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  • THE OPEN QUESTION on the forum right now is on KT AnalysisThis is part of our TWO QUESTIONS PER WEEK initiative. One question is launched on Tuesday and the other on Friday, both at 5 PM IST. Best answers are recognized well on this most active Lean Six Sigma forum. (Benchmark Six Sigma Forum ranks first on Google Search based on popularity) 

     

    The most recent answered question along with the best response(s) can be seen here - Thematic AnalysisAll questions can be seen here. The entire Dictionary of Business Excellence terms is here.

     

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  • ALL TIME GB TOP SCORERS

     

    Name

    Score (%)

    City/Year

     
     

    Purvi Gupta

    100

    Del 2019

     

    Bhawana Sethi

    100

    Del 2015

     

    Adyan Prabhakaran

    100

    Hyd 2014

     

    Thirumoorthi.M

    99

    Chn 2019

     

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    99

    Mum 2019

     

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    99

    Kol 2017

     

    Vidula Valavalkar

    99

    Hyd 2014

     

    Vishal Tillu

    99

    Mum 2014

     

    Yashwanth J G

    99

    Bng 2013

     

    Jyothi Kanuri

    99

    Hyd 2013

     

    Vrajesh Parekh

    99

    Mum 2013

     

    Gnanasekaran D

    99

    Chn 2012

     

    Benoy Ramachandran

    99

    Chn 2012

     

     

    Muthu Naveen S

    99

    Mum 2012

     

    Ketan Trivedi

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    Mum 2012

     

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    Del 2011

     

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    Komal Bansal

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    Mum 2011

     

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    Mum 2011

     

    Ritik Gupta

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    Pun 2011

     

    Amit Kumar Makkar

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    Del 2010

     

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    Clarence Wong

    99

    Hyd 2010

     

    Devendra Singh Baghel

    99

    Hyd 2010

     

    Varun Hemrajani

    99

    Pun 2010

     
         

    Here is the complete list of all time Lean Six Sigma Green Belt Top Scorers

  • ALL TIME BB TOP SCORERS

     

    Name

    Score (%)

    City/Year

     
     

    Kunal Obhrai

    98

    Del 2019

     

    Mahesh P K

    98

    Bng 2017

     

    Balaji M

    97

    Bng 2017

     

    Rohit Arora

    97

    Bng 2017

     

    Amit Kumar Makkar

    97

    Del 2015

     

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    96

    Pun 2019

     

    Sunil M. Bhat

    96

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    Rohan Chavali

    96

    Del 2017

     

    Apoorve Arya

    96

    Mum 2014

     

    Sandeep P.R. 

    96

    Chn 2013

     

    Awojide Martins Olabisi

    95

    Mum 2020

     

    Zeshan Abubacker

    95

    Bng 2019

     

    Kumar Kaushal

    95

    Del 2019

     

    Vishal Kanojia

    95

    Hyd 2019

     

    Swati Malhotra

    95

    Mum 2019

     

    Nithin Sandhyala

    95

    Bng 2017

     

    Abhishek Arora

    95

    Del 2017

     

    Satishkumar Jain

    95

    Mum 2017

     

    Atirakshit Bhatt

    95

    Mum 2017

     

    Narendra Anil Murdeshwar

    95

    Pun 2017

     

    Rupinder Kaur Narang

    95

    Del 2016

     

    S Sujay Kumar

    95

    Mum 2016

     

    Kuljinder Kaur

    95

    Del 2015

     

    Vetrivendhan K P

    95

    Bng 2014

     

    Sunil Bissa

    95

    Chn 2013

     

    Mayank Gupta

    95

    Pun 2011

     

    Here is the complete list of all time Lean Six Sigma Black Belt Top Scorers

  • Posts

    • Qualitative Analysis usually takes a back seat while we work with numbers, however it is equally important.   To be honest, I was not expecting that there will be such wonderful answers to this question (and I strongly recommend reading all answers). It was a difficult choice to make, however, I still had to make a choice.   Answer from Rahul Arora has been selected as the winner for pointing out how Thematic Analysis can be utilized to understand the VOC to identify a potential project area.
    • Q 482. Explain the KT Analysis method of problem solving using an example. Does it provide any advantage over other problem solving approaches?   Note for website visitors - Two questions are asked every week on this platform. One on Tuesday and the other on Friday. All questions so far can be seen here - https://www.benchmarksixsigma.com/forum/lean-six-sigma-business-excellence-questions/ Please visit the forum home page at https://www.benchmarksixsigma.com/forum/ to respond to the latest question open till the next Tuesday/ Friday evening 5 PM as per Indian Standard Time. Questions launched on Tuesdays are open till Friday and questions launched on Friday are open till Tuesday.  When you respond to this question, your answer will not be visible till it is reviewed. Only non-plagiarised (plagiarism below 5-10%) responses will be approved. If you have doubts about plagiarism, please check your answer with a plagiarism checker tool like https://smallseotools.com/plagiarism-checker/ before submitting.  The best answer is always shown at the top among responses and the author finds honorable mention in our Business Excellence dictionary at https://www.benchmarksixsigma.com/forum/business-excellence-dictionary-glossary/ along with the related term
    • What is Thematic Analysis  Thematic analysis is commonly stated as the study of patterns of meaning. In other words, If you want to analyze the themes within your data set to identify meaning. It is the most common forms of analysis within qualitative research - to identify, analyze and interpreting patterns of meaning or themes within quality data.   What is Qualitative Data It is the data on which participants write descriptively and it can be obtained from questionnaires, interviews, focus groups, case studies, social media profiles, survey responses etc. - these data are generally non-numerical. And qualitative research methods have been used in various areas like, sociology, political science, psychology, educational research etc. Some samples of qualitative data      The hair was smooth and silky     The girls have brown, black, blonde, and red hair     The room was very airy and bright with white curtains      Different Approaches      · Inductive Approach: No pre-conceptions about themes,  instead we generating themes.     · Deductive Approach: Already have a set of themes that we expect to generate from the data.      · Semantic Approach: No need to understand the subjective meaning of the data.      · Latent Approach: Need to dive into the data and understand its meaning.      Steps To Do Thematic Analysis This is an iterative process which helps to go from messy data to the most important themes in the data. There are commonly used six steps developed by Braun and Clarke which can follow -      · Data familiarization : reading, re-reading, taking notes     · Initial codes generation: coding interesting feature across the entire data set     · Searching themes: collecting codes related to potential themes     · Reviewing themes : review and compare themes against data set     · Defining Themes: generating clear definitions and names for each theme     · Writing Up: the final analysis of selected extracts and producing a report of the analysis   Advantages and  Disadvantages  Advantages -      - It has a lot of flexibility in interpreting the data     - It helps to approach large data sets easily into broad themes by sorting them. Disadvantages -     - it involves the risk of missing nuances in the data     - It is often quite subjective     - Need to carefully reflect on your interpretations
    • In qualitative research, we can use thematic analysis to determine something about people's views, opinions, knowledge, experiences, or values from a set of qualitative data, such as interview transcripts, social media profiles, or survey responses. We can use thematic analysis to answer the following types of research questions: In a hospital setting, how do patients perceive doctors? In terms of climate change, what do non-experts think? What is the role of gender in high school history? The six steps Braun and Clarke develop can help us decide if the thematic analysis is right for you and how you will analyse our data. Step 1: Familiarization Familiarizing ourselves with our data is the first step. Getting an overview of all the data we collected is essential before we analyse individual items. Step 2: Coding Once the data is analysed, it needs to be coded. It is, in essence, the process of highlighting sections of a text, usually a phrase or a sentence, and creating shorthand labels for it. We can quickly gain an overview of the main points and common meanings that recur across the data by using these codes. Step 3: Generating themes Next, we review the codes we've created, identify patterns among them, and begin generating themes. Themes are more general than codes. In most cases, you'll combine several codes into one theme. As an example, we might combine the codes as follows: Again, what we decide will depend on what we are trying to discover. We are looking for themes that tell us something useful about the data for our purposes. Step 4: Reviewing themes We need to make sure our themes are useful and accurate representations of the data. In this step, we compare our themes with the actual data set.  For eg, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion. Step 5: Defining and naming themes Now that we have the final list of themes, it’s time to label and describe each of them. Defining themes will involve formulating precisely what we mean by each theme and figuring out how it helps us understand the data. Naming themes will involve coming up with a succinct and easily understandable name for each theme. Step 6: Writing up At last, we’ll write up our analysis of the data. A thematic analysis needs to begin with an introduction that establishes our research question, aims, and approach. We should also include a methodology section, describing how we collected the data and explaining how we conducted the analysis itself. The results or findings usually address each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.  
    • Thematic analysis is method used to identify patterns or clusters of related data. This method is used to analyse qualitative / non - numeric data. This type of analysis is predominantly performed on data collected through survey's.   Most of the organisations roll out employee survey to understand how satisfied and motivated the employees are working for the organisation.    In this example, org ABC rolled out a survey and the results were not really good. They decided to initiate a Six Sigma project to improve the ESAT score by X%. As the data collected is from the survey results and it's qualitative in nature, they use thematic analysis to identify patterns or clusters of related data, themes emerging from the respondents concerns, employees perceived problems etc.,     
    • What is Thematic Analysis, Thematic analysis is a study of patterns, a methodology used to analyse qualitative data (i.e. non-numerical data’s like audio or video, or audio) for understanding the opinions, experiences, or concepts. This analysis will be used to gather in-depth insights into a problem or to generate new ideas for research. There are 3 approaches/ways to do this analysis, they are, 1.     Inductive approach – This approach derives meaning and creating themes from data without any preconceptions. (Will do the analysis without any idea of what themes will emerge, hence the themes will be determined by the data)   2.     Deductive approach – In this approach, we start the analysis with a set of themes that we already expect to find from the data. (Will do this analysis after getting the knowledge from research or existing theory about the data)   3.     Semantic approach – In this approach, we ignore the underlying meaning of data, but will identify the themes based on what is openly stated or written. (This approach is taken when investigating opinions and viewpoints, as these tend to be understandable)   4.     Latent approach - This approach focuses on underlying meanings and relatively looks at the reasons for semantic content, which involves an element of interpretation, where data is not just engaged because of face value, but meanings are also theorized. Note – I personally prefer the Latent approach though we have the option of choosing any of these four as per the analysis requirement.   Application, This analysis is useful during an interview or transcripts or during psychological research to examine the data to identify the patterns of meaning that come up repeatedly. How to do this analysis, There are different approaches to conducting thematic analysis, the most familiar type is the  six-step process, Step 1 - Familiarization, In this step the analyser makes himself familiar with the data that needs to be analysed.  This may include reading and re-reading the whole data thus having an overview of its context and taking notes of it. Step 2 - Coding, In step 2, the analyser highlights or labels the keywords or group of keywords, or even the entire phrases in the data that indicate some meaning. This meaning will come in handy when the analyser trying to clutch the essence of the data. In this example - The survey questing is “How has social media changed over the years?”, and we are interviewing a person who is 40+ years old and working in a middle school.  And receiving the opinion, “I think these social media platforms such as YOUTUBE/FACEBOOK and LINKEDIN are not for the oldies anymore. Because the current trends are rapidly changing and evolving every day. Hence it becomes difficult for people like me to keep up with them. This difficulty makes us feel disconnected.” Further, we need to derive codes for the key phrases like – Quickly changing/ Uninterested/ Discomfort, etc., Step 3 - Generating themes, For the above-mentioned example, we can have a theme called “NOT SATISFIED” for the codes we derived from the interview mentioned. This step will give the analyser a brief idea about how many codes are being used repeatedly and which ones of them will be useful and which need to be discarded. Step 4 - Reviewing themes, In this step, the analyser compares the themes with the original data and looks for any missing points or irrelevant results, and may modify the themes by checking on how they satisfy and/or justify the data intended. Step 5 – Defining and naming themes, Next step, the analyser will do the naming for the themes depending on what they indicate and what we will understand from the data. Step 6 - Writing up, in the last step, using all the results we may conclude that social media has evolved so much that the elder generations find it difficult to understand and interact with which results in their dissatisfaction. This is the method I prefer and practice to regulate a perfect Thematic Analysis. Thanks
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