• Qualitative analysis of interview data: A step-by-step guide

    The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *it surprises you; *the interviewee explicitly states that it is important; *you have ...

    published: 19 May 2013
  • Excel and Questionnaires: How to enter the data and create the charts

    This is a tutorial on how to enter the results of your questionnaires in Excel 2010. It then shows you how to create frequency tables (using the countif function not the frequency function). The next stage is creating charts.

    published: 14 Feb 2013
  • Keeping Track of Qualitative Research Data using Excel

    This screen cast demonstrates the use of Microsoft Excel to organize information for qualitative research.

    published: 07 Jul 2014
  • How to apply predictive analytics to customer data

    Empower your front line to make effective and informed decisions at point of contact during customer interactions. IBM predictive customer analytics solutions apply predictive analytics to your customer data to help you deliver enhanced capabilities to your front line. Find out more about what IBM predictive customer analytics solutions can do for your business by visiting https://www.ibm.com/analytics/us/en/business/predictive-customer-intelligence/. Subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_center?add_user=ibmbigdata

    published: 19 Apr 2016
  • SPSS Questionnaire/Survey Data Entry - Part 1

    How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.) Survey data Survey data entry Questionnaire data entry Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, biv...

    published: 21 Apr 2014
  • Part 1 - Using Excel for Open-ended Question Data Analysis

    Completing data analysis on open-ended questions using Excel. For analyzing multiple responses to an open-ended question see Part 2: https://youtu.be/J_whxIVjNiY Note: Selecting "HD" in the video settings (click on the "gear" icon) makes it easier to view the data entries

    published: 22 Mar 2013
  • Southern States Unlocks More Customer Insight without Coding

    Southern States were looking to understand what they could do as an organization to create more loyal customers. Alteryx enables them to build predictive models and explore their data with drag-and-drop tools instead of coding." "...most people don't need to be writing code here in the 21st century, especially people who are integrated into the business."

    published: 15 May 2013
  • Introduction to Data Science with R - Data Analysis Part 1

    Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc

    published: 09 Nov 2014
  • How to analyze satisfaction survey data in Excel

    Purchase the spreadsheet (formulas included!) that's used in this tutorial for $5: https://gum.co/satisfactionsurvey

    published: 13 Dec 2012
  • How To Create A Simple Database In Excel (VBA)

    How To Create A Simple Database In Excel (VBA)

    published: 26 Mar 2015
  • Erasure Coding for SUSE Enterprise Storage

    Erasure Coding for SUSE Enterprise Storage This ChalkTalk gives you a brief overview of how Erasure Coding works and why it’s important to your customers. Vivid imagery walks you through the opportunity: Erasure Coding as the next generation of RAID that gives customers redundancy far beyond RAID’s one- or two-failure model...but at a fraction of the price. With erasure coding, customers can finely tune a balance between the cost of storing data against the need for data resiliency and availability, based on each business’s unique needs.

    published: 12 Nov 2015
  • Creating a web database application in 5 minutes using AppGini

    This screen cast will guide you through using AppGini to create a database web-application from scratch. In a few minutes, we'll create a simple product catalog, set it up and see it in action. 00:07 If you are looking for a way to make a user-friendly, fast and feature-rich web application in a very short time, that's when AppGini comes handy. This tutorial shows you how to create a web application in 5 minutes using AppGini. 00:21 First we'll start a new project. Let's give it a name: in our example we'll name it "product catalog". 00:31 Then we'll add two tables one for "categories" and another one for "items". 00:37 You can assign an icon for each table by clicking on the box at the left of the table name. 00:48 Next, we'll add fields to each table. Each table MUST contain a prima...

    published: 19 Jan 2015
  • Final Year Project Toolkit: Collecting Data -- Quantitative Coding

    This session has been developed through the Learning from WOeRK project at Plymouth University and seeks to support learning in the work place. For an overview of all related modules and resources please visit http://cpdoer.net/collections/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.0 UK: England & Wales License

    published: 29 Sep 2011
  • Data Science Demo - Customer Churn Analysis

    This introduction to Data Science provides a demonstration of analyzing customer data to predict churn using the R programming language. MetaScale walks through the stops necessary to train and test multiple algorithms in order to provide the most accurate model for predicting when a customer will leave the company.

    published: 27 Sep 2015
  • Enter data and define variables in SPSS

    How to define variables and enter data into SPSS (v20) ASK SPSS Tutorial Series

    published: 12 Aug 2013
  • SPSS Tutorial: Entering and coding Likert-scale data

    This video shows how one can input Likert-scale data, such as from surveys and questionnaires, into SPSS as well as how to code this data for statistical analysis.

    published: 27 Jul 2014
Qualitative analysis of interview data: A step-by-step guide

Qualitative analysis of interview data: A step-by-step guide

  • Order:
  • Duration: 6:51
  • Updated: 19 May 2013
  • views: 426532
videos
The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *it surprises you; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. 3.10. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark This tutorial showed how to focus on segments in the transcripts and how to put codes together and create categories. However, it is important to remember that it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Good luck with your study. Text and video (including audio) © Kent Löfgren, Sweden
https://wn.com/Qualitative_Analysis_Of_Interview_Data_A_Step_By_Step_Guide
Excel and Questionnaires: How to enter the data and create the charts

Excel and Questionnaires: How to enter the data and create the charts

  • Order:
  • Duration: 14:37
  • Updated: 14 Feb 2013
  • views: 246468
videos
This is a tutorial on how to enter the results of your questionnaires in Excel 2010. It then shows you how to create frequency tables (using the countif function not the frequency function). The next stage is creating charts.
https://wn.com/Excel_And_Questionnaires_How_To_Enter_The_Data_And_Create_The_Charts
Keeping Track of  Qualitative Research Data using Excel

Keeping Track of Qualitative Research Data using Excel

  • Order:
  • Duration: 10:33
  • Updated: 07 Jul 2014
  • views: 16815
videos
This screen cast demonstrates the use of Microsoft Excel to organize information for qualitative research.
https://wn.com/Keeping_Track_Of_Qualitative_Research_Data_Using_Excel
How to apply predictive analytics to customer data

How to apply predictive analytics to customer data

  • Order:
  • Duration: 3:35
  • Updated: 19 Apr 2016
  • views: 1886
videos
Empower your front line to make effective and informed decisions at point of contact during customer interactions. IBM predictive customer analytics solutions apply predictive analytics to your customer data to help you deliver enhanced capabilities to your front line. Find out more about what IBM predictive customer analytics solutions can do for your business by visiting https://www.ibm.com/analytics/us/en/business/predictive-customer-intelligence/. Subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_center?add_user=ibmbigdata
https://wn.com/How_To_Apply_Predictive_Analytics_To_Customer_Data
SPSS Questionnaire/Survey Data Entry - Part 1

SPSS Questionnaire/Survey Data Entry - Part 1

  • Order:
  • Duration: 4:27
  • Updated: 21 Apr 2014
  • views: 232559
videos
How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.) Survey data Survey data entry Questionnaire data entry Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor Video Transcript: In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.
https://wn.com/Spss_Questionnaire_Survey_Data_Entry_Part_1
Part 1 - Using Excel for Open-ended Question Data Analysis

Part 1 - Using Excel for Open-ended Question Data Analysis

  • Order:
  • Duration: 14:02
  • Updated: 22 Mar 2013
  • views: 107086
videos
Completing data analysis on open-ended questions using Excel. For analyzing multiple responses to an open-ended question see Part 2: https://youtu.be/J_whxIVjNiY Note: Selecting "HD" in the video settings (click on the "gear" icon) makes it easier to view the data entries
https://wn.com/Part_1_Using_Excel_For_Open_Ended_Question_Data_Analysis
Southern States Unlocks More Customer Insight without Coding

Southern States Unlocks More Customer Insight without Coding

  • Order:
  • Duration: 1:31
  • Updated: 15 May 2013
  • views: 307
videos
Southern States were looking to understand what they could do as an organization to create more loyal customers. Alteryx enables them to build predictive models and explore their data with drag-and-drop tools instead of coding." "...most people don't need to be writing code here in the 21st century, especially people who are integrated into the business."
https://wn.com/Southern_States_Unlocks_More_Customer_Insight_Without_Coding
Introduction to Data Science with R - Data Analysis Part 1

Introduction to Data Science with R - Data Analysis Part 1

  • Order:
  • Duration: 1:21:50
  • Updated: 09 Nov 2014
  • views: 467573
videos
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
https://wn.com/Introduction_To_Data_Science_With_R_Data_Analysis_Part_1
How to analyze satisfaction survey data in Excel

How to analyze satisfaction survey data in Excel

  • Order:
  • Duration: 4:17
  • Updated: 13 Dec 2012
  • views: 250429
videos
Purchase the spreadsheet (formulas included!) that's used in this tutorial for $5: https://gum.co/satisfactionsurvey
https://wn.com/How_To_Analyze_Satisfaction_Survey_Data_In_Excel
How To Create A Simple Database In Excel (VBA)

How To Create A Simple Database In Excel (VBA)

  • Order:
  • Duration: 31:06
  • Updated: 26 Mar 2015
  • views: 585073
videos
How To Create A Simple Database In Excel (VBA)
https://wn.com/How_To_Create_A_Simple_Database_In_Excel_(Vba)
Erasure Coding for SUSE Enterprise Storage

Erasure Coding for SUSE Enterprise Storage

  • Order:
  • Duration: 2:33
  • Updated: 12 Nov 2015
  • views: 1053
videos
Erasure Coding for SUSE Enterprise Storage This ChalkTalk gives you a brief overview of how Erasure Coding works and why it’s important to your customers. Vivid imagery walks you through the opportunity: Erasure Coding as the next generation of RAID that gives customers redundancy far beyond RAID’s one- or two-failure model...but at a fraction of the price. With erasure coding, customers can finely tune a balance between the cost of storing data against the need for data resiliency and availability, based on each business’s unique needs.
https://wn.com/Erasure_Coding_For_Suse_Enterprise_Storage
Creating a web database application in 5 minutes using AppGini

Creating a web database application in 5 minutes using AppGini

  • Order:
  • Duration: 5:26
  • Updated: 19 Jan 2015
  • views: 103980
videos
This screen cast will guide you through using AppGini to create a database web-application from scratch. In a few minutes, we'll create a simple product catalog, set it up and see it in action. 00:07 If you are looking for a way to make a user-friendly, fast and feature-rich web application in a very short time, that's when AppGini comes handy. This tutorial shows you how to create a web application in 5 minutes using AppGini. 00:21 First we'll start a new project. Let's give it a name: in our example we'll name it "product catalog". 00:31 Then we'll add two tables one for "categories" and another one for "items". 00:37 You can assign an icon for each table by clicking on the box at the left of the table name. 00:48 Next, we'll add fields to each table. Each table MUST contain a primary key field. If you name a field "ID", it will automatically become a primary key. You can add as many fields and tables as you need. 01:16 Now, let us try out some of AppGini's features. 01:18 If we set a field as a look-up, it would be displayed to users as a drop-down, listing data from another table. This makes your web application more organized. We'll set the "category" field as a look-up field simply by clicking on the "look-up field" tab, selecting "category" as the "parent table" then "category name" as the "parent caption field". 01:40 In addition, if you want to list items under each category, you'll need to configure the "categories" table by opening the "parent/children settings", select the "items" table, check "enabled", "show icon" and optionally rename the "tab title" to a more descriptive one. 01:57 All you need to do now is to generate your web application by clicking the magic stick icon. Choose your application path and AppGini will do the job for you. 02:14 These are the files generated by AppGini. 02:20 Let us visit our web application and see this in action. 02:37 This is the setup data page. Once we are done filling it, we start using our application. 02:52 As you can see, both tables appear on the first page. Let's go to the "Categories" table and enter some data. Here you can see the table fields. 02:59 To start adding a new record simply click "Add new". You will automatically be directed to the Data entry page, also known as Detail view form. 03:11 We'll enter Dresses and T-shirts as an example. Simply press "back" to see how the table looks like. 03:22 Once we are done, we jump to "items" table and start adding our data for this table as well. 03:43 Do you see how the "category" field is displayed? It's a drop down menu, because we set it up as a look-up field in AppGini. The drop down lists the categories that we entered a moment ago in the "categories" table. 03:59 AppGini offers special features for fields with immutable data like "size" and "target groups". 04:07 This is how the "items" table looks like after adding our data. 04:12 Now let us go back to AppGini to see how to make use of its features. 04:16 To create a drop-down list, click on the field name, then on "options list", and type all values you need, separating options with double semicolon. You can choose among three options for how to display your options list. In our case we will choose the "drop-down list". 04:36 We'll perform the same steps for the "target group" field, but this time we'll choose "radio buttons" instead of "drop-down list". 04:48 Once we generate, we get back to our web application, refresh the page and start adding a new record. 05:04 So this way our "size" field became a drop-down field and our "target group" field became a radio buttons field. 05:24 Finally, this is how our table looks like. To get to know more about AppGini, please visit our homepage for more video tours.
https://wn.com/Creating_A_Web_Database_Application_In_5_Minutes_Using_Appgini
Final Year Project Toolkit: Collecting Data -- Quantitative Coding

Final Year Project Toolkit: Collecting Data -- Quantitative Coding

  • Order:
  • Duration: 32:58
  • Updated: 29 Sep 2011
  • views: 389
videos
This session has been developed through the Learning from WOeRK project at Plymouth University and seeks to support learning in the work place. For an overview of all related modules and resources please visit http://cpdoer.net/collections/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.0 UK: England & Wales License
https://wn.com/Final_Year_Project_Toolkit_Collecting_Data_Quantitative_Coding
Data Science Demo - Customer Churn Analysis

Data Science Demo - Customer Churn Analysis

  • Order:
  • Duration: 9:30
  • Updated: 27 Sep 2015
  • views: 11330
videos
This introduction to Data Science provides a demonstration of analyzing customer data to predict churn using the R programming language. MetaScale walks through the stops necessary to train and test multiple algorithms in order to provide the most accurate model for predicting when a customer will leave the company.
https://wn.com/Data_Science_Demo_Customer_Churn_Analysis
Enter data and define variables in SPSS

Enter data and define variables in SPSS

  • Order:
  • Duration: 8:02
  • Updated: 12 Aug 2013
  • views: 230574
videos
How to define variables and enter data into SPSS (v20) ASK SPSS Tutorial Series
https://wn.com/Enter_Data_And_Define_Variables_In_Spss
SPSS Tutorial: Entering and coding Likert-scale data

SPSS Tutorial: Entering and coding Likert-scale data

  • Order:
  • Duration: 9:23
  • Updated: 27 Jul 2014
  • views: 40056
videos
This video shows how one can input Likert-scale data, such as from surveys and questionnaires, into SPSS as well as how to code this data for statistical analysis.
https://wn.com/Spss_Tutorial_Entering_And_Coding_Likert_Scale_Data
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