysis, and how to structure a research report. Conducting thorough research is important for managers to solve problems, make informed decisions, understand competition and risks, and invest resources effectively. The scientific process typically involves observation, problem identification, developing a theoretical framework, generating hypotheses, research design, data collection and analysis, interpretation, and implementation.
This document provides an overview of big data analytics and data visualization. It discusses key concepts like data wrangling, exploring patterns, drawing conclusions, and communicating findings. Common techniques are also summarized, including classification, clustering, association rules, and predictive analytics. Specific algorithms like decision trees, k-means clustering, and hierarchical clustering are explained. The CRISP-DM process model and applications of analytics in areas like customer understanding and process optimization are also covered at a high level. Visualization is presented as an important part of the overall analytics process.
Research design decisions and be competent in the process of reliable data co...Stats Statswork
Research Design may be described as the researchers scheme of outlining the flow of his project. It is based on research design, that the researcher goes about gathering data to answer his research question. It enables the researcher to prioritize his work, create better questionnaires and arrive at conclusions with greater clarity. Statswork offers statistical services as per the requirements of the customers. When you Order statistical Services at Statswork, we promise you the following – Always on Time, outstanding customer support, and High-quality Subject Matter Experts.
Learn More: http://bit.ly/2S312hb
Why Statswork?
Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics Across Methodologies | Wide Range Of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
Contact Us:
Website: www.statswork.com/
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
Estudios Clinicos Epidemiologia OdontologiaSergio Uribe
This document describes the development of a visual research package using multidimensional scaling and other techniques to help designers understand users' emotional and social responses to designed objects. The package allows flexible online research through methods like free sorting, grouping, and semantic differentials. It presents results visually through plots, networks and other interactive displays. Case studies show how the package could be used by furniture, cutlery and bathroom companies to gain insights for new product development and marketing. The goal is a tool that provides quick, visual and easily manipulated exploratory research to support designers.
The document discusses research design and provides details on different types of research designs:
1. Exploratory research designs which aim to gain insights through literature reviews, experience surveys, and analysis of examples.
2. Descriptive and diagnostic research designs which describe characteristics or determine associations through surveys and observations.
3. Hypothesis-testing research designs which test hypotheses through experiments while controlling variables.
The document also covers topics like qualitative and quantitative research, different sampling designs, and the need for a well-planned research design to efficiently answer research questions.
Here are some potential responses to your questions:
1. A potential research topic could be "Assessing market demand and identifying target customer segments for a new coffee shop near a university and school."
2. A problem statement could be "It is unclear whether there is sufficient demand in the local area to support a new coffee shop and which customer groups would be most likely to frequent the shop."
3. Objectives could include:
- Determine size and characteristics of potential customer base in the local area
- Identify key customer segments to target with the new coffee shop
- Assess level of demand and spending potential of different customer groups
- Gather customer feedback on preferred shop attributes, products/services, pricing etc.
How do you write an original research article and have it published? – PubricaPubrica
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The document discusses research design and provides details on different types of research designs. It begins by defining research design and outlines the key decisions that must be made, including what, where, when, how much, and how data will be collected and analyzed. It then discusses different types of research designs for exploratory, descriptive, diagnostic, and hypothesis-testing studies. Specific methods for qualitative and quantitative research designs are also outlined.
Similar to Analysis Techniques - Rapid Session (20)
The questions we ask ourselves at the idea generation stage of design play a critical role in the nature of the ideas generated. Bold questions beget bold ideas; and incrementalism begins in the same way.
In this talk Steve Baty will look at how problem framing and reframing can impact the ideas teams generate, and how problem statements can be ‘tuned’ to better deliver feasible concepts within your organisation. We’ll look at some recent examples from the work at Meld Studios as well as some well-known case studies from around the world.
During May 2013 the Meld Studios team conducted a three-week long observational and contextual research project for a cafe chain in Western Australia. After 110 hours of observation in 18 locations, nearly 200 interviews, and thousands of photographs we had collected a lot of data, and learned a lot more about the conduct of field research.
This presentation looks at the research objectives, research plan, our experiences in the field, and reflect on the extent to which we successfully captured – and were able to communicate – what we saw, heard, smelled, touched and felt.
Two models of design-driven innovation - UX AustraliaSteve Baty
The drive for innovation in products and services and a culture of ‘fail early; fail often’ has bred a desire for very early prototypes. This approach lends itself to an entire industry tackling a problem or for the venture capitalists funding them. It can be broadly characterised as hypothesis-led. It is much less appropriate or advantageous for an individual project team within an established industry attempting to reinvent an existing product/service category. For these teams, an insight-led approach in which multiple concepts are developed in parallel is more appropriate.
This presentation will give an introduction to each of these two dominant models of design-driven innovation. It will look at the advantages and disadvantages of each; and look at the issue of localised optimal solutions and what this means for innovation.
Showrooming is the practice of visiting a physical retail space to try out a product, and then using the Web to comparison shop and purchase online. Seen as a major threat to traditional retail, showrooming is also a major opportunity.
This document discusses the importance of empathy in design through the use of personas. It defines personas as a tool that helps designers understand users by determining what a product should do, communicating needs to teams, and building consensus. Successful personas require first-hand research, including the whole team, developing intimate knowledge of each persona, relevance to objectives, and rich scenarios. Empathy is key - designers must immerse themselves in users' perspectives. The document also discusses how empathy is vital for disruptive innovation and blue ocean strategy, which involve understanding non-customers' viewpoints.
Implementation Role Models for Service DesignersSteve Baty
As service designers our work typically ends with the design ‘blueprint’ and our involvement is often cut short of the implementation work so critical to the quality of the service.
What is the most appropriate model for service designers when the project reaches implementation:
* conductor
* film director;
* screenplay writer?
The document discusses the benefits of meditation for reducing stress and anxiety. Regular meditation practice can help calm the mind and body by lowering heart rate and blood pressure. Making meditation a part of a daily routine, even if just 10-15 minutes per day, can have mental and physical health benefits over time by helping people feel more relaxed and focused.
Steve Baty is a UX strategist and principal of Meld. He discusses using design and strategy to solve business problems. He talks about applying concepts from science like conducting experiments and systems thinking to management. Baty advocates exploring novel ideas and alternatives rather than reinforcing the status quo. He cites examples like the Toyota Production System that changed manufacturing through a company-wide design philosophy.
The document discusses UX strategy and the role of strategy in UX projects and teams. It covers several aspects of developing a UX strategy including defining the audience and their goals, mental models, context of use, capabilities, and experience. The strategy helps create alignment across the organization and balance both short-term tactical work and long-term strategic work. The document provides examples of strategies for companies like Amazon and Google.
The document discusses analyzing quantitative user experience data. It will cover topics like time-to-completion, task completion rates, A/B testing, and page-view data. Examples are provided of what quantitative data may look like, including times for users to complete tasks. Methods for calculating averages, variance and standard deviation of data are also outlined. A/B testing is discussed as a way to compare two approaches and measure differences in metrics like click-through rates.
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2. Deconstructing analysis techniques
For as long as we continue to discuss
the analysis of research as a 'black
box' where "magic happens", we will
fail to learn, improve and innovate in
the areas of design research
techniques.
- me, February, 2009.
5. Deconstructing analysis techniques
Transformation: Processing the data
to arrive at some new representation
of the observations. Unlike
manipulation, transformation has the
effect of changing the data.
7. Deconstructing analysis techniques
Aggregation: closely related to
summarization, this technique draws
together data from multiple sources.
Such collections typically represent a
“higher-level” view made up from the
underlying individual data sets.
9. Deconstructing analysis techniques
Abstraction: the process of stripping
out the particulars - information that
relates to a specific example - so that
more general characteristics come to
the fore.
11. Deconstructing analysis techniques
Synthesis: The process of drawing
together concepts, ideas, objects and
other qualitative data in new
configurations, or to create something
entirely new.
13. Deconstructing analysis techniques
Visualization: this technique is about
giving the data a visual dimension.
Instead of lists of items, or rows of
numbers in a spreadsheet, a chart or
graph or some form of illustration.