The document discusses qualitative data analysis. It notes that qualitative data comes in the form of words from sources like interviews, focus groups, open-ended questions, videos, experiences online, and news articles. Qualitative data analysis aims to make valid inferences from large amounts of collected data and may involve repeated sampling, collection, and analysis. There are generally three steps to qualitative data analysis: data reduction, data display, and drawing conclusions. Data reduction involves selecting, coding, and categorizing data to reduce it. [END SUMMARY]
Online Service Rating Prediction by Removing Paid Users and Jaccard CoefficientIRJET Journal
This document summarizes a research paper that proposes a new method for online service rating prediction. The method first filters out paid users from rating datasets using visibility and interest metrics. It then learns the latent feature values of users and items based on interpersonal interest similarity, personal interest, rating similarity between friends, and Jaccard coefficient of common friends. The method is evaluated on precision, recall, detection rate and false alarm rate and shown to outperform an existing method called EURB on different sized datasets.
IRJET- Predicting Review Ratings for Product MarketingIRJET Journal
This document discusses predicting review ratings for product marketing using big data analysis. It proposes using Hadoop tools like HDFS, MapReduce, Hive and Pig to analyze large amounts of product review data from sources like blogs in order to provide more accurate predictions of review ratings. The system would gather reviews, convert unstructured data to structured data, analyze the data using Hadoop queries to determine popular products and trends. It would then display the results as bar charts and pie charts comparing review ratings for products. Experiments show the Hadoop-based system provides results faster than traditional databases for large datasets over 100MB in size.
Developing A Universal Approach to Cleansing Customer and Product DataFindWhitePapers
Take a look at this review of current industry problems concerning data quality, and learn more about how companies are addressing quality problems with customer, product, and other types of corporate data. Read about products and use cases from SAP to see how vendors are supporting data cleansing.
- Lauren Johnston is working in the Engineering department of Vascutek, a medical device company, on a project investigating product yield loss. She recorded 6 months of manufacturing data, conducted testing, and led a project team.
- She analyzed the manufacturing data using pivot tables and charts, identifying the dates, product sizes, materials, and geometries with the highest failure rates. Comparing interviews with operators performing quality tests allowed her to determine if technique differences affected results.
- Trends in the data will impact future activities - samples will be sent for external testing, an experimental protocol will be conducted, and tank cleaning will be enforced and monitored for improvements in yield.
IRJET - Sentiment Similarity Analysis and Building Users Trust from E-Commerc...IRJET Journal
This document proposes a trust reputation system for e-commerce reviews that uses sentiment similarity analysis of user reviews. It analyzes user reviews to estimate user similarity and trust. The system displays pre-fabricated reviews to newly registered users and analyzes their responses to calculate a trust score for the user based on how similar their sentiments are to trustworthy reviews. It then uses the trust scores and review ratings to calculate an overall trust score for products. The goal is to help users make more informed purchasing decisions by analyzing review sentiments and user trustworthiness.
Customer Churn prediction in ECommerce Sector.pdfvirajkhot5
Retaining customers is a challenging issue that is encountering most of organizations, particularly businesses operating in e-commerce sector. According to Wu et al., (2017), it is much more difficult to retain the existing customers as compared to attract new ones because existing customers provides high value in ecommerce; however, to attract new customers, companies need to invest a lot of money for making them as loyal customers. This study will develop a prediction model for E-commerce sector to correlate the key attributes leading to churn.
The document discusses data mining and its evolution from early database systems. It defines data mining as a process of finding patterns in data through techniques like forecasting, classification, clustering, association, and sequencing. The document outlines the standard phases of data mining including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. It provides examples of how organizations use data mining for applications such as fraud detection, customer profiling, and targeting.
Online Service Rating Prediction by Removing Paid Users and Jaccard CoefficientIRJET Journal
This document summarizes a research paper that proposes a new method for online service rating prediction. The method first filters out paid users from rating datasets using visibility and interest metrics. It then learns the latent feature values of users and items based on interpersonal interest similarity, personal interest, rating similarity between friends, and Jaccard coefficient of common friends. The method is evaluated on precision, recall, detection rate and false alarm rate and shown to outperform an existing method called EURB on different sized datasets.
IRJET- Predicting Review Ratings for Product MarketingIRJET Journal
This document discusses predicting review ratings for product marketing using big data analysis. It proposes using Hadoop tools like HDFS, MapReduce, Hive and Pig to analyze large amounts of product review data from sources like blogs in order to provide more accurate predictions of review ratings. The system would gather reviews, convert unstructured data to structured data, analyze the data using Hadoop queries to determine popular products and trends. It would then display the results as bar charts and pie charts comparing review ratings for products. Experiments show the Hadoop-based system provides results faster than traditional databases for large datasets over 100MB in size.
Developing A Universal Approach to Cleansing Customer and Product DataFindWhitePapers
Take a look at this review of current industry problems concerning data quality, and learn more about how companies are addressing quality problems with customer, product, and other types of corporate data. Read about products and use cases from SAP to see how vendors are supporting data cleansing.
- Lauren Johnston is working in the Engineering department of Vascutek, a medical device company, on a project investigating product yield loss. She recorded 6 months of manufacturing data, conducted testing, and led a project team.
- She analyzed the manufacturing data using pivot tables and charts, identifying the dates, product sizes, materials, and geometries with the highest failure rates. Comparing interviews with operators performing quality tests allowed her to determine if technique differences affected results.
- Trends in the data will impact future activities - samples will be sent for external testing, an experimental protocol will be conducted, and tank cleaning will be enforced and monitored for improvements in yield.
IRJET - Sentiment Similarity Analysis and Building Users Trust from E-Commerc...IRJET Journal
This document proposes a trust reputation system for e-commerce reviews that uses sentiment similarity analysis of user reviews. It analyzes user reviews to estimate user similarity and trust. The system displays pre-fabricated reviews to newly registered users and analyzes their responses to calculate a trust score for the user based on how similar their sentiments are to trustworthy reviews. It then uses the trust scores and review ratings to calculate an overall trust score for products. The goal is to help users make more informed purchasing decisions by analyzing review sentiments and user trustworthiness.
Customer Churn prediction in ECommerce Sector.pdfvirajkhot5
Retaining customers is a challenging issue that is encountering most of organizations, particularly businesses operating in e-commerce sector. According to Wu et al., (2017), it is much more difficult to retain the existing customers as compared to attract new ones because existing customers provides high value in ecommerce; however, to attract new customers, companies need to invest a lot of money for making them as loyal customers. This study will develop a prediction model for E-commerce sector to correlate the key attributes leading to churn.
The document discusses data mining and its evolution from early database systems. It defines data mining as a process of finding patterns in data through techniques like forecasting, classification, clustering, association, and sequencing. The document outlines the standard phases of data mining including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. It provides examples of how organizations use data mining for applications such as fraud detection, customer profiling, and targeting.
This document discusses how customer data is collected and used by various companies. Records are created at each step of a customer's transaction including phone calls, orders, credit card authorization, and shipping. Companies use these detailed records to learn about customer behaviors over time to target promotions, predict future purchases, and improve customer relationships. Data mining techniques are applied to large data warehouses to discover patterns in customer data and gain business insights.
To share a component in our appraisal process specifically, the application of the 360-degree feedback system as I introduce a performance management system and the goals of organization, its strengths and limitations, discover challenges, identify areas of opportunity and applications for improvement.
Gain an overview of data verification and validation, the methods and techniques used to keep data clean as well as new business practices in the industry that help in maintaining data quality and preventing data decay.
Adopt new approaches of “Think Blue” and “Think Green”, in order to create a pollution free virtual environment.
Check out more - http://www.infocheckpoint.com/Images/pdf/Expand-your-Enterprise-Exponentially-whitepaper.pdf
This document discusses customer advocacy in business-to-business (B2B) contexts. It argues that customer advocacy exists in B2B as brand impression and word-of-mouth are important drivers of supplier choice, even with business constraints like pricing and regulations. Meeting basic customer needs is essential to building trust and loyalty. The document presents research showing that service quality, relationship quality, satisfaction, and trust influence customer retention and advocacy in B2B similarly to business-to-consumer. It provides an example of a computer company that improved advocacy by focusing service on problem ownership.
Data Governance in the age of Social MediaExperian
Data is key to all of us. Regardless if you are a banker, retailer, marketer or underwriter, we all strive to know the most about our prospects and customers. We need to know their likes, wants, pain points and a foresight into their interest. And we need to know it before the prospect or customer does. Given the never-ending need for further insights, many of us continually look for new data sources to provide this competitive edge. This is just good business. But there is a need to understand both the predictability and persistence of the data and the insights it provides.
This presentation explores:
The regulatory landscape
The new data sources being tested and used
The implications upon your data governance infrastructure
The path to ensuring your use of the data does not become more of a burden than a benefit
The majority of organizations (54%) use people analytics to improve HR effectiveness today. Organizations more frequently rely on people analytics to improve business outcomes, organizational performance and achieve labor cost savings.
People Analytics allows HR to gain a more strategic role in the organization and clearly show its impact.
Advanced organizations use data to analyze the workforce proactively, make predictions, and create and monitor comprehensive workforce plans to achieve financial success.
HR data has become an strategic priority, but it takes efforts in order to enable the usage of it.
31MARCH 2015 THE CPA JOURNALDemonstrating Professional.docxgilbertkpeters11344
31MARCH 2015 / THE CPA JOURNAL
Demonstrating Professional
Skepticism
A C C O U N T I N G & A U D I T I N G
a u d i t i n g
By Douglas M. Boyle and Brian W. Carpenter
Insights from Recent Research for Auditors of Financial Statements
Demonstrating professional skepticism while conducting an audit is an important, well-documented expectation of the pro-
fession. A skeptical mindset ensures that auditors approach audits recognizing that it is always possible that fraud is present.
Unfortunately, recent audit deficiencies and failures have raised questions as to whether auditors exhibit an appropriate degree
of skepticism. This article reviews the profession’s guidance regarding professional skepticism, summarizes the results of
studies examining audit deficiencies and failures attributed to a lack of sufficient professional skepticism, and offers sug-
gestions to aid auditors in exhibiting appropriate levels of professional skepticism in audit engagements.
In Brief
MARCH 2015 / THE CPA JOURNAL32
P
rofessional skepticism is a concept
of critical importance to the audit
profession. Statements on Auditing
Standards (SAS) 1, Responsibilities
and Functions of the Independent Auditor,
clearly states that “Due care requires
[emphasis added] the auditor to exercise
professional skepticism” (AU 230.07). SAS
99, Consideration of Fraud in a Financial
Statement Audit, further clarifies the essen-
tial role of professional skepticism by dis-
cussing and restating its elements and
necessity in conducting an audit:
Professional skepticism is an attitude that
includes a questioning mind and a crit-
ical assessment of audit evidence. The
auditor should conduct the engagement
with a mindset that recognizes the pos-
sibility that a material misstatement
due to fraud could be present, regardless
of any past experience with the entity
and regardless of the auditor’s belief
about management's honesty and integri-
ty. Furthermore, professional skepticism
requires an ongoing questioning of
whether the information and evidence
obtained suggests that a material mis-
statement due to fraud has occurred. In
exercising professional skepticism in
gathering and evaluating evidence, the
auditor should not be satisfied with less-
than-persuasive evidence because of a
belief that management is honest.
(p. 1724; AU 316.13)
In Staff Audit Practice Alert 10,
“Maintaining and Applying Professional
Skepticism in Audits” (Dec. 4, 2012), the
PCAOB underscores this emphasis by stat-
ing the following:
Professional skepticism is essential to the
performance of effective audits under
Public Company Accounting Oversight
Board standards. Those standards require
that professional skepticism be applied
throughout the audit by each individual
auditor on the engagement team. (p. 1)
In addition to the above sources, through-
out the professional literature, there are
similar calls for auditors to exhibit appro-
priate levels of professional skepticism
through a questioning mind.
Trust is a big part of how well e-commerce works and it is also a big part of how people decide what to
buy. As more and more business is done online, companies that want to build customer loyalty and
involvement must understand the complex relationship between trust and e-commerce. This study paper
looks at the many distinct aspects of trust in e-commerce and how it affects people’s actions. By looking at
the various aspects and causes of trust and how to build and keep trust, this study shows how businesses
can create a trustworthy online environment that makes customers happier and leads to long-term success.
Trust is a big part of how well e-commerce works and it is also a big part of how people decide what to
buy. As more and more business is done online, companies that want to build customer loyalty and
involvement must understand the complex relationship between trust and e-commerce. This study paper
looks at the many distinct aspects of trust in e-commerce and how it affects people’s actions. By looking at
the various aspects and causes of trust and how to build and keep trust, this study shows how businesses
can create a trustworthy online environment that makes customers happier and leads to long-term success.
This document discusses using data and metrics to improve customer experience. It begins by introducing the presenters and their perspective on experience design. It then makes the case that experience is defined by customer perception of service delivery. Data can help uncover issues and validate solutions, but companies often struggle to use data effectively or define proper metrics. The document advocates understanding qualitative and quantitative data, as well as the difference between data and metrics. It also provides an overview of tools that can help with discovery, experimentation, and operationalizing insights.
Update on the progress of supply chain leaders on progress on the Supply Chain Effective Frontier (balancing growth, profitability, cycles and complexity). Philippe Lambotte, SVP of Merck, recommends a seat at the table, focus on supply chain strategy, eliminate the white noise, and stay the course.
A Novel Technique for Improving Group Recommendation in Recommender SystemIRJET Journal
This document summarizes a research paper that proposes a novel technique to improve group recommendation in recommender systems by increasing diversity while maintaining accuracy. The paper first discusses existing recommendation and personalization techniques that suffer from low diversity. It then proposes three optimization-based approaches to directly control diversity levels by either specifying a desired diversity level or maximizing diversity. The proposed approaches were shown to outperform existing re-ranking techniques in terms of both accuracy and diversity. The objective is to replace similar recommended items with new items to provide more recommendations to users and increase their willingness to purchase items.
Succeeding in the Web 3.0: from mobile to data, how will you empower consumer...Guadalupe Pagalday
The document discusses moving from Web Research 2.0 to Web Research 3.0 by empowering not just consumers but also researchers and clients. Web Research 3.0 will achieve new levels of relevance for clients by making them active participants in shaping and mining qualitative data for insights, as well as collaborating across organizations. This empowerment of clients will maximize business value through client relevance.
This document discusses measuring customer satisfaction using Net Promoter Score (NPS). It begins by providing an overview of NPS, noting its promise to deliver results with minimal effort but also acknowledging criticisms of the method. The document then examines using NPS for small and medium enterprises, discussing how to design a customer satisfaction measurement system and integrate it within an organization's management processes. It also reviews previous studies that have criticized NPS, finding it a poor predictor of loyalty and satisfaction. The document concludes by discussing how to properly define customer satisfaction, customers, and quality to develop an effective customer satisfaction measurement approach.
This document provides an overview of a case study on depression. It describes the participants in the study, which included 18 patients who met the inclusion criteria of having major depressive disorder. The study aimed to assess the effectiveness of cognitive behavioral therapy (CBT) for treating depression. Patients received 8 to 16 sessions of individual CBT and completed assessments before and after treatment to measure changes in depression symptoms. The results showed that CBT was effective at reducing depressive symptoms, with the majority of patients no longer meeting the criteria for major depressive disorder after treatment. CBT was found to be a viable treatment option for depression.
- Analytics leaders have integrated analytics across their entire organizations and achieved substantial benefits like improved financial performance and faster decision making. They display characteristics like a data-driven culture and transparency in decision making.
- While most organizations recognize the need for better decision making, many lack formal, consistent processes. Nearly half report a lack of transparency in key decisions.
- Individuals and organizations are evolving to meet increasing demands for timely, data-driven decisions. People are enhancing their analytics skills and forging closer relationships with analytics professionals to leverage data insights.
The document discusses how leading organizations are evolving to adopt more data-driven decision making cultures. It finds that organizations face increasing pressure to make decisions faster amid shrinking time windows. As a result, many organizations are enhancing employee skills to better integrate analytics, balancing data with experience, and forging new relationships between decision makers and analytics professionals. The most advanced organizations are developing best practices that distribute data and tools widely to promote transparency.
A Business-first Approach to Building Data Governance ProgramsPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. In this presentation, we share a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term.
- Mariska Hargitay is an American actress known for her role as Olivia Benson on Law & Order: Special Victims Unit.
- She has used her celebrity platform to advocate for victims of sexual assault and help reform laws surrounding the backlog of untested rape kits.
- Through the Joyful Heart Foundation, which she founded, Hargitay has helped pass laws to process untested rape kits and support victims of sexual assault.
1) The document discusses the evolution of data-driven decision making in leading organizations. It finds that 11% of surveyed organizations have integrated analytics across the entire company and are considered "analytics leaders".
2) These analytics leaders report greater benefits from analytics like improved financial performance and faster decision making. They also display characteristics like a data-based culture and transparent decision processes.
3) As pressure increases to make decisions more quickly, individuals and organizations are developing new skills to leverage analytics tools and forge closer relationships with analytics professionals to make evidence-based decisions.
This document discusses how customer data is collected and used by various companies. Records are created at each step of a customer's transaction including phone calls, orders, credit card authorization, and shipping. Companies use these detailed records to learn about customer behaviors over time to target promotions, predict future purchases, and improve customer relationships. Data mining techniques are applied to large data warehouses to discover patterns in customer data and gain business insights.
To share a component in our appraisal process specifically, the application of the 360-degree feedback system as I introduce a performance management system and the goals of organization, its strengths and limitations, discover challenges, identify areas of opportunity and applications for improvement.
Gain an overview of data verification and validation, the methods and techniques used to keep data clean as well as new business practices in the industry that help in maintaining data quality and preventing data decay.
Adopt new approaches of “Think Blue” and “Think Green”, in order to create a pollution free virtual environment.
Check out more - http://www.infocheckpoint.com/Images/pdf/Expand-your-Enterprise-Exponentially-whitepaper.pdf
This document discusses customer advocacy in business-to-business (B2B) contexts. It argues that customer advocacy exists in B2B as brand impression and word-of-mouth are important drivers of supplier choice, even with business constraints like pricing and regulations. Meeting basic customer needs is essential to building trust and loyalty. The document presents research showing that service quality, relationship quality, satisfaction, and trust influence customer retention and advocacy in B2B similarly to business-to-consumer. It provides an example of a computer company that improved advocacy by focusing service on problem ownership.
Data Governance in the age of Social MediaExperian
Data is key to all of us. Regardless if you are a banker, retailer, marketer or underwriter, we all strive to know the most about our prospects and customers. We need to know their likes, wants, pain points and a foresight into their interest. And we need to know it before the prospect or customer does. Given the never-ending need for further insights, many of us continually look for new data sources to provide this competitive edge. This is just good business. But there is a need to understand both the predictability and persistence of the data and the insights it provides.
This presentation explores:
The regulatory landscape
The new data sources being tested and used
The implications upon your data governance infrastructure
The path to ensuring your use of the data does not become more of a burden than a benefit
The majority of organizations (54%) use people analytics to improve HR effectiveness today. Organizations more frequently rely on people analytics to improve business outcomes, organizational performance and achieve labor cost savings.
People Analytics allows HR to gain a more strategic role in the organization and clearly show its impact.
Advanced organizations use data to analyze the workforce proactively, make predictions, and create and monitor comprehensive workforce plans to achieve financial success.
HR data has become an strategic priority, but it takes efforts in order to enable the usage of it.
31MARCH 2015 THE CPA JOURNALDemonstrating Professional.docxgilbertkpeters11344
31MARCH 2015 / THE CPA JOURNAL
Demonstrating Professional
Skepticism
A C C O U N T I N G & A U D I T I N G
a u d i t i n g
By Douglas M. Boyle and Brian W. Carpenter
Insights from Recent Research for Auditors of Financial Statements
Demonstrating professional skepticism while conducting an audit is an important, well-documented expectation of the pro-
fession. A skeptical mindset ensures that auditors approach audits recognizing that it is always possible that fraud is present.
Unfortunately, recent audit deficiencies and failures have raised questions as to whether auditors exhibit an appropriate degree
of skepticism. This article reviews the profession’s guidance regarding professional skepticism, summarizes the results of
studies examining audit deficiencies and failures attributed to a lack of sufficient professional skepticism, and offers sug-
gestions to aid auditors in exhibiting appropriate levels of professional skepticism in audit engagements.
In Brief
MARCH 2015 / THE CPA JOURNAL32
P
rofessional skepticism is a concept
of critical importance to the audit
profession. Statements on Auditing
Standards (SAS) 1, Responsibilities
and Functions of the Independent Auditor,
clearly states that “Due care requires
[emphasis added] the auditor to exercise
professional skepticism” (AU 230.07). SAS
99, Consideration of Fraud in a Financial
Statement Audit, further clarifies the essen-
tial role of professional skepticism by dis-
cussing and restating its elements and
necessity in conducting an audit:
Professional skepticism is an attitude that
includes a questioning mind and a crit-
ical assessment of audit evidence. The
auditor should conduct the engagement
with a mindset that recognizes the pos-
sibility that a material misstatement
due to fraud could be present, regardless
of any past experience with the entity
and regardless of the auditor’s belief
about management's honesty and integri-
ty. Furthermore, professional skepticism
requires an ongoing questioning of
whether the information and evidence
obtained suggests that a material mis-
statement due to fraud has occurred. In
exercising professional skepticism in
gathering and evaluating evidence, the
auditor should not be satisfied with less-
than-persuasive evidence because of a
belief that management is honest.
(p. 1724; AU 316.13)
In Staff Audit Practice Alert 10,
“Maintaining and Applying Professional
Skepticism in Audits” (Dec. 4, 2012), the
PCAOB underscores this emphasis by stat-
ing the following:
Professional skepticism is essential to the
performance of effective audits under
Public Company Accounting Oversight
Board standards. Those standards require
that professional skepticism be applied
throughout the audit by each individual
auditor on the engagement team. (p. 1)
In addition to the above sources, through-
out the professional literature, there are
similar calls for auditors to exhibit appro-
priate levels of professional skepticism
through a questioning mind.
Trust is a big part of how well e-commerce works and it is also a big part of how people decide what to
buy. As more and more business is done online, companies that want to build customer loyalty and
involvement must understand the complex relationship between trust and e-commerce. This study paper
looks at the many distinct aspects of trust in e-commerce and how it affects people’s actions. By looking at
the various aspects and causes of trust and how to build and keep trust, this study shows how businesses
can create a trustworthy online environment that makes customers happier and leads to long-term success.
Trust is a big part of how well e-commerce works and it is also a big part of how people decide what to
buy. As more and more business is done online, companies that want to build customer loyalty and
involvement must understand the complex relationship between trust and e-commerce. This study paper
looks at the many distinct aspects of trust in e-commerce and how it affects people’s actions. By looking at
the various aspects and causes of trust and how to build and keep trust, this study shows how businesses
can create a trustworthy online environment that makes customers happier and leads to long-term success.
This document discusses using data and metrics to improve customer experience. It begins by introducing the presenters and their perspective on experience design. It then makes the case that experience is defined by customer perception of service delivery. Data can help uncover issues and validate solutions, but companies often struggle to use data effectively or define proper metrics. The document advocates understanding qualitative and quantitative data, as well as the difference between data and metrics. It also provides an overview of tools that can help with discovery, experimentation, and operationalizing insights.
Update on the progress of supply chain leaders on progress on the Supply Chain Effective Frontier (balancing growth, profitability, cycles and complexity). Philippe Lambotte, SVP of Merck, recommends a seat at the table, focus on supply chain strategy, eliminate the white noise, and stay the course.
A Novel Technique for Improving Group Recommendation in Recommender SystemIRJET Journal
This document summarizes a research paper that proposes a novel technique to improve group recommendation in recommender systems by increasing diversity while maintaining accuracy. The paper first discusses existing recommendation and personalization techniques that suffer from low diversity. It then proposes three optimization-based approaches to directly control diversity levels by either specifying a desired diversity level or maximizing diversity. The proposed approaches were shown to outperform existing re-ranking techniques in terms of both accuracy and diversity. The objective is to replace similar recommended items with new items to provide more recommendations to users and increase their willingness to purchase items.
Succeeding in the Web 3.0: from mobile to data, how will you empower consumer...Guadalupe Pagalday
The document discusses moving from Web Research 2.0 to Web Research 3.0 by empowering not just consumers but also researchers and clients. Web Research 3.0 will achieve new levels of relevance for clients by making them active participants in shaping and mining qualitative data for insights, as well as collaborating across organizations. This empowerment of clients will maximize business value through client relevance.
This document discusses measuring customer satisfaction using Net Promoter Score (NPS). It begins by providing an overview of NPS, noting its promise to deliver results with minimal effort but also acknowledging criticisms of the method. The document then examines using NPS for small and medium enterprises, discussing how to design a customer satisfaction measurement system and integrate it within an organization's management processes. It also reviews previous studies that have criticized NPS, finding it a poor predictor of loyalty and satisfaction. The document concludes by discussing how to properly define customer satisfaction, customers, and quality to develop an effective customer satisfaction measurement approach.
This document provides an overview of a case study on depression. It describes the participants in the study, which included 18 patients who met the inclusion criteria of having major depressive disorder. The study aimed to assess the effectiveness of cognitive behavioral therapy (CBT) for treating depression. Patients received 8 to 16 sessions of individual CBT and completed assessments before and after treatment to measure changes in depression symptoms. The results showed that CBT was effective at reducing depressive symptoms, with the majority of patients no longer meeting the criteria for major depressive disorder after treatment. CBT was found to be a viable treatment option for depression.
- Analytics leaders have integrated analytics across their entire organizations and achieved substantial benefits like improved financial performance and faster decision making. They display characteristics like a data-driven culture and transparency in decision making.
- While most organizations recognize the need for better decision making, many lack formal, consistent processes. Nearly half report a lack of transparency in key decisions.
- Individuals and organizations are evolving to meet increasing demands for timely, data-driven decisions. People are enhancing their analytics skills and forging closer relationships with analytics professionals to leverage data insights.
The document discusses how leading organizations are evolving to adopt more data-driven decision making cultures. It finds that organizations face increasing pressure to make decisions faster amid shrinking time windows. As a result, many organizations are enhancing employee skills to better integrate analytics, balancing data with experience, and forging new relationships between decision makers and analytics professionals. The most advanced organizations are developing best practices that distribute data and tools widely to promote transparency.
A Business-first Approach to Building Data Governance ProgramsPrecisely
Traditional data governance initiatives fail by focusing too heavily on policies, compliance, and enforcement, which quickly lose business interest and support. This leaves data management and governance leaders having to continually make the case for data governance to secure business adoption. In this presentation, we share a lean, business-first data governance approach that connects key initiatives to governance capabilities and quickly delivers business value for the long-term.
- Mariska Hargitay is an American actress known for her role as Olivia Benson on Law & Order: Special Victims Unit.
- She has used her celebrity platform to advocate for victims of sexual assault and help reform laws surrounding the backlog of untested rape kits.
- Through the Joyful Heart Foundation, which she founded, Hargitay has helped pass laws to process untested rape kits and support victims of sexual assault.
1) The document discusses the evolution of data-driven decision making in leading organizations. It finds that 11% of surveyed organizations have integrated analytics across the entire company and are considered "analytics leaders".
2) These analytics leaders report greater benefits from analytics like improved financial performance and faster decision making. They also display characteristics like a data-based culture and transparent decision processes.
3) As pressure increases to make decisions more quickly, individuals and organizations are developing new skills to leverage analytics tools and forge closer relationships with analytics professionals to make evidence-based decisions.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
2. Qualitative Data Analysis
QUALITATIVE DATA ARE
DATA IN THE FORM OF WORDS
EXAMPLES OF QUALITATIVE DATA ARE INTERVIEW NOTES,
TRANSCRIPTS OF FOCUS GROUPS, ANSWERS TO OPEN-ENDED
QUESTIONS, TRANSCRIPTIONS OF VIDEO RECORDINGS, ACCOUNTS
OF EXPERIENCES WITH A PRODUCT ON THE INTERNET, NEWS
ARTICLES, AND THE LIKE
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
3. Qualitative Data Analysis
THE ANALYSIS OF QUALITATIVE DATA IS AIMED AT
MAKING VALID INFERENCES FROM THE OFTEN
OVERWHELMING AMOUNT OF COLLECTED DATA
QUALITATIVE RESEARCH MAY INVOLVE
REPEATING SAMPLING, COLLECTION OF
DATA, AND ANAYLSIS OF DATA
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
4. Qualitative Data Analysis
AS A RESULT, QUALITATIVE DATA ANALYSIS MAY START
AFTER ONLY SOME OF THE DATA HAVE BEEN
COLLECTED
THE PROBLEM IS THAT, THERE ARE RELATIVELY FEW
WELL-ESTABLISHED AND COMMONLY ACCEPTED
RULES AND GUIDELINES FOR ANALYZING
QUALITATIVE DATA
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
5. Qualitative Data Analysis
THERE ARE GENERALLY 3 STEPS IN QUALITATIVE DATA ANALYSIS :
DATA REDUCTION, DATA DISPLAY,
AND THE DRAWING OF CONCLUSION
DATA REDUCTION REFERS TO THE PROCESS OF SELECTING,
CODING, AND CATEGORIZING THE DATA
DATA DISPLAY REFERS TO WAYS OF PRESENTING THE DATA
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
6. Qualitative Data Analysis
DATA CODING MAY HELP YOU SIMULTANEOUSLY TO DEVELOP IDEAS
ON HOW THE DATA MAY BE DISPLAYED, AS WELL AS TO DRAW
SOME PRELIMINARY CONCLUSIONS. IN TURN, PRELIMINARY
CONCLUSIONS MAY FEED BACK INTO THE WAY THE RAW DATA ARE
CODED, CATEGORIZED, AND DISPLAYED
THERE ARE GENERALLY 3 STEPS IN QUALITATIVE DATA ANALYSIS :
DATA REDUCTION, DATA DISPLAY,
AND THE DRAWING OF CONCLUSION
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
7. Qualitative Data Analysis Data
reduction
QUALITATIVE DATA COLLECTION PRODUCES
LARGE AMOUNTS OF DATA
THE FIRST STEP IN DATA ANALYSIS IS
THEREFORE THE REDUCTION OF DATA
THROUGH CODING AND CATEGORIZATION
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
8. Qualitative Data Analysis Data
reduction
CODING IS THE ANALYTIC PROCESS THROUGH WHICH
THE QUALITATIVE DATA THAT YOU HAVE GATHERED
ARE REDUCED, REARRANGED, AND INTEGRATED TO
FORM THEORY
THE PURPOSE OF CODING IS TO HELP YOU TO
DRAW MEANINGFUL CONCLUSIONS ABOUT
THE DATA
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
9. Qualitative Data Analysis Data
reduction
CODING IS OFTEN AN ITERATIVE PROCESS, YOU MAY
HAVE TO RETURN TO YOUR DATA REPEATEDLY TO
INCREASE YOUR UNDERSTANDING OF THE DATA
CODING BEGINS WITH SELECTING THE
CODING UNIT. INDEED, QUALITATIVE DATA
CAN BE ANALYZED AT MANY LEVELS
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
berulang
10. Qualitative Data Analysis Data
reduction
EXAMPLES OF CODING UNITS INCLUDE
WORDS, SENTENCES, PARAGRAPHS, AND
THEMES
THE SMALLEST UNIT THAT IS GENERALLY
USED IS THE WORD
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
11. Qualitative Data Analysis Data
reduction
A LARGER, AND OFTEN MORE USEFUL, UNIT OF
CONTENT ANALYSIS IS THE THEME : “A SINGLE
ASSERTION ABOUT A SUBJECT” (Kassarjian, 1977, p.12)
WHEN YOU ARE USING THE THEME AS A CODING
UNIT, YOU ARE PRIMARILY LOOKING FOR THE
EXPRESSION OF AN IDEA (MINICHIELLO ET. AL, 1990)
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
forceful statement
12. Qualitative Data Analysis Data
reduction
AFTER THE MEAL I ASKED FOR THE CHECK. THE WAITRESS
NODDED AND I EXPECTED TO GET THE CHECK. AFTER THREE
CIGARETTES THERE WAS STILL NO CHECK. I LOOKED AROUND
AND SAW THAT THE WAITRESS WAS HAVING A LIVELY
CONVERSATION WITH THE BARTENDER
THE CRITICAL INCIDENT CONTAINS TWO THEMES :
1. THE WAITRESS DOES NOT PROVIDE SERVICE AT THE TIME
PROMISES TO :
”THE WAITRESS NODDED AND I EXPECTED TO GET THE CHECK.
AFTER THREE CIGARETTES THERE WAS STILL NO CHECK”
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
13. Qualitative Data Analysis Data
reduction
AFTER THE MEAL I ASKED FOR THE CHECK. THE WAITRESS
NODDED AND I EXPECTED TO GET THE CHECK. AFTER THREE
CIGARETTES THERE WAS STILL NO CHECK. I LOOKED AROUND
AND SAW THAT THE WAITRESS WAS HAVING A LIVELY
CONVERSATION WITH THE BARTENDER
THE CRITICAL INCIDENT CONTAINS TWO THEMES :
2. THE WAITRESS PAYS LITTLE ATTENTION TO THE CUSTOMER :
SHE IS NOT LATE BECAUSE SHE IS VERY BUSY; INSTEAD OF
BRINGING THE CHECK, SHE IS ENGAGED IN A LIVELY
CONVERSATION WITH THE BARTENDER
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
14. Qualitative Data Analysis Data
reduction
ACCORDINGLY, THE AFOREMENTIONED CRITICAL
INCIDENT WAS CODED AS “DELIVERY PROMISES”
(THAT WAS BROKEN) AND “PERSONAL
ATTENTION” (THAT WAS NOT PROVIDED)
THIS EXAMPLE ILLUSTRATES HOW THE CODES
“DELIVER PROMISES” AND “PERSONAL
ATTENTION” HELP TO REDUCE THE DATA TO A
MORE MANAGEABLE AMOUNT
NOTE THAT PROPER CODING
NOT ONLY INVOLVES
REDUCING THE DATA BUT
ALSO MAKING SURE THAT NO
RELEVANT DATA ARE
ELIMINATED
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
16. Qualitative Data Analysis
Creating concept,
propositions, and
mini-theory
SEE THE EXPLANATION FROM FILE
“PENELITIAN KUALITATIF MITOSIS-BISNIS”
17. Qualitative Data Analysis
Validity and
reliability in
qualitative
research
IT IS IMPORTANT THAT THE CONCLUSIONS
THAT YOU HAVE DRAWN ARE VERIFIED IN
ONE WAY OR ANOTHER
THAT IS, YOU MUST MAKE SURE THAT THE
CONCLUSIONS THAT YOU DERIVE FROM YOUR
QUALITATIVE DATA ARE PLAUSIBLE, RELIABLE,
AND VALID
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
seeming reasonable
18. Qualitative Data Analysis
Validity and
reliability in
qualitative
research
RELIABILITY IN QUALITATIVE DATA ANALYSIS
INCLUDES CATEGORY AND INTERJUDGE
RELIABILITY
RELIABILITY AND VALIDITY HAVE A SLIGHTLY
DIFFERENT MEANING IN QUALITATIVE RESEARCH
IN COMPARISON TO QUANTITATIVE RESEARCH
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
19. Qualitative Data Analysis
Validity and
reliability in
qualitative
research
CATEGORY RELIABILITY “DEPENDS ON THE
ANALYST’S ABILITY TO FORMULATE CATEGORIES
AND PRESENT TO COMPETENT JUDGES
DEFINITIONS OF THE CATEGORIES SO THEY WILL
AGREE ON WHICH TERMS OF A CERTAIN
POPULATION BELONG IN A CATEGORY AND WHICH
DO NOT (Kassarjian, 1977, p.14)
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
20. Qualitative Data Analysis
Validity and
reliability in
qualitative
research
THUS, CATEGORY RELIABILITY RELATES TO THE
EXTENT TO WHICH JUDGES ARE ABLE TO USE
CATEGORY DEFINITIONS TO CLASSIFY THE
QUALITATIVE DATA
WELL-DEFINED CATEGORIES WILL LEAD TO
HIGHER CATEGORY RELIABILITY AND EVENTUALLY
TO HIGHER INTERJUDGE RELIABILITY (Kassarjian,
1977)
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
21. Qualitative Data Analysis
Validity and
reliability in
qualitative
research
HOWEVER, CATEGORIES THAT ARE DEFINED IN A
VERY BROAD MANNER WILL ALSO LEAD TO HIGHER
CATEGORY RELIABILITY
THIS CAN LEAD TO THE OVERSIMPLIFICATION OF
CATEGORIES, WHICH REDUCES THE RELEVANCE OF
THE RESEARCH FINDINGS
For instance, McKellar (1949) in an attempt to classify instigation of anger
distinguished between NEED SITUATION and PERSONALITY SITUATION.
NEED SITUATION were defined as “any interference with the pursuit of a
personal goal” such as missing a bus
PERSONALITY SITUATION included the imposition of physical or mental pain
or the violation of personal values, status, and possession.
This classification, which focuses on whether an anger-provoking event can
be classified as a PERSONALITY SITUATION or a NEED SITUATION, will
undoubtedly lead high category and interjudge reliability, but it seems to be
too broad to be relevant to service firm management trying to avoid
customer anger.
Therefore, Kassarjian (1977) suggests that the researcher must find a
balance between category reliability and the relevance of categories
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
22. Qualitative Data Analysis
Validity and
reliability in
qualitative
research
INTERJUDGE RELIABILITY CAN BE DEFINED AS A
DEGREE OF CONSISTENCY BETWEEN CODERS
PROCESSING THE SAME DATA (Kassarjian, 1977)
A COMMONLY USED MEASURE OF INTERJUDGE
RELIABILITY IS THE PERCENTAGE OF CODING
AGREEMENTS OUT OF THE TOTAL NUMBER OF
CODING DECISIONS
AS A GENERAL GUIDELINE,
AGREEMENT RATES AT OR
ABOVE 80% ARE CONSIDERED TO
BE SATISFACTORY
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
23. Qualitative Data Analysis
Validity and
reliability in
qualitative
research
VALIDITY WAS DEFINED AS THE EXTENT TO WHICH
AN INSTRUMENT MEASURES WHAT IT PURPORTS
TO MEASURE
IT REFERS TO THE EXTENT TO WHICH THE RESEARCH RESULTS :
1. ACCURATELY REPRESENT THE COLLECTED DATA (INTERNAL
VALIDITY)
2. CAN BE GENERALIZED OR TRANFERRED TO OTHER CONTEXTS
OR SETTINGS (EXTERNAL VALIDITY)
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
The meaning or substance of something
24. Qualitative Data Analysis
Validity and
reliability in
qualitative
research
TWO METHODS THAT HAVE BEEN DEVELOPED TO
ACHIEVE VALIDITY IN QUALITATIVE RESEARCH ARE :
• SUPPORTING GENERALIZATIONS BY COUNT OF EVENT.
THIS CAN ADDRESS COMMON CONCERNS ABOUT THE REPORTING OF
QUALITATIVE DATA : THAT ANECDOTES SUPPORTING THE RESEARCHER’S
THEORY HAVE BEEN SELECTED, OR THAT TOO MUCH ATTENTION HAS
BEEN PAID TO A SMALL NUMBER OF EVENTS, AT THE EXPENSE OF MORE
COMMON ONES
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
25. Qualitative Data Analysis
Validity and
reliability in
qualitative
research
TWO METHODS THAT HAVE BEEN DEVELOPED TO
ACHIEVE VALIDITY IN QUALITATIVE RESEARCH ARE :
• ENSURING REPRESENTATIVENESS OF CASES AND THE
INCLUSION OF DEVIANT CASES (CASES THAT MAY CONTRADICT
YOUR THEORY). THE SELECTION OF DEVIANT CASES PROVIDES
A STRONG TEST OF YOUR THEORY
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
26. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
CONTENT
ANALYSIS
CONTENT ANALYSIS IS AN OBSERVATIONAL RESEARCH METHOD
THAT IS USED TO SYSTEMATICALLY EVALUATE THE SYMBOLIC
CONTENTS OF ALL FORM OF RECORDED COMMUNICATIONS
(KOLBE & BURNETT, 1991)
CONTENT ANALYSIS CAN BE USED TO ANALYZE
NEWSPAPERS, WEBSITES, ADVERTISEMENTS,
RECORDINGS OF INTERVIEWS, AND THE LIKE
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
27. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
CONTENT
ANALYSIS
THE METHOD OF CONTENT ANALYSIS ENABLES THE RESEARCHER
TO ANALYZE (LARGE AMOUNT OF) TEXTUAL INFORMATION AND
SYSTEMATICALLY IDENTIFY ITS PROPERTIES, SUCH AS THE
PRESENCE OF CERTAIN WORDS, CONCEPTS, CHARACTERS,
THEMES, OR SENTENCES
TO CONDUCT A CONTENT ANALYSIS ON A TEXT, THE
TEXT IS CODED INTO CATEGORIES AND THEN ANALYZED
USING CONCEPTUAL ANALYSIS OR RELATIONAL ANALYSIS
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
28. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
CONTENT
ANALYSIS
CONCEPTUAL ANALYSIS ESTABLISHES THE
EXISTANCE AND FREQUENCY OF CONCEPTS (SUCH
AS WORDS, THEMES, OR CHARACTERS) IN A TEXT
CONCEPTUAL ANALYSIS ANALYZES AND INTERPRETS
TEXT BY CODING THE TEXT INTO MANAGEABLE
CONTENT CATEGORIES
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
29. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
CONTENT
ANALYSIS
RELATIONAL ANALYSIS BUILDS ON CONCEPTUAL
ANALYSIS BY EXAMINING THE RELATIONSHIP
AMONG CONCEPT IN A TEXT
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
30. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
CONTENT
ANALYSIS
THE RESULTS OF CONCEPTUAL OR RELATIONAL ANALYSIS ARE
USED TO MAKE INFERENCES ABOUT THE MESSAGE WITHIN THE
TEXT, THE EFFECTS OF ENVIRONMENTAL VARIABLES ON
MESSAGE CONTENT, THE EFFECTS OF MESSAGES ON THE
RECEIVER , AND SO ON
ALONG THESE LINES, CONTENT ANALYSIS HAS BEEN USED TO ANALYZE
PRESS COVERAGE OF ELECTION CAMPAIGNS, TO ASSESS THE EFFECTS OF
THE CONTENT OF ADVERTISEMENTS ON CONSUMER BEHAVIOR, AND TO
PROVIDE A SYSTEMATIC OVERVIEW OF TOOLS THAT ONLINE MEDIA USE TO
ENCOURAGE INTERACTIVE COMMUNICATION PROCESSES
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
31. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
NARRATIVE
ANALYSIS
A NARRATIVE IS A STORY OR “AN ACCOUNT
INVOLVING THE NARRATION OF A SERIES OF
EVENTS IN A PLOTTED SEQUENCE WITH UNFOLDS
IN TIME” (DENSIN, 2000)
NARRATIVE ANALYSIS IS AN APPROACH THAT AIMS TO
ELICIT AND SCRUTINIZE THE STORIES WE TELL ABOUT
OURSELVES AND THEIR IMPLICATIONS FOR OUR LIVES
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
32. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
NARRATIVE
ANALYSIS
NARRATIVE DATA ARE OFTEN COLLECTED
VIA INTERVIEWS.
THESE INTERVIEWS ARE DESIGNED TO ENCOURAGE THE
PARTICIPANT TO DESCRIBE A CERTAIN INCIDENT IN THE
CONTEXT OF HIS OR HER LIFE HISTORY
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
33. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
NARRATIVE
ANALYSIS
NARRATIVE ANALYSIS HAS THUS BEEN USED TO STUDY
IMPULSIVE BUYING (ROOK, 1987), COSTOMERS’
RESPONSES TO ADVERTISEMENTS (MICK & BUHL, 1992),
AND RELATIONSHIPS BETWEEN SERVICE PROVIDERS AND
CONSUMERS (STERN, THOMSON & ARNOULD, 1998)
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
34. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
ANALYTIC
INDUCTION
ANALYTIC INDUCTION IS AN APPROACH TO QUALITATIVE DATA
ANALYSIS IN WHICH UNIVERSAL EXPLANATION OF PHENOMENA
ARE SOUGHT BY THE COLLECTION OF (QUALITATIVE) DATA
UNTIL NO CASES THAT ARE INCONSISTENT WITH A
HYPOTHETICAL EXPLANATION OF A PHENOMENON ARE FOUND
ANALYTIC INDUCTION STARTS WITH A (ROUGH)
DEFINITION OF A PROBLEM (“WHY DO PEOPLE USE
MARIJUANA” IS A FAMOUS EXAMPLE),
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
35. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
ANALYTIC
INDUCTION
ANALYTIC INDUCTION IS AN APPROACH TO QUALITATIVE DATA
ANALYSIS IN WHICH UNIVERSAL EXPLANATION OF PHENOMENA
ARE SOUGHT BY THE COLLECTION OF (QUALITATIVE) DATA
UNTIL NO CASES THAT ARE INCONSISTENT WITH A
HYPOTHETICAL EXPLANATION OF A PHENOMENON ARE FOUND
AND THEN PROCEEDS WITH THE EXAMINATION OF
CASES (e.g. THE COLLECTION OF DATA VIA IN-
DEPTH INTERVIEWS).
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
36. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
ANALYTIC
INDUCTION
ANALYTIC INDUCTION IS AN APPROACH TO QUALITATIVE DATA
ANALYSIS IN WHICH UNIVERSAL EXPLANATION OF PHENOMENA
ARE SOUGHT BY THE COLLECTION OF (QUALITATIVE) DATA
UNTIL NO CASES THAT ARE INCONSISTENT WITH A
HYPOTHETICAL EXPLANATION OF A PHENOMENON ARE FOUND
IF A CASE IS INCONSISTENT WITH THE
RESEARCHER’S HYPOTHESIS (e.g. “I USE
MARIJUANA FOR HEALTH REASONS”),
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
37. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
ANALYTIC
INDUCTION
ANALYTIC INDUCTION IS AN APPROACH TO QUALITATIVE DATA
ANALYSIS IN WHICH UNIVERSAL EXPLANATION OF PHENOMENA
ARE SOUGHT BY THE COLLECTION OF (QUALITATIVE) DATA
UNTIL NO CASES THAT ARE INCONSISTENT WITH A
HYPOTHETICAL EXPLANATION OF A PHENOMENON ARE FOUND
THE RESEARCHER EITHER REDEFINES THE
HYPOTHESIS OR EXCLUDES THE “DEVIANT” CASE
THAT DOES NOT CONFIRM THE HYPOTHESIS
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013
38. Qualitative Data Analysis
Some other methods
of gathering and
analyzing qualitative
data
ANALYTIC
INDUCTION
ANALYTIC INDUCTION IS AN APPROACH TO QUALITATIVE DATA
ANALYSIS IN WHICH UNIVERSAL EXPLANATION OF PHENOMENA
ARE SOUGHT BY THE COLLECTION OF (QUALITATIVE) DATA
UNTIL NO CASES THAT ARE INCONSISTENT WITH A
HYPOTHETICAL EXPLANATION OF A PHENOMENON ARE FOUND
ANALYTIC INDUCTION INVOLVES INDUCTIVE – RATHER THAN
DEDUCTIVE – REASONING, ALLOWING FOR THE MODIFICATION
OF A HYPOTHETICAL EXPLANATION FOR PHENOMENA
THROUGHOUT THE PROCESS OF DOING RESEARCH
Ref. : Sekaran,and Bougie. Research Methods for Business:
A Skill Building Approarch. 2013