Friese S. Qualitative Data Analysis with ATLAS.ti 3ed 2019

16 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI me all data segments coded with “attitude towards the environment” but only for females who live in London as compared to females who live in the countryside’. You will learn about and work with groups in all later chapters. SKILLS TRAINING 2.3 WORKING WITH THE ENTITY MANAGERS The entity managers allow access to the entities, where you can review, edit, manage and query them. Figure 2.12 Access the entity managers from the Home ribbon • Open the Document Manager by clicking on the Documents button in the Home tab. • Look at the other managers as well. You will notice that they all look similar. I will explain the specifics of each manager when we begin to set up a project. An alternative way to open a manager is to double-click on the branch for Documents, Codes, Memos, etc. in the Project Explorer in the navigation panel on the left. Figure 2.13 Document Manager

GETTING TO KNOW ATLAS.TI 17 Each manager has a list of the entities it manages and some detailed information about them. At the bottom of the list, you will find a comment field in each manager and in some managers also a preview field. In the Document Manager and Quotation Manager, you can preview the content of either the selected document or the selected quotation. In the Memo Manager, the memo content is shown next to the comment field. Another common element is the side panel on the left-hand side, which can be used to quickly access and filter the elements listed in the managers via groups, or codes in the quotation manager. • Activate or deactivate the side panel by selecting the first button in the View tab, i.e. Document Groups in the Document Manager. • The relative size of the side panel, list, preview and comment pane can be modified by dragging the split bar between the panes. Try it out. The cursor changes when the mouse moves over the split bar. You can resize the adjacent panes by dragging the split bar to the desired position. The blue status bar at the bottom of the screen shows the number of items listed in the manager. The Document Manager shown in Figure 2.14, for instance, contains 30 documents. The entity managers allow comfortable sorting and filtering. With a click on a column header all items are sorted in ascending or descending order based on the entries in the selected column. You can also enter a word in the search field to filter all items in the manager. Another way to filter is via the groups. When selecting a group, only the members of the selected group are shown in the list. • Open the Code Manager: sort the entries with a click on the column header. • Adjust the width of the columns as fitting. • Grab the splitter at the bottom of the list of codes, above the comment field, and move it up and down. • Select the code group ‘effects of parenting’. The list of codes will be filtered, plus you see a light-yellow bar that shows which filter is currently active. • To see all codes again, remove the filter by clicking on the X on the right-hand side of the light-yellow bar. • Click on some other code groups. See how this facilitates the navigation of the code list and provides easy access to smaller groups of codes. SKILLS TRAINING 2.4 WORKING WITH DOCKED AND FLOATED WINDOWS When you first open a manager or other windows, they will be opened either in floating or docked mode, depending on your preference settings. Figure 2.14 Entity Manager View options

18 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI • To check your preference settings, select File/Options/Project Preferences (Window Location and Size). Now let’s practice how to dock and undock windows. • To dock a window, click on the icon at the top right-hand side as shown in Figure 2.15. Figure 2.15 Floated Code Manager Figure 2.16 Docked Code Manager • To float the window again, click on the drop-down arrow as shown in Figure 2.16 and select Float.

GETTING TO KNOW ATLAS.TI 19 A floating window disappears in the background if you click outside the window. If you prefer the window to stay on top of other windows, right-click on the button at the top left of the window and select Always On Top. SKILLS TRAINING 2.5 LOADING DOCUMENTS • To load a document, open the document tree in the Project Explorer on the left-hand side and double-click document D1 and D3. Figure 2.17 Loading documents The documents are loaded into two tabs in the main work space. The tab of the currently loaded document is colored in yellow. Figure 2.18 Documents load in tabs next to each other

20 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI SKILLS TRAINING 2.6 CREATING TAB GROUPS If you want to see the documents side by side, you can move one of the documents into a different tab group: • Click on the down arrow and select New Tab Group/Right. You can move it into a tab group to the right, left, below or above. Figure 2.19 Create a new tab group This can also be done with any other windows you open, be it your list of codes, a memo or a network. You can individualize your work space as it suits your needs. An example is shown in Figure 2.20. Figure 2.20 Partition your screen as suitable for the task

GETTING TO KNOW ATLAS.TI 21 SKILLS TRAINING 2.7 SIMPLE DATA RETRIEVAL In this exercise I want to give you an idea of the benefits of coding. • In order not to be distracted by too many open windows on your screen, close all windows. A quick way of doing this is by clicking on the drop-down arrow in the header and selecting Close All. • Open the Code Manager. • Click with your cursor on any code in the list. Next, press the letter R on your keyboard. The cursor jumps to the first code in the list that starts with the letter R, which is REASONS FOR HC (HC = having children). • Double-click on the code ‘reasons for hc: altruism’. The list of quotation opens that is coded with this code. • Move the list to the right or left side of the screen. It will dock automatically. In case it does not work, click on the little drop-down arrow and select Dock. The quotation list is shown in the navigation panel on the left. This way it does not block the view. • Click on a quotation. This opens the document that contains this quotation. The selected quotation is highlighted in blue. • Click on a few more quotations. SKILLS TRAINING 2.8 LOOKING AT A NETWORK • Close the quotation list in the navigation panel by clicking on the X. • Open the Network branch in the Project Explorer and select the network ‘Positive effects of parenting’. • Grab a node and move it to a different position. • Let ATLAS.ti do the layouting. Select the Layout button in the ribbon and play around with some of the layout options. You will learn more about this in Chapter 7. • After you have selected a layout that you like, click on the Routing button and select Polyline Rerouting. Figure 2.21 Example of a network

22 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI SKILLS TRAINING 2.9 PREVIEWING THE QUERY TOOL Here is what you have done so far. You learned about the entity types in ATLAS.ti and how you can access them via the navigator or via an entity manager. I also showed you how you can retrieve and review the quotations coded with a code. You can also ask more complex questions based on multiple codes. For this you use the Query Tool. • To open the Query Tool (Figure 2.22), select the Analyze tab and from there the Query Tool button. Figure 2.22 The Query Tool Note that there are 14 operators, organized into three groups (set, semantic and proximity operators), which you can use to combine codes and groups of codes to ask questions about your data. In addition, you can restrict searches to subsets of data via the Scope Tool. This allows you to set tasks such as: ‘Find all data segments coded with “reasons for not having children” that have also been coded with “effects of parenting: negative”.’ You can compare the blog data to the survey data, or within the survey data compare male and female respondents. Let’s click a simple code co-occurrence query (Figure 2.23): • Select the Co-Occurs button. • You can now enter two codes or code groups into the nodes with the red dot. Double-click on the code ‘children: > happiness’. Next, double-click on the code ‘fam: have children’.

GETTING TO KNOW ATLAS.TI 23 • Below the query you see the list of quotations that result from the query (Figure 2.23). Expressed in words, the query retrieves all quotations where respondents who have children wrote that they are happier because they have children. If you double-click on a quotation, it is displayed in the context of the document. • You can compare this with the statements of those who do not want children by deleting the ‘#fam: have children’ code from the query and using the ‘#fam: don’t want children’ code instead. • You may also be interested in reading the quotations about being less happy because of children (children: <happiness) written by those who have children and those who do not want to have children by exchanging the codes. Figure 2.23 Example query You will learn more about the Query Tool along with other analysis tools in Chapter 6. REVIEW QUESTIONS 1 What are the six main entity types in ATLAS.ti? 2 If you are familiar with version 7 of ATLAS.ti, what has changed? What type of new elements and interface features have been added in version 8? 3 What can be displayed in the navigation panel on the left-hand side? 4 What are entity managers and what are they useful for? 5 How can you dock and float windows?

24 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI 6 How can you display documents side by side? How can you display the Code Manager next to a document? 7 How can you navigate through a long list of codes? 8 How can you read the quotations of a code? 9 What is a network? 10 What can you do in the Query Tool?

Embarking on the journey – data and project management 3 Starting with this chapter, I will take you on a journey – metaphorically speaking – to explore a data landscape. Think of your ATLAS.ti project as an excursion into unknown territory. The data material is the terrain that you want to study; the chosen analytic approach is your pathway through it. The tools and functions provided by ATLAS.ti are your equipment to examine what there is to discover. The preparation of the data material is like choosing the right time for the journey. Rain and storm can complicate a planned excursion. This also applies to your project, if, for example, during the transcription, the peculiarities of a computer-aided analysis are not taken into consideration, or if the data file formats are not chosen optimally. A well-designed project set-up is like carefully planning your trip, so you do not make a wrong turn at the first intersection and end up in a dead end. The first part of this chapter gives an overview of the data file formats supported by ATLAS.ti and a few things you need to pay attention to. The recommendations and suggestions are derived from everyday user problems and questions that I have come across in the past. In addition, I include some transcription guidelines relating to the technicalities of the software. Following these guidelines will ease your work with ATLAS.ti at later stages of your analysis. In the second part of this chapter, you will learn how to set up a single-user project. Different scenarios on how to set up a team project are discussed in Chapter 9, at the end of the book.

26 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI Figure 3.1 Looking at data like a landscape to be explored Chapter 9 is not only relevant if you are working in a classic team situation, where multiple researchers analyze data together. Please also read it if you are working alone but intend to check for inter-coder agreement. This means that you eventually will share your project with other coders, so you work in a team of at least three. LEARNING OBJECTIVES ATLAS.ti supports most textual, graphical and multimedia formats. In this chapter, you will learn how to prepare several types of data and files for analysis and the issues you should pay attention to: how to manage your project data, where ATLAS.ti saves project data, how to create backups and how to transfer your project to another computer. Project management is often neglected; it is admittedly rather boring. But you never know when the next disk failure will occur or if someone will steal your laptop (which I hope never happens). Then you will be glad if you have a current backup of your project somewhere in a safe place. The organization of project data is another topic that we will look at in more detail in this chapter. You will learn how to identify cases in your data and how to handle them

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 27 in ATLAS.ti. It’s important to get your project set up right from the start, so you can take full advantage of the analysis tools later. Furthermore, you will create initial memos, and I will give you some ideas on how to use memos when you start your project work. Once the project is set up, I’ll show you how to start data exploration. SKILLS TRAININGS Skills training 3.1: setting up a project Skills training 3.2: organizing project documents Skills training 3.3: managing your project Skills training 3.4: exporting projects for project transfer or backup Skills training 3.5: creating project snapshots Skills training 3.6: changing the default location for ATLAS.ti project data Skills training 3.7: preparing and importing survey data Skills training 3.8: importing reference manager data for a literature review Skills training 3.9: writing memos in the early stages of analysis Skills training 3.10: exploring your data — creating word clouds Skills training 3.11: keyword in context search DATA PREPARATION In principle, ATLAS.ti supports most textual, graphical and multimedia formats (see Table 3.1). Table 3.1 Supported file formats Type of data Format Specific features/considerations Text .txt (plain text), .rtf (rich text), .doc(x), .odt (OpenOffice), .htm and .html Can be modified within ATLAS.ti in a later version. It was not yet available at the time of writing. PDF Image and text format Make sure that documents are scanned with character recognition. If the scan is an image rather than a text PDF, you cannot retrieve text. ATLAS.ti treats it just like an image document. Image .mpg, .gif., .jpeg, .jpg, .png, .tif and .tiff Resize large images. For analysis you do not need the highest resolution. Adapt the size to whatever resolution your computer screen can display. (Continued)

28 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI Type of data Format Specific features/considerations Audio Windows: .aac, .m4a, .mp3, .wav Mac: .aac, .m4a, .mp3, .mp4 The recommended format is: .mp3 files with AAC audio When you work across platforms, make sure you use a format that plays on a Mac as well as in Windows. This excludes the use of .wav and .wmv files. Video .3g2, .3gp, .3gp2, .3gpp, .asf, .avi, .m4v, .mov, .mp4, .wmv Mac: .avi, .m4v, .mov, mp4 The recommended format is: .mp4 files with AAC audio and H.264 video Geo data OpenStreetMap is used as data source If GoogleEarth is installed on your computer, you can also browse a selected location in GoogleEarth. Survey data (Excel) Results from an online survey can be imported as case-based documents (commonly used for the analysis of openended questions). The column headers in the Excel file need to follow certain formatting rules. Variables from surveys are imported as document groups. Reference Manager Articles and meta data from reference managers like EndNote, Zotero, Mendeley, Reference Manager, a.o. Your reference manager must be able to export the data in XML format for EndNote. This is the format that ATLAS. ti imports. RIS files will be supported in the future. Evernote If you collect data in Evernote, you can import them directly from there. Documents with multiple images are not supported. Twitter You can collect data from Twitter, searching for keywords, hashtags, users, etc. ATLAS.ti can collect tweets that are not older than one week. The tweets from one search will be collected in one document. Thus, with each search, ATLAS.ti adds a new document to your project. In order to use this option, you need to sign in with your own Twitter account. The final choice is made by Twitter; queries at different times or on different computers may result in different tweets. Table 3.1 (Continued) Text documents If you are familiar with an older version of ATLAS.ti, you might remember that there were issues at times with certain Word file formats. With version 8, this no longer applies. You can add plain text files, rich text files, doc(x), odt files from OpenOffice, htm and html files. An issue might be if Word files have lots of pictures and figures. Such files can become quite sizable and you may experience long loading times. If so, save the Word file in PDF format as it reduces file size quite considerably.

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 29 PDFs ATLAS.ti supports native PDF; this means the files are displayed within ATLAS.ti just as they look in their native PDF environment. Nothing is lost, and the full information is available to you when coding the data. Misunderstandings sometimes arise in distinguishing between textual and image PDFs. When you scan a document, activate character recognition to turn the document into a text rather than an image PDF. Image PDFs are treated like all other image documents by ATLAS.ti. Further, you need to be aware that all retrieved text from text PDFs is rich text. You will lose the original layout when creating an output of coded PDF segments. PDFs are bit more difficult to code than Word files. Therefore, do not make them your standard, catch-all format. PDFs are a choice when the original layout is important for analysis purposes or when the documents are already available as PDFs. Audio and video files When analyzing audio or video data, there are a few things that you need to consider. • If your ATLAS.ti project should work on both platforms (Mac and Windows), you need to select a file format that can play on both platforms. This means not to use Windows media files like wmv and wav files. • Having the choice to analyze audio files does not mean you can save yourself the process of transcription, if you are interested in analyzing the content of what was said. Analyzing audio files is relevant for those researchers who want to code things that are difficult to transcribe like pitch of voice, sad, hesitant, enthusiastic, angry voices or music. Coding an audio file and listening to coded segments takes longer than coding and reading text. The time you save by not transcribing is consumed again when working with the audio data in ATLAS.ti. • When importing a video file, ATLAS.ti Windows creates preview images from key frames. A key frame is defined as one of the frames in a video that gives the best summary of the video content. In ATLAS.ti Windows, they are displayed vertically next to the margin area and help you navigate the video. You can set key-frame rates in most good encoding software. If you have a typical talking-head video or something else with little motion, you can get away with a slow key-frame rate. If you are shooting something with a lot of motion like a sporting event or a dance recital, a faster key-frame rate is necessary. The standard rate is to include a key frame every 5 seconds. The Mac version does not create preview images; instead you see a preview of every frame if you move down the margin area with your mouse. • Video files can be quite sizable. Therefore, instead of importing them you can link them to a project. Once the video files are imported and you begin to work on the project, you cannot reverse the process and turn imported documents into linked files. I recommend that before preparing all your audio or video files, you create a short trial file for testing in ATLAS.ti. After you have figured out the settings and format, you can prepare the rest of the material.

30 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI Multimedia transcripts It is possible to link transcripts with audio and video files via timestamps. This means you can watch a video or listen to an audio recording in synch with the transcript or jump from a section in your transcript and play the video sequence described by the transcript. At the time of writing, it had only been implemented in the Mac version (see screenshot below), but it will have been implemented in the Windows version by the time the book is published. I will not discuss this feature in the book but want to point out that it exists. Once document editing becomes available again, you will be able to prepare transcripts in ATLAS.ti using this function. Figure 3.2 Audio file with transcript Image files When working with images, there is no point in adding the full-quality image taken with a 14-million-or-more-pixel camera to ATLAS.ti. The image will not fit on your screen as the resolution is way too high, so you will end up resizing the images in ATLAS.ti to make them suitable for analytic purposes. I cannot give a one-size-fits-all optimal image size because it depends on the resolution and size of the screen. However, a good starting point is 1024 × 768 pixels. Excel files (survey import) You need to follow a specific syntax when preparing an Excel file for import. Special characters in front of the header columns define which part of the spreadsheet will be added as document, as group or as code. I explain in Skills training 3.7 how to prepare Excel files for import.

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 31 File size and document length Theoretically, size restriction is not an issue. However, you should bear in mind that your computer’s processing speed and storage ability affect its performance. Excessively large documents can be uncomfortable to work with, even if you have an extremely sophisticated computer. The crucial issue is not always the file size, but rather, in the case of multimedia files, the length of playing time. For text documents, the number and size of embedded objects may cause extraordinarily long load times. There is a high likelihood that if a text document loads slowly in ATLAS.ti, it will also load slowly in other applications like Word. Language support You can use documents in any language, and you can also create codes, write comments and memos in any language. At the time of writing, the following interface languages were available: English, German, Spanish, Portuguese and simplified Chinese. COLLECTING DATA WITH THE ATLAS.TI MOBILE APP With the ATLAS.ti mobile app you can begin to collect and analyze data in the field. Or perhaps you want to work on a document while on a long train journey, flight or boat trip. Email the document to yourself so it is available on your iPad and Android device.2 Then you can add it to an ATLAS.ti mobile project, read it, write comments and perform first coding work. You can later merge the result with your existing ATLAS.ti desktop project. Text, PDF (iPad only), image, audio and video files are supported. For further information on working with ATLAS.ti mobile, visit the ATLAS.ti website. TRANSCRIPTION GUIDELINES At the time of writing, the function of transcribing data in ATLAS.ti was not available because text documents could not be edited, but at the time you have this book in your hands it might be possible. Below, I offer some guidelines for transcription that will ease the later analysis process. Guidelines for interview transcripts • Mark all speakers unambiguously and enter an empty line between each speaker in turn. If you want to use the auto coding feature, this will allow you to code hits within a given speaker unit. 2The apps are available free of charge.

32 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI • In the sample transcript below, the paragraph marker is visible, showing when the Enter button was pressed. The two speakers in the transcript are marked with unique identifiers: INT: is used for the interviewer and AL: for Alexander, the interviewee. ‘Interviewer’ or ‘Alexander’ would be impractical as markers because those words might appear in the text itself, but the character combinations INT: and AL: are not likely to be found anywhere else. This is essential for using the Auto Coding Tool. • If one speaker talks for a long time, break the speech into multiple paragraphs (see sample transcript below). The reason for this is the ATLAS.ti referencing system for quotations. Each quotation has an ID, and paragraph numbers are used to indicate where it starts and finishes – so if you code just one sentence in a longer paragraph, the reference for the resulting quotation might not be precise enough. Figure 3.3 Organizing a transcript This way of organizing the transcript can be used for any documents that include structuring elements, like dates in historical documents, emails or letters. If you format documents in this way, you can get the most out of the Auto Coding Tool, even if you may not know at this point whether you want to use it. It is sensible to get used to all the above formatting rules as early as possible. Although neglecting these best-practice rules will not have a negative effect initially, you may later regret not having used them from the beginning. Guidelines for focus group transcripts Everything I wrote above for interview transcripts also applies to focus group transcripts. If you want to analyze answers of individual respondents or group of respondents, you need

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 33 to code each speaker unit. You cannot use document groups for this. If a focus group is comprised of four female and four male participants, you can neither add the transcript to the document group female nor the document group male. ATLAS.ti can recognize speakers if they have a unique ID like ‘Anne:’ or ‘@Anne:’. Based on these, ATLAS.ti finds all speaker units, and you can automatically code them with both speaker name and other attributes (see Skills training 4.13). ABOUT DATA FILE NAMES If you prepare your own data, I recommend that you name your documents in a way that is useful for the analysis. I numbered the documents of the sample project from 1 to 5, which helps working with the data in a teaching context. If you prepare a project for analysis, the situation is different. Look at Figure 3.4 for an example of how NOT to do it. The names of the many files added to the project consist of the word ‘transcript’, a number and the date of transcription: ‘transcript 1_ 20171105’, for example. This is not a good choice because the document name does not include any information that could be helpful for the analysis. A better choice is for the document names to support the analytic process. Naming a transcript ‘transcript’ – as in the example above – is only useful if you are working with other data sources as well. Numbering them and adding the date of transcription is useful for the process of transcription but not for your analysis. More informative are file names that include the criteria that you already know are important for your analysis like gender, age, profession, location and the date of the interview. Naming your files in this way has the advantage that the documents are already sorted by these criteria. This helps, for instance, to create documents groups in ATLAS.ti for analytic purposes (see Skills training 3.2). In addition, a good analytic name gives valuable information when retrieving data and, overall, adds transparency to your project. If alphabetical order is not useful for your purposes, or if you do not assign all the data at once, you can always change the position of the document in the Document Manager (see ‘Renumbering documents’, Skills training 3.5). It may not always be possible to know from the very beginning what might be a good analytic name – or perhaps you have already created a project before reading my suggestions. In that case, you can rename each document in ATLAS.ti: Figure 3.4 ‘Bad’ data file names Figure 3.5 ‘Good’ data file names for analytic purposes

34 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI • Right-click on a document in the Document Manager and select Rename. I am frequently asked how to present and report on a project that has been analyzed with ATLAS.ti. Look at your document names in the Document Manager. This is where to start – use the Document Manager to explain your sampling. If well chosen, the names will already include some of the major sampling criteria. PROJECT MANAGEMENT IN ATLAS.TI ATLAS.ti project management entails an understanding of how ATLAS.ti handles and accesses documents. Most problems can be avoided with a little informed planning about issues such as file locations, and the need to copy, move and transfer ATLAS.ti projects across disks, networks and computers. For more general considerations in designing and conducting qualitative research in a software environment, see di Gregorio and Davidson (2008: chapter 2). The aim of this section is to help you understand what is happening when you add documents to a project and to introduce you to a few technical issues that happen behind the scenes. Working with ATLAS.ti involves users, files and computers. An ATLAS.ti project can be as simple as a single person working with one project and a few documents on a stand-alone computer. It can be as complex as large teams working on different computers in a network or at different geographic locations; working on several projects at once; moving files between users, computers and networks; merging partial projects into compiled projects; and many other scenarios. First, however, you need to know a few basics about how ATLAS.ti handles data and to understand that a well-managed project begins even before you enter any data: that is, when thinking of names for your files as explained above. Let’s assume that you have conducted an interview study and have 20 audio-recorded interviews. You transfer the audio files to your computer and begin to transcribe and save the resulting text files somewhere on the computer, using your own system for organizing and storing them. Next, you want to analyze the data with the help of ATLAS.ti. You open ATLAS.ti and begin to add data to your project. When adding documents to a project, they are copied, converted and stamped with a unique ID and become internal ATLAS.ti files. This means ATLAS.ti no longer needs the original files. However, I recommend that you keep a backup copy of the original source files. The unique ID consists of a combination of letters and numbers and looks something like this: 0e5418ea58a94fd28b7d5bd937f884a2. It allows ATLAS.ti to recognize each document unambiguously as the name of a document, and not even a combination of name and size means that the content of two documents are indeed the same. This is especially important for merging projects when working in a team, or if you want a second coder to code parts of your data to assess inter-coder agreement. In both cases, you need to share projects, and you need to make certain that if coders work on the same documents that these documents have the same ID in each project. The way you ensure this is for one

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 35 person to oversee setting up the project and adding documents. Everybody else who will be working on the project needs to wait for that person to share with them a project bundle file. You will learn all the necessary steps in the skills trainings below. Your ATLAS.ti project is saved within the ATLAS.ti environment. This is a folder on your computer under the AppData\Roaming directory (Windows) or the Applications folder (Mac) on your computer. If you want to view or touch your project as a file, either to share it, to transfer it to another computer or to make a backup copy, you need to export it. The default location for all ATLAS.ti projects is on the C drive/hard drive as this is where the AppData\Roaming folder is located. As there are situations where it is not possible to work on the C drive, ATLAS.ti has a function that allows you either to move the library to a different location or to create new libraries. You can create as many new libraries as you want. This, however, means that you are responsible for taking care of your libraries. The AppData\Roaming folder is a hidden folder: you can access it, but you need to know how. Therefore, it is unlikely that you will lose or corrupt any of the files by accident. If you work on your own computer and you have enough space on your C drive, I recommend that you leave everything as is and work at the default location. If you work with ATLAS.ti in a computer lab where you do not have your own user profile, you could create a library on an external disk and take it with you. Another possibility is to export the project after each work session and import it when you continue to work on the project. Reasons for creating a new library at a location of your choice: • You are not allowed to work on the C drive because you work with sensitive data and must work at a specific location on a server. • You don’t have enough space on your C drive. • You work in a computer lab, and user data on the C drive are removed every night. • You want libraries for different purposes. This is interesting for those who work with lots of projects – for example, for teaching purposes. I, for instance, have created a library for all the sample projects that I need for this book. When I teach, I create a library just for the course I am teaching. When the course is finished, I remove the library as I no longer need the projects. Different from version 7, libraries in version 8 cannot be shared by different users. When you work in a team, each team member works within his or her own library. Projects are shared and united by creating project bundle files and by merging them. One last thing that you need to know is that libraries cannot be created or moved to a cloud sharing service like Dropbox, OneDrive and Google Drive. One purpose of cloud sharing services is that they synchronize data across different devices. This could quickly mess up an ATLAS.ti library and result in incoherent projects. To avoid this, ATLAS.ti prohibits creating or moving libraries to such locations. As ATLAS.ti may not catch all available cloud sharing services, you may be able to outsmart the program. This may, however, have dreadful consequences, as nothing is worse than losing an already coded data set and having to code it all over again. So, be smart and do not try to outwit the program.

36 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI SKILLS TRAINING 3.1 SETTING UP A PROJECT The workflow for setting up a project looks as follows: Figure 3.6 Workflow for setting up a single-user project To follow the skills training you can either use your own data or download the sample data from the companion website. I recommend that you practice with the sample data before you apply what you learn to your own data. • Download and unzip the sample data set. The name of the unzipped project folder is Children & Happiness project_sample data. Creating a new project • Open ATLAS.ti and create a new project: if you have just started ATLAS.ti, click on the button: Create New Project in the opening window. If a project is already open, click on File / New. This opens the backdrop (Figure 3.7). From there select the Create New Project button. • Enter a project name like ‘My first project’ and click on Create. Figure 3.7 Create a new project

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 37 Adding documents • To add documents to a project, click on the Add Documents button in the Home tab, or click on the dialog-box launcher (drop-down arrow). If you click on ‘Add Documents’ you can select individual files. If you want to add the content of a folder or link multimedia files (audio/video) to your project, click on the dialog-box launcher and select the appropriate option. All added or linked documents are numbered consecutively, starting with D1, D2, D3 and so on. The assignment of the numbers is determined by its position in the list of documents. The default sort order is by name, i.e. in alphabetical order for each batch of documents that you import. Commenting your data and keeping track of analytic thoughts You can enter a comment for each document. This may not be necessary for all types of projects, but users often do not think of adding information that they already have. My advice is to include all information in your ATLAS.ti project that is relevant for the analysis. When analyzing interview transcripts, researchers often write an interview protocol. But instead of adding it to their ATLAS.ti project, they store the protocols as Word files in some other folder. I recommend copying and pasting the protocols into the comment field of the respective document, so you have all information at one place. The likelihood that you will look at the protocols again is much greater when they become part of your ATLAS.ti project. When working with newspaper articles or reports, add information about the source, such as a description of the newspaper, its circulation, readership and from where you retrieved the document. If the article or report is available online, you can also add the link to the original source. This is how it works: • Select a document in the Document Manager. The pane on the bottom right-hand side is the comment field. • Type a few words into the comment field or copy and paste text from Word. • To save the comment, click on another document in the Document Manager. This action automatically saves your entry. Or click on the save icon – the one with the down arrow. Each document that has a comment shows a little yellow Post-it in the document icon. In the Windows version, you also see a tilde (~) after each document name. If you do not see it, you may have to extend the name column (Figure 3.8).

38 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI Figure 3.8 Commented document Saving the project • After you have added and commented your documents, save the project by selecting File/Save or click on the Save button in the quick-access toolbar. SKILLS TRAINING 3.2 ORGANIZING PROJECT DOCUMENTS When you start a project, you should first consider where and at what level the cases are in your data. Is each document a case that you want to compare to other cases? Or are several documents a case, such as all female respondents compared to all male respondents? Other examples include interviewees from different organizations, locations, occupations or age groups; notes from March, April or May; observations of place A, B or C; and articles from magazine or newspaper X, Y or Z Cases may also be embedded within documents – for example, the different speakers in focus groups. The example project used in this book contains two documents containing comments from more than 100 people. Each comment corresponds to one case. Depending on whether the case is at the document level or within the documents, you must deal with it differently in ATLAS.ti. If the case is at the document level, you need to create document groups (see below). If the cases are inside the documents, you must code them. For focus groups or other structured data, ATLAS.ti can do this for you (see Skills training 4.13). For the two mentioned documents in the example project, there

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 39 is no further information about the individual writers. This must be determined from the context and thus coded manually. You can look later at the coding of documents 3 and 5 in the coded version of the example project to see how this has been done. In this chapter we deal with the document level only. You will learn more about coding in Chapters 4 and 5. Often data come from various sources, locations, respondents, etc. To ease the handling of the different types of data, they can be organized into document groups. Document groups allow quick access to subsets of your data. They can be used for analytic comparisons in later stages of the analysis or for administrative purposes in team projects by, for instance, creating a group that holds all documents for coder 1, another group that contains the documents for coder 2 and so on. Examples of document groups are the classic sociodemographic variables of gender, age groups, material status, profession, location, etc. For an analysis of newspaper articles, you may want to group by country, circulation and type of newspaper. It is possible to add each document to more than one group; it is not an exclusive either/ or allocation. In a classical interview study, you may want to group a document into groups like gender: female, marital status: single and profession: high-school teacher. Groups can be combined in many ways. For instance, you can compare all comments from the blogs to statements made in the scientific reports. Or, in a different data set, you may want to compare all of the comments made on a particular topic by all female elementary-school teachers to all comments on the same topic made by female high-school teachers. The document groups in ATLAS.ti are a prerequisite for such questions being asked. A document can only meaningfully be added to a group if the mentioned characteristics refer to the whole of the document. For example, if you interview a couple (a man and a woman), or conduct a group interview with ten people, you can assign the interview neither to the group male nor to the group female. As described above, this is an example of cases embedded within the document. In this instance, the variables male and female must be assigned directly to the statements of each person via codes. Groups can be created either in the side panel of the document manager or in the group manager. Creating groups in a manager • Open the Document Manager with a click on the Documents button in the Home tab. • Select some documents and drag and drop the selected documents into the side panel on the left. If you are working with the sample data, select document D1 and D5 (Figure 3.9). You can use common Windows selections techniques holding down the Ctrl or Shift key. • Enter a name for the group – for example, research articles – and click Create. • Create a second group for documents D2, D3 and D4 that you name ‘blog data’.

40 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI Figure 3.9 Creating document groups in the Document Manager The document groups can be used to easily access subsets of your data. • Click on the group blogs in the side panel. As a result, only the three documents of this group are displayed in the Document Manager. Above the list you will see a light-yellow bar indicating that a filter has been set and which one. This filter is local and only affects the current window – in this case, the Document Manager. In Chapter 6 you will learn about global filters. Global filters affect the entire project and can be used for analytical purposes. For now, I want you to remember that every time you see that light-yellow bar over a list, you have set a local filter. Figure 3.10 Filtering the list of entities by groups • To remove the filter, click on the X on the right-hand side. Conversely, one can see in which group a document appears. • If you select a document that is part of a group, the group name is highlighted. In the figure below, for example, you can see that the document ‘case 4’ is male, has a high-school education, has children and is part of the group ‘Effects of parenting: negative’.

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 41 Figure 3.11 Checking group membership If you want to remove items from a group in a manager, proceed as follows: • Select a group in the side panel. • Select the items that you want to remove from this group in the list of items on the right. Use the Ctrl or Shift key to select multiple items. • Right-click and select Remove from group. Figure 3.12 Removing an entity from a group using the side panel Creating groups in the Group Manager Another way to create groups is in the Group Manager. I prefer to create groups in managers, but everyone develops their own preferences.

42 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI • To open a group manager, select the Home tab. Click on the drop-down for documents, codes, memos or networks and select the respective Group Manager. • In the Group Manager, select the button New Group and enter a name – for example, ‘reader comments’. Figure 3.13 Adding documents to groups in the Group Manager • Next select one or more documents on the right-hand side in the pane ‘Documents not in group’ and move them to the left-hand-side ‘Documents in group’ by clicking on the button with the left arrow (<). You can also double-click each item that you want to move. Removing members works the opposite way as shown above: • Select a group. In the pane ‘documents in group' select those items that you want to remove from the group and click on the button with the right arrow (>) or double-click on each item to remove it. Deleting a group • Select a group in the group manager and click on the Delete button in the ribbon. In the side panel of the manager, right-click and select the Delete Group(s) option. Deleting multiple groups needs to be done in the side panel of a manager or in the Project Explorer.

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 43 Renaming a group • Select an entry in the side panel, the Group Manager or the Project Explorer, right-click and select Rename (or press F2, or select the Rename button in the ribbon). SKILLS TRAINING 3.3 MANAGING YOUR PROJECT Removing documents from a project If you accidentally have added documents or want to remove them from your project for other reasons, you can remove them. • Select one or more documents in the Project Explorer or the Document Manager, right-click and select Delete Document(s) from the context menu or click on the Delete button in the ribbon. Figure 3.14 Removing documents from a project If you delete a document by mistake, you can always use the Undo option in the quickaccess toolbar. Renumbering documents If you remove documents from a project, the document numbers do not automatically adjust. You may wonder why this is the case as this results in gaps in the sequence of document numbers. If you have been working with your data for a while, you will know who is behind document 17 or document 28. You may also have included quotation references in some of your writings. Quotation references are based on document numbers. Therefore, you do not want ATLAS.ti to automatically adjust the numbering. • Open the Document Manager. • Select the Utilities tab in the contextual document ribbon and from there select Renumber Documents.

44 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI The renumbering option also allows you to sort documents in a different order: sort the documents by name by clicking on the Name column and renaming the documents so that they appear in the desired order. If you then renumber the documents, the numbering follows the alphabetical sorting. Deleting a project You can delete projects from the opening screen, either when you start ATLAS.ti or when closing all projects. • To delete a project, select it in the opening screen, right-click and select the option Delete Project. • You will be asked to confirm the deletion as this is a permanent action that cannot be undone. SKILLS TRAINING 3.4 EXPORTING PROJECTS FOR PROJECT TRANSFER OR BACKUP I recommend that you store a copy of your project on a server, in the cloud or on an external drive. Computer hard disks can fail; laptops can be stolen. Therefore, it is best to store a copy of your project somewhere else. You do this by exporting a project bundle file. Project bundle files also need to be used to transfer projects between computers. They can be opened by both ATLAS.ti 8 Mac and Windows. • To create a backup of your project, select File/Export. • Click on the Project Bundle button. This opens the File Manager. Select a location for storing the project bundle file. The default name for the bundle will be the project name plus the name of the currently logged in user and the date: project name (user name YYYY-MM-DD). Figure 3.15 Creating a project bundle file You can rename the bundle file at this stage. However, renaming the bundle does not automatically change the name of your project. Think of the project bundle file like a box that holds your project. Putting a different label on the outside of the box does not change

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 45 anything that is inside, which is your project with all your coded segments, comments, memos, networks and the documents that you added to the project. Make it a habit to create a project bundle file after each work session and store it in a safe place. You may keep a few rolling copies of the project bundle and from time to time remove older versions. Later in the book, you will find suggestions on when to save a special backup copy in the form of a project bundle file to document the research process. SKILLS TRAINING 3.5 CREATING PROJECT SNAPSHOTS You may want to create a snapshot: • To preserve certain stages of your project to review them later. • As a backup of your project file. • As a fallback version before a merge, in case something turns out differently than you expected. • As a copy of your project that you want to use as template for another project. When creating a snapshot, ATLAS.ti automatically adds the following to the project name: (Snapshot YYYY-MM-DD hh:min:sec). You can also enter any other name. • To create a snapshot, select File/Snapshot. • Accept the default name or enter another unique name and click Create Snapshot. Figure 3.16 Default name for project snapshots The snapshot is created in the background. If you want to continue to work with the snapshot, you need to close the original project and open the snapshot. Just like ATLAS.ti project files, snapshots are saved internally in the ATLAS.ti environment. A snapshot has the same ID as the project from which it is created. This is important when merging projects (see Skills training 9.1).

46 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI SKILLS TRAINING 3.6 CHANGING THE DEFAULT LOCATION FOR ATLAS.TI PROJECT DATA As explained above, the default location for ATLAS.ti project files is on the C drive. As it is not possible for all users to work on the C drive, there is the possibility to create a userdefined location where all your ATLAS.ti data are stored. It is possible to work with multiple libraries. Theoretically, you could create a new empty library every time you start a new project. This, however, demands careful data management on your part as you need to keep track where these different libraries are located. • If you have loaded a project, save and close it. You cannot change the library location when a project is currently open. • At the opening screen, select Options at the bottom left of your screen and then Switch Library. Figure 3.17 Starting the Switch Library process For all users familiar with ATLAS.ti 7: this is DIFFERENT from version 7, where you could create project-specific libraries that could be shared. In ATLAS.ti 8, each user works within her or his own ATLAS.ti environment. The location where ATLAS.ti stores your data can be within another folder on the C drive, any other drive on your computer, a server or an external drive. It is not possible, however, to use a cloud sharing service like Dropbox because the specific way in which such systems work can jeopardize the integrity of your ATLAS.ti library.

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 47 • You will see a message that informs you where the current library is located. Click Next. A wizard opens that guides you through the process. The next choice you need to make is whether you want to open an existing library, move the library to a different location or create a new empty library. Figure 3.18 Options for library switching Open an existing library. Select this option if you are working with multiple libraries and want to access an existing library elsewhere. Move the library to a different location. Select this option if you want or need to move your current library to another location – for example, because you can no longer work on the C drive. It is possible to keep a copy of the library in the old location. Create a new empty library. This option allows you to create a new library in a location of your choice. If you already have projects, none of them will be moved to the new library. To fill this library, you must either create new projects or import existing ones in the form of a project bundle file. Let’s create a new library: • The library needs to be created in an empty folder. I suggest you create a new folder in your File Manager first. Then proceed. • Select Create a new empty library and click Next. • Select where this new library will be created. Click on the file-loader icon to browse through your file system. After selecting the new empty folder, click Next. Figure 3.19 Select a location where you want your new library to be stored

48 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI • Confirm the new location and click Finish. Wait until ATLAS.ti returns to the opening screen. The opening screen will be empty, and you can begin to fill it with new projects. To go back to the default library that has the project you created in Skills training 3.1: • Select Options at the bottom left of your screen and then Switch Library. You will see a message that informs you where the current library is located. Click Next. • Select Open an existing library and click Next. • Select Default Library Location as location and click Next. (If at some later point you want to access any of the new libraries you have created, select ‘Choose Library Location’. If you click on the drop-down arrow, ATLAS.ti lists all locations where libraries are stored.) Figure 3.20 Switching back to the default location When ATLAS.ti shows the opening screen again, you are back at the default library location. SKILLS TRAINING 3.7 PREPARING AND IMPORTING SURVEY DATA In paper-and-pencil surveys, respondents often did not write a lot when asked an openended question. This changed with the use of online surveys. Respondents now often write quite a lot and their responses are already in electronic format. Thus, there is no need to retype it. Survey data are imported via an Excel file to ATLAS.ti. Most online survey tools offer an Excel output, which can then be used to prepare the data for ATLAS.ti. You can also use this function if you have data that is structured like a survey. The Internet is great for finding information of all sorts. For instance, as part of a study, I was interested

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 49 in how the computer game Minecraft was evaluated and found a website where educators, parents and those playing the game were offering their opinions. On another occasion, I wanted to know how viewers rated the TV series The Big Bang Theory. I did not only find full transcripts of every episode online: I also found ratings of the series episodes over a range of several years. In both cases, I did not have to conduct a survey myself. The data I found could be prepared in an Excel file in a way analogous to answers to open-ended survey questions. You can download both examples from the companion website. Preparing survey data ATLAS.ti imports survey data case-based. This means each document holds the answers of one respondent. Attributes that describe each respondent, like male, type of occupation, age range, education, etc., are turned into document groups by ATLAS.ti. Whether information is interpreted as content or as attribute is defined by the syntax you use in the Excel table, as in Table 3.2. Table 3.2 Syntax for preparing survey data Header in Excel :Gender :has children # of children Cell value in Excel Male Yes 1 Document group in ATLAS.ti Gender::male has children::yes # of children::1 Header in Excel :Marital status .bring happiness .fulfillment & purpose Cell value in Excel divorced 1 0 Document group in ATLAS.ti Marital status::divorced bring happiness No group If you add a colon (:) in front of the variable name, ATLAS.ti creates a document group for each cell value. Note that in ATLAS.ti two colons (::) are used to separate the variable name and the value. This is not a typo. If you put a period/full stop (.) in front of the variable name, only those cases that have a 1 or a yes in the cell will be added to a document group. In the above example, survey respondents were asked in one question whether they believe that children contribute to happiness and in another whether they believe children bring fulfillment and purpose. Both questions could be answered with a ‘yes’ or ‘no’. Thus, on a limited scale, it is also possible to add quantitative information to your ATLAS.ti project. As you will use document groups to compare cases, think about the kinds of comparisons you want to make and then decide which quantitative survey questions are useful to bring into ATLAS.ti. All columns in the Excel table that hold the answers to open-ended questions, or the content that you want to analyze in case you captured online data as described above, do not require any syntax. The column name will be used as code to pre-code all open-ended questions.

50 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI Table 3.3 Syntax for open-ended questions SQ1::Reasons for having children SQ2::Reasons for not having children Children whittle away your time in ways that are ultimately beneficial: they have an uncanny knack for getting rid of the meaningless hobbies that used to consume you. Responsibility. All of life’s prior responsibilities pale in comparison. If you decide to have a child, that new person must absolutely be your top priority. As your child will remind you when he or she is older and something goes wrong, ‘I didn’t ask to be born.’ A child is a lifelong commitment to a person who is innocent of this choice. However, you may not want the entire (long) question to be used as the code name. In this case, you can use the question ID – SQ1 and SQ2 – as the code name and the question as a code comment. To do this, separate the question ID and the question with two colons (::). ATLAS.ti then uses the text after the two colons as a code comment. The advantage is that the code label is shorter, and the full label does not appear in the body of the text, which can, for instance, affect word clouds and word-frequency counts in unwanted ways (see Skills training 3.10). Importing survey data • Download the Children & Happiness Sample Survey Data from the companion website. • Create a new project. • Select the Import/Export tab and then the Survey button. • Select the Excel file to be imported and click Open. The import procedure starts. You see a progress report and ATLAS.ti informs you when the import is finished. Inspecting the imported data • Open the Document Manager by double-clicking on the Document branch in the Project Explorer and look at what has been added to the project: Figure 3.21 Document Manager after importing survey data

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 51 If you do not specify a name for each case, the default document name for each case is case 1, case 2, case 3 and so on. In the example, the name/synonym of each respondent was known and entered as ‘author’. Based on the information provided in the Excel table, document groups were created. The highlighted respondent case 6, for example, has some college education, is a female, single and has no children. She answered the question ‘Do children bring happiness?’ with ‘Yes’. ATLAS.ti automatically creates a group that contains all survey data. This is a useful option in case you work with data from multiple sources. Each open-ended question was also automatically coded: • Double-click case 6 to open it, and open the Code Manager alongside as shown in Figure 3.22. Figure 3.22 Coded document content after survey import The second code does not have a comment and the full-length header name is used as the code label because no syntax was added in Excel. The header was labeled: SQ2: Reasons for not having children If you had added a second colon (:), only the question number SQ2 would have been used as code. If your data comes in waves, you can add the new data to the existing Excel file and import it again. ATLAS.ti will only import the new information. Working with survey data Analyzing survey data requires that you know how to code (see Chapter 4). You may want to explore the data first by creating word clouds or word lists (see Skills training 3.10) and by using the auto coding feature. After coding, further analysis options that are often used with survey data are the Code Document Table, the Query Tool in combination with the Scope option and possibly also the Code Co-occurrence Table. For further information, see Chapter 6.

52 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI SKILLS TRAINING 3.8 IMPORTING REFERENCE MANAGER DATA FOR A LITERATURE REVIEW To complete this skills training, you can prepare your own data or you can use the examples provide on the companion website. ATLAS.ti is frequently used for assisting with literature reviews. ATLAS.ti 8 has a new feature that allows you to import articles or other literature you have collected directly from a reference manager. You can use EndNote or any other reference manager that can export data for EndNote in XML format (e.g. Reference Manager, Mendeley, Zotero). In a later version of ATLAS.ti, the RIS format may also be supported. You might collect a great many documents in your reference manager for a first screening. The purpose of using ATLAS.ti for literature reviews is to take a closer look at those articles that you have already identified as being relevant – for instance, for writing the theory chapter. Another purpose could be to generate theoretically derived codes that you can apply to your data. In the following, I show you how a selection of documents can be exported from Mendeley and Zotero so that you can import them to ATLAS.ti. • In Mendeley, you highlight the documents that you want to export, right-click and select Export. When saving the file, you can select the following file formats: Figure 3.23 Exporting an EndNote XML file in Mendeley • Select EndNote XML as the file type and save the file. In Zotero: • Select the documents that you want to export. • Select File/Export library. Figure 3.24 Exporting an Endnote XML file in Zotero

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 53 • Zotero offers about ten different formats to export to. Select Endnote XML. • In addition, you can export your notes and the full content of the documents. Select both options. The notes will be imported as document comments in ATLAS.ti. To start importing the file into ATLAS.ti: • Select the Import/Export tab and the button Reference Manager. Figure 3.25 Select import options By default, ATLAS.ti creates document groups for authors, periodical name and type of document (journal article, book, book chapter, etc.). Further grouping options are Publisher, Edition, Volume, Language, Issue, Number and Source. In a future version, all of these will be optional. Select only those options that are relevant for retrieval and comparison purposes. If you do not have multiple documents per edition, issue or volume, there is no need to create a group for each of them. If you import documents from more than one reference manager, it is useful to group documents by source, so that you know where they came from. As document names, ATLAS.ti uses the name that is stored in the Reference Manager. For easier sorting and organization, it is beneficial to add the name of the first author and the publishing year to the document name when importing.

54 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI If only the document abstract is available and not the full document, you can select to import the abstract as a document. • Make your selections and click on the Import button. • To inspect the imported documents, open the Document Manager. Figure 3.26 Document Manager after importing reference manager data The imported documents are sorted by author and year in alphabetical order. Further, document groups were created based on the available information. Document 3, for instance, was published in Technical Report CTIT-02-34 in 2002. The text entered as note in Mendeley was imported as comment. If there are no notes for the document, the URL of the document is added into the comment field (if available). SKILLS TRAINING 3.9 WRITING MEMOS IN THE EARLY STAGES OF ANALYSIS As you can see in Figure 3.27, I included lots of benches in the data landscape that you are exploring. The benches signal points of reflection on our journey as an important part of data analysis. Reflection is important throughout the entire research process. As Richards (2009: 51) writes: ‘From the start of your project, get into a habit of “telling” what’s going on, rather than “writing it up”. […] Telling is much more purposive than writing – and much easier to do.’ All the memos and comments you write in ATLAS.ti do not have to be stylistically perfect. There is no need to check grammar or spelling. Just write down what comes to your mind. Do not make it unnecessarily difficult by thinking that the text must already be good enough for it to be used for the final report. This is all for later (see Chapter 8). Take time for reflection – writing is an important part of analysis.

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 55 Writing is an important part of analysis in qualitative research. When using software, it is easy to fall into the code trap. Always remember that coding the data is only a means to an end – to think, retrieve and query data (Corbin and Strauss, 2008; Richards, 2009; Richards and Morse, 2013). Below I describe the kinds of memos that you can start writing early in the analysis process. Read more about how you can use the memos in ATLAS.ti to systematically prepare texts for your research report in Skills training 5.7. Research diary. If you are writing a thesis or dissertation, or working on a student or scientific research project, it is a good idea to write a research diary that you can later refer to when writing up your method section and to document the analytic process. You could do this in a Word document but using an ATLAS.ti memo has some advantages. After an analysis session in ATLAS.ti, you can immediately write down what you have done and timestamp it without having to open another program. It becomes part of your evolving project and can later be submitted together with your project data and your analytic work in ATLAS.ti. Supervisors can follow your analytic steps and for teachers it is useful when grading a project. But there is more to it than just adding transparency: research diaries are useful reminders for the analyst, too. It is difficult to keep everything in mind when a project continues over months or even years. The research diary can be reviewed, for example, when it comes to writing the method chapter for a thesis or a paper publication. Project memo. To stay focused, adding a memo with your research questions can be done at an early stage of analysis. You probably already have a list of research questions or at least some ideas. You can add further questions and ideas to this list with progressing analysis. Idea memo. If you have a great idea but no time to follow it up right away, write it down before it gets lost. However, do not write a memo for every single idea you have! Collect all the good ideas in one memo that you might entitle ‘Great ideas to follow up’. To-do memo. Like the idea memo, you can have a memo that contains a to-do list for the next work session or a plan for the next week or analysis period. Team memos. When you work in a team, all the members can add a team memo to their sub projects. In the team memo, members can write down things that they want to discuss at the next team meeting. If you put all the team memos together after merging, you already have your agenda for the next meeting. Figure 3.27 Take time for reflection – writing is an important part of analysis

56 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI Theory or literature memos. This type can be used to add information from secondary sources to the project, such as excerpts from the relevant literature, main theoretical concepts, etc. These memos partly serve as reminders; instead of having to switch programs or look through a stack of papers to remind you of important theoretical concepts and their definitions, they are right there within your ATLAS.ti project. Additionally, these memos can be used to collect empirical evidence for theories proposed in the literature. When you come across a data segment that ties in with ideas proposed in the literature by other authors, you can connect the respective memo to this data segment. See Skills training 5.7. Creating a memo Let’s create a research diary. This is a ‘free’ memo, which means it is not connected to anything else in the project. In later chapters, you will see that memos can also be linked to codes, quotations and other memos. • In the ribbon, click on the drop-down arrow of the New Entities button and select New Memo. You are prompted to enter a title. Enter ‘research diary’. Figure 3.28 Research diary opened in a tab group below a document

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 57 The memo editor opens as a docked window. If you prefer, you can float it or move it into a new tab group either to the right or down (Figure 3.28). When writing a research diary, it is a good idea to keep track of when you wrote what. This can be done easily by inserting the date above each entry. The shortcut Ctrl+D for inserting the date and time was not yet implemented at the time of writing, but it probably will have been by the time you read this. • Save the memo by clicking on the Save button in the ribbon. If you close the memo without saving, ATLAS.ti will remind you to save it. If you open the Memo Manager, you see that the text you have written into the memo editor also shows up in the lower part of the Memo Manager on the left-hand side. You can also continue to write the memo there, but it is less comfortable, and you have fewer editing options. • To open the Memo Manager, click on the Memos button in the Home ribbon or double-click on the Memos branch in the Project Explorer. • If you select the memo ‘research diary’, you see its content in the text pane below. Figure 3.29 Memo Manager The Memo Manager offers several other options. You can write a comment for each memo. These could be notes to yourself like ‘use this memo in Chapter 5’ or ‘look up the article by Smith (2012)’; or a supervisor could comment on some of your analytic memos, team members can comment on each other’s writings, and so on. You can set memo types that you can use to sort and organize your memos. Memos can also be converted into documents for further analysis. And if you want to create reports based on memos, you also need to do this from the Memo Manager. When to use comments and memos in ATLAS.ti Memos, technically speaking, provide a writing space like the comment field. In ATLAS.ti, I highly recommend that you make a distinction between comments and memos. Memos are

58 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI an entity class of their own. Comments are always attached to the entity they describe – a document, a quotation, a code, a link or a network. Memos have a title, you can select a type for each memo, and ATLAS.ti keeps track of the creation and modification dates. This should already hint that memos are different from comments (see also Konopásek, 2007). Users often treat them like a comment function and discover after a while that this is not very fruitful, or else they do not use memos at all. This is not to say that many analysts do not write memos, they probably do – but using a word processor rather than writing directly in ATLAS.ti. The disadvantage if you do this is that you are away from the data and you cannot link a Word document to a segment in your data. Within ATLAS.ti, you can link your analytic thoughts to the data segments that support them (see Skills training 5.7). In Tables 3.4 and 3.5, you will find some ideas on where to write down what type of information. Table 3.4 Use of comments Type of information Type of comment Don’t do Meta information about a document: source, where and how you found or generated it Interview protocols Document comment Information about the person, like gender, age, profession or company, industry, etc., is handled via document groups; you do not need to write them into the comment field Comments about data segments: if you have a thought on a data segment you are reading, coding or reviewing, open the comment field for the quotation Quotation comment (see Skills trainings 4.8, 4.15 and 5.6 for examples on how to work with quotation comments) Do not add a memo to single segments that you simply want to comment on First ideas about what you mean with a code; with time a proper code definition Teamwork: coding rules Teamwork: example quotes Code comment (see Skills training 4.10) Don’t neglect writing code comments – it is important that you think about what a code means and how to define it Table 3.5 Use of memos Type of information Memo category Type Research diary Memo method Ideas/open questions about the coding system Code memo memo Things you want to discuss at the next team meeting Team memo memo Project description List of research questions you already know of Project memo memo Research questions you generate throughout the analysis process Idea memo or project memo memo Ideas for relationships Idea memo memo Analysis and interpretation: one or more memos per research question Research-question memo (see Skills training 5.7) analysis Important definitions, theories or models from the literature that you need to refer to from time to time Theory memo theory

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 59 In 1998, Fielding and Lee reported that CAQDAS users having difficulties with the idea of writing memos within the software and after coding, abandoned the package they were using and completed the analysis manually. Now, many years later, this still appears to be an issue. Because writing memos is an essential step in qualitative data analysis, I would like to make some suggestions on how to utilize memos within ATLAS.ti and the kind of advantages they offer over writing notes on a piece of paper or in a word processor. Memos and diagrams are more than just repositories of thoughts. They are working and living documents. When an analyst sits down to write a memo or do a diagram, a certain degree of analysis occurs. The very act of writing memos and doing diagrams forces the analyst to think about the data. And it is in thinking that analysis occurs. (Corbin and Strauss, 2008: 118) Writing is thinking. It is natural to believe that you need to be clear in your mind what you are trying to express first before you can write it down. However, most of the time, the opposite is true. You may think you have a clear idea, but it is only when you write it down that you can be certain that you do (or sadly, sometimes, that you do not). (Gibbs, 2005) Memos, thus, represent analytic work in progress and you can use some of the writing later as building blocks for your research report (see Chapter 8). It is probably not by accident that Juliet Corbin includes a lot about memo writing in the third edition of Basics of Qualitative Research; she wants to show readers how it can be done. In a talk about the book at the CAQD 2008 Conference, she linked the poor quality of many of today’s qualitative research projects to a failure to use memos. Along the same lines, Birks et al. (2008) devotes an entire journal article to memo writing, criticizing the limited exploration of its value in most qualitative methodologies. Freeman (2017) states that it is one of the issues facing novice researchers to understand that writing is inseparable from analysis. I can only encourage you do it: write a lot while you code and later when you query the data. If you do, you will find out why it is useful – but you have got to do it. As Freeman put it: the challenge for novice researchers is ‘Needing to do analysis to understand analysis’ (2017: 3). When you need more input regarding how to write memos, see, for example, the third or fourth edition of Basics of Qualitative Research by Corbin and Strauss (2008/2015), Wolcott (2009) or Charmaz (2014). I will teach you about possible types of memos and how to set up analytic memos in ATLAS.ti in a way that makes your research transparent and helps you engage with your data and enter an internal dialog, advancing your analytic thoughts and ways of thinking. Learning how to write good memos is experiential. Reading about it and seeing examples of how it can be done is one part, but you need to do it yourself and practice it. SKILLS TRAINING 3.10 EXPLORING YOUR DATA – CREATING WORD CLOUDS A quick way to get a feeling for the content of a text document is by creating a word cloud.

60 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI • To create a word cloud for one or multiple text documents, select the document(s) in the Documents branch of the Project Explorer or the Document Manager, right-click and select the option Word Cloud. Alternatively, you could create a word list that shows all the words that occur in a tabular format. In addition to the frequency per word across all selected documents, the table also includes the word length, the frequency per word for each document and the percentage distribution. The latter two are optional and can be selected in the ribbon via the options Detail and Percent. The skills training will only be about the word clouds. I invite you to explore the word lists by yourself. If you use a content-analysis approach, the list of words in table format might be useful for a report. It can be exported as an Excel table. Since you probably still have the project with the survey data loaded, I suggest comparing the words men and women used to describe reasons for and against having children. • In the Document Groups branch of the Project Explorer, select the document group ‘gender::female’, right-click and select Word Cloud. • Repeat this for the document group ‘gender::male’ and move the second word cloud into a tab group of its own so you can see both word clouds side by side. Or, if you have a big screen, you can place two floating windows next to each other. Figure 3.30 Word clouds comparing male and female responses Words like ‘and’, ‘you’ and ‘to’ come up as very frequent. To make the word clouds more meaningful, it is often useful to work with stop-word lists. All words that are in the list will not be counted. ATLAS.ti offers four default stop-word lists: one for English-, Spanish-, German- and Portuguese-language data. If you need other languages, look at https://www. ranks.nl/stopwords or use other online sources.

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 61 • To apply a stop-word list to your data, select the Exclude option in the ribbon and select the English stop-word list from the drop-down menu. Figure 3.31 Choosing a stop-word list The word clouds will change. You may want to clean up further by excluding words like ‘SQ1’, ‘SQ2’, ‘reason’ and ‘children’/‘child’. It is obvious from the question that it is about children. Therefore, it does not need to be included in the cloud. I also excluded the word ‘will’. If you hover over a word, you will see the number of times it has been used. If you right-click, you have three options: to add the selected word to the stop-word list, to copy it to the clipboard and to search (for the word) in context. The second option is useful if you want to use the word for auto coding purposes. To check whether it is a worthwhile word to code, look it up in context first. Figure 3.32 Reviewing frequencies and adding words to stop lists When applying a stop list and removing some additional words, the word clouds look something like what you see in Figure 3.33. Can you already see differences in the responses by men and women? As the two questions and their answers – ‘Reasons for having children’ and ‘Reasons for not having children’ – were coded when importing the data, we can further explore possible differences in the responses by men and women by creating a word cloud for each question (Figure 3.34). To create word clouds for the two questions SQ1 and SQ2, you can open either the Quotation Manager or the Code Manager. If you want to import an existing stop-word list, click on the Edit button. This opens the Stop and Go Lists Manager. There you will find the Import Lists button and other options.

62 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI Figure 3.33 Refined word clouds comparing answers of female and male respondents • In the Code Manager, select the code ‘SQ1’ in the main list; in the Quotation Manager, select the code in the side panel. Then click on the Word Cloud button in the ribbon. • Repeat this for ‘SQ2’ and move the second word cloud once again to a tab group of its own, so you can see both clouds side by side. Figure 3.34 Comparing words used in the responses to questions 1 and 2 In the answers to the question ‘Reasons to have children’, you will see many more positive words compared to the answers to the question ‘Reasons not to have children’.

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 63 You can break it down further, by comparing men’s and women’s responses to both questions. The easiest way to do this is by setting a global filter for the two document groups ‘gender::male’ and ‘gender::female’. • Close all word clouds. • Right-click on the document group ‘gender::female’ in the Project Explorer and select Set Global Filter. The global filter setting is indicated by a colored bar above each affected list. In the Project Explorer documents and quotations are filtered: in the Document Manager the list of documents. This means all subsequent analysis is only done on the documents of the filtered group. All other documents are hidden. You will learn more about global filter settings in Chapter 6. Figure 3.35 Setting a global filter for the document group ‘Gender::female’ Figure 3.36 Quotation list filtered by a global and a local filter • Open the Quotation Manager and select the code ‘SQ1’ (Figure 3.37). Note that there are now two bars on top of the quotation list. As a global document filter also affects the quotation list, the global filter bar is orange (= color of quotations). The selected code in the side panel is the second, local filter for the list.

64 QUALITATIVE DATA ANALYSIS WITH ATLAS.TI Figure 3.37 Quotation Manager with an activated global and local filter Figure 3.38 Word clouds comparing male and female respondents by question

EMBARKING ON THE JOURNEY – DATA AND PROJECT MANAGEMENT 65 • Click on the Word Cloud button in the ribbon to create a word cloud of all quotations for the code ‘SQ1 (Reason for having children)’ by female respondents. • Repeat this process for ‘SQ2: Reasons for not having children’. • Save the two word clouds as an image file by clicking the Save button on the ribbon. Or, alternatively, create a screenshot or view them side by side in ATLAS.ti in two tab groups. • Repeat the process for all male respondents by changing the global document filter to ‘gender::male’. Figure 3.38 shows all four word clouds in one image for ease of comparison. For most projects the word clouds are not a final result, but they can be a starting point. They can give you ideas for coding, or you can use the auto coding function (not covered in this book) based on the words you see in the word clouds. SKILLS TRAINING 3.11 KEYWORD IN CONTEXT SEARCH If you want to see the context of a word, you can access the project-wide search directly from a word cloud or word list. • Right-click on the word ‘time’ , for instance, and select Search in Context. Figure 3.39 Open the project-wide search tool This opens a new window that shows all sentences where the keyword that you entered as search term occurs in the documents. Note in Figure 3.40 that the global filter is still set and that the results only reflect the answers of female respondents. You can deactivate the filter to see where the word ‘time’ occurs in all responses.