NVivo 10 Memos - University of Colorado Boulder 10 Memos Table of Contents Number Name Page QUERI...

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NVivo 10 Memos

Kristi Jackson

[email protected]

www.Quer.com

801 Pennsylvania, #205

Denver, CO 80203

(303) 832-9502

This packet of Memos is not intended to be a self-teaching guide, nor

does it explicitly discuss the full architecture of NVivo or the purpose

of all NVivo tools. As such, it is not likely to be helpful to anyone

who has not participated in guided activities pertaining to the various

tools.

The present materials are not necessarily organized according to the

order in which you might choose to use the software tools. Qualitative

research is an iterative process, researchers embark on different

courses of managing their data (depending on their specific qualitative

methodology, pragmatic issues regarding their data, their theoretical

approach, and their individual style), and two researchers might take

very different approaches to the same data set. Therefore, you should

think of these materials as reminders/notes that you might dip into at

different times/places than other researchers.

NVivo 10 Memos

Table of Contents

Number Name Page

QUERI Memo 1 Source Materials 1

QUERI Memo 2 Orientation to the Screen 4

QUERI Memo 3 Preparing and Importing Text Files 9

QUERI Memo 4 Web pages 16

QUERI Memo 5 Datasets, Facebook, LinkedIn and Twitter 19

QUERI Memo 6 Audio, Video and Photo Files 23

QUERI Memo 7 Nodes and Coding 29

QUERI Memo 8 Coding Stripes 37

QUERI Memo 9 Links 40

QUERI Memo 10 “Select Project Items” Window 51

QUERI Memo 11 Cases 52

QUERI Memo 12 Classifications, Attributes, and Values 57

QUERI Memo 13 Framework Matrices 69

QUERI Memo 14 Queries Overview 72

QUERI Memo 15 Text Search Queries 81

QUERI Memo 16 Matrix Coding Queries 86

QUERI Memo 17 Copying and Merging Projects 97

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Kristi Jackson [email protected]

QUERI 1 NVivo 10 Training Materials

QUERI Memo1: Source Materials

Understanding Sources

NVivo’s name for the bulk of the qualitative

material you will be working with

interpretively is “Source” material.

The four kinds of Source

materials in NVivo are:

Internals

Externals

Memos

Framework Matrices

“Navigation View”

As shown above, you can create subfolders (e.g. “Area and Township”) to further organize items

in these four existing folders. You will learn more about navigating around the screen in NVivo

in a subsequent Queri Memo, but the screen shot above is from the “Navigation View” in NVivo

to help you start familiarizing yourself with the interface.

An Internal in NVivo may be a document (an interview, focus group transcript, literature

review document, proposal, observational notes, or a pdf file), video, audio, photograph, a screen

captured from a web page (including hyperlinks, but excluding other web content such as Flash

plug-ins, embedded video, or audio), or the exchanges/posts from on-line social media

(Facebook, Twitter and Linkedin). An Internal may also be a bibliographic database that you

import from EndNote, RefWorks, or Zotero. Finally, you can even bring in a table of data from

Excel that has some qualitative fields you intend to code, as well as some quantitative (nominal,

ordinal, or interval value) fields that you may want to use to compare subpopulations (e.g. a

demographic field for “Gender”, or a field with Likert scale scores from a survey question). In

NVivo, this table-based file is known as a “Dataset.” So, in NVivo, a “Dataset” DOES NOT

pertain to ALL of your data, just the data in table-based Excel file that you choose to import into

your “Internals” folder. Not all projects will contain a “Dataset” – they might only contain

interview transcripts, video, and photographs, for instance.

A Note on “Datasets”

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QUERI NVivo 10 Training Materials 2

There is an additional potential point of confusion about “Datasets” that should perhaps

be noted at the outset, before you import such a table into your project. NVivo provides a

tool for comparing subpopulations in your project via a table of data such as demographic

values or Likert scale sores. In NVivo, this place where demographics are most

efficiently stored and managed is called a “Classification sheet.” There are a few ways of

creating a Classification Sheet in NVivo (to be discussed in detail later). One way is to

convert the demographic fields from a “Dataset” file (after this “Dataset” file has been

imported into the “Internals” folder of your project) into the columns of a Classification

Sheet. THERE ARE OTHER WAYS to get demographics and (other kinds of values)

into a Classification sheet without using a “Dataset.” In fact, if you have a table that

ONLY contains demographics (no qualitative fields), you can just import the Excel file as

a Classification Sheet and avoid using Datasets altogether. See the NVivo Help files

regarding Datasets and Classifications, or turn to the Queri Memo on Classifications for

more information.

The following data types may be imported into the Internals folder of project (Note that any

demographic data or any nominal, ordinal or interval values that you intend to use to compare

subgroups is handled in a different part of the project: Classifications)

Documents (interview transcripts, etc.

� Microsoft Word Document (docx)

� Microsoft Word 97 - 2003 Document (doc)

� Adobe PDF (pdf)

� Rich text (rtf)

� Plain text (txt)

Audio

� MPEG-1 Audio Layer 3 (mp3)

� MPEG-4 (m4a)

� Microsoft Windows Media (wma, wav)

Video

� MPEG file formats (mpg, mpeg, mpe, mp4)

� Windows Media (avi, wmv)

� QuickTime (mov, qt)

� 3GMobile Phone format (3gp)

� AVCHD high Definition Video formats (mts, m2ts)

Pictures

� Windows bitmap (bmp)

� Graphic Interchange Format (gif)

� Joint Photographics Expert Group (jpg, jpeg)

� Tagged Image File Format (tif, tiff)

Datasets

� Microsoft Excel workbook (xlsx)

� Microsoft Excel 97 - 2003 workbook (xls)

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� A database table or view—for example, a Microsoft Access or SQL Server

database table. Facebook, LinkedIn and Twitter data are imported into NVivo as

datasets.

� Comma or tab-delimited text files (.txt)

Web pages

� Collected with NVivo’s NCapture tool, which is installed as a web browser

extension your Internet Explorer (7 or later).

o Most web pages are handled inside NVivo the same way as other PDFs

o Facebook, Twitter, and LinkedIn data are automatically handled as

Dataset tables (see above).

Bibliographic records

Note that some items in the bibliographic record, such as the publication date) are located

outside of the “Internals” folder of a project.

� EndNote X2, X3, X4 or X5 (exported first as .xml before importing to NVivo)

� RefWorks (exported first as RIS before importing to NVivo)

� Zotero (exported first as RIS before importing to NVivo)

An External is any kind of file that cannot be imported into the project (such as a power

point presentation). The name “External” is used to describe these items, because this material is

stored outside of the NVivo database. These Externals need some place to be organized or

anchored in NVivo, so the Source area includes a folder for “Externals.” In NVivo, External files

either point to the digital file that exists somewhere else on your computer, or they refer to non-

digital data (such as a painting, a tractor, or a medical instrument) that exists outside of your

computer altogether.

A Memo is just like an Internal, from NVivo’s perspective (you may code it, include it in

your queries, add demographic values to it, etc.), but the software allows you to sort them

separately since they tend to have a different nuance in a project. While you can code your

Memos, just like you code your Internals, most researchers frequently want to look only in their

Memos or only in their Internals depending on the researcher inquiry at the time. The Memo

subfolder in NVivo makes this easy to do.

Framework Matrices provide a strategy for summarizing data in a table with columns

that represent themes and rows that represent cases.

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QUERI Memo 2: Orientation to the Screen

Understanding the Screen

To get grounded in the framework of the NVivo screen, open the sample data that accompanies

the software. Just launch NVivo and then choose the “Sample” project (after clicking on it, just

wait until it opens). This dataset is about perceptions among coastal residents in North Carolina

regarding the changes in their physical environment due to commerce, tourism, urban expansion,

etc. It also contains photographs of coastal areas and other data. Follow these brief activities so

you understand how the database is structured prior to working with your own data.

When you open an NVivo project that already has items imported and perhaps some coding as

well, you will see three main windows as you navigate into the data. These three windows

comprise the common architecture of the project, through which you will access most of the

tools. Once you learn these three windows you will be able to smoothly follow the Help File

instructions (and these QUERI Memos).

NAVIGATION VIEW

• The window on the very left hand side of

the screen is the NAVIGATION VIEW.

This column allows you to access the

major tools in the software (Sources,

Nodes, Sets, etc.).

• If you click on the button “Sources”

you’ll see the subfolders above the list of

buttons change.

• Now click on “Nodes” and you’ll see that

they change again.

• The subfolders that appear in the top part

of the screen are the various items

subsumed under that umbrella category.

• The buttons represent umbrella terms

• After opening a project you will see a lot

of icons in the tool bars at the top of the

screen. Remember that you can access

almost all of the tools by Right Clicking

on the item of interest.

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Working with the Screen

From the sample data, select “Sources” in the Navigation View. Then select the minus sign next

to “Internals.” Then click the plus sign. You will see a lot of plus/minus signs next to items in

NVivo. When you click a plus sign, the software “shows” you the items nested underneath and

when you click a minus sign, the software “hides” these items.

Now click on the subfolder for “Interviews.” You will see the list of Interviews appear. Don’t

worry – we’ll talk about how to prepare and import these later. For now, just orient yourself to

the architecture of where items are stored.

This entire window is called the LIST VIEW because it provides a list of the items contained in

the subfolder you just selected (in this instance, it’s the Interviews subfolder).

In the Navigation View, select the subfolder for “Memos” and watch the list change.

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Double-click on the yellow icon next to Charles (it’s usually better to use the icons than the

names of the items unless you actually want to change the names of the items). His interview

will open underneath the List View, in what is called the DETAIL VIEW

Taken altogether, these are the three main windows that allow you to navigate into the detail of

your data: Navigation View, List View, and Detail View.

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Most researchers prefer a slightly different configuration on their screen so that the Navigation,

List and Detail views are organized in three columns from left to right. To change this (and it

can be easily changed back) go to “View” from the Main Menu � Detail View � Right.

Detail View command

NAVIGATION VIEW LIST VIEW DETAIL VIEW

Move your mouse so your arrow hovers over the vertical blue line between the List View and the

Detail View until you get a double-headed arrow. Left click, and drag this line to the left. You

can resize the width of any of the three columns to suit your preference with these vertical blue

lines (there’s another one between the Navigation View and the List View).

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So What?

By remembering these three columns, you will be able to follow the help files in NVivo! Most

of the tools (the Buttons in the Navigation View) have this architecture of drilling down to

greater detail by starting in the Navigation View, selecting from the List View, and then being

able to work in the Detail View.

Note that if you double-click on Dorothy from the List View, her interview will open up in

Detail View. You now have two Tabs that you may use to bring either Charles or Dorothy to the

foreground.

Also note that if you select another button from the Navigation View, the items in the Detail

View will stay open, although the items in the List View change. This allows you to have many

different items open in Detail View (a Model, a Query, two Internals, and a Node, for instance)

as you create webs of connections and think about different items in the database. When the

number of Tabs feels cluttered, or you simply want to close a Tab because you are done

examining that item, just select the black X to the right of the Tab, and that item will close.

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QUERI Memo 3: Preparing and Importing Text Files

About Text Files

Text File types

The following text file types are supported (may be imported):

.doc & .docx (Word files) .txt (Text files) .rtf (Rich text files) .pdf (Portable data files)

If some (or all) of your qualitative data is organized in a table format in a program such as

Excel, you will learn how to import this data as a “Dataset” in NVivo. If all of your data is

organized in such a table, you should look at the NVivo help files for instructions, as the

formatting issues below will not apply to your data.

Languages

NVivo will accommodate text data in any language supported by Microsoft (e.g. Korean,

Turkish, Chinese) and can be used to employ text searches based on the characters of these

alphabets.

Working with Text Files

Document Description

The first paragraph of the file may serve as the “description” of the file. Items often found in

the description include: 1) Name of participant, 2) Name of researcher, 3) Date of collection,

4) Location of collection, . . . and any key demographics that help situate the data. This

description must be strung together in a paragraph, not as a list. It must also occur prior to

ANY HARD RETURN in the document, and should not exceed 511 characters (or it will be

truncated). The first paragraph of the file may serve as the “description” of the file. Items

often found in the description include: 1) Name of participant, 2) Name of researcher, 3)

Date of collection, 4) Location of collection, . . . and any key demographics that help situate

the data. This description must be strung together in a paragraph (a hard return tells NVivo

that you are done with your description). During document import, you will need to change

one of the defaults in order to tell NVivo to place this “description” in the file properties of

the NVivo Source.

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Color, Bold, Italics

Any color, bold, or italics in the original text file will be imported into NVivo.

• NVivo does not do anything “smart” with colors, italics, etc. For example, you

cannot ask NVivo to find the word “trust” but only if it is blue font; NVivo displays

blue font, but cannot sort by this format.

o The most common use of font color, bold, etc. is in the visual differentiation

between the interviewer speech and the respondent speech. This is a feature

that facilitates researcher thinking and interpretation; It is not a feature that

NVivo uses to sort data.

o Researchers using discourse analysis techniques, or Jeffersonian transcription

protocols may find this kind of formatting more helpful than researchers who

are using other approaches.

o Tip: If you were not using NVivo, but would choose to apply these visuals

within an MSWord file, then you will probably want to use these visuals in

NVivo as well. If you were not using NVivo and would not bother with these

visuals within an MSWord file, then you probably will not want to use these

visuals in NVivo.

Heading Levels

In MSWord, you may use “Styles” to add “Heading Levels” in a file. If you do not know

what Heading levels are in MSWord, search the Microsoft help for “Add a heading”. IT IS

VERY IMPORTANT THAT YOU UNDERSTAND a “heading level” is more than a font

color or size, it is a special tag inside the file that establishes a way to “pre-code” the text

document.

Heading 1

Heading 2

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Heading levels are often used to differentiate speakers and/or to differentiate questions from

the interview guide. After importing a file formatted such as the one above, you can use a

tool in NVivo called “Auto Code” that will create, for instance, a single code with all of the

answers to question 1, and another for question 2 (etc.). Or, it can create a code with all of

the text from Linda and a code with all of the text from Charles. These can be used to sort or

filter the data during analysis.

Other Formatting Issues

The following formatting issues should also be considered:

• Tables may be embedded in the text file

o Text may be coded within a cell

o Note that coded text from a table appears in the node (the code) at the entire

“Row” level, and therefore very large rows can become cumbersome in the

output. To see a visual of this issue, import a table with a row that has more

than 10 lines of text, and only code one line from this row to a node (coding is

covered later). Open the node to see the visual display of the coding from this

cell. Tips for managing this issue:

� Import tables with relatively short rows.

� Never import a document that is two columns by one row.

� Avoid a document that is one long table.

� Use tables sparingly.

• Photographs, Clip art (and other embedded objects) will appear in the text file inside

NVivo, but it is always a good idea to pilot a file before preparing all of them!

• Lines, arrows, shapes, headers, footers, line numbering and page numbers are ignored

• Bulleted lists will import and text boxes will import, but text boxes can only be coded

in their entirety (you cannot code just a portion of the text from a text box into one of

your thematic piles [known as “Nodes” in NVivo. You’ll learn more about Nodes in

a subsequent Memo.

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Working with Your Project

If you haven’t done so already, you should create your own project. First, close the sample data

by selecting File from the menu � Close project.

From the “Welcome Window” in NVivo, select the button that says “New Project”

Then give your project a Title (and a description if you wish).

Note that you can use the “Browse” button to place your project in a specific folder on your

computer. IT IS HIGHLY RECOMMENDED THAT YOU DO NOT PLACE YOUR

PROJECT ON A SERVER OR A FLASH DRIVE. WORK OFF OF YOUR C DRIVE AND

WHEN NECESSARY, PLACE BACKUP COPIES OF YOUR PROJECT ONTO A SERVER

OR FLASH DRIVE. DO NOT OPEN A PROJECT WHILE IT IS ON AN EXTERNAL

LOCATION UNLESS YOU ARE USING THE AUXILLIARY PROGRAM, “NVIVO

SERVRER.” Additional information about backing up, saving, moving, and merging projects

while working on your C Drive is in the final Queri Memo of this packet.

The “Welcome Window” has a list of your most recently opened projects, but not necessarily

every project on your computer. Select “Open Project” to look for additional projects.

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Working with Text File Import

Files must be closed before they can be imported.

• From the Navigation View select “Sources,” and then select the Source subfolder

named “Internals.” If this is the first Source you are importing, your List View will be

empty. Right Click in this large empty white space on your screen (you must be

active in this location to import).

• You may also import directly into a subfolder that you create to organize your data.

• Right Click � Import. You may choose any of the categorized file types (Documents,

PDFs, Audio, Video, etc.).

o If you choose any of these options (Documents, Audio, Video, etc.), you will

only be able to import data supported for that type of file (e.g. if you select

“Audio” you will only be able to import .mp3, .m4a, .wma or .wmv files

during this import), and then you will return to these steps again for other file

types.

o If you want to import several different file types at once, select Import

Internals, and then choose one of the available options (e.g. “Import

Documents”) � Browse and then in the next screen go to the bottom of the

window, and choose “Supported Files” from the drop-down arrow. You can

now import multiple file types at the same time.

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• If you formatted your transcripts so that the first paragraph represents a description of

the file, select the “More” button after choosing the files you want to import

• Then check the “Use first paragraph to create description” box

• Don’t worry if you forget to check this box – you can add descriptions later, but if

you have over 15 files, it is more efficient to do this here.

• Notice there is also a check box about coding sources at new nodes. There are at

least seven ways to start managing your “units of analysis” (such as people, who will

have the attribute values of male, female, transgendered, etc.). You will learn about

these in a subsequent Queri Memo, so we will leave this box unchecked as you are

first learning the software.

• Then click “OK”

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• On the top right hand of your screen, just below the red X, you will see a question

mark that will give you access to the NVivo Help

Consider exploring a few items that have already been covered to obtain a different

way of explaining the software and to begin familiarizing yourself with NVivo.

Examples of items to search for include: Sources, Customize the workspace, and

Crete a new project.

So What?

Because of the many considerations regarding the preparation of the data, you may want to

pilot things first. Instead of preparing all 40 of your files, prepare two (or even just a few

pages of two files). Then import this data and conduct some pilot coding and pilot searching.

You may change your mind about how you want to prepare the rest of your data after a

simple pilot.

Because NVivo sorts information in Nodes by the alphabetical listing of the Source files, you

might use alpha/numeric characters (at the beginning of my files names) that represent the

most important categories in the data that you will want to compare. This varies by study.

• For instance, in a study of twenty communities, with 4 different surveys, among a range

of types of people in the community, with 4 researchers in the field. The name of a file

might be “a3d2” where: a = Mapleton; 3 = survey #3; d = Director of the program; 2 =

Interviewer #2

• This way, all data is organized first at the community level (all files from Mapleton were

in the first part of the list of Sources), and so on.

• Of course, you can use attributes later to more specifically compare subpopulations in the

data set, but with the above naming convention you will ALWAYS be looking at the data

by community -- which can move you more efficiently through the analysis.

• Tips:

o Write a list of the most important subcategories of files in the data set (time could be

a factor, for instance)

o Place pseudonyms, e.g. “Laura” at the end of the file name, rather than the beginning.

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QUERI Memo 4: Web pages

About Web Pages NCapture takes web pages and converts them into PDF files so they can be imported into NVivo.

If you want to work with content from a web page, you will need to install NCapture, a free

browser extension for Internet Explorer (7 or later), provided by QSR and available as in install

when you install NVivo. Most of the web content (text, images, and hyperlinks) is collected into

a file that you can later import into NVivo. Other types of content from the web page are not

captured (Flash plug-ins, embedded video or audios, etc.). While you capture the page you can

also record ideas and identify nodes that you want to use to code the entire page.

Working with Web Pages

Installing NCapture

If you are unsure whether you installed NCapture, the tool in Internet Explorer that collects web

data and allows it to be easily imported into NVivo, do one of the following:

• In Internet Explorer 8 and 9, click Tools and then click Manage Add-ons.

• In Internet Explorer 7, click the Tools button, point to Manage Add-ons, and then click

Enable or Disable Add-ons.

If NCapture for NVivo is included in the list, it is installed. If not, go to the QSR web site to

download and install.

Showing NCapture in the Command bar of Internet Explorer

When you open Internet Explorer you should see the NCapture button in the toolbar

If you don’t see NCapture, you simply have so many commands on the toolbar that it is hidden.

To show NCapture:

• In Internet Explorer 8 and 9, right-click the Command bar, point to Customize, and then

click Add or Remove Commands.

• In Internet Explorer 7, right-click the Command bar, point to Customize Command Bar,

and then click Add or Remove Commands.

Click the NCapture button in the Current toolbar buttons list, and then click Move Up to bring

the NCapture button to (or near to) the top of the list.

If you are using Internet Explorer 9 and the Command bar has been hidden, you will need to

show the command bar (Refer to Internet Explorer help for more information)

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Capture a web page

• Before you capture a web page, you should create a destination folder on your computer

that will be used to store the captured material (unless you want to use the default folder

created for you in the “My Documents” folder called “NCapture.”

• While you are on a web page, click the NCapture button at the top of the toolbar.

• In this window you can rename the source, add a description if desired, and start writing a

memo that will be linked to the PDF after it is imported.

• You can also type in the name of any node (new or existing at the top level of your Nodes

folder) that you would like to code the entire page to.

• Use the Browse button to choose a location, and then select “Capture.”

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Import a web page

• From the Navigation View select “Sources,” and then select the Source subfolder named

“Internals.” (You may also import directly into a subfolder that you create to organize

your data.)

• If this is the first Source you are importing, your List View will be empty. Right Click in

this large empty white space on your screen (you must be active in this location to

import).

• Right Click � Import � Import from NCapture

• Use the “Browse” button to locate the folder where you stored the captured data, and

choose the three radio buttons to see the list of available captures.

• Select the items you want to import � Import

• You may code the source just as you’d code any PDF document.

So What? • The file is imported and is identified with the same symbol as a PDF document.

o If you want to easily differentiate the web pages from other PDF files (such as

journal articles), it would be wise to either:

� Place the web data in a separate folder inside NVivo

� Give the source a name that distinguishes it from other files (for

example, place a W at the front of the file name). Once you identify

such a convention, you can add it during import if you wish.

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QUERI Memo 5: Datasets, Facebook, LinkedIn and Twitter

About Datasets A Dataset is a table of data that contains both qualitative and quantitative fields, and is often the

result of a download from an on-line survey, such as Survey Monkey. In addition, NCapture (a

browser add-on for Internet Explorer [7 or later]) can collect Facebook, LinkedIn or Twitter data

for you and in doing so it converts the material into a Dataset.

Working with Datasets While your Dataset table contains all of the information for each Case in your project, you’ll

need to work through a few steps to convert it into components that NVivo can use effectively.

While it may seem counter-intuitive at first to “break apart” the Dataset, you’ll be converting it

into the same structure as other types of data in NVivo, and in the end this makes the various

types of data more compatible throughout the analytical process.

In this Memo you will learn how to:

• Prepare an Excel file as a Dataset

• Import a Dataset

• Code the qualitative data in a Dataset

In addition, you may want to manipulate the Dataset in other ways, and these are found in other

Memos in this packet. You might need to:

• Create a Case Node (or Unit of Analysis) for each row, and each Node will contain the

qualitative data for that Case (Queri Memo 11: Cases)

• Create a Classification type for these Nodes (Queri Memo 12: Classifications, Attributes

and Values)

• Add Attribute Values to your Cases from the Dataset (Queri Memo 12: Classifications,

Attributes and Values)

Prepare an Excel file as a Dataset

For each dataset that you want to create, your data must be gathered into one of the following:

• Microsoft Excel spreadsheet (.xlsx or .xls)

• Tab or comma delimited text file (.txt)

In addition:

• Each record must be on a separate line, and each field must be separated by the delimiter.

• You cannot select multiple worksheets (within a spreadsheet), so the data you want to

import must be gathered into a single worksheet.

• The maximum amount of data that can be imported into a single dataset is 256 fields

(columns) and 1,048,576 records (rows).

To create a Dataset in Excel:

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• Create a table with the Case identifier in Cell 1A (e.g. “Respondent”).

• Add the Attributes in the first row (A), as well as column names that identify the data that

is not a forced choice (e.g. the column below that was a ‘fill in the blank’: “The natural

environment Down East is . . .”).

• Fill in the table.

Respondent Township Income tied

to resources Pace of development

The natural environment Down East is

DE001 Straits no, never undecided beautiful

DE002 Harkers Island yes too fast good

DE003 Marshallberg no, but was too fast peaceful, beautiful serene -

DE004 Smyrna no, never too fast becoming poorer with pollution due to chemical runoff from local farms. It has ruined the fishing business.

• Save and close the table

Import a Dataset from Excel

Note: The steps for importing a Facebook, LinkedIn or Twitter file are slightly different. If

you are importing this kind of a table, see the NVivo help files on “import from Facebook,”

Import From LinkedIn,” and “Import from Twitter.

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“Internals.” If this is the first Source you are importing, your List View will be empty.

Right Click in this large empty white space on your screen (you must be active in this

location to import).

• You may also import directly into a subfolder that you create to organize your data.

• Right Click � Import� Import Dataset

o Step 1

� Use the Browse button to find the file � Open � Next

o Step 2

� Select the sheet that has the data in it (in case you have multiple sheets) �

Next

o Step 3

� This provides an opportunity for you to change any qualifiers (e.g. the date

format might not be in Four Digit Years), but this is usually not necessary.

o Step 4

� THIS IS A CRITICAL STEP. ALTHOUGH NVIVO IS USUALLY QUITE

FLEXIBLE, IT IS IMPERATIVE THAT YOU CHOOSE THE CORRECT

RADIO BUTTONS IN THIS SCREEN AS YOU CANNOT CHANGE

YOUR MIND ABOUT THEM AFTER YOU IMPORT THE DATASET!

� Click on any column and you will see that NVivo has done its best to choose

either the

• Codable Field (qualitative data) or

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• Classifying Field (quantitative; nominal/ordinal/interval values)

� Select each column and make sure the correct Analysis Type (Codable or

Classifying) has been chosen for each column.

o Step 5

� This window provides an opportunity to rename the file after it is imported.

Change it if you wish, but you can change this later after the Dataset is

imported if you wish � Finish.

Code the qualitative data in a Dataset

Data in codable fields can be coded using any of the strategies available to code text files

(drag-and-drop, etc.) See the QUERI Memo on Nodes and Coding for additional information.

So what? As with all other data types, it is a good idea to experiment with a subset of the data. If you

import a small amount, you can experiment with the various approaches to analyzing a Dataset.

Once you are confident that you have imported the data in a way that supports your analysis,

then you can import all the data, and begin coding.

Remember to consider the additional things you can do with Datasets in the following memos

• Create a Case Node (or Unit of Analysis) for each row, and each Node will contain the

qualitative data for that Case (Queri Memo 11: Cases)

• Create a Classification type for these Nodes (Queri Memo 12: Classifications, Attributes

and Values)

• Add Attribute Values to your Cases from the Dataset (Queri Memo 12: Classifications,

Attributes and Values)

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QUERI Memo 6: Audio, Video, and Photo Files

Understanding Media Files and Media Transcripts

Media File Types

The following media file types are supported (may be imported):

• Audio recordings in .mp3, .m4a, .wma, .wav

• Video recordings in .mpg, .mpeg, .mpe, .wmv, .avi, .mov, .qt, .mp4, .3gp, mts, .m2ts

• Pictures in .bmp, .gif, .jpg, .jpeg, .tif, or .tiff

Media File Locations

When working with an audio or video source you can choose to:

• Embed the media file in your NVivo project

• Store the media file externally

No matter where you choose to store the file, you can still code, annotate and work with the

source in the same way.

Benefits of Embedding Media Files

• Media files embedded in a project become part of the project—if you move your

project file (.nvp), the embedded media files move as well.

• Media files stored outside of your project are linked to the project but are not part of

it —if you send your project to a colleague you will need to include the media files

separately.

• Also, if you move linked media files to a new location (using the Windows file

system), you will need to 're-link' them in your project.

Benefits of Storing Media Files Externally

• Embedding large media files (over 20MB) may impact the performance of your

NVivo project

• If you store large media files externally, you can analyze as much media as your

computer can accommodate—without jeopardizing project performance

Working with Media Transcripts or Summaries

Transcripts or text summaries of media files are optional in NVivo.

If you will be working with transcripts, you should keep the following in mind:

• If you choose to add verbatim or summary text to the media files, you may “hide” or

“show” this text data while you are working with the media files.

• You may either prepare the transcript outside of the NVivo project and then import it, or

you may play audio and video files inside NVivo and transcribe as you watch or listen.

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• “Timespan” is the term in NVivo that refers to a transcript entry for a portion of audio or

video (e.g. the 45 seconds when interaction pertains to a teacher’s explanation of

classroom expectations).

o You can enter transcribed text or summary information for a timespan in either

table or paragraph format prior to importing it into the project.

o The transcript can be in Word (.doc), rich text (.rtf), text format (.txt) or Portable

Document Format (.pdf).

o NVivo measures timespan up to a tenth of a second, if desired.

o The separate timespan entries are based on Timestamps (e.g. 1:25:01), and may

be delimited through:

� Paragraph or Line breaks

� Table rows (though this option is not available in plain text documents

[.txt])

� Tab delimited text files

• Preparing Transcripts prior to importing them into NVivo

o The following sample displays the formatting for tables in MS Word: (see the

NVivo help for additional information and alternatives)

Timespan Content Speaker

1 0:00.1 - 0:02.9 What are your favorite places? Henry

2 0:02.9 - 0:13.3 Down East? I'd say xxx of course. That's where I always grew up, and that's where I live at. That...

Ken

3 0:13.3 - 0:15.3 Home Henry

4 0:15.3 - 0:17.2 Home. It's my home. I've been here all my life. Ken

5 0:17.2 - 0:23.9 This area...this particular spot in particular? This is a nice spot. It'd be easy to see...

Henry

6 0:23.9 - 0:32.3 Yeah, yeah. I've got to get good access to the water, luckily

'cause passed down from our family.

Ken

7 0:32.3 - 0:45.3 So, what about the natural environment or landscape Down East? Are there any things that you particularly value about the

environment here?

Henry

• If you would rather prepare transcripts inside NVivo while playing the audio or video

files, read the following section on importing the media files first, and then read the

section in this Memo on: “Working With Media and Media Transcripts Inside NVivo”

Working with Media File Import

Files must be closed before they can be imported.

• From the Navigation View select “Sources,” and then select the Source subfolder

named “Internals” (you must be active in this location to import).

• Right click in the empty space of the “List View”.

• Import �Audio/Video

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• Use the Browse button to select the files � OK

Working with Media and Media Transcripts Inside NVivo

Playing Audio and Video Files

When working in audio/video sources, you can use options on the Media menu to manage the

files.

• Open one of your Media Sources, and the Media Ribbon will also open at the top of the

screen.

You can select from three play mode options available with the GREEN “play” triangle on the

Media Toolbar:

• Normal

• Synchronize (as the video or audio plays, the transcript also scrolls)

• Transcribe (rows of a transcript are added as timespans are selected).

For more information on all of the options for playing media, see the NVivo Help files.

To Select a Section of a Media File

When working in audio/video sources you can select sections of the media file for transcribing,

playing, coding, linking, and annotating. To select a section of a media file you have several

options:

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Option 1 (if you know the time count you want to select): Click and drag the mouse over a

section of the timeline (creating the blue box outline from beginning to end)

Option 2: To select a section of a media file while the file is playing:

• Play the media file or drag the play-head (the blue oval) to the point where you want to

start selecting.

• On the Media toolbar click the Start Selection button

• Click the Finish Selection button when you want to stop the selection

• This will also create a blue box outline on your selected timespan

Importing Media Transcripts

• In NVivo, open the video or audio source that you want to import transcript entries into.

• Make sure it is in “Edit” mode by selecting the “Click to Edit” line if it is available

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• On the Media Tab, click Transcript Rows

The Import Transcript Entries dialog box is displayed.

• Click the Browse button on the “Import from” field.

• Locate and select the document containing the transcript table.

• Click Open.

• From the “Create one transcript row” for each drop down list, choose “Table row” if you

prepared your data in a table (other options are available – see the Help files)

• In the bottom window, “Transcript Field Mappings”, choose Timestamp, Content,

Speaker, etc. to match up the Import Data Field with the Transcript Field.

• Click OK.

Embedding or Linking to a Media File

See the first page of this Memo for a reminder about the benefits/disadvantages of Embedded

and Linked Media files.

To define whether a selected file is embedded or stored externally:

• In Navigation View, click the Internals folder.

• In List View, click the required source.

• Right click and select Audio/Video Properties. The Audio/Video Properties dialog box is

displayed.

• Click the Audio or Video tab.

• To embed the file in the project, click the “Embedded in project” option.

• To store the file externally, click “Not embedded – file location” and specify the location

using the Browse button.

• Click OK

So What?

In the QUERI Memo on Nodes and Coding you will learn more about how to flag portions of

Media Files or Media Transcripts with conceptual categories (Nodes).

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• You can code the audio or video directly by clicking and dragging the mouse over a

portion of the Timeline to select and code the required content.

• You can also code the text in the Content column of the transcript.

Although there are a variety of items that NVivo can import into a database, PILOT the file

import before preparing the entire data set.

Because NVivo sorts information in Nodes by the alphabetical listing of the Source files, you

might use alpha/numeric characters (at the beginning of my files names) that represent the

most important categories in the data that you will want to compare. This varies by study.

• For instance, in a study of twenty communities, with 4 different surveys, among a range

of types of people in the community, with 4 researchers in the field. The name of a file

might be “a3d2” where: a = Mapleton; 3 = survey #3; d = Director of the program; 2 =

Interviewer #2

• This way, all data is organized first at the community level (all files from Mapleton were

in the first part of the list of Sources), and so on.

• Of course, you can use attributes later to more specifically compare subpopulations in the

data set, but with the above naming convention you will ALWAYS be looking at the data

by community -- which can move you more efficiently through the analysis.

• Tips:

o Write a list of the most important subcategories of files in the data set (time could be

a factor, for instance)

o Place pseudonyms, e.g. “Laura” at the end of the file name, rather than the beginning.

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QUERI Memo 7: Nodes and Coding

Understanding Nodes and Coding • Coding is the process of bundling text into thematic piles. These bundles/piles can be

returned to for the purpose of focusing on an area and developing interpretations/reports.

The bundles can also be used in searches (divided by subpopulations in the data set – for

instance).

• You will notice the term for these bundles in NVivo is a “Node”. This term helps researchers

make a distinction between:

o A “Node” – the noun (the “bundle” or “pile” that contains data)

o A “Code” – the verb (the act of getting data into the “Node”)

o While this distinction between “Node” and “Code” may seem unnecessary, it can

help clarify the distinctions between structure and process.

• There are three primary strategies through which data can be placed in a Node, and within

each of these three strategies there are several technical options.

o Open Coding: Interpreting the meaning of a specific portion of text or media and

then placing this content in a Node. This Queri Memo focuses on Open Coding.

o Auto Coding: Using Heading Levels (see “Style options” in Microsoft Word for

additional information) to create Nodes. This is more of an administrative

process than an interpretive process. Instructions are provided later in this Memo.

o Queries: Search engine results that may be saved as Nodes by using the “Query

Options” tab. See the Memo on Text Queries for an example.

Working with Nodes and Coding

Creating a Node

• In the Navigation View go to Nodes.

• You may create subfolders for different types of nodes if you wish, by Right-Clicking on

the Node folder. However, Nodes can be easily moved around later (similar to moving

files around on your computer), so do not worry about getting it perfect from the start.

Right Click in the

empty space of the

List View and

choose New Node

In the subsequent

window provide a

name and a

description � OK

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Coding Text Via “Drag and Drop”

• Open the source or node you want to code in Detail View

• Open the required node folder to display the nodes in List View

• Select the content you want to code, let go with your mouse, return to the highlighted

portion with your cursor, and drag the reference to the desired node(s)

• The content is coded at the node. If you open the node (by double-clicking on it) you can

see coded content.

• There are several other ways to code text other than “drag and drop” (including the

“Quick Coder” toolbar at the bottom of the page, or using right click to drill into the

coding options). See the help files for more information.

Uncoding Data

• As you review a node, it is likely you will find some data that does not belong

• To uncode data from these nodes you have several options (Look in the Help files for

“uncode”)

• One option is provided below for uncoding data from within a node:

o Highlight the information you would like to uncode

o Right click and select “Uncode Selection”

� “Uncode Selection at This Node”

o You should see the node recalculating briefly, and then the undesired data will go

away (note that you have not deleted the text from the original file, you have

simply uncoded it from the current Node).

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• To uncode data from within a source, two options are provided below:

o Right click on the coding stripe that represents the data you want to uncode

and select “Uncode” OR

o Highlight the next you want to uncode, right click, “Uncode Selection” � “At

existing nodes”. (if this window is confusing, take a look at the Query Memo

on the ‘’Select Project Items’ Window”)

Auto coding Text Data

If you have heading levels inserted into your file (try this on the Sample data, by selecting some

of the Interviews):

• Right Click � Select “Auto Code.”

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o Select the Style you want to use to code, such as Heading 1 and use the

arrows in the middle of the two windows to push it to the “Selected

paragraph styles” window.

o If a parent Node already exists (e.g. a Node called “People”), leave the

“Under” window default at “Existing Node”. Otherwise, change it to “New

Node” and use the “Select” button on the bottom right to find this Node

o Provide a Name if you are creating a new Node.

o Select “OK”

o Go to “Nodes” in the Navigation View and look at the Nodes you created

under the specified Location.

• You may auto code other data types as well (Video transcripts, Datasets, etc.). See

the Help files for more information.

Coding Media

You may code the Media files vie the audio/video stream, or via the transcript

Drag and drop coding:

• You may drag a selected timespan from the timeline (the portion surrounded by a blue

box) to a Node, just as you drag text to a node.

• You may also drag a row to a node (but not portions of a row from inside a node via this

strategy. If you want to code portions of a row, see “another way to code a transcript”

below)

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So What? • This issue of how to code – the technical moves in the software that place data in Nodes – is

far less complicated than the issue of what to code, and why. While coding is a common

process, most of the literature on qualitative coding focuses on how to collect, format, and

interpret data, rather than how to make decisions about what data goes in which Nodes.

• Three guides on coding are summarized below

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Glaser, B. & Strauss, A. (1999). The Discovery of Grounded Theory. New York: Aldine de Gruyter.

1. Purpose of writing book

A. “our principal aim is to stimulate other theorists to codify and publish their own

methods for generating theory” (p. 8)

B. “discovering theory as a process” is a central theme of the book (p.9)

2. Grounded Theory

A. GT does not allow for “exampling” – providing examples for speculative theory (p. 5)

B. There is a middle ground between grounded and logico-deductive theorizing, by

choosing examples systematically and allowing them to feed back to give theoretical

control over formulations (p. 5)

C. Theory should predict, explain, & be relevant (p. 5)

D. “Generating a theory from data means that most hypotheses and concepts not only

come from the data, but are systematically worked out in relation to the data during

the course of the research “ (p. 6)

E. “It does not take a ‘genius’ to generate a useful grounded theory” (p. 11)

3. Constant Comparative Method (p. 101)

A. To develop theory, one must constantly redesign and reintegrate theoretical notions

with the material under study, “by using explicit coding and analytic procedures” (p.

102)

B. Theoretical sampling: The joint process of collecting, coding and analyzing data. The

initial decisions are not made on a preconceived theoretical framework, but the

process of data collection is progressively controlled by the emerging theory.

C. Four stages:

i. Comparing incidents applicable to each category: Coding each incident into as

many categories of analysis as possible. Compare data coded into categories with

previously coded data in the same categories. Stop coding, and write a memo.

ii. Integrating categories and their properties: “This process starts out in a small

way; memos and possible conferences are short. But as the coding continues, the

constant comparative units change from comparison of incident with incident to

comparison of incident with properties of the category that resulted from initial

comparisons of incidents.”

iii. Delimiting theory occurs at two levels

a The theory

b The categories

iv. Writing the theory

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Spradley, James A. (1979). The Ethnographic Interview. New York: Hold, Rinehart and Winston.

1. Domains: “Any symbolic category that includes other categories is a domain. All the

members of a domain share at least one feature of meaning. In the process of discovering

domains we look especially for the similarities that exist among folk terms. Domains are the

first and most important unit of analysis in ethnographic research.” (p.100)

A. Cover Terms: Names for a category of cultural knowledge. Friend, tree (and then

there are types of friends or types of trees)

B. All domains have two or more included terms (oak, pine, aspen)

C. All domains have a single semantic relationship. A Volvo “is a kind of” car. (the

semantic relationship is “is a kind of”)

D. Every domain has a boundary (no, this isn’t a tree, it’s a bush”).

E. Identifying and analyzing folk domains is one of the most difficult tasks faced by

ethnographers.

2. Procedures:

A. A preliminary search of folk domains can be name for things. (As above)

B. Next, we have domain analysis. Asking informants structural questions that confirm

or refute hypothesized domains.

C. Thirdly, we have structural questions.

D. Fourth, componential analysis.

3. For the domain analysis:

A. First, look at semantic relationships (most cultures only have two dozen or so).

B. They identify 13 types of semantic relationships in Papago.

C. Casagrande and Hale (1967) identified universal semantic relationships. All known

languages employ the relationship of strict inclusion (a Subaru is a kind of car).

D. Spradley has proposed 8 universal semantic relationships (p. 111):

i. Strict inclusion: X is a kind of Y

ii. Spatial: X is a place in Y, X is a part of Y

iii. Cause-effect: X is a result of Y, X is a cause of Y

iv. Rationale: X is a reason for doing Y

v. Location for action: X is a place for doing Y

vi. Function: X is used for Y

vii. Means-end: X is a way to do Y

viii.Sequence: X is a step (stage) in Y

ix. Attribution: X is an attribute (characteristic) of Y

4. Spradley also provides a detailed guide for data collection, the hypothesizing of domains,

and the return to respondents for clarifications on domains and their structures the structural

analysis and the componential analysis)

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LeCompte M & Schensul J. J. (1999) The Ethnographer’s Toolkit: Analyzing and Interpreting Ethnographic Data (Book 5). Walnut Creek: Alta Mira Press.

1. “What is a code? Codes are names or symbols used to stand for a group of similar terms,

ideas or phenomena that the researcher has noticed in his or her data set. In order to

determine the relative frequency of co-occurrence of items or other phenomena, researchers

have to code the data.”(p. 55).

2. Where do codes come from? (p. 58)

A. Borrowing from other researchers

B. Deductively from a conceptual framework

C. Precoded data: “Precoded data are data that have been categorized so that a given

number or letter represents a single kind of information about a given person, group,

setting or other researcher-defined unit of analysis.” (p. 61)

D. Codes can come from theory, or be theory-less (p. 66)

E. “In all of these cases, however, the codes are applied on top of the data, whether the

application is done during the data collection phases of research or after the data are

all collected.” (p. 66).

3. “We believe that theorizing – or building formative theory – resembles the kind of thinking

that people do in everyday life – except that theorizing is done in a more systematic manner .

. . The cognitive processes involved are:” (p. 69).

A. Perceiving

B. Comparing

C. Contrasting

D. Aggregating

E. Ordering

F. Establishing linkages and relationships

G. Speculating

4. Comparisons among scholars

A. Glaser and Strauss in grounded theory refer to the identification of items through

“constant comparison” (p. 75). The authors say this is similar to, and not in contrast

with Spradley’s approach via domain analysis.

B. However, they contrast this approach with Lofland and Lofland (1971), who “move

from the microscopic constituents and details of human interaction to a more

macroscopic perspective on how those constituents are aggregated into the behavior

and beliefs of larger groups.” (p. 79). Rather than beginning with item identification

(Glaser/Strauss and Spradley), Lofland and Lofland begin with a hierarchically

organized set of categories:

Acts and Actors Activities Settings

Ways of Participating Relationships Meanings

5. “Computers are merely hand and extremely fast labeling and retrieval tools. Researchers

also must remember that they alone can tell or program the computer to retrieve and count

data in specific ways; the machines do not do it automatically.” (p. 92)

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QUERI Memo 8: Coding Stripes

Understanding Coding Stripes

This tool opens vertical ribbons of color adjacent to the text and these ribbons are aligned with

the portions of text that have been coded to particular nodes.

• This tool is only accessible when a Document or Node is open in Detail View.

• Select a stripe and see the data you have associated with the node.

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Working with Coding Stripes

To turn on Coding Stripes

• Open a Node or Source, and put your cursor in the Detail View

• Go to View from the Main Menu, and select Coding Stripes

Understanding the many options for viewing the Coding Stripes requires an understanding of

some key terms:

• There are three fairly self-explanatory options

o “Most” displays the applied Nodes in descending order from left to right

o “Least” displays the applied Nodes in ascending order from left to right

o “Recently” displays the nodes applied in chronological order of application (from

recent on the left to distant on the right)

• Approximately half way down the list of Coding Stripe features, you will notice a

“Coding Density Only” option.

o If desired, you may turn on only this feature, although the Coding Density stripe

automatically appears alongside the other viewing options.

o The more nodes that have been applied to a portion of text, the darker the Density

Stripe becomes

o If you hover over the Coding Density stripe with your cursor, you will see a list of

all the nodes that have been applied to that portion of text.

• Select Items

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o When you choose this option you are taken to a “Select Project Items” Window.

� For more information on how to click through the “Select Project Items”

Window (which you will again be able to access from other tools in the

software), see the QUERI Memo on Selecting Project Items window

� To create pre-defined, always-ready sets of Nodes, see “About Sets” in the

NVivo Help files.

o When “Selected Items” is checked, NVivo is reminding you that you have

selected particular nodes to view.

• Near the end of the list of Coding Stripe features, you will notice a “Number of Stripes”

option.

o This option is only available if you have one of the Coding Stripe features open

adjacent to your text.

o This option allows you to view as many as 200 (!) stripes in the Coding Stripe

window.

o You may change the default of how many stripes open with each item by going to

File from the Main Menu � Options � “Display” tab.

• Finally, you can ask NVivo to show you stripes according to the colors you have assigned

to Nodes, rather than the defaults NVivo gives them by going to View from the main

menu. On the far right you will see “Color Scheme”

So What?

• After clicking on a particular ribbon of color in the Coding Stripe window, all text that

has been applied to that Node will become highlighted.

• The Coding Stripes are “live” and hyperlinked with the rest of the database, which means

o You may double-click on any ribbon of color and open up the entire Node

o You may right click on any ribbon of color and “Uncode” that patch of text (note

this does not remove all text from the file that has been coded to that Node, only

the particular length of the ribbon you have selected)

• You may print off a file of your text and Coding Stripes by going to File from the Main

Menu, and then Print. Choose the “Print Preview” option to see how subsequent pages

will be produced after the page of text, that align coding stripes according to the view you

have selected on your screen.

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QUERI Memo 9: Links

Understanding Links

The four kinds of Links in NVivo are: Annotations, Memo Links, See Also Links, and

Hyperlinks.

An Annotation is similar to the “comments” feature in Microsoft Word. With

annotations, you are able to add comments, clarifying remarks, or brief questions to particular

pieces of text, without actually altering the original file (interview, focus group, etc.).

A Memo Link is a pathway that connects an NVivo Memo with either an Internal, a

Node, or an External (See QUERI Memo2 for more information on these Source types). This

allows you to connect, for instance, your own thinking and writing to one of their respondents or

one of your nodes. “Memo Links” represent one of the many ways Memos may be linked to

other things in (and out) of NVivo, so the term “Memo Link” may mislead you into thinking it is

the only option you have for linking a Memo. The unique characteristic of the “Memo Link” is

that it is paired with an Internal or Node; each Internal or Node may only have ONE Memo

Link, and that Memo may be accessed from ANYWHERE inside of the Internal or Node to

which it is linked.

A Hyperlink is the one link of all four that allows you to connect with something that

exists OUTSIDE of the database. Examples include Power Point presentations, GIS mapping,

and web sites. ANY file that your computer can open (because it has the necessary program to

launch the file) may be linked into a Inernal, External, or Memo. A Hyperlink is simply a

pathway from inside of NVivo to another file elsewhere in your virtual environment such as your

C Drive, a server, or the internet.

A See Also Link is pathway from one particular piece of text in a project to another piece

of text. For example, you may link an idea in a Memo to the interview text that served as a

catalyst for this idea, or you may link a statement in an interview with an apparently

contradictory statement occurring elsewhere in the same (or in another) interview. While you

may also use this link to connect a particular piece of text with an entire item (such as an

interview or a node) in NVivo, this is a less frequently used option in most projects. The “See

Also” Link is perhaps one of the most complex, but it is also incredibly useful as a way of

connecting important pieces of data. Researchers often use these inside of their Memos to link to

the relevant data. A very detailed subsection has been provided on “See Also” Links to help you

explore their potential.

To see all of the links that have been created in your project, go to Links from the Navigation

View.

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Understanding Annotations

An annotation is like a comment in Microsoft word and allows you to attach notes to particular

pieces of text.

• They appear at the bottom of the page in the Detail View and can be hidden or revealed

as desired.

• The anchoring text to which an annotation is attached will be highlighted in blue – and

you cannot make this blue highlighting go away from the screen. It is your cue that

you’ve attached a comment.

• Annotations have a size limitation of 1,024 characters (approximately 325 words)

• If an Annotation is added to a portion of text and that text is also coded at a node (either

before or after adding the Annotation), the Annotation will “follow” the text to that node,

and it appears exactly as it does in the Internal.

• When you export any internal that contains annotations, they appear as end notes in the

exported Microsoft Word document.

Working with Annotations

Creating an annotation is very simple.

• Highlight a portion of text in an Internal (or select the Timespan in an audio/video file or

a segment of a photograph)

• Right click and select Links � Annotation � New Annotation

• A window appears at the bottom of the screen where you can enter up to 1,024 characters

of text.

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• A check box is available in the ribbon that lets you hide/show the Annotations.

• To export an Internal or a Node and the accompanying Annotations, simply select the

item from the List View, right click and “Export”

Understanding Memo Links

A Memo Link is a link between a Memo and either another Internal or another Node. Memos

are items in the database that can be found in the Internals folder, and if you wish, some of these

Memos may be linked to other items in the database (but remember that Memos can also be “free

standing” or unconnected to other items). Memo Links differ from Annotations in four key

respects:

• Annotations have a limit of 1024 characters, while Memos can be as long as you’d like

them to be. In fact, in some projects there will be more text in the Memos than in the raw

data.

• Annotations are anchored in a specific spot within running text, while Memos are linked

globally. In other words, they can be “jumped to” from anywhere inside the text.

• You may link as many Annotations as you wish inside an Internal or Node. However,

only one Memo Link may be attached to any Internal or any Node.

• The text within Annotations cannot be coded, while text within a Memo is codable.

A common use is to use a Memo Link to write up a summary of Anna, one of your cases, and

then link this Memo to Anna’s interview. Or, you may write up a summary Memo of your Node

for “Trust” and create a Memo Link to connect this to the actual Node.

Working with Memo Links

Creating a Memo Link is very simple. You can either create a new Memo and Memo Link at the

same time, or you can ju8st create the Memo Link to an existing Memo.

• If you are creating the new Memo and the new Memo Link at the same time

o Right click in the List View or the Detail View of any Internal or Node.

o Select Links � Memo Link � Link to new Memo.

o Name the new Memo � OK

o Begin typing in this Memo. You will be able to jump to this memo from

anywhere within the text of the item in which you just did your “right click”.

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• If the Memo already exists but you want to link it to another item, follow the steps above

but “Link to existing Memo” instead of to a new Memo.

Memo Link icons are visible in the List View next to the Memo as well as the item to which the

Memo is linked.

Understanding Hyperlinks

Hyperlinks differ from all the other Links in one crucial respect. All other Links are pathways

that are created between items that are embedded within the project. Hyperlinks, however, are

links to items that are not inside the database. Because NVivo only imports documents with

certain extensions (.txt, .rtf, mp3, etc.) there may be items you want to examine as part of your

research but cannot import into NVivo. This may be a Power Point presentation or a web site,

for instance.

• Hyperlinks are created with relative (rather than absolute) links, so you must be careful

about renaming or moving the items that are outside of NVivo. Doing so will likely

break the link.

• You may link ANYTHING into the NVivo project via a hyperlink, as long as your

computer has the program required to run that file (e.g. you must have Power Point or

Internet Explorer if you are linking to a file with these formats)

• Hyperlinks in NVivo appear as they do on many web sites, as blue underlined text.

Working with Hyperlinks

Creating a Hyperlink is very simple

• Highlight a portion of text in an Internal (or select the Timespan in an audio/video file or

a segment of a photograph)

• Right click and select Links � Hyperlink � New Hyperlink

• A window appears at the bottom of the screen where you can enter a web address or

select “Browse” to find the file on your computer.

• Unlike a web site, you cannot simply select the blue text to launch the link. You must

hold down your control key (Ctrl) and then click once with your left mouse on the blue

underlined text.

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Understanding “See Also” Links

See Also Links allow you to connect almost any piece of the data (or your notes) to any other

piece. To use an example, you may want to Link the location where Raul talks about an

education policy to a copy of the policy to which he is referring (you imported Raul’s interview

and the policy into your project).

Internal: Raul’s Interview Internal: Education Policy

• Every “See Also” Link connects two items in the database and is created by a researcher

who makes the observation in the data. NVivo does not create the Links automatically.

• A “See Also” Link is like a pathway on a web site that lets you jump to a different

location within the web site. In NVivo, you are simply jumping to another spot in the

database.

• A helpful note about the name, “See Also” Link: You might understand this terminology

better by reversing the words and thinking of it as an “Also See” Link. For instance, here

I am in Raul’s file where he discusses the policy, and I would like to “Also See” the

actual policy.

• “See Also” Links can be turned into end notes in reports that are exported from

NVivo as Microsoft Word files (or as hyperlinks in an HTML file)!!

“See Also” Links take many forms and can be created different ways, and some researchers

initially find the multitude of “See Also” Link options (and their distinction from other Links

[Annotations, Memo Links, and Hyperlinks]) a little overwhelming. However, you can master

this Link quite easily if you come to terms with several “pairs” of issues.

• The first pair has already been discussed: “See Also” Links connect two items in the

database. However, there are several other issues that can also be understood via “pairs”

of options, each of which is addressed in its own section below:

o What is the destination content (two options)?

o What are you creating (two options)?

Raul; June 22, 2008;

Education Council Office; 45

minutes; by Kristi Jackson

Interviewer:

What policies are most

difficult to implement, and

why?

Raul:

The Community Service

Requirement policy is

definitely the hardest, because

we cannot find enough

sponsors for the children in

community settings.

Interviewer: Why is it so difficult to . . .

Community Service Requirement

A Policy of the

Drake District Education Council

Recognizing the importance

of practical experiences in the

lives of youth, the District of

Drake has established a part-

time volunteer coordinator

position for the next fiscal

year to oversee the

assignment of youth

volunteers to local community

service agencies. Each

student will be required to . . .

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o Where do you create “See Also” Links (two options)?

o How do “See Also” Links appear in reports (two options)?

What is the destination content (two options)?

Every “See Also” Link begins in a specific location, such as a comment in Raul’s interview, or a

reflection written in one of your Memos. However, there are two options for the

content of the link’s destination (it may be a specific location or link to an entire item)

• From a Specific Location to another Specific Location

o Researcher interpretation: Anna contradicts herself between her first and second

interview (or between the beginning and end of the same interview).

o Researcher action: This Link connects Anna’s words, “I think this group has a

verylow level of trust” to the spot where she later says “we trusted each other.”

Internal: Anna - Time 1 Internal: Anna - Time 2

Anna; June 25, 2008; 120


Interviewer:

Tell me about how well the

members of the group get

along with each other.

Anna:

I think this group has a very

low level of trust. This is

because of all of the dynamics

among the participants before

we came together. You see,

it’s not as though people were

strangers, and I just think that

it all started on the wrong

foot. We needed earlier . . .

Anna; July 12, 2008; 45


Interviewer:

What did the group do to

work through the recent

choice between the recreation

center improvements and the

community liaison staff

person?

Anna:

Well, even though there were

different agendas, the rec.

center supporters were willing

to put those plans on hold

since we trusted each other to

hire a person who could . . .

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• From a Specific Location to the Entire Item

o Researcher interpretation: Anna makes a comment that contradicts what everyone

else says about “trust”, which is one of your Nodes.

o Researcher action: This Link connects the exact spot where Anna says, “I think

this group has a very low level of trust” to the Node called “Trust.”

Internal: Anna’s Interview Node: Trust

What are you creating (two options)?

The Link can be to an Existing item, or to a New item.

• A Link to an Existing item

o This is a Link to an item or passage that already exists. Both of the examples

above represent this action.

� The first example Links Anna’s comment to the existing Node for “trust.”

� The second example Links Anna’s comment to another existing verbatim

from Anna’s second interview.

• A Link as well as the Node or Internal that is the destination of the link

o This is a Link to an item that does not yet exist. For this, we need a new example.

� Researcher interpretation: You write paragraph in your “Emerging Ideas”

memo with new reflections about a new theme, “Loyalty.”

� Researcher action: While you write up this paragraph, you decide to create

a Node for this new idea to track subsequent comments about loyalty from

your participants. You therefore simultaneously create the new Node

called “Loyalty” while Linking the reflection in the memo to this new

Node. You therefore successfully document the initial idea that lead to the

creation of the Node.

Memo: Emerging Ideas New Node: Loyalty

Anna; June 25, 2008; 120


Interviewer:




Anna:










Trust

This Node has

references from

other participants

about the high level

of trust among the

group members.

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Where can you create “See Also” links (two options)?

You can create “See Also” Links in the following two kinds of project items

• Sources (Internals, Externals, Memos)

• Nodes (Free, Tree, Case, Relationship)

These can be connected in any of the following combinations

o Source � Source

o Node � Node

o Source � Node

o Node � Source

You cannot apply “See Also” Links to or from any of the following classes of items

• Sets

• Queries

• Models

• Classifications

How do “See Also” Links appear when exported?

You may export any Internal or Node containing a “See Also” Link, and simultaneously choose

whether or not you want the output to show the content of the links. The export is easy, and can

be saved as either

• A Microsoft Word file (where the links appear as end notes!)

• An HTML file (where the links appear as hyperlinks).

Kristi Jackson: Emerging

ideas

6/23/2008 4:58 PM

The early questions in the

interview protocol may have

missed the importance of the

way existing loyalties

influence the ability of

participants to trust. In other

words, there may be some sort

of “trust readiness” that we

should explore as a dimension

of existing loyalties

Loyalty

This Node does not

contain any text

references, because

you just created it

while you created

the Link. It is ready

for data when you

return to coding.

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Working with “See Also” Links

“See Also” Links go in one direction: From x to y. When you are ready to make a “See Also”

Link, the best way to begin is to identify the destination (y). Ask yourself, “Is the destination a

specific passage (a verbatim, a particular piece of text, etc. – as in the example where Anna

contradicts herself) or is it the entire item (an entire Node, an entire Memo, etc. – as in the

example of linking the paragraph in your memo about loyalty to a Node called “Loyalty”)?

Once you have the answer to this question, you will use one of the starting points below

If the destination content is a Specific passage . . .

You should go to the specific passage that is the destination of the Link, such as “Anna – Time

2” below.

• Select the destination text with your cursor

• Go to the Menu to Edit � Copy.

Internal: Anna - Time 1 Internal: Anna - Time 2

• Navigate to the desired origin of the Link (the place where you want to see the Link).

• Highlight the desired origin, and then go to the main Menu to Edit � Paste As See Also

Link.

• You will see pink highlight on your screen, indicating a new See Also Link.

o At the bottom of the page a Tab will appear to track your See Also Links for the

current item.

� Go to the Main Menu to View � See Also Links, and the Tab will

disappear.

� Return to View � See Also Links, and the Tab will reappear.

Anna; June 25, 2008; 120


Interviewer:




Anna:










Anna; July 12, 2008; 45


Interviewer:

What did the group do to

work through the recent

choice between the recreation

center improvements and the

community liaison staff

person?

Anna:

Well, even though there were

different agendas, the rec.

center supporters were willing

to put those plans on hold

since they trusted the other

group to hire a person who . .

.

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� You may also use the check box on the toolbar to hide or show

o Double-click on the recent link under the “See Also Link” Tab at the bottom of

the screen, and you will jump to the destination (even if it is not currently open!).

o IMPORTANT: This Link ONLY goes in one direction. If you want the link to

go both directions, you have to do it again but reverse the origin and destination.

If the destination content is the Entire Item

Go to the specific passage that will be the origin of the link, such as the text where Anna talks

about trust

Internal: Anna’s Interview Node: Trust

• Highlight the text � Right click� Links � See Also Link � New

• In the subsequent window use the drop-down arrow to identify whether or not the item

exists already or whether you want to create something new (e.g. a document, a node, a

memo, etc.)

• If you are linking to an existing item, such as the node for “Trust,” use the “Select”

button to find it. Click “OK”

Anna; June 25, 2008; 120


Interviewer:




Anna:










Trust

This Node has

references from

other participants

about the high level

of trust among the group members.

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Generating MS Word Reports To view the See Also Links exported as end notes in Microsoft Word files, select a Node or

Internal containing a See Also Link from the List View. Right click and “Export” the item with

the See Also Link box checked. Save wherever you’d like and then open in MS Word.

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QUERI Memo 10: Select Project Items Window

Understanding the “Select Project Items” Window

This window may appear when you drill far enough into several different tools, including

Coding, Coding Stripes, Highlighting, Queries, Models, and Reports. This window will allow

you to select the exact items of interest.

Working with the “Select Project Items” Window

The key to maneuvering through this interface, which is accessible through several tools

(Coding, Coding Stripes, Highlighting, Query, and Modeler), is to note that it has two windows.

Above one window there is a check box next to “Automatically select subfolders”, and above the

other there is a check box for “Automatically select hierarchy”. Each of these check boxes

pertains to the list below it.

• For instance, if the “Automatically select subfolders” option is checked, then if you click

on the box next to “Themes” you will automatically have chosen ALL Themes

o To see this, click on the FOLDER or ICON to the right of the box of any Node or

Node folder (such as “Themes”). Now select the check box and all nodes will be

selected.

o This is the bonus of working with the two check boxes at the top of the main

window – they allow you to efficiently select pre-identified groups/hierarchies of

items already in your project.

• It is important to remember that in the left window, clicking on the BOX will select all

subfolders and items in those subfolders (this is likely to be a big grab if you have done a

lot of work in the project). However, clicking on the ICONS to the right of the box on

the left window simply opens up a list of possibilities on the right.

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QUERI Memo 11: Cases

Understanding Cases • If you intend to compare subpopulations in your dataset (men compared to women,

younger participants compared to older participants), you have to start planning around

your Units of Analysis (People, such as Barbara, Charles, and Dorothy). In NVivo, there

are two, overarching ways that these can be organized. They can either be organized as

Sources, or as Nodes.

• Because of some issues pertaining to maximizing flexibility in the software, this section

of the instructions focuses our attention on Nodes as Units of Analysis, rather than

Sources. While it is possible that you will want to think of your Sources as Units of

Analysis, there are some ceilings in the ways these can be used and modified over time in

your study. See the subsequent Queri Memo on Classifications, Attributes, and Memos

for clarification.

o Like other Nodes, A Node for one of your Units of Analysis (e.g. Charles or

Dorothy) is a container, or a boundary within which qualitative data is held. In

addition, these Nodes can be Classified according to a broad category, like

“Person”

o Alternatively, and perhaps in the same project, you might have a Node for

“Otway,” a city, and in this same project, Otway would be classified as a “City”

(rather than a “Person”)

• To prepare you for the steps we will take in the next subsection, it may be helpful to be

forewarned that there are four steps in NVivo that a researcher usually takes to make a

such a Node viable:

o Create the container (a node) for each Unit of Analysis (e.g. a node for “Charles,”

a node for “Dorothy,” a node for “Otway” etc.)

o Code all of the qualitative data from a particular Unit of Analysis into the

corresponding Node (e.g. everything Charles says in his interview, everything he

said in the focus group in which he participated, his journal entry, etc. all go into

his one Node. In addition all the interviewee data from people who live in Otway

go into the “Otway” node)

o Classify the Charles Node as a “Person,” and classify the Otway Node as a “City”

o Apply all of the demographic and other quantitative data points – nominal,

ordinal, or interval – to the specific Nodes (e.g. Charles is a “male,” “over 40,”

etc.). Again, NVivo’s terms for the demographic data (such as Gender � male)

are Attributes � values.

• Once these four steps have occurred (though they may not necessarily occur in this order,

and all of them can be accomplished in a variety of ways), the Attribute values can be

used as a filter in the data set (to see, for instance, how women talk differently than men

about their perception of “Habitat”).

• While the steps are a bit complex, this structure in the software allows you to have

multiple kinds of Units of Analysis (People as well as Cities) and to count and ask

questions about these different units. This can be especially helpful in large data sets.

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Working with Cases

You have many options (a total of seven!) to create cases (and not all of them are detailed here).

You may:

• Turn Sources into a Cases

o While you are importing data or

o After data has been imported

• Take portions of Sources and turn them into Cases

o Using heading levels to Auto Code

o Using any of the Open Coding strategies you learned while coding to themes (e.g.

drag-and-drop coding)

• Import a Classification Sheet (an Excel file) with the nominal, ordinal, and/or interval

data to simultaneously create Cases and import as well as assign the values

• Use a Dataset to create Cases (also by using the Auto Code tool)

• Use a Text Search query to locate speakers and convert the results into Cases (this

depends on whether or not the data is formatted in a way that allows an easy capture of

text by speaker).

In the instructions below, we will show you the two most common strategies:

• Turn Sources into Cases after the data has been imported

• Use a Dataset to create Cases

To create Cases using other methods see the NVivo help files (“Import documents and PDFs,”

“Automatic coding in document sources,” “Import (or export) classification sheets,” “Run a Text

Search query.”

Creating Cases from Sources

• To keep your more thematic nodes separate from your Units of Analysis Nodes, we

recommend creating a subfolder in Nodes called “Units of Analysis”. Simply right click

on the Node folder in the Navigation View to create this folder.

• Create a subfolder under “Units of Analysis” for each kind of unit. For instance, you

might create one folder for “People” and another folder for “Cities.”

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• Go to Sources in the Navigation view.

o Select all of your Internal files in the List View (or the files in one of your

subfolders under “Internals”), right click on them � Create As � Create As Case

Nodes.

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o Select the appropriate subfolder to nest these under, such as “Interview

Participants”

o Before you select “OK” you may also “Classify” these nodes if you have

Classifications. If you do not, don’t worry, because we will use another method

later on.

• To see what you just accomplished, go to the Navigation View and select Nodes � Units

of Analysis � People. You now have a node for each person. If you had two interviews

for Charles, you would simply right click on one and “cut,” and then go to the other, right

click, and “Merge into selected node.” Rename this node if it makes sense to do so (so it

is called “Charles” instead of “Charles Interview 1”).

Use a Dataset to create Cases

Before using the Auto Code tool to create Cases from a Dataset, you first need to create a place

where the individual Case Nodes will be placed

• You may have already created a folder in your Nodes area called “Units of Analysis.”

• If not, go to the Navigation View and select Nodes �� Node folder �� right-click ��

New Folder �� Name it “Units of Analysis”

• Go to the Navigation View and select Sources.

o Right-click on the Dataset in List View; choose to Auto Code. This will open the

auto code dataset wizard.

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• Work your way through the wizard. to code rows into classified nodes:

o Step 1:

� Code at nodes for each value in a column �� Next o Step 2:

� Use the drop-down arrow to select the column you want to use to name the

nodes (it is usually the first column in the Dataset).

� You probably do not need to create a hierarchical structure for your nodes.

If so, you can check the box and select form the available columns.

� The preview window at the bottom of the screen will provide an image of

the Case Nodes that will be created.

� �� Next

o Step 3:

� The wizard will show you the columns that have codable text in them. If

they are on the left side of the screen, select the ones you want to include

in the Cases and move them to the right. Usually you would select all of

them.

� The two windows at the bottom give you a preview of the Case Node

structure and confirm the qualitative data that will be included in each

Case.

� �� Next o Step 4:

� This screen allows you to either create a New Node or to select an

Existing Node where you want locate the new classification nodes (select

the folder or parent).

� In either case, be sure to use the Select button to choose your “Units of

Analysis” folder.

� Again you will see preview windows at the bottom of this screen.

� �� Finish

• Go to the Navigation View �� Nodes �� “Units of analysis” to see the Nodes you

created. Open one to see the qualitative data in that Node.

So What?

The utility of the Unit of Analysis node usually depends on getting attributes (nominal, ordinal,

or interval values – such as “male” and “female”) associated with each Node. See the next

Memo on Classifications, Attributes and Values for instructions on how to assign these values to

the Nodes you just created.

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QUERI Memo 12: Classifications, Attributes and Values

Understanding Classifications

A warning about the use of Source Classifications and Node Classifications: Before this

discussion even begins, it may be helpful for you to know that Queri STRONGLY recommends

that you use Node Classifications for everything EXCEPT imported bibliographic databases (see

the NVivo help files for a description of what happens to bibliographic databases when they are

imported into NVivo for additional information). So, if you want to compare men versus women

or political parties A, B and C, then use Node Classifications instead. This may seem counter-

intuitive at first, but 1) because Source Classifications cannot be used in Coding Queries (see the

memo on queries for a discussion of this query), and 2) because your “units of analysis” might

change during a project (discussed below), using Source Classifications is more likely to paint

you into a corner. In sum: Here at Queri, we ALWAYS use Node Classifications unless we are

importing a bibliographic database.

Before we talk through this conceptual distinction further, it will help to see where they are

located in a project. If you open the sample data set, you will see, under Classifications in the

Navigation View, these two types, Node Classifications and Source Classifications.

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• Click on “Source Classifications” in the Navigation View. In the List View, select the

plus sign next to “Book”.

o Double click on the Attribute “Year” and you will see a Tab for “Values.” This

Tab contains the possible values for the publication year of the book.

o “Book” is called a “Source Classification” in NVivo

o “Year” is an “Attribute” (in quantitative data this is known as a variable)

o “2000” is a “Value” for a particular attribute (and there are always multiple value

options, e.g. “2000” “1999” etc.)

o These three terms form a logical hierarchy.

o We strongly recommend that you ONLY use Source Classifications if you are

importing a bibliographic database, or to sort your data by values that pertain to

the data types you are managing (such as a source classification for “on-site

interviews” an attribute for “researcher” and the values of “Kristi,” “Susan,”

“Peter,” etc. for the researcher who conducted the interview.

o In sum, if you are using NVivo for a literature review, your bibliographic records

would be best managed with Source Classifications if you want to compare

different authors, different publishers, or literature produced in different years.

Attribute

Value

Source Classification Book

Year

2000 or 1999, etc.

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• A slightly different way of being able to compare things is available in Node

Classifications.

o We STRONGLY recommend that you use Node Classifications to sort your data

by values (male, female, old, young) that pertain to the units of analysis you are

interested in examining. This is especially true if your units of analysis are not a

one-to-one match with your Source files. For example:

� The study design may have entailed interviews with the same participants

at two different times. If you have two documents representing the two

interviews for Charles, but you want to know, across the entire study, how

many men talk about “Habitat”, then you don’t want to always count

Charles twice. Charles is one man.

� With nodes, we can gather all of the data collected from Charles (his two

interviews, his journal entries, and his video diary, etc.), and put them in

one place, so he can be counted as one unit of analysis (also sometimes

referred to as one case).

• To see examples in the sample data set of these kinds of Node Classifications, click on

“Node Classifications” in the Navigation View. In the List View, select the plus sign

next to “Person.”

o “Person” is called a Node Classification in NVivo

o “Gender” is an Attribute (in quantitative data, known as a variable)

o Double click on the Attribute “Gender” and you will see a Tab for “Values.” This

Tab contains the possible values of “male” and “female”

.

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There are three strategies you can use to create Classifications, Attributes and Values and

assigning them in NVivo:

• Step-by-step inside the software

• Importing an Excel file (also known as a Classification Sheet) with the data

• Taking a Dataset file and using the tool to “Classify nodes from a Dataset.”

The three methods are described below.

Working with Classifications, Attributes and Values

Creating Classifications, Attributes and Values and assigning them step-by-step inside NVivo

• Select “Classifications” in the Navigation View

• Select Node Classifications

• Go to the empty space in the list view, right click � New Classification

o NVivo provides some ready-made classifications, but chances are slim that they’ll

match up with your study. It’s probably better to create what you need.

o Note that Classifications can be set up and attributes assigned automatically by

importing data from a spreadsheet. These instructions are in the next subsection,

“Creating Classifications, Attributes and Values by importing an Excel spreadsheet”

• Select your new Classification in the List View (e.g. “Person”, right click � New Attribute

o Name your attribute, e.g. “Gender”

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� Select the Tab for “Values” and add the relevant values (e.g. “Male,”

“Female” “Transgendered Male,” “Transgendered Female,” etc.)

• Before you can add specific values to your Nodes or Sources, you must assign a

classification to each of your nodes in “Units of Analysis”. To do this, select the node(s)

or source(s) in List View, right click � Classification.

• Now you are ready to assign specific values to your classified Nodes.

o Go to Explore from the menu/ribbon � Node Classification Sheet � “Person” (for

example).

� The Classification Sheet will open in Detail View, with any nodes to which

you have assigned that classification. The nodes are in rows, and the

attributes you have created in columns. Currently the values will all be listed

as Unassigned.

� Click in a cell, and select an appropriate value from the drop-down list for

that cell.

� If you wish to create a new value ‘on the run’, double-click in the cell and

then overtype the current entry with your new value. The new value will be

added to the list and made available for further classification nodes.

� Rows and columns in the Classification Sheet cannot be deleted. You need to

go to the actual node in the List View of Nodes (for rows) or the actual

Attribute in the List View of Classifications to delete them (alternatively, you

can simply “Declassify” a Node by right-clicking on it � Classification �

No Classification.

o For a fast way to do this in a project by importing all of these values with Excel, see

the next section.

Creating Classifications, Attributes and Values and assigning them by Importing an Excel File

• Open Excel and set up the data in the following fashion (it is a good idea to do a pilot

with a small number of columns and rows as a test before bringing in a large

Classification Sheet)

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o Cell 1A is the name of the Classification (it is okay if it doesn’t exist yet inside

NVivo – the import process will create it!) o Attributes are identified in the first row, and Units of Analysis (nodes in this instance)

are in the first column.

o Fill in the table with the appropriate values.

o Close it before you import.

• To import the table into NVivo, go to External Data from the menu � Classification

Sheets

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o Step 1: Use “Browse” to find the file � Next

o Step 2: Choose the correct Classification Type (Node or Source?).

� Leave the first check box selected

� Select the Second check box to ensure that

all relevant nodes or sources get classified if they haven’t yet been classified. (If you’ve

done it already it won’t hurt to do it again).

� Check the bottom check box if you are

trying to overwright errors that exist inside

NVivo in an existing Classification Sheet. If

you don’t have any yet, it doesn’t matter

whether you check it or not

� Select Next

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o Step 3:

� If your Excel file had names in the first column (as in the example above,

rather than hierarchical names such as “Nodes\\Units of

Analysis\\People\\Charles”, then you want to change the selection to the

first radio button, “As Names”

� Then use the Select button to identify the location of these “Units of

Analysis”

� Uncheck the box to “Create new nodes if they do not exist.” There’s a good

reason that this is here (you can create your nodes with this step, which you

might want to do occasionally, but it is generally a less efficient strategy

overall). However, if you have a typo in your table for the names of one of

your cases (e.g. “Chalres” instead of “Charles”) you will end up with two

nodes. The Charles node will have all of his qualitative data, but the Chalres

node will have the quantitative data. It’s a fairly easy fix, but confusing when

you are just learning the software. Save yourself the confusion by

unchecking this box.

� Select “Next”

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o Step 4:

� Change the final default options for dates, times and numbers if desired.

� Select “Finish” and the Classification Sheet will import

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Creating Classifications, Attributes and Values and assigning them with a Dataset

Create the Classification

The wizard to assign Attributes from the Dataset will do so only for an existing classification.

So, if you don’t have one yet, you’ll need to create one.

• To create a new classification, go to the Navigation View �� Classifications �� Node

Classifications. Then go to the List View and right-click in the empty space �� New

Classification � provide a name for your Classification (e.g. Person, Organization,

Policy).

Create the Units of Analysis (or Cases)

If you haven’t already created a Case for each row of your Dataset (these would be somewhere

in your Nodes area), return to the Queri Memo on Cases and follow the steps to create cases with

a Dataset.

Add Attribute Values to the Cases

Now that you have a Node Classification and you’ve created Case Nodes, you are ready to add

the values to the Cases (and you’ll simultaneously complete the Classification System structure).

• Go to the Navigation View and choose Sources.

• Select the Dataset in the List View.

• From the Ribbon at the top of the screen, select Create �� Classify Nodes from Dataset.

o Step 1:

� �� Next o Step 2:

� Change the drop-down arrow to Existing Node

� Use the Select button to find the parent Node for the Units of Analysis you

created.

� Uncheck “Create nodes if they do not exist in this location” just to make

sure you don’t inadvertently create new Nodes instead of matching up the

Attribute Values with the Nodes you already have (you created them

earlier in the Memo on Cases.

� Select the Classification for the Nodes with the drop-down arrow

� Check the box to “Update the classification of existing nodes”

� �� Next

o Step 3

� Use the drop-down to choose the column with the Node names (usually

the first column)

� �� Next

o Step 4

� From the available Attribute columns on the left, choose the ones you

want to assign to the Cases (you can probably ignore the first one, which

is the column containing the Case Nodes. Using these would be redundant,

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and very unlikely to be useful, but it won’t hurt the database or prevent

you from using the other attributes.

� �� Finish

So What?

• Attributes may be added to the project at any time. While it may be helpful to think

about Attributes early on in the project, the relevant Attributes in some studies may not

be apparent until much of the coding, memoing, and linking is finished.

• The reason it might be helpful to think through the Classifications, Attributes, and Values

prior to collecting or transcribing data, is that it could influence the way you prepare your

qualitative data.

o For instance, if you know that you want to compare men and women (values) as

the options for gender (an attribute) that pertain to a person (classification), then

you know you’ll need to get each person into their own Node.

� If you conduct three interviews one day in a single household and know

that you’ll want to eventually compare how men and women in the

households talk about their Natural Environment (a Thematic Node), then

you will want to put each individual interview in a separate document

(NOT all three of them in one document).

� Why? In NVivo, you can select all of the individual interviews and

instantly turn them into Nodes. However, if you put all three interviews

into one document, you somehow have to individually code these

interviews into nodes (Auto coding and Open coding would be two

options for doing this, as detailed in see the QUERI Memo on Coding, but

these strategies – especially Open coding -- are usually less efficient).

• Regardless of the timing of your management of Classifications, Attributes, and Values

or the number of times you may import a classification sheet, NVivo creates one, large,

aggregated table for each kind of classification (sorted alphabetically by Attribute Name)

as the default.

Classification sheet for

“City”

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Classification sheet for

“Person”

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QUERI Memo 13: Framework Matrices

About Framework Matrices

Framework matrices provide a table format that was originally designed to add cases in the rows

and thematic nodes in the columns. However, you can add any node to columns/rows. You can

either generate a table that is empty so you can summarize the intersecting content, or you can

have NVivo generate the table for you with all of the intersecting content visible in the cell. Try

this one in the sample data to determine if you might be interested in running one on your data.

Working with Framework Matrices

Framework matrices are fairly easy to generate.


“Framework Matrices.”

• Right-click in the List View � New Framework Matrix

• Name it “Cases X Natural Environment nodes”

• Go to the Rows � Select the plus sign next to Nodes � Folder Places and People > �

Barbara, � Betty, � Charles � OK

• Optional: Choose attributes to sort the rows in the column on the right

• Go to the Columns tab Select > � Automatically select descendant nodes � Natural

environment � OK � OK

You are looking at an empty table with the columns and rows specified. On the right hand side of

the screen, you see ALL of the data for the item (person, such as Barbara) selected in a row.

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You can do six main things in/with this table to further the analysis:

1. Change the view on the right hand side of the screen so it displays only the intersection of the

active cell:

• Go to “View” from the main menu, and select Framework Matirx � Cell Coding

• Click on any cell you will see only the intersecting content in the Associated view on

the right.

2. Write your own comments/summaries in the empty cell based on the content to the right:

• Place your cursor in cell 1B (Barbara x Ecosystem services).

• Read the first sentence in the Associated view on the right.

• Type “Maximized for community resiliency” in the cell.

• You can continue in this fashion to create a summary of the data.

3. Link your specific summaries to the data to which it refers

• Highlight the first sentence in Barbara’s text (“I would like . . .”), right-click � Links

� Summary Link � New Summary Link.

• Pink highlight has been added to your summary, and if you select this pink area, you

see the data to which it refers.

• As you continue typing and creating summary links, you can choose any summary in

the cell and see the associated data.

• You can also add links later: highlight the text to be associated (in the cell and then in

the associated view on the right) right-click � Links � Summary Link � New

Summary Link.

4. Have NVivo Auto Summarize in the table for you. This places all of the intersecting content

(verbatim) directly in the cell, which you can then edit (or simply export as a table for

manipulation in Excel). A warning about this strategy – you will be permanently adding data

to the cell (unless you delete one cell’s text at a time), even if you delete the Framework

Matrix from the project, since the cell results are retained in the database.

• Analyze ribbon � Auto Summarize

• You may now summarize the data by editing the content in the cell.

5. Export

• Right click in the table � Export Framework Matrix

6. Display options

• You may alter the ways you see the display on the screen by going to “View” from

the main menu � Framework Matrix.

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So What? Some important things to know about the Framework Matrix (additional information is in the

help files)

• The Framework method was developed by the National Centre for Social Research

(NatCen).

• You may place any combination of nodes, sets, or search Folders in the columns/rows of

a Framework Matrix. However, only nodes in the sets and search folders will be

displayed in the Framework Matrix.

• If you run a subsequent Framework Matrix with a column/row combination (a specific

cell) that has already been summarized in NVivo, this summary will appear in the table.

• If you delete a Framework Matrix, you do not lose your summaries, but you will need to

run another Framework Matrix with the same column/row combination in order to access

the summary for a cell.

• You may import Framework Matrices from another project

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QUERI Memo 14: Queries Overview

Understanding Queries • A “Query” in NVivo is like an engine that you customize in order to answer the specific

questions you have of your data. The engines “runs” through your project to collect the

information you requested.

• Not every project has a node for “communication” and not every project has an attribute

for “gender,” so researchers won’t always want to know “how do men and women talk

differently about communication?” This is the reason that each project contains unique

Queries.

• A Query is similar to a “macro” a “syntax” or a “command” (as they are often called in

other software packages). If you do not know what these terms mean, not to worry,

because NVivo will help guide you through the process of developing your Query.

• In sum, Queries (or searches) are commands that find what you’ve told them to find.

Different Queries are needed in different proejct, so you as a researcher need to construct

the Queries in your particular database.

NVivo has a wizard built in to every query that helps you create the specific instructions, and

once you create these instructions you can save them and run the Query again at any time. A

quick orientation may help you prepare for a specific Query before we explain some of the key

Queries in greater detail.

• As you are learning NVivo and building your skills in the construction of Queries, one

move is the MOST IMPORTANT (and it’s easy!): ALWAYS check the box next to

“Add To Project”. This is like selecting the “Save” button in SPSS and forces NVivo to

save the engine language so you can adjust and improve it if you realize it wasn’t exactly

what you wanted. Saving the Query cannot harm you database, so ALWAYS check this

box on the top left (until you are fluent with the Query language and are certain that you

are ready to bypass this box):

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Common Building Blocks in Queries In most Queries, you will see three tabs as soon as you select the “Add To Project” check box

mentioned above:

• General

• “X” Criteria (e.g. “Text Search” Criteria, “Matrix Coding” Criteria, etc.)

• Query Options

• About the three tabs:

o General: This tab provides two windows:

� Name: This is the name of the Query engine, so when you create ten of

them you can tell one from the other. It’s like naming a file in your

windows explorer. You are NOT providing any real instructions for the

Query engine here – you are simply giving it a name.

� Description: This is handy because you are likely to forget the reasons

you created this search or exactly what question the Query is designed to

answer. Always add a description here, just as you always add a node

description (e.g. “A search for the words: talk, talking, talked, talks, write,

wrote, written, writes, writing”). This is just a log – you are NOT

providing any real instructions for the Query engine here – you are simply

giving it a description.

o “X” Criteria: Depending on the Query, this tab will present you with several

windows for adding the specific Query instructions. This is the place you do most

of the work to think through the components of the NVivo database and the

specific data you want to retrieve.

o Query Options: This tab (present in every Query except the “word frequency”

Query) provides a range of options for how to manage your results. You might

want to preview the results but not be forced to save them (if they are very large,

for instance, you may not want to take up the room), you may ask for additional

context around the finds, you may want to save the results as a node, etc.

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Seven Types of Queries There are seven types of Queries in NVivo 8 (NVivo 7 does not have the “Coding Comparison

Query” but it does have a Coder Comparison report. These are different ways to compare coding

among researchers).

• Word Frequency Query: Catalogues the words used most often in the data.

o No prior knowledge of the data set is required to run this Query, and it is certainly

the easiest to “run.” Simply create a new “Word Frequency Query” and click on

the “run” button to use all of the existing defaults.

o This is a wide net and captures question numbers, etc. unless the documents are

formatted with heading levels or unique characters that allow the researcher to

include/exclude certain sections of text.

o The Query can be modified to include/exclude any document, node, annotations,

etc.

o This is the only Query without a “Query Results” tab, because results are not

saved. The output is easy to run any time, and can be exported (saved outside

NVivo) if desired.

• Text Query: Finds simple word searches and stem searches.

o Find all mentions of a particular word (e.g. “help”)

o Use “Special” button to add instructions.

� For example “help*” (without the quotations) will find “help,” “helping,”

“helper,” etc.

� In addition, “help OR assist” (without the quotations) will find either of

the words specified, and you may add as many synonyms to the Query as

you would like.

• Coding Query: Allows for a single, complex question with multiple items.

o This Query depends entirely on the bundles of nodes and attributes you have

created in your project.

o For instance, find all the women over 40 who talk about trust anywhere in their

document and see if they say anything about family.

• Matrix Coding Query: Pairs of items are combined and displayed in a table, similar to a

cross-tab.

o This Query allows researchers to essentially run multiple searches at one time,

because each cell in the output represents a different search.

o For instance, Rows can be populated with Male and Female (two, separate rows),

and Columns can be populated with the Nodes for trust, loyalty, and love. (three,

separate columns).

� If this Query is run as an “intersection”, each of the six resulting cells will

contain numeric and textual output for the six different combinations

(male/trust, male/loyalty, male/love, female/trust, female/loyalty,

female/love).

o In addition to running Nodes by Attributes (as in the example above), the Query

can run Nodes by Nodes, Sets by Nodes, Attributes by Attributes, Cases by

Nodes, etc.

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• Compound Query: Finds specified content in or near other specific content.

o This is one of the more complicated and less often used Queries.

o Most qualitative researchers call this a “proximity” search, rather than a

“compound” search, though the name “compound” comes from the fact that the

Query is using compound logic (e.g. I want to find the word “passion” but only if

it is NEAR any discussions of volunteering outside of the country by women in

the youngest age group) “communication” BEFORE they talk about “conflict” as

they describe the emerging collaboration in their community group.

� This Query is particularly useful when chronology in story-telling is

relevant.

o This Query is most certainly best learned on your own data set when you have a

complicated question about the CLOSENESS of data on the page.

• Grouped Query:

o Use Group queries to find items that are associated in a particular way with other

items in your project.

o For example, you might have a hunch that Interview with Charles and Interview

with Susan covered very different issues and therefore have been coded

differently. You could explore that hunch with a Group query. When you run the

query, the results are displayed in Detail View with the coded nodes grouped

under Interview with Charles and Interview with Susan.

• Coding Comparison Query: Compares coders and provides a reliability measure via

Kappa.

o The Query allows the researcher to select the Nodes of interest and the Internals

of Interest, in addition to the specific “users” or coders.

o Results are displayed one Node at a time, for all the Internals chosen, and

provides a Kappa Score as well as percentage Agreement and Disagreement for

each Node in each Internal.

o The numbers or percentages may be “drilled into” in order to see the exact text.

Boolean and Compound Logic The above materials imply some recurring “logic” in the Queries. The two primary types of

logic are “Boolean” (after mathematician George Boole) and “Compound” (also known as

“proximity”).

• You will see the following Boolean and Compound “operators” in many of the Queries.

o Boolean: AND (intersection), OR (union), NOT (negation/less)

o Compound: NEAR, PRECEDING, SURROUNDING

• The subsequent pages contain diagrams and examples of these types of logic.

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Boolean Diagrams

The first diagram in each pair represents a page (with horizontal stripes corresponding to text lines).

The second diagram in each pair represents relationships among Nodes (codes).

C = Classroom Experiences; L = Learning Experiences; �� = Other codes

AND (Intersection) OR (Union)

NOT Coded by (Negation) NOT Coded by (Difference)

Compound Diagrams

Classroom Experiences AND Learning Experiences Classroom Experiences OR Learning Experiences

All coded data that is NOT in Classroom Experiences Classroom Experiences LESS Learning Experiences

C L

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L

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C ��

C

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��

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Boolean Examples

AND: Finds only the text indexed by all of the given set of two or more nodes.

To examine the illegal activities in neighborhoods that involve police contact, you conduct interviews with residents of all ages, and you ask them about their own illegal activities, the illegal activities they

observe in the community, and the police contact that they have either experienced or observed. As

you code more and more data, you may want to look at the data indexed by both police involvement and illegal activities by adolescents. This will provide you with the kinds of interventions that police

had with the younger people in the neighborhood (but not the intervention police had with older people

in the neighborhood, and not all illegal activities by adolescents). Note that you may have more than

one node in an “intersection” search. If you also coded for the time of day during which illegal activities occurred, you could add evening to the nodes above to find only the illegal activities by

adolescents that occurred in the evening and also resulted in police intervention. The more nodes you

have in your intersection search, the more you trim down your findings.

OR: Finds all text units indexed by any of the nodes selected.

In a study that looks at the formal and informal relationships in Asian-American communities, you

may have categories for family, friends and neighbors. If you would like to examine all of these categories together, you would run a “union” search on family, friends, and neighbors.

NOT (negation): Units not indexed by the named node.

You and three other staff have coded twenty, open-ended employee evaluation forms from an intensive, three-week company training. Because the interviews trickled in, and you want to make sure

your staff coded all the documents, wonder if you have missed something. Simply copy all of your

nodes, merge them into one new node, and search for anything that is “not in” that node. This will allow you to examine the data, if any, that did not get coded anywhere.

NOT (difference): Finds all text units indexed by the first node, but only when these units are

not indexed by any of the other (one or more) nodes.

In several interviews with victims of crimes, the relationship between the victim and offender are

discussed. Some of the categories include: violent crimes, assistance, consequences, friend, family,

coworker and stranger. You want to examine violent crimes, but not those that are committed by

individuals who are close to the victim. Furthermore, you realize that although you coded all violent crimes, you may have omitted a code for the relationship when the perpetrator was a distant

acquaintance or a neighbor. To conduct this search and still capture all of the strangers,

acquaintances and neighbors, you want to select all violent crimes, but exclude from the data the instances when friends, family and co-worker are also indexed at the violent node. When you browse

this search you are able to analyze the data, and also re-code for any of the categories you may have

missed, such as neighbor and acquaintance.

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Compound Diagrams

The three diagrams each represent a page (with horizontal stripes corresponding to text lines).

Near Content Preceding Content

Surrounding Content

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Compound Examples

NEAR content: Finds all text for the first and second node within the stated distance.

You can then choose whether you want to retrieve just the index references for the first of each such

pair (that is, cases of the first node that were found) or the second, or both. This search can be viewed

as a “generous” intersection because you are not requiring that the exact same text be coded into different nodes, just that text within a certain distance is coded at the specified nodes.

In interviews with people who work in early childhood education, you are curious about their

professional path and their career history. Many of them discuss their first months at a new job, their struggles, their successes, and reasons they moved on to the next job. You are curious about the

relationship between their comfort at a provider and the timing of their job transition. You have a

node for not comfortable and a node for new to the job. In some cases, discomfort might lead to a new job, and in others it might result from coming into a new job. To investigate this further, you run a

“co-occurrence” search on these two nodes to examine the data.

PRECEDING content: Finds all pairs of the nodes within the stated distance, provided the text range for the first node starts in the document at or before the start of the text range for the

second.

Like “Co-occurrence”, but puts a restriction on the sequence of the two nodes. You can then choose

whether you want to retrieve just the first of each such pair (that is, cases of the first node that were found) or the second pair, or both.

Using the example for co-occurrence, you specify that you would like to look at only the data regarding not comfortable when it follows discussions of new to the job. This will narrow down the

search that you ran in the example above.

SURROUNDING content: Selects the text unit ranges for the first node, provided the text unit

range lies entirely inside a text unit range for the second node.

You interview individuals about their definition of intimacy in a variety of settings, including

romance, sports teams, friendships, and work. When defining intimacy in the context of friendships, respondents often discuss respect. You note that discussions of respect seem to occur most often in the

context of friendship, and want to look at all instances in which respect is cradled inside the instances

of friendship. You may compare the volume of data in this search node to the volume of data in your

respect node, to find out how much of the data referring to respect occurred within discussions of friendship.

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Matrices

And, Or, Not, Near, Preceding, Surrounding may all be used in a Matrix Search. This is a

framework within which the Logics can be applied.

A B C

1

1.A 1.B 1.C

2

2.A 2.B 2.C

3

3.A 3.B 3.C

4

4.A 4.B 4.C

Matrix: Takes all the first named nodes or attributes and all the second named nodes or

attributes and pairs them in all possible combinations across these two groups.

A cross tabulation of text data with several columns and rows. To examine the types of interventions

most commonly employed by family members when a loved one abuses chemical substances, you

want to compare all of the substances identified in your study (alcohol, cocaine, marijuana) with all of

the intervention strategies identified in your study (family confrontation, police, counseling). In this case, your run will contain nine different finds (or cells) for the nine different pairs. By running a

“matrix” with the substance nodes (alcohol, cocaine, marijuana) on one axis (rows, for instance) by

the interventions (family confrontation, police, counseling) on the other axis (columns, for instance you will gather all of the data that is indexed by each pair.

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QUERI Memo 15: Text Search Queries

Understanding Text Search Queries

• Text Search Queries are used to “mine” the text data for key words and phrases.

• The architecture/options within this particular type of Query is borrowed from other

“text mining” software packages.

• These Queries can be used to get a quick preview of the use of certain kinds of words,

and they can also be saved and converted into Nodes (which can then be used like any

other Node – in Reports, additional Queries, Models, etc.)

Working with Text Search Queries

Starting a New Query

• From the Navigation View, select “Queries”

• Right click in the empty white space of the List View to create a New Query � Text

Search.

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Entering the Information

• Check the top left box “Add To Project”

o This opens the “General” Tab where you name the query. Note that you are not

creating the actual syntax of the query in this window, you are simply naming it

in order to easily find it the next time you wish to run the query.

o Enter a “Name” (and “Description” if desired)

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• Select the middle tab “Text Search Criteria” To specify the search engine syntax.

o In the “Search for” window, add the words/terms of interest.

� If you are using a phrase like “border crossing” be sure to include

quotation marks around the phrase. Do not use quotation marks for

single words.

� If you want all versions of the word, use an asterisk to indicate any

characters may follow the root. For instance, to find “help”, “helper”,

“helpful”, “helped”, or “helping”, simply type: help*

� Use a synonym, by adding “OR” (without quotes) between synonyms.

See the example in the screen shot below:

o The “Special” button on the right side of the window provides additional

options. See the NVivo Help files for further information on each of these

options.

o The “Finding matches” slide bar provides a range of options including stemmed

words and synonyms. See the Help files for detailed descriptions of these

options. This tool is based on WordNet (out of Princeton). For additional

information on this tool, go directly to the source at

http://wordnet.princeton.edu/.

� You cannot use the asterisk and the slide bar at the same time.

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o The “Search In” window default (“Text”) is to search the text in your files,

excluding annotations. You may use the dropdown arrow to select only your

annotations or your annotations as well as the text.

o The “Of” window specifies the files you would like to include in the search.

Note the default of “All Sources” includes Documents, Memos, and Externals.

� To identify an alternative subset of these items, use the drop down arrow

and the “Select” button (See QUERI Memo on ‘Select Project Items’

Window for assistance navigating the subsequent window”).

o The “Where” window allows you to specify the researcher who conducted the

coding

• Select the last tab “Query Options” to tell NVivo how you want the results to be

managed.

o In the “Option” window, use the dropdown arrow to select the desired option

(start with “Create Results as New Node” if you want this to be a start of a new

node, and put it alongside your other thematic nodes).

o Use the “Select” button to choose the location of the new node, such as your

“Themes” Node.

o The bottom third of the window allows you to select additional context around

the word(s) or phrase(s) you specified in the “Text Search Criteria” tab.

� Use “Broad Context” to capture the specific text as well as the

paragraphs in which this text appears.

� We recommend leaving it at “None” in order to easily see the word in

context. Try it both ways and experiment to determine your preferred

option.

• When the information for all three tabs is complete, select the “Run” button on the

bottom left.

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Uncoding Irrelevant Data from Your Text Search

• As you review the results, it is likely you will find some data that does not belong

• To uncode data from these results you have several options

• One option is provided below:

o Highlight the information you would like to remove

o Right click and select “Uncode”

� “Uncode Selection at This Node”

o You should see the node recalculating briefly, and then the undesired data will

go away (note that you have not deleted the text from the original file, you have

simply uncoded it from the current Node).

So What?

A Tip for Efficiently Looking Through Text Searches

Rather than spreading the context in the search, simply run the search without spreading.

The results provide no context, but you have three options for examining this context:

• Select the blue hyperlink of the name of a document in which there are finds and

you will be navigated to that document. The search terms will be highlighted in

yellow.

• Right click on any passage and “open referenced source”.

• Right click and select “coding context”. Choose the desired range.

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QUERI Memo 16: Matrix Coding Queries

Understanding the Matrix Coding Query • A Matrix search is comprised of columns and rows.

• Each column/row contains either a project item (such as a Node, Source, or Set) or an

Attribute condition (such as male or female).

• The table below is a sample, with rows populated by Attribute values (the location of a

participant’s city as either Inland or Coastal), and columns populated by a Node (the

text query for the word Boundary).

• An operation is selected for the entire table that determines the action taken in each

cross-referenced cell (AND, OR, NOT, Near, Preceding, Surrounding).

o The “AND” (also often referred to as the “intersection”) search is often the most

intuitive place to start if you are new to the software or to this Boolean and

compound logic.

• The table provides output in numeric as well as text form.

o When examining the numeric output, the software can provide various counts.

o Click in the Detail View and then right click � Matrix cell content. The

software counts by

� Sources – Memos, documents, externals

� Nodes that organize units of analysis such as people, organizations, or

policies.

• Return to this window after choosing “Nodes” and then go to

“Classifications” to obtain numbers on your subpopulations.

� Words – Any string of text separated by a space on either end.

� References – A continuous passage of selected text (this could be a few

words, a paragraph, a page, etc.)

� Duration – pertaining to audio and video files

� Row and Column percentage – this isn’t what you think it is (a

conversion into rates), so don’t use it.

� Presence – the word “yes” indicates there is some text in the cell, and

“no” indicates and empty cell.

o To see the text in any cell, you may double click on any cell with a value other

than zero.

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Working with Matrix Coding Queries

Starting a New Query

• From the Navigation View, select “Queries”

Right click in the empty white space of the List View to create a New Query � Matrix

Coding.

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Entering The Information

• Check the top left box “Add To Project”

o This opens the “General” Tab where you name the query. Note that you are not

creating the actual syntax of the query in this window, you are simply naming it

in order to easily find it the next time you wish to run the query.

o Enter a “Name” (and “Description” if desired)

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• Select the middle tab “Text Search Criteria” To specify the search engine syntax.

o In the “Rows” tab, add the items of interest. (e.g. Nodes)

� Click on the “Select” button at the bottom of the Row tab.

� Use the “Select Project Items” Window to identify the items of interest.

See the QUERI Memo on the “Select Project Items Window” for further

assistance if needed.

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• Select “OK” after choosing the desired items.

� DON’T FORGET to select the new button that is now available to the

right of “Select” that says “Add to List”, or the items you chose from the

previous window will not appear in the Rows.

o In the “Columns” tab, add the items of interest (e.g. Attribute Values) by

following the same instructions for the Rows.

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� DON’T FORGET to select the new button that is now available to the

right of “Select” that says “Add to List”, or the items you chose from the

previous window will not appear in the Columns.

o The “In” window (at the bottom of the Search Criteria tab) has a default set

at“All Sources”. This searches all of the Source Files, excluding annotations

and other Links. You may use the dropdown arrow to select only a portion of

the data if you prefer.

o The “Where” window allows you to specify the researcher who conducted the

coding

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• Select the last tab “Query Options” to tell NVivo how you want the results to be

managed.

o In the “Option” window, use the dropdown arrow to select the desired option

(start with “Create Results as New Matrix”)

o Enter a name for the new node in the “Name” window, and a description if

desired.

• Run the search by selecting the “Run” button at the bottom left.

o The Matrix results will open in a tab and can be found later in the Results folder

of your Queries tool.

o The syntax lives in your Queries folder and can be opened or run again any time.

� Right click on the query engine syntax and select “Run Query”

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So What?

• The prior Matrix Intersection is a common approach because it allows for an efficient

comparison of subpopulations within each Node.

• However, there are additional options. Matrix queries can be used to cut across many

kinds of project items. For instance, consider the following Matrix Intersections, in

which the columns and rows are modified:

o Units of Analysis (or Cases) BY Nodes compares the distribution of coding

across Cases

o Documents BY Nodes compares the distribution of coding across

Documents

o Nodes BY Nodes reveals the co-occurrence of coding at any two nodes in

the data set

o Questions BY Nodes provides a map of the way different themes emerged in

response to different questions.

o Attributes BY Attributes facilitates the creation of “interaction effects” that

can be used in subsequent searches

• Consider the third option above, a Matrix Intersection of Nodes BY Nodes.

o Each cell in the table above represents the number of people in the project

who identified the person/entity responsible (rows) with a particular

recommendation (columns)

� Two individuals believe they are responsible regarding the strategy

of “Better Communication”

� Nine individuals believe an outside entity is responsible regarding

the strategy to “Increase Safety”

• This capability in the software has implications for the development of your coding

structure. Software packages have provided a new, often elegant solution to the chaos

of a voluminous coding structure.

o Many experienced users of software are able to help newcomers convert a

coding structure of, for instance, 1,000 Nodes, into a structure of 150 (there

is no implicit formula – these numbers are simply examples).

o The grand significance of this simplification, and the paradox that can be

difficult to grasp, is that the simplified coding structure – the one with 150

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Nodes, can actually provide a researcher with MORE than the coding

structure of 1,000 Nodes.

• On the following page you will see an illustration of this simplification, although

complete understanding of the implications may require a conversation with a more

experienced software user.

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Coding Structure Option #1

Potentially Problematic

The text in this passage goes to one, specific node

Visual A

Visual B

“I think it's a better police presence -- even if we have to pay. The

police need to keep us safe.”

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Coding Structure Option #2

Potential Solution

The text in this passage goes to two different “vistas” (angles/views)

Visual A

Visual B

“I think it's a better police presence -- even if we have to pay. The

police need to keep us safe.”

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QUERI Memo 17: Copying and Merging Projects

Understanding the Copy Feature

• An NVivo project is a single file, like an Excel spreadsheet, a Microsoft Word document,

or an Access database.

• Similar to an Access database (and most other databases), you can open up different

items in the database, such as an interview or a focus group, and you can have more than

one item open at a time. This is most noticeable from the Detail View, where you are

able to have several tabs open at once, and to open/close these items without actually

saving the whole database.

• You do, however, need to periodically save all the work that you’ve been doing, even

though NVivo keeps track of all of this work as you open/close items in the database.

• Just like a Microsoft Word document, you can copy any project from the Windows

Explorer, or copy/move the project in Windows Explorer from your C Drive, for

instance, to a Thumb/Flash Drive. However, QUERI does not recommend this if you are

going to be crating many backup files over time. When a project is copied, and renamed,

the actual Project Title remains the same, even though the File Name changes. This

reality is apparently familiar to programmers, but is not likely to be second nature to

qualitative researchers. To understand the difference between the Project Title and the

File Name, and to effectively manage these without getting confused, QUERI

recommends the following process for copying projects.

Working with the Copy Feature

• Prior to copying a project, you’ll want to save it. So, from inside the project, Select

“File” from the main menu and “Save”

• You may have noticed that there is not a “Save As” option (often familiar to Microsoft

Word and Excel users). However, there is a “Copy Project” option. Go to “File” from

the main menu and Manage � “Copy Project”

o From the top window (“Save in”) you may select the location for the copy

o From the bottom window you may provide a new name

o QUERI Recommends creating a folder for your copies, and adding the date to the

end of the project name in the format: Year_Month_Date (see the following

screen shot)

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• Once you click on the “Save” button, you will have a backup copy of your project, but

you will still be working in the project you most recently had open (this is why it is not a

“Save As” and instead is a “Copy Project”

Understanding the Project Merge Feature

• Built into the NVivo software is the ability to Import project “A” into project “B” (or

project “A,” “B,” and “C” into project “D”, etc.)

• This feature is especially helpful under two (although there are more) common

circumstances:

o Teams are faced with the reality that only one person can be in any one project at

a time but need to get work done quickly. They therefore make copies of the

project and each researcher works in a copy which will be merged into the master

version of the project (aggregated) later.

o Researchers are located in geographically dispersed areas and instead of trading

the digital project back and forth (or storing it on an accessible server), they

determine it is more appropriate to work in copies of the project that can be

aggregated later.

• These projects may have one or more items common (the same documents, nodes,

memos, attributes, queries, models, links, etc.) OR they many not contain any items in

common (different documents, different nodes, etc.).

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o For instance, there is an interview for “Anna” in both projects (the same

interview), but Anna in project “A” may be coded to different nodes than project

“B”.

o This could be because the researchers have different conceptual

frameworks/assumptions or it could be a result of divisions of labor: One

researcher’s task was to look for “Strategies” and the other researchers task was to

look for “Resources”.

o Some (but not all) f the coding may overlap, but some of it is likely to be unique

to project “A” and project “B”. Merging allows for all of this coding to be rolled

up into one project.

• The researcher has the option of “merging” items that the projects have in common, or

the researcher can specify that any duplicates should not be merged and should instead be

left as separate items in the newly merged project (although this is less common in

practice).

• Unique items across the projects will remain unique items, but become part of one

aggregated project.”

• Merging projects is very easy, providing researchers are aware of the rules for merging.

KNOW THESE RULES BEFORE COPYING AND WORKING IN SEPARATE

PROJECTS BECAUSE FAILURE TO FOLLOW THESE RULES MAY RESULT IN

AN INABILITY TO MERGE PROJECTS SUCCESSFULLY

Working with the Project Merge Feature

The Simple Rules if You Want Items to Merge • Items in project “A” and project “B” will merge as long as three conditions are met

o The items have the same NAME. What does this mean?

� “Trust” in project “A” is also called “Trust” in project “B”

� “Anna” in project “A” is also called “Anna” in project “B”

� Etc.

o The items are in the same hierarchical location with the projects. What does this

mean?

� “Trust” in project “A” is under the parent node “Teamwork”, and in project

“B” the “Trust” node is also under “Teamwork” (if it is under

“Communication” in project B, the two “Trust” nodes will not merge. This is

fairly easily resolved later as the researcher may manually merge two or more

nodes after the projects have been combined, but this can become tedious if it

happens often).

� “Anna” in project “A” is nested under the “Interviews” folder, which is under

“Internals”, and “Anna” in project “B” is in the same nesting of folders (if

“Anna” in project “B” is in a different folder, such as the “Internals” folder,

the two versions of “Anna” will not merge.

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o A third condition applies to all Internals in the project: Internals such as “Anna”

will only merge if they contain the exact same text characters. What does this

mean?

� If a researcher working in project “A” finds a typo in “Anna” and fixes that

typo, but the researcher in project “B” does not, the two versions of “Anna”

will not merge, even though the documents are the same name and in the same

hierarchical location.

� For this reason it is critical that none of the researchers edit any of the

documents in their version of the project. Researchers may CODE as

differently as they would like, but they may not edit the text in the Internal

file.

• To help ensure this, make all internals are “Read Only” by selecting the

check box for “Create as Read Only” as the internals are imported OR,

select the document from the List View after it has been imported, right

click, select Document Properties and then select “Read Only”. Explain

the reason for “Read Only” status to all researchers so they are aware of

the importance of this status and do not change it.

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Using the Project Merge Feature • Before you get too far along in your work with different copies of a project, run a

sample Merge to iron out any issues. This will potentially save you MASSIVE

amounts of time later.

• Although we intuitively use the term “merge” to describe this process, NVivo

actually “Imports” one project into another project. This is technically called a

“Project Import”.

• Because you will be importing one project into another, you should first MAKE

COPIES of all relevant projects first.

• Open the 'target' project you want to import into.

o From the External Data menu select, “Project.”

o Select the Browse button and find the project you want to import.

o Click the “Open” button.

o The name of the project you are importing is displayed in the Project to

import field.

o Click the “All (including content)” option, if you want to include all items

in the imported projects.

o Choose how you want to handle duplicate items. You can choose to merge

duplicates or create new project items.

So What?

If you are planning to compare the work of different researchers

• If you are hoping to Merge and compare the work of different researchers (by

using the coder comparison query, for instance) you must change one of the

defaults in NVivo if these researchers are working on the same computer (either

in the same copy of the project, or in two different copies of the project).

• To change this default, open NVivo on the computer, and before opening a

project, go to “Tools” from the main menu and then to “Options.”

o Under the “General” Tab, you will see a check box to “Prompt for user on

launch.”

o This box should be checked so that the software tracks individual researchers

who are using the same computer.

o This strategy is also recommended (though not required) if researchers are

using different computers.

• Subsequently, each time a researcher launches the software on the computer,

they’ll be asked to provide their login Name and Initials.

Questions that may Influence Your Teamwork and Project Merge Strategies

• Are we coding different documents to the same nodes?

• Are we coding the same documents to different nodes?

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• Howe often can we share/merge projects?

• Do we want to identify a standard set of codes up front, or do we want to let

things organically emerge (or both)?

• Do we care about coder reliability? If so, see the coding comparison query and

see the help files for “users” in order to establish the various researcher IDs and

passwords.

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