MS Excel - Statistical Functions
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Lecture By: Mr. Pavan Lalwani, Tutorials Point India Private Limited

Views: 78532
Tutorials Point (India) Pvt. Ltd.

This video shows how to graph the Probability Density Function and the Cumulative Density Function of normal random variables.

Views: 32421
Darryl Morrell

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:09) Overview of % Cumulative Frequency
2. (00:42) Formulas to create Cumulative Frequency Distribution and % Cumulative Frequency Distribution.
3. (04:17) % Cumulative Frequency (Ogive) Chart using X-Y Scatter Chart
4. (07:36) PivotTable to create Frequency Distribution and % Cumulative Frequency Distribution.
5. (10:30) Combine Chart that shows Frequency Distribution as a Column Chart with No Gap width and % Cumulative Frequency as a Line.
6. (11:43) Custom Number Formatting to hide all values using three semi-colons

Views: 46499
ExcelIsFun

Activity 3.7 Statistical Analysis with Excel

Views: 177
Jeff Ransford

www.ozanozcan.us

Views: 282576
ozanteaching

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:11) Discussion about Binominal Experiments and Probability Distributions
2. (11:00)Example 1: Experiment is attempting four sales calls. This example show how to create the Binomial Distribution in Table and Column Chart. This example show the 5 possibilities for comparative operators while using the BINOM.DIST function.
3. (24:10) Example 2: Experiment concerns late airline flights. This example show the 5 possibilities for comparative operators while using the BINOM.DIST function.
4. (29:47) Example 3: Experiment concerns West Seattle Bridge “Stuck in Traffic”.
5. (31:39)Example 4: Experiment concerns Student’s probability of withdrawing from Accounting 202. This example show how to use two BINOM.DIST function to calculate probabilities between two values.
6. (35:24) As number of trials get large, distribution approaches a Bell Shape. As the probability approaches 0.5, the distribution approaches a Bell Shape.

Views: 12284
ExcelIsFun

This is a short demonstration of how to analysis data in Microsoft Excel. We will manually create some basic descriptive statistics (including average, median, standard deviation, minimum, maximum and count), as well as using the Analysis Toolpak to generate the same (plus more).
00:50 Inserting rows
01:28 Using Ctrl + Shift + Down Arrow
02:00 Absolute and relative referencing
02:00 Manually creating descriptive statistics
02:50 Function help
04:15 Using Analysis Toolpak to generate descriptive statistics
The example data used in this video is available from: http://bit.ly/1cZVGE3
This video is a collaboration between drdavebond and Dr Paul F. Burke of the UTS Business School.

Views: 4153
drdavebond

Learn day wise excel formula pdf tutorial
visit www.selfadhyan.in
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Self Adhyan Gurujj
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Self Adhyan Guruji

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:12) Review Handwritten PDF Notes about Probability, Random Events, Sample Points, Sample Space, Count Rule For Size of Sample Space, Listing all Sample Points with Tree Diagram and Table, Methods of Probability such as Classical, Relative Frequency and Subjective, Requirements for Probability, Events and Probability of Events
2. (14:30) In Excel: Experiment of Flipping Coin Three Times: Find all Sample Points, Calculate Probabilities and practice finding Probabilities of Events by listing all the sample points and then adding all the probabilities for each Sample Point to get the Probability of the Event
3. (21:09) Frequency Distribution Built from Sample Space in order to find Probabilities for Events
4. (23:46) In Excel: Experiment of Rolling Two Die: Find all Sample Points, Calculate Probabilities and practice finding Probabilities of Events by listing all the sample points and then adding all the probabilities for each Sample Point to get the Probability of the Event

Views: 15917
ExcelIsFun

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:24) Look at Data Set that exhibits the characteristics of a Poisson Experiment and compare it to the results of the POISSON.DIST to helps us understand why when can use this function to make probability predictions.
2. (05:57) Chart a POISSON Distribution with a column chart
3. (09:16) Discussion of POISSON experiment and POISSON.DIST Discrete Probability Distribution
4. (12:23) Use POISSON.DIST for arrivals at Dick’s Hamburger Restaurant
5. (17:14) Use POISSON.DIST for arrivals at an Accounting Tutoring Center
6. (18:20) Use POISSON.DIST for water line breaks in a 100 Mile stretch of pipe

Views: 8690
ExcelIsFun

An introduction to exploratory data analysis that includes discussion of descriptive statistics, graphs, outliers, and robust statistics.

Views: 35263
Prof. Patrick Meyer

Predict who survives the Titanic disaster using Excel.
Logistic regression allows us to predict a categorical outcome using categorical and numeric data. For example, we might want to decide which college alumni will agree to make a donation based on their age, gender, graduation date, and prior history of donating. Or we might want to predict whether or not a loan will default based on credit score, purpose of the loan, geographic location, marital status, and income. Logistic regression will allow us to use the information we have to predict the likelihood of the event we're interested in. Linear Regression helps us answer the question, "What value should we expect?" while logistic regression tells us "How likely is it?"
Given a set of inputs, a logistic regression equation will return a value between 0 and 1, representing the probability that the event will occur. Based on that probability, we might then choose to either take or not take a particular action. For example, we might decide that if the likelihood that an alumni will donate is below 5%, then we're not going to ask them for a donation. Or if the probability of default on a loan is above 20%, then we might refuse to issue a loan or offer it at a higher interest rate.
How we choose the cutoff depends on a cost-benefit analysis. For example, even if there is only a 10% chance of an alumni donating, but the call only takes two minutes and the average donation is 100 dollars, it is probably worthwhile to call.

Views: 191572
Data Analysis Videos

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:09) Overview of Frequency Distributions for Quantitative Variable
2. (02:02) Create Frequency Distribution with PivotTable for Grade Data where NUMBERS ARE DECIMALS (important distinction for grouping feature in a PivotTable)
3. (03:08) Grouping Feature in a PivotTable for creating Classes or Categories for a Decimal Quantitative Variable. Class that are created are 0-10, 10-20, 20-30, etc. Extensive Discussion about how to create classes or categories that are NOT Ambiguous.
4. (05:03) Upper Limit for Class/Category is Not Included when the numbers are Decimals.
5. (05:58) Aggregate Function for Number Values defaults to Count when you have Grouped Numbers in the Row area of the PivotTable.
6. (06:32) Double Click PivotTable to Extract Records that match the criteria from the Row area of the PivotTable
7. (09:16) Use Find and Replace feature to create non-ambiguous labels in a Grouped Decimal Number PivotTable.
8. (10:20) Create Histogram for Quantitative Variable (Grouped Numbers) for Grade Data. This Histogram has Frequencies at the top of each column and the gap width is zero. The colors for each column are different.
9. (13:13) Create Frequency Distribution with PivotTable for Grade Data where numbers are WHOLE NUMBERS (important distinction for grouping feature in a PivotTable)
10. (14:33) Methods for determining Number of Classes and Class Width for a Quantitative Variable
11. (18:19) When grouping Whole Numbers in a PivotTable the classes that are created are not ambiguous. We get classes like: 16-22, 23-29, 30-36. Etc.
12. (20:07) Create Histogram for Quantitative Variable (Grouped Numbers) for Age Data. This Histogram has Frequencies in the vertical axis and the gap width is zero. The colors for each column are the same.
13. (22:00) Discussion about Skew, Histogram shape and Histogram distribution of column heights.
14. (25:37) Relative Frequency and Percent Frequency Distribution built with a PivotTable based on Age Data that is shown as a Whole Number.
15. (27:48) Formulas
16. (27:44) Create Frequency Distribution with Formulas for Grade Data.
17. (30:03) Text Formulas for Category Labels
18. (30:40) COUNTIFS function with Comparative Operators Joined to Lower and Upper Limits from the Cells.
19. (33:17) Relative/Percent Frequency Formula.
20. (34:00) Create Histogram for Grade Data based on Frequency Distribution created with formulas.
21. (35:56) See that we can change the categories be more precise when we use formulas.
22. (38:20) Link Data Labels in Chart to cells in the spreadsheet
23. (39:16) See how formulas allow Frequency Distribution Formulas and Histogram Chart update automatically when raw data change. See different grade distributions with Histogram.
24. (40:45) Summary

Views: 165297
ExcelIsFun

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:12) What is the t Distribution and when do we have to use it? Create Confidence Intervals when Population Standard Deviation is not known
2. (04:13) Formulas for t distribution to build Confidence Intervals when Population Standard Deviation is not known. Math formulas and Excel functions
3. (05:21) Example 1: Printer Manufacturer Example to create Confidence Interval
4. (06:41) Printer Manufacturer Example: Calculate Sample Mean, Sample Standard Deviation, Sample Size, Degrees of Freedom, Standard Error, Alpha and Alpha/2
5. (08:16) Printer Manufacturer Example: T.INV function to calculate t
6. (09:24)Printer Manufacturer Example: CONFIDENCE.T function to calculate Margin of Error
7. (11:38)Printer Manufacturer Example: Data Analysis Descriptive Statistics, Summary Statistics, Confidence Interval for Mean
8. (14:57) Restaurant Rating Example: All 3 Methods: 1) T.INV function, 2) CONFIDENCE.T function, 3) Data Analysis Descriptive Statistics

Views: 6630
ExcelIsFun

Learn day wise excel formula pdf tutorial
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Self Adhyan Guruji

Finding the arithmetic mean in Excel

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Opeyemi Ajayi

Garrett Ranking Method used to rank the set of variable in the statistical analysis

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My Easy Statistics

Amir H. Ghaseminejad explains an example for designing a process consistant with six sigma strategy.

Views: 135817
Amir H. Ghaseminejad

How to perform a simple t-test in Microsoft Excel

Views: 1253365
Jim Grange

WATCH PART 2: https://www.youtube.com/watch?v=g530cnFfk8Y
Download file used in the video: http://www.excelcampus.com/pivot-table-checklist-yt
In this video series you will learn how to create an interactive dashboard using Pivot Tables and Pivot Charts.
Works with Excel 2003, 2007, 2010, 2013 for Windows & Excel 2011 for Mac
Don't worry if you have never created a Pivot Table before, I cover the basics of formatting your source data and creating your first Pivot Table as well. You will also get to see an add-in I developed named PivotPal that makes it easier to work with some aspects of Pivot Tables.
Download the files to follow along at the following link.
http://www.excelcampus.com/pivot-table-checklist-yt
I have another video that shows how to reformat the pivot chart in Excel 2010. In the video above I'm using Excel 2013 and the menus are different from Excel 2007/2010. Here is the link to that video. http://www.youtube.com/watch?v=Jt_QqG-vRRw
Get PivotPal: http://www.excelcampus.com/pivotpal
Free webinar on The 5 Secrets to Understanding Pivot Tables: https://www.excelcampus.com/pivot-webinar-yt
Subscribe to my free newsletter: http://www.excelcampus.com/newsletter

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Excel Campus - Jon

This video shows how to conduct a one-sample hypothesis t-test for the mean in Microsoft Excel using the built-in Data Analysis (from raw data).
How to load Data Analysis in Excel: https://youtu.be/SqpSwxJ9t2k

Views: 108351
Joshua Emmanuel

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:43) Categorical Data vs. Quantitative Data
2. (02:00) Scales of Measurement (Levels of Measurement): Nominal, Ordinal, Interval, Ratio
3. (14:42) Cross Sectional Data vs. Time Series Data
4. (15:48) Graphical Display of types of Data
5. (16:22) How to Enter Data into the spreadsheet and use the Auto Complete (Auto Text) to your benefit
6. (18:50) How to create a new Excel Workbook to do your Homework from the Textbook

Views: 35568
ExcelIsFun

http://j.mp/21A6CCa

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Jocelyn Collins

Use simple data analysis techniques in SPSS to analyze survey questions.

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Claus Ebster

In this video, I will show you how to import PDF files into IDEA. This is the same concept for all report files.

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SAF Business Analytics

Our Excel training videos on YouTube cover formulas, functions and VBA. Useful for beginners as well as advanced learners. New upload every Thursday.
For details you can visit our website:
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Often many statistical documnets are avaiable to us in PDF format. The PDF file format has many advantages:
1. Adobe Acrobat Reader is free and you can view the PDF files on any computer as long as you have this reader.
2. PDF files often take less space on the computer
3. Viewing PDF files in different magnifications makes it more readable
4. Nowadays it's easy to convert a word document into a PDF file using MS Word
5. Actually PDF has become the format of choice for distributed documents online
However, if you wish to perform calculations and analysis on the numerical data in a PDF file you can either retype the data (time consuming) or use some interesting free or paid tools to convert the data into Excel.
You can subscribe at the following for a paid conversion: https://www.acrobat.com/exportpdf/en/convert-pdf-to-excel.html
$19.99/year
This website http://www.zamzar.com/convert/pdf-to-xls/ offers free PDF to XLS conversion.
Another tool you can try is available at convertpdftoexcel.net.
Get the book Excel 2016 Power Programming with VBA: http://amzn.to/2kDP35V
If you are from India you can get this book here: http://amzn.to/2jzJGqU

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Dinesh Kumar Takyar

[Here is my XLS @ http://trtl.bz/2AgvfRo] A function is a viable probability function if it has a valid CDF (i.e., is bounded by zero and one) which is the integral of the probability density function (pdf). The inverse CDF (aka, quantile function) returns the quantile associated with a probability, q = F^(-1)(p), whereas the CDF returns the probability associated with a quantile: p = F(q).
As mentioned, the =POISSON_INV(.) is not built into excel. Rather, it is available from the wonderful and *free* add-on published by Charles Zaiontz and available at http://www.real-statistics.com
Discuss this video here in our FRM forum: https://trtl.bz/2O7chQg

Views: 16949
Bionic Turtle

http://thedoctoraljourney.com/ This tutorial demonstrates how to conduct a One Way ANOVA in SPSS.
For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.

Views: 473980
The Doctoral Journey

Data Analytics for Beginners -Introduction to Data Analytics
https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm_medium=VM&utm_source=youtube
Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial.
Here are the topics covered in this training video:
• Data Analysis and Interpretation
• Why do I need an Analysis Plan?
• Key components of a Data Analysis Plan
• Analyzing and Interpreting Quantitative Data
• Analyzing Survey Data
• What is Business Analytics?
• Application and Industry facts
• Importance of Business analytics
• Types of Analytics & examples
• Data for Business Analytics
• Understanding Data Types
• Categorical Variables
• Data Coding
• Coding Systems
• Coding, coding tip
• Data Cleaning
• Univariate Data Analysis
• Statistics Describing a continuous variable distribution
• Standard deviation
• Distribution and percentiles
• Analysis of categorical data
• Observed Vs Expected Distribution
• Identifying and solving business use cases
• Recognizing, defining, structuring and analyzing the problem
• Interpreting results and making the decision
• Case Study
Get started with Data Analytics with this tutorial. Happy Learning
For more updates on courses and tips follow us on:
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LinkedIn: https://www.linkedin.com/company/acadgild

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ACADGILD

Powerful statistical analysis tools are available in the Excel is add-in data analysis package. This is an example of generating a randomly sampled Poisson distribution (1000 values) and plotting the frequency of the result.

Views: 8008
APMonitor.com

This video is about how to create 43 Statistical Functions with Examples in MS Excel Office Spreadsheet 2016 - Part 3.
00:05 - Steyx formula with example
00:54 - Trend formula with example
02:20 - Trimmean formula with example
03:34 - Vara formula with example
04:54 - Varpa formula with example
06:16 - Slope formula with example
07:31 - Small formula with example
09:10 - Standardize formula with example
10:23 - Stdeva formula with example
11:37 - Stdevpa formula with example
12:53 - Skew.p formula with example
14:04 - Skew formula with example
15:18 - Rsq formula with example
16:30 - Prob formula with example
17:48 - Phi formula with example
18:47 - Permutationa formula with example
20:12 - Permut formula with example
21:30 - Pearson formula with example
22:31 - Mina formula with example
24:00 - Min formula with example
25:09 - Median formula with example
26:51 - Maxa formula with example
28:24 - Max formula with example
29:39 - Logest formula with example
30:33 - Linest formula with example
32:04 - Large formula with example
33:17 - Kurt formula with example
34:45 - Intercept formula with example
36:09 - Harmean formula with example
37:25 - Growth formula with example
38:45 - Geomean formula with example
40:07 - Gauss formula with example
40:54 - Gammaln.precise formula with example
41:50 - Gammaln formula with example
42:49 - Gamma formula with example
43:40 - Frequency formula with example
45:51 - Forecast.linear formula with example
47:39 - Forecast.ets.stat formula with example
49:07 - Forecast.ets.seasonality formula with example
50:24 - Forecast.ets.confint formula with example
51:41 - Forecast.ets formula with example
52:53 - fisherinv formula with example
53:48 - fisher formula with example
This is Do It Yourself (DIY) free online ms office excel spreadsheet tutorial without voice instruction.
Excel Analytics Tutorial : https://www.youtube.com/playlist?list=PLZnHzQfaP-kOY6XVgxq5KX94m9dvCp4I5
Excel Charts for Data Visualization : https://www.youtube.com/playlist?list=PLZnHzQfaP-kOfeUdxNXPmIgAIlvr5qWJu
Creative Excel Graphs : https://www.youtube.com/playlist?list=PLZnHzQfaP-kOnddZhSPMWRbhv2pbL0CO2
Fast Track 30 Days Excel Course : https://www.youtube.com/playlist?list=PLZnHzQfaP-kOQCzq7fXw-lNOtmDrlifAZ
450+ Excel Formula Examples : https://www.youtube.com/playlist?list=PLZnHzQfaP-kPCFREvK67qsro3KIwoThVS
Difference between Formulas : https://www.youtube.com/playlist?list=PLZnHzQfaP-kPAMQruqKZ0IAUye7lWn90J
MIS Report & Dashboard : https://www.youtube.com/playlist?list=PLZnHzQfaP-kOWD8qL60zxfh6ksMpZTg2D
Basic Level Excel Interview Test : https://www.youtube.com/playlist?list=PLZnHzQfaP-kP8moHPW31fno6Z1LFMAKNr
Intermediate Level Excel Interview Test : https://www.youtube.com/playlist?list=PLZnHzQfaP-kPHlAIsHczOHO-02RoDI5Z9
Advance Level Excel Interview Test : https://www.youtube.com/playlist?list=PLZnHzQfaP-kMA9eKZTzDNjPnHpxkgu7Ag
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Excel (A-Z)

Updated video 2018: SPSS for Beginners - Introduction https://youtu.be/_zFBUfZEBWQ
This video provides an introduction to SPSS/PASW. It shows how to navigate between Data View and Variable View, and shows how to modify properties of variables.

Views: 1571349
Research By Design

Get my free 3 hour Introduction to Excel 2013 course. Get 17 training videos to help you learn MS Excel 2013. Click here to get the free course: https://simonsezit.leadpages.net/free-excel-2013-course/e/
In this video, discover how to use various statistical functions available in Microsoft Excel 2013.
Get the full course on Excel 2013 course here: http://www.simonsezit.com/courses/microsoft/learn-microsoft-excel-2013-advanced-training-tutorials/
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Hello again and welcome back to our course on Excel 2013 Advanced. In this section I’m going to start to look at the statistical functions in Excel 2013. I’m not going to assume that you’re a statistician and have a deep and broad knowledge of statistics, but most people have enough exposure to statistics in everyday life nowadays to understand the kinds of terms and terminology that I’m going to use.
Microsoft Excel has been used in statistics and by statisticians for quite a few years now and it’s actually a pretty strong tool. As we’ll see during this section and later on in the course, not only can we do some very useful numeric work but we can also use some of the charting and graphing features of Excel 2013 to look at statistical questions and statistical issues. There are some specialist pieces of software available to do a lot of these sort of statistical number crunching that you might need but the advantage of Excel, of course, is that it does many other things as well and many more people have copies of Microsoft Excel. So if you’ve got a particular statistical problem to solve or a piece of work to do, the chances are that Excel can help you to solve that problem or do that job for you if it can’t then you probably will need to look at one of the more specialized products.
Now for the purposes of this course I’ve divided the application of the statistical functions in Excel 2013 into three groups, and those three groups are: description, prediction, and inference. The first one we’re going to look at is description and by that I mean we take some statistics, in our case, a set of numbers and we look at ways of describing that set of numbers. So we’re really just looking at fact finding. We’re saying what are the features of this set of numbers that will help us, for instance, understand what the numbers tell us or maybe even compare them with other similar or related sets of numbers? I’m then going to look at prediction and in particular at regression and how we can use some existing measurements to predict what might happen in the future.
The third category in relation to using Excel 2013 in statistics is inference.
By inference what we mean is the ability to infer something about a population by measuring a sample from that population. So typically we’re talking about things like doing opinion polls, maybe taking 10 or 100 or 1,000 people, getting their opinion, measuring some feature, and from that measurement inferring something about the population. Now this is an area that requires quite a bit more statistical knowledge so I’m not going to go into it in great detail but I’ll look at one or two examples of how Excel can help with inferential statistics.
Now I’m looking at the list of statistical functions in Excel Help here and one of the things you’ll notice straightaway is that it is a very, very long list of function. It’s one of the areas of Excel where the most functions are available. And if you’re not a statistician, many of those won’t really mean a lot to you. I’m not going to worry too much about those very detailed statistical functions, the ones that involve quite a lot of technical knowledge. I’m going to look at the more straightforward ones in this section. But I would like to point something out. If you’ve used an earlier version of Excel and if you’ve used statistical functions before, many of those functions have been superseded in Excel 2010 and Excel 2013 with functions of a similar name but with extensions. For instance, the quartile functions that I’ll look at a little bit later QUARTILE.EXC and QUARTILE.INC, for example.
Sorry, we couldn't fit the entire video transcription here since YouTube only allows 5000 characters.

Views: 60941
Simon Sez IT

This video is a review of the normal density function and its key properties. Subscribe to our channel or visit our website for more financial risk videos! http://www.bionicturtle.com

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Bionic Turtle

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;
*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. 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
Nb: 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.
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