This document provides an agenda and materials for a one-day workshop on qualitative data analysis. The workshop will include two exercises. The first involves selecting quotes, assigning codes, and creating memos from narrative data. The second uses grounded theory methods to map themes, quotes and codes from the data. The workshop aims to teach participants tools for analyzing text, documents and images within and across different settings.
Directed versus undirected network analysis of student essaysRoy Clariana
IWALS 2018
6th International Workshop on Advanced Learning Sciences
Perspectives on the Learner: Cognition, Brain, and Education
University of Pittsburgh, USA JUNE 6-8, 2018
Data collection chapter 15 from the companion website for educational researchYamith José Fandiño Parra
This is a slide show of chapter 15 from Educational Research: Competencies for Analysis and Applications. Primarily intended for instructor use in the classroom, it is also available for students’ study use or to review as an advance organizer before class lectures or discussions. Key chapter concepts are presented in an easy-to-read format.
EXPERT OPINION AND COHERENCE BASED TOPIC MODELINGijnlc
In this paper, we propose a novel algorithm that rearrange the topic assignment results obtained from topic
modeling algorithms, including NMF and LDA. The effectiveness of the algorithm is measured by how much
the results conform to expert opinion, which is a data structure called TDAG that we defined to represent the
probability that a pair of highly correlated words appear together. In order to make sure that the internal
structure does not get changed too much from the rearrangement, coherence, which is a well known metric
for measuring the effectiveness of topic modeling, is used to control the balance of the internal structure.
We developed two ways to systematically obtain the expert opinion from data, depending on whether the
data has relevant expert writing or not. The final algorithm which takes into account both coherence and
expert opinion is presented. Finally we compare amount of adjustments needed to be done for each topic
modeling method, NMF and LDA.
Directed versus undirected network analysis of student essaysRoy Clariana
IWALS 2018
6th International Workshop on Advanced Learning Sciences
Perspectives on the Learner: Cognition, Brain, and Education
University of Pittsburgh, USA JUNE 6-8, 2018
Data collection chapter 15 from the companion website for educational researchYamith José Fandiño Parra
This is a slide show of chapter 15 from Educational Research: Competencies for Analysis and Applications. Primarily intended for instructor use in the classroom, it is also available for students’ study use or to review as an advance organizer before class lectures or discussions. Key chapter concepts are presented in an easy-to-read format.
EXPERT OPINION AND COHERENCE BASED TOPIC MODELINGijnlc
In this paper, we propose a novel algorithm that rearrange the topic assignment results obtained from topic
modeling algorithms, including NMF and LDA. The effectiveness of the algorithm is measured by how much
the results conform to expert opinion, which is a data structure called TDAG that we defined to represent the
probability that a pair of highly correlated words appear together. In order to make sure that the internal
structure does not get changed too much from the rearrangement, coherence, which is a well known metric
for measuring the effectiveness of topic modeling, is used to control the balance of the internal structure.
We developed two ways to systematically obtain the expert opinion from data, depending on whether the
data has relevant expert writing or not. The final algorithm which takes into account both coherence and
expert opinion is presented. Finally we compare amount of adjustments needed to be done for each topic
modeling method, NMF and LDA.
A Review on Neural Network Question Answering Systemsijaia
In recent years neural networks (NN) are being used increasingly on Question Answering (QA) systems and
they seem to be successful in addressing different issues and challenges that these systems exhibit. This
paper presents a review to summarize the state of the art in question answering systems implemented using
neural networks. It identifies the main research topics and considers the most relevant research challenges.
Furthermore, it analyzes contributions, limitations, evaluation techniques, and directions proposed for
future research.
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
Forrester's Kate Leggett recently blogged about the business benefits of improving the Agent Desktop experience, and I was inspired to add additional color commentary to this as I am currently working with a client who has some significant Agent Desktop challenges. So below is a summary of the Top 6 KPIs and Business Benefits associated with improving your Customer Service Agent's Desktop experience. The 6 KPIs are below:
Improving Agent Handle Time.
Increasing First Call Resolution.
Improving Agent Satisfaction & Turnover.
Improving Training & Ramp Up Time.
Improve Customer Satisfaction.
Increase Revenue.
Let’s compare! Practical perspectives on the use of an international comparat...CesToronto
Used appropriately and carefully, international comparisons (reviews, case studies, etc.) can inform the design of your evaluation or performance measurement study, engage a broad range of stakeholders, and greatly add value to your findings and recommendations.
Drawing on experience with several such approaches in evaluations covering public safety, health surveillance, environmental assessment, and technology development, this presentation will discuss the rationale and key practical considerations to ensure the successful implementation of an international comparative design.
Specifically, the presentation will review when to use these methods (advantages/disadvantages), and provide concrete tools and tips to overcome common challenges. It will also discuss how to facilitate engagement and collaboration for both the subject matter community and the evaluation and performance management community, within Canada and across borders.
A Review on Neural Network Question Answering Systemsijaia
In recent years neural networks (NN) are being used increasingly on Question Answering (QA) systems and
they seem to be successful in addressing different issues and challenges that these systems exhibit. This
paper presents a review to summarize the state of the art in question answering systems implemented using
neural networks. It identifies the main research topics and considers the most relevant research challenges.
Furthermore, it analyzes contributions, limitations, evaluation techniques, and directions proposed for
future research.
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
Forrester's Kate Leggett recently blogged about the business benefits of improving the Agent Desktop experience, and I was inspired to add additional color commentary to this as I am currently working with a client who has some significant Agent Desktop challenges. So below is a summary of the Top 6 KPIs and Business Benefits associated with improving your Customer Service Agent's Desktop experience. The 6 KPIs are below:
Improving Agent Handle Time.
Increasing First Call Resolution.
Improving Agent Satisfaction & Turnover.
Improving Training & Ramp Up Time.
Improve Customer Satisfaction.
Increase Revenue.
Let’s compare! Practical perspectives on the use of an international comparat...CesToronto
Used appropriately and carefully, international comparisons (reviews, case studies, etc.) can inform the design of your evaluation or performance measurement study, engage a broad range of stakeholders, and greatly add value to your findings and recommendations.
Drawing on experience with several such approaches in evaluations covering public safety, health surveillance, environmental assessment, and technology development, this presentation will discuss the rationale and key practical considerations to ensure the successful implementation of an international comparative design.
Specifically, the presentation will review when to use these methods (advantages/disadvantages), and provide concrete tools and tips to overcome common challenges. It will also discuss how to facilitate engagement and collaboration for both the subject matter community and the evaluation and performance management community, within Canada and across borders.
Вы наверняка знакомы с планированием маршрутов, при котором Вы добавляете один адрес за другим в Google Картах или другой предпочитаемой Вами навигационной системе.
При планировании маршрутов, Вы посещаете пункты в той последовательности, которую Вы задаете. Все так просто!
Однако если каждый Ваш маршрут должен включать более 10 мест, Вам необходима оптимизация маршрутов, чтобы заново установить последовательность пунктов в маршрутах с целью исключения зигзагообразной езды по всему городу.
Вам необходима оптимизация маршрутов, потому что человеку очень сложно мысленно представить себе и определить порядок, в котором необходимо посещать пункты, особенно если речь идет о десятках пунктов, расположенных в незнакомой части города.
Workshop nwav 47 - LVS - Tool for Quantitative Data AnalysisOlga Scrivner
In the format of hands-on session, this workshop will introduce participants to the Language Variation Suite (LVS), a user-friendly interactive web application built in R. LVS provides access to advanced statistical methods and visualization techniques, such as mixed-effects modeling, conditional and random tree analyses, cluster analysis. These advanced methods enable researchers to handle imbalanced data, measure individual and group variation, estimate significance, and rank variables according to their significance.
Kelly technologies is the best data science training institute in hyderabad.We provide our trainings by industrial real time experts so that our students know about real time market technology.
Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...Gabriel Moreira
This talk introduces the main techniques of Recommender Systems and Topic Modeling. Then, we present a case of how we've combined those techniques to build Smart Canvas, a SaaS that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to a content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics.
Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform.
Short-Bio: Gabriel Moreira is a scientist passionate about solving problems with data. He is Head of Machine Learning at CI&T and Doctoral student at Instituto Tecnológico de Aeronáutica - ITA. where he has also got his Masters on Science. His current research interests are recommender systems and deep learning.
https://www.meetup.com/pt-BR/machine-learning-big-data-engenharia/events/239037949/
Why aren't Evaluators using Digital Media Analytics?CesToronto
Whether it’s through blogs, tweets, or even the comments section of an online newspaper, the world is increasingly talking online. However, the potential uses for the massive amounts of information available on the internet remain largely untapped in the sphere of evaluation.
This presentation will explore innovative methods to extract these insights from the large and complex collections of digital data publicly available online. In particular, we will examine the unprecedented uses, and potential limitations, of digital media analytics to:
• Measure the outcomes of public outreach, advocacy, communications, and information sharing programs;
• Establish current and retroactive baselines;
• Conduct “borderless” data collection to gain insights from other countries, as well as disapora communities in Canada;
• Identify unknown stakeholder groups and create detailed stakeholder maps; and,
• Provide context and insight to inform further data collection.
Evaluation pal program monitoring and evaluation technologyCesToronto
In this session, Dr. Cugelman will discuss his work to develop an automated program monitoring and evaluation technology, called Evaluation Pal. He launched Evaluation Pal in 2011, then in 2012, pilot tested it for an evaluation of the Green Infrastructure Ontario Coalition which was submitted to the Ontario Trillium Foundation. Soon after, MaRS' Social Innovation Generation accepted it into their incubator program.
In this session, Dr. Cugelman will provide a tour of the tool, and use the Green Infrastructure Ontario case study to demonstrate how automated data collection can be used in the program evaluation process. This presentation will also provide an opportunity to discuss the challenges and opportunities of using technology to aid program evaluation.
Concurrent session #6 (Tuesday June 11th, 15:45-17:15)
Negotiating evaluation desigg in developmental evaluation: an emerging framework for shared decision-making
by Heather Smith Fowler, Dominique Leonard, & Neil Price
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Effective monitoring and evaluation (M&E) systems are essential to learning and accountability. M&E system reviews provides perspective on what is working well, where there are gaps in coverage or weaknesses that need to be addressed, how the M&E information is actually used in decision making, and whether the system is efficient. This session will demonstrate the methods, tools and results in assessing the functioning of the M&E systems of the World Bank Group’s private sector operations in two specialized agencies: the International Finance Corporation and the Multilateral Investment Guarantee Agency.
Living the Values of Engagement and the Strategic View with Program Evaluatio...CesToronto
William Reid, MNP LLP
Dr. Laura Tate, Community Action Initiative
Scott Graham, Social Planning and Research Council of BC
The BC Community Action Initiative was established to facilitate community-based, partnership-driven, and culturally safe approaches that promote mental health; prevent harmful use of substances; and improve services and supports for those affected by mental health/illness and problematic substance use. Over a three year period, SPARC BC in partnership with MNP LLP is leading a provincial evaluation with both formative and summative aspects, and that is further supported by quarterly performance reporting and digital stories. This presentation will provide for an overview of, and sharing of experiences from both the program being evaluated and those evaluating, the BC Community Action Initiative. In so doing, it will speak to the shared discoveries of collaboration as the evaluation studies and performance reporting unfolded; the engagement of a multi-stakeholder Leadership Council and community project evaluators; and, orienting a provincial evaluation in a fashion that tells the story of system-wide results along with community collaboration, action, and impacts.
Evaluation for Development: matching evaluation to the right user, the right...CesToronto
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While donors typically control evaluation agendas, grantees may be better placed to commission and use evaluations. We will present experiences of handing over control of evaluation to grantees, with practical and political issues that arise.
In development as elsewhere, agencies are frustrated when evaluation does not accurately capture the results they aim to achieve. Often simple metrics and methods are inadequate in complex systems. We will describe challenges of articulating results appropriately so evaluation doesn’t miss, let alone undermine, results. We will also share experiences of using complex systems approach to assessing outcomes to match the values and purpose of the evaluand.
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Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
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Qda ces 2013 toronto workshop
1. Qualitative Data Analysis
Within and Across Settings
Beginner Level Workshop
June 9, 2013
Toronto
Reed Early, MA CE
rearly@telus.net
250 748 0550
http://www3.telus.net/reedspace/shared/
1:00 Overview of QDA and mixed methods.
2:00 Exercise 1 involving quotes, codes, memos and content analysis, using narrative data.
3:00 Exercise 2 in grounded theory that maps themes, quotes and codes.
Alternate Exercise: Theorizing Using Matrix Analysis or Software Sampler
3:45 Wrap up – lessons learned and evaluation
Objective: Participants learn a range of tools for use in analysis of text, documents and images.
2. Contents
Qualitative Data Analysis – Within and Across Settings
Overview of field of QDA ....................................................................................................... 1
Exercise 1 involving quotes, codes, memos and content analysis, using narrative data .......... 5
Exercise 2 in grounded theory, network maps of themes, quotes and codes............................ 8
Methods Matched to Data and Task ....................................................................................... 10
Alternate Exercise: theorizing using matrix analysis across settings ..................................... 11
QDA Software sampler .......................................................................................................... 14
HANDOUTS
1. Summary of Qualitative Methods – matrix .............................................................. 15
2. Websites, Software, and Internet Resources ............................................................. 16
References
Data – Incidents of Support 1, 2, 3
3. 1
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Qualitative Data Analysis
Overview of field of QDA
Outcomes – learners understand the origin and current practice of QDA methods in general,
grounded theory in particular, and possible applications of the methods.
Methods
useful for inductive research
useful in naturalistic inquiry
qualitative methods growing consensus
collection ↔ analysis ↔ collection
Qualitative Data Analysis (QDA)
open
exploratory
useful when questions to ask not yet been defined
allows insights
Overview of QDA
(nested diagram)
QDA cycle
(begin with question, data collection, continue ad infinitum)
Research process
(all stages connected)
Work plan of research process
4. 2
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Characteristics of Qualitative Data
constructivist - words have many meanings
context bound i.e. “cast of thousands"
uses inflection i.e. "THIS was good."
can be sorted in many ways
QD by itself has meaning i.e. “apple”
Sources of Qualitative Data
interviews
focus Groups
field observations (GPS data)
survey comments
historical records
secondary data
photos, paintings, songs ...
Types of Qualitative Data
structured text, (writings, stories, survey comments, news articles, books etc)
unstructured text (transcription, open interviews, focus groups, conversation)
audio recordings (as above, music)
visual recordings (graphics, art, pictures)
location specific data Google Earth GPS
5. 3
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Principles of QDA (J. Morse)
Data entry (gathering)
Comprehending (immersion)
Synthesizing (sifting)
Theorizing (sorting)
Re-Contextualizing (emerging theory)
Data entry (analogous demo)
not easily mechanized
important part of process
often done by analyst
concurrent with analysis
transcribe thoroughly, as soon as possible
write memos (reflect)
coding (start with few)
Comprehending (immersion)
begin while entering data
start QDA immediately
“live with it”
line by line examination
create new questions for collection
Synthesizing (sifting) “selecting quotations” (decontextualize)
use inductive categories
find common threads
compare transcripts
aggregate stories
Theorizing (sorting) “coding”
ask questions of the data
find alternative explanations
allow sufficient time
be open to insights
Re-Contextualizing
develop theoretical “elegance”
apply to other settings
examine fit to literature/research
describe emerging theory
Data Management Principles
stay close to the data
be sensitive to emergent theory
allow recontextualizing
it is a non-linear process
6. 4
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
QDA method options – everyday analogues
i) Content Analysis - like movie ratings by the censorship bd
ii) Grounded Theory – like a mystery solved by ordinary citizens
iii) Matrix Analysis – like a map’s matrix of campsite services
iv) Phenomenology – like a movie documentary
Displaying results
v) display code frequencies
Code Count % Codes Cases % Cases
1.1 Defines Mgmt Structure, Roles, Resp 36 1.20% 21 5.30%
1.1 Framework Contents 42 1.40% 23 5.80%
1.1 Key People Involved 119 3.90% 59 14.80%
1.1 Lines of Authority 46 1.50% 26 6.50%
1.2 Approval and Endorsement 25 0.80% 17 4.30%
1.2 Key Elements of Strategic Plan Exist 72 2.40% 36 9.00%
1.2 Plan Communicated 55 1.80% 26 6.50%
1.2 Plan Updated 18 0.60% 14 3.50%
7. 5
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Exercise 1, quotes, codes & memos (within a setting)
Outcomes – learners will practice the use of quotes, codes and memos and will understand the steps
in relation to comprehending (immersion), and synthesizing (sifting).
Equipment: scissors, glue stick, 12 blank index cards, post-it notes, data
1) Immersion: Read 1. An Incident of Support (p17) quickly to understand the context.
(comprehending)
2) Selecting Quotations: Read it again and underline each distinct behaviour or incident
(full sentences – non-overlapping). Look for a phrase that captures several sentences
Be selective - underline less than half the text (approx 8-12 quotes) (synthesizing).
3) Memoing: As you are selecting quotations make memos to yourself (on post-it notes).
On them record ideas for a code i.e. “shared need is the bridge”. Also include ideas for
a theory, notes about the speaker, additional data to collect, etc. (i.e. needed for
theorizing).
4) Selecting quotations: Cut out the quotations underlined in step 2 above. Tape or
paste them onto the bottom of the index cards, one per card. Affix post-it notes to their
relevant quotes. You should end up with about 10-12 cards (synthesizing)
5) Coding: Group the cards into 5 to 10 piles based on similarity of behaviour/incident.
Write a fitting descriptive code name on a blank piece of paper i.e. “mutual needs are
supportive” and place it by the pile. A code name can be 3-6 words (like a theme),
expressing a topic a verb and a descriptor. If a pile gets too large split it into
subcategories. If two smaller piles may be merged if they have a commonality
(synthesizing).
6) Order the codes: Now consider the codes you created and reorder the piles left to
right along some continuum related to the codes (tangible-specific, subjective-
objective, serious-funny, etc). Identify the order (theorizing).
7) Choose an illustrative quote: From each pile choose a particularly clear quote that
expresses the code best and put it on the top of the pile. On the next pages “Code
List” make a master list of codes, in your chosen order, and beside write the illustrative
quote. (synthesizing)
8. 6
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Code List
Code Name Illustrative Quotation
Mutual needs are
supportive.
She said that her children were in need of other children
to play with and she needed an adult to talk to.
9. 7
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Grounded Theory
Primary documents (comprehending)
i) Immerse yourself in the primary documents (all types)
ii) begin as data are collected
iii) read/view/listen to the data
Quotations (synthesizing)
i) select and mark salient quotations/passages
ii) compare each line to other relevant data constantly
Coding (theorizing)
i) assign codes in margin
ii) group, sort, categorize codes into families
iii) collect new data based on emerging theory, memos, new codes…until
“saturation” is reached
Memos (aids in all processes)
i) record insights on memos or post-it notes ie: ideas for emerging theories,
thematic ideas, linked memos
Network (re-contextualizing)
i) create a work view (mind map) see p9
ii) add and arrange network nodes (quotes, memos and codes)
iii) collect more data as needed
Generate Theory
i) (Optional Exercise) Make a matrix of themes (rows) by roles (cols)
ii) Fill in cells with either a selected quote or “*” to indicate missing data.
iii) Look for patterns, empty cells, areas of convergence.
iv) Generate an explanation and provide a short quote to support your "theory".
(optional exercise p11)
10. 8
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Exercise 2: Grounded theory and network maps (across settings)
Outcome – participants will generate theory to explain the interactions in context. Learners will
generate understanding based on emerging theory (theorizing) in terms of the context
(re-contextualizing). They will draw a map depicting the evidence and create an explanation
(theory) emerging from it.
1. Explanatory statements: Re-examine the code list from Exercise 1. Pay attention to
any explanatory quotes – statements that answer why something occurred. Ask
questions of the data. Why did it go the way it did? Look for reasons why, and
alternative explanations. Allow time – this is a creative process. Be open to insights.
2. Reexamine the original transcript: Look for explanatory quotes or examples of “lay
theory” or grounded theory.
3. Grounded Theory: Choose one brief explanatory statement paraphrased from the
data that seems to represent the data (grounded theory).
4. Mind Map: Take the Grounded Theory Network Map on the next page (or enlarged
copy). In the centre summarize that explanatory statement.
5. Codes: In the bubbles around the central idea write (in pencil) the codes from the
code list (example shown). Arrange them in an order that makes sense, in relation to
the central explanatory idea (grounded theory).
6. Quotations and memos: Near each code (bubble) attach with glue (or re-write) the
applicable quotation on the card. Also beside the code affix applicable memos (post-it
notes). Note: you will not use all codes, quotes and memos; be selective and include
only those relevant to support the theory. Other theories may require more maps.
7. Examine the Map: Consider the map. Look at it from a distance, and close up. Look
for patterns and new explanations (several grounded theories may emerge).
11. 9
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Grounded Theory Network Map (across settings)
Central organizing question
Mutual needs are
supportive.
Card: She said that her
children were in need of
other children to play
with and she needed an
adult to talk to.
Post-It: Shared
need is the
bridge
12. 10
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Methods Matched to Type of Data (my preferences)
structured text
i) Content Analysis (i.e. coding of survey comments by themes)
ii) Matrix Analysis (i.e. coding as above and by speaker, role, time etc)
unstructured
iii) Phenomenology (i.e. essence of the lived experience described)
iv) Grounded Theory (i.e. sense making by participants)
audio voice / music
v) Matrix Analysis, Grounded Theory
video photos / scenes
vi) Matrix Analysis, Grounded Theory
Methods Matched to Principle Task
description
vii) Content analysis
explanation
viii)Matrix analysis
derive new ideas and insights
ix) Phenomenology
test significance
x) Matrix analysis, Quantitative
map theoretical relation
xi) Grounded Theory, Network Mapping
Crosstab (matrix) of codes and variables (program and time) (see Exercise next page)
Proj A Proj B Proj C
Time
1
Time
2
Time 3
Chi-
square
P value
finding 1.1.x 2 1 3 9.924 0.357
finding 1.2.x 1 9 1 2 17.447 0.042
finding 1.3.x 1 5 1 10.624 0.302
finding 1.4.x 1 2 12 4 2 41.016 0
finding 2.1.x 3 1 2 1 20.759 0.014
finding 2.2.x 1 2 2.13 0.989
finding 2.3.x 20 2 1 3 4 2 16.514 0.057
Risks 6 2 2 17 4 4 33.709 0
Best Practices 4 1 1 3 6 4 49.178 0
13. 11
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
(Optional) Exercise 3: Theorizing Using Matrix Analysis (across settings)
Outcomes – learners will use matrix of roles and themes to theorize an explanation of male and female
support giving, grounded in two narratives.
Equipment: scissors, tape, data – Incidents of Support #2 and #3
1. Read Support Incident #2, the mother/daughter. Read Support Incident #3 by Les
Brown about Mr. Washington and the student. (can be done ahead of time)
2. Quotations and Coding. Reread Support Incident #2. Beside each of the three codes
(page over), write (or cut and paste) the chosen underlined quote from the incident.
3. More Quotations and Coding. Now reread Support Incident #3, Mr Washington, looking
for similar quotes. If you find some (and you may not) put them in this column. At the
same time be looking for new ideas (based on themes) to put in rows 4 and 5
4. Matrix. Down the left side add new code names beside 4 and 5. Fill in cells with either
a selected quote or an “*” to indicate missing data.
5. Give it a thorough look for patterns, empty cells, areas of convergence.
6. Below write an explanation (theory or testable statement) about possible patterns for
female and male support giving. Include a short quote from each incident to support
your "theory".
14. 12
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Mother (incident 2) Mr. Washington (incident 3)
1. Being there is
supportive
2. Tangible action
is supportive
3. Support yields
positive outcomes
4.____________
5. __________
15. 13
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Multi Dimensional Scaling 2D and 3D maps
16. 14
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Show rotating Code Correlation 3D in QDA Miner
Q&A Questions and answers
Some QDA Methods (and Software)
1. Content Analysis
Word, QDA Miner Word-SimStat, Excel, Atlas-ti
2. Matrix analysis
Nvivo, QDA Miner Word-SimStat,
3. Grounded Theory Mapping
Atlas-ti, QDA Miner Word-SimStat,
4. Phenomenology - using mind maps
Inspiration, Visio
5. Concept Mapping
Concept Systems, QDA Miner Word-SimStat,
QDA software
1. QDA Miner Word-SimStat (Provalis, CAN)
2. Atlas-ti (Scientific Software, GER)
3. NVivo (QSR, AU)
4. Inspiration (USA)
5. Concept Systems (USA)
6. Excel, Access, SPSS (USA)
See Handouts.
http://www3.telus.net/reedspace/shared/
Reed Early
rearly@telus.net
250 748 0550
17. 15
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Handout 1. Summary of Qualitative Methods
Method Software Data Principle
Task
+ / - Application/
time
Content Analysis
From sociology
Word
processing,
Atlas-ti, QDA
Miner-
WordStat,
Static data
such as photos,
newspapers,
books etc
Count
frequencies of
recurring key
words/phrases
and themes
Simple to
grasp and use,
quantified,
reliable and
rigorous but
shallow
> 100 items/
observations in
5-10 hours
Matrix Analysis from
social science from
Matthew Miles and
Michael Huberman
NVivo, word
processor
Atlas-ti/SPSS,
QDA Miner-
WordStat
transcripts,
interviews,
photos, books,
newspapers
Reduce bulk of
data, memo
and code long
transcripts,
pull out quotes
simple,
displays well,
raw text data is
preserved, but
two
dimensional
50 interview or
3 focus groups
takes ~3 days
to analyze
Grounded Theory
from Sociologists
Anselm Strauss,
Barney Glaser and
Corbin
QDA Miner-
WordStat,Atlas
-ti, NVivo
Text,
interviews,
focus groups,
audio tapes,
video tapes
Code and
examine inter-
relations/
patterns of
codes/themes
deep analysis,
sensitive to lay
theory but time
consuming,
50 interview or
3 focus groups
transcripts, ~5
days to analyze.
Phenomenology from
philosophy from
Antonio Giorgi, Egon
Guba and Yvonne
Lincoln and others
Mind Maps
use Inspiration,
Visio
tapes,
interviews,
anecdotes,
diaries,
literature
Derive new
ideas and
insights, map
ideas
sensitive to
definitions,
looks
interesting, but
less precise
50 interview or
3 focus groups
~ 2 days to
analyze
Multi-Dimensional
Scaling (Concept
maps) advanced by
Bill Trochim.
Concept
Systems,
SPSS, QDA
Miner-
WordStat
Any set of
codes or
ranked, sorted
concepts , GPS
special data
Generate,
analyse, map
and confirm
ideas
empowers
participants,
displays well,
preserves own
words, but
complicated to
interpret
50 interview or
3 focus groups
~3 days to
analyze
Post Coding from
quantitative
sociological survey
techniques
Access,
database,
Excel, QDA
Miner-
WordStat,
survey
comment lines,
line entries in
log books,
short quotes
Test
significance of
ideas (i.e.
statistical
significance)
done in stats
program, fast,
but lacks depth
200 surveys ~1
day to analyze
18. 16
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
Handout 2. Website, Software and Internet Resources
Click on links in MSWord or at www3.telus.net/reedspace/QDA2013workshop.html
NON-COMMERCIAL COMMENT (useful, current links etc)
√ Text Analysis Info
www.textanalysis.info/
A free information source for information that
deals with the analysis of content of human
communication, mostly but not limited to text.
√Scoop It http://www.scoop.it/t/qualitative-quantitative-data-
analysis-management
A currently active Q&A about Qual Quan data
analysis.
√ Qualitative Research Web Sites
http://www.nova.edu/ssss/QR/web.html
Good links to Action Research, Qualitative Data
Analysis, and Ethnography and other sites
√ Computer Assisted Qualitative Data
Analysis Software
http://www.surrey.ac.uk/sociology/research/r
esearchcentres/caqdas/
CAQDAS at the Sociology Dept in U. of Surrey
UK announcing upcoming seminars and providing
slideshows.
√International Institute for Qualitative
Methodologies
http://www.uofaweb.ualberta.ca/iiqm/
The International Institute site at U. of Alberta
announcing conferences, workshops and training.
√Qualitative Research Web Rings
http://www.ringsurf.com/netring?ring=Qualit
ativeResearch;action=list
A number of web rings of interest to graduate
students and faculty interested in all aspects of
qualitative research. Includes YouTube.
√ Qual Page
http://www.qualitativeresearch.uga.edu/QualPage/
Resources for qualitative research from University
of Georgia. Another contribution of links
originated by a member of the web ring.
19. 17
Qualitative Data Analysis – Within and Across Settings
Reed Early, MA CE rearly@telus.net 250 748 0550
COMMERCIAL COMMENTS
√ Atlas.ti Workbench
http://www.atlasti.com
Danish maker of Atlas ti offers a demo, workshop
schedule, user groups and a variety of extras.
√ Qualitative Solutions and Research
http://www.qsrinternational.com
The Latrobe people who started it all with
Nud-Ist. And N-Vivo. Demos, prices etc
√ Concept Systems Mapping software
http://www.conceptsystems.com
Bill Trochim's excellent and expensive hybrid
hard-art qual/quan software
√ Provalis Research
http://provalisresearch.com/products
Canadian Normand Peladeau of provides QDA
Miner and companion program WordStat (content
analysis and text mining) .
√ Inspiration software
http://www.inspiration.com
Simple, traditional but very effective network
mapping software (manual) for analysis and ideas,
from Oregon
√ Qualitative Research and Consulting
http://www.quarc.de
List of QDA software workshops and seminars,
from Germany
√ Ethnograph by Qualis Research
http://www.qualisresearch.com
From Utah, the demo, workshop schedule and lots
more.
√ HyperResearch by ResearchWare
http://www.researchware.com
Simple description of the software and links from
Mass.
√ MaxQDA
http://www.maxqda.com
Organize, evaluate, and interpret data, create easy-
to-read reports and visualisations, and connect and
share with other researchers. From Germany.
√ Sage Publishing
http://www.sagepub.com
A source for QDA books and support
√ SPSS Text Analytics for Surveys SPSS, the Statistical Package for the Social
Sciences. Some QDA software (i.e.ATLAS.ti’s
export function) creates a syntax file to permit
SPSS to work with it.
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References
Denzin, N. And Lincoln, Y. (Eds) (1994). Handbook of Qualitative Research,
Thousand Oaks CA: Sage.
Fielding and Lee (1998). Computer Analysis and Qualitative Research. Thousand Oaks
CA: Sage.
Friese, S. (Presentation 2004). Computer Aided Qualitative Data Analysis of
Multimedia Data. http://caqdas.soc.surrey.ac.uk/.
Froggatt, K.A (2001). Using Computers in the analysis of qualitative data. Froggatt,
K.A. (2001) The analysis of qualitative data: processes and pitfalls. Palliative
Medicine, (15) 517-520.
Lewins, A and Silver, C (June 2005). Choosing a CAQDAS Package – A Working
Paper by Ann Lewins and Christina Silver. http://caqdas.soc.surrey.ac.uk .
Miles, M. and Huberman, M. (1994). Qualitative Data Analysis: A Sourcebook of New
Methods, (2nd Edition). Thousand Oaks CA: Sage.
Morse, J. M. and Field, P.A. (1995). Qualitative Research Methods for Health
Professionals. Thousand Oaks CA: Sage.
Strauss, A (1987). Qualitative Analysis for Social Scientists. New York : Cambridge
University.
21. 19
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1. An Incident of Support
My children were age one, three and five and we had just
moved out to the country. We were thirty miles from town
and were able to build a home on the quarter section of land
we had recently purchased. We were living in a small cabin
on the property which was without electricity, telephone and
running water or central heating. Each time we entered the
gates was like stepping back in time. We used kerosene,
had a well, and kept warm with a small space heater. I
cooked on a two burner camp stove that rested on boards
suspended on two saw horses. My nearest neighbour was
two miles away; I had unreliable transportation and no way
of communication. What began as fun and adventurous in
the summer became difficult when the winter snows arrived
and the days were short and cold. I felt lonely and stranded
with three preschool children.
Each Friday I went to town to shop for groceries, go to a
laundromat and visit the public library. Socialization and
communication were the problems I tried to solve on this
one day per week. I remember thinking that it was a sad
state of affairs for me to need the interactions with
salespersons for adult company. I often felt sorry for myself,
but was determined to be strong for the benefit of the
children. I did indeed become self sufficient in running of
the "home". At this time I was not employed in the outside
work force.
One particularly cold December day I was lingering in the
store because I was not looking forward to the chore of
entertaining my children in a laundromat for the next few
hours. A Scottish lady that I had previously met socially
approached me to wish us a Merry Christmas. After we had
conversed for a while she invited us to her home for lunch. I
explained that I couldn't because I still had the marketing
and the laundry to do before going home. She convinced
me that if I came to her house I would be doing her a favour.
She said that her children were in need of other children to
play with and she needed an adult to talk to. I was surprised
to see that even in a large community there could be
loneliness. Her children were the same ages as mine and
they also began a journey of long lasting friendship which
exists even today.
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During lunch she offered to me to use her laundry facilities. I
gratefully accepted because I enjoyed her company and
knew that I could fulfill my need for adult contact for a longer
period of time. I felt somewhat selfish about the possible
imposition on her household, but she assured me that she
needed the stimulation of the company of another woman
just as I did. It became a habit to go to her home on Friday
to do laundry, as she insisted. She could not see why I
should be paying for facilities when hers were not being
used that day.
As the winter progressed and our friendship flowered, we
took turns looking after each other's children while the other
savoured personal time. I used my time to quickly do the
marketing and visit the library. Without the added
distraction of children my time was efficiently and
wonderfully spent. Occasionally my friend would scoot me
out for a whole afternoon while she stayed in with the
children. It was difficult to discourage her from doing my
laundry as well, but I had to draw the line somewhere!
There were times when we accepted a dinner invitation at
her insistence. Occasionally we gratefully accepted
because I knew what it would be like for my children to arrive
back at the cold dark cabin, hungry and tired.
In a few short months the days became longer and the
weather warmer and I continued to visit my friend on a
weekly basis. I tried many times to speak of payment. She
would not hear of it and gently said that it would be an insult
to her to speak of payment for what she offered so freely. I
felt obligated to her because I did not think that I was
offering anything to her in return.
It took some negotiating and much thought but we finally
arrived at a solution which was satisfactory to both of us.
She said that since she had done a service for me I must
now find a way to pass on a service to someone else. When
I had done so I was to let her know the circumstances.
Three years later I took time off work to care for the children
of another friend for ten days while she recovered from
surgery. I related the events to both of my friends because I
felt that I had paid my debt of gratitude. My Scottish friend
was delighted and felt rewarded, as did I.
Anonymous
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Qualitative Data Analysis – Within and Across Settings
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2. Incident of Support
A daughter of age twenty had just recently returned to live
with her mother. After living at home a few months she
decided to move out again but this time she planned to live
with her boyfriend in a different town. Both her parents
were very upset with her decision because she had only
known her boyfriend for a few months and they believed that
people should be married before they live together. Even
though the mother did not believe that her daughter was
making the right decision she still supported her.
The mother supported her daughter in several ways. First
she helped resolve conflict between the father and daughter
by helping them each see both sides of the situation. She
also let her daughter know that she loved her unconditionally
and that even though she did not agree with what her
daughter was doing she would still be there when she was
needed. She told her daughter that she believed in her
ability to make her own decisions and that she would respect
whatever her daughter decided. Another example of
support that the mother demonstrated was to help her
daughter pack and make necessary arrangements to move.
I think this proved to the daughter that her mother was
sincere in what she had said.
During this situation many thoughts were going though my
head. First I thought it must be hard for the mother to
support her daughter in a decision that she did not agree
with. I also thought that this daughter was very lucky to have
such an understanding and supportive mother. Thinking
these things brought up some feelings for me. I felt a lot of
respect towards the mother because she was able to put her
own feelings aside in order to support here daughter. I also
felt happy for the daughter because she had someone in her
life that loved her unconditionally.
By watching the expressions on her faces I saw a lot of love
and respect between a daughter and a mother, but most of
all I saw how much the daughter appreciated her mother's
support. Seeing the results of the support was very
exciting. In this situation the mother's supportive nature to
her daughter paid off. After a few months of living together
the daughter got engaged to her boyfriend. Now the mother
has a new son-in-law and her daughter has been happily
married for three years.
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Qualitative Data Analysis – Within and Across Settings
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3. Incident of Support by Les Brown
One day in eleventh grade I went into a classroom to wait for
a friend of mine. When I went into the room the teacher, Mr
Washington suddenly appeared and asked me to go to the
board to write something, to work something out. I told him I
could not do it. He said "Why not?".
I said "Because I am not one of your students. He said it
doesn't matter. I said "I can't". He said "Why not?"
I paused, because I was somewhat embarrassed and said
"Because I am Educably Mentally Retarded". He came from
behind his desk and he looked at me and he said "Don't
ever say that again. Someone else's opinion of you does
not have to become your reality".
It was a very liberating moment for me. On one hand I was
humiliated because the other students laughed at me. They
knew I was in Special Education. But on the other hand I
was liberated because he began to bring to my attention that
I did not have to live within the context of what another
person's view of me was.
And so Mr Washington became my mentor. Prior to this
experience I had failed twice in school. I was identified as
Educably Mentally Retarded in the fifth grade, was put back
into fourth grade, and failed again when I was in eighth
grade. So this person made a dramatic difference in my life.
Mr Washington believed that nobody rises to low
expectations. This man always gave students the feeling
that he had high expectations for them and we strove to live
up to them.
One day when I was still a junior I heard him giving a speech
to some graduating seniors. He said "you have greatness
within you. You have something special. If just one of you
can get a glimpse of a larger vision of yourself, of who you
really are, then in a historical context the world will never be
the same again. You can make your parents proud. You
can make your school proud. You can make your
community proud. You can touch millions of lives. He was
talking to the seniors but it seemed like the speech was for
me.
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I remembered when they gave him a standing ovation.
Afterwards I caught up to him in the parking lot and after
short conversation said "Mr Washington,........Is there
greatness within me sir?" He said "Yes, Mr Brown".
"But what about the fact that I failed English and math and
history, and that I'm going to summer school? I'm slower
than most kids. I'm not as smart as my brother or sister who
is going to University".
He said "It doesn't matter. It just means that you have to
work harder. Your grades don't determine who you are or
what you can produce in your life."
I said I want to buy my mother a home. "You can do that"
He turned and walked a way again.
"Mr Washington", I said..."Um I'm the one sir, You remember
me, remember my name. One day you're gonna hear it .
I'm gonna make you proud. I'm the one sir."
School was a real struggle for me. I was passed from one
grade to the next because I was not a bad kid....Mr
Washington became my drama instructor in senior year,
even though I was in Special Ed. The principal realized the
kind of bonding that had taken place and the impact he'd
made on me because I had begun to do well academically.
For the first time in my life I made the honour roll. I wanted
to travel on a trip with the drama department and you had to
be on the honour roll to make the trip out of town. That was
a miracle for me!
Mr Washington restructured my own picture of who I am.
He gave me a larger vision of myself, beyond my mental
conditioning and my circumstances.
Years later I produced five specials that appeared on public
television. I had some friends call Mr Washington when my
program "You Deserve" was on....I was sitting by the phone
when he called and said..."May I speak to Mr. Brown
please?"
"Oh, Mr Washington, its you" I said.
"You were the one, weren't you?
"Yes, Sir I was" I said.
From A Third Serving of Chicken Soup for the Soul
by Jack Canfield and Mark Victor Hansen