Newsletter 2015-5


Dear ATLAS.ti users:

You now have in your hands the June issue of INSIDE ATLAS.ti: The QDA Newsletter. In this issue of INSIDE ATLAS.ti, we will share with you news related to the ATLAS.ti software and company programs, published work on research using the software, and events organized by our partners worldwide. Following, we will update you on the training activities and conferences that will take place in June and July of this year. Next, Dr. Susanne Friese discusses the use of ATLAS.ti 7 for Windows in mixed methods research.

In this issue of INSIDE ATLAS.ti we also present the Africa Institute for ATLAS.ti, an important initiative aimed at supporting users in the African continent. As always, we close this June issue of INSIDE ATLAS.ti with a list of the different means available to learn about ATLAS.ti and to get in touch with us.

Enjoy!


News

Case studies on applications of ATLAS.ti

We invite the international community of users to submit brief descriptions of how they use ATLAS.ti in research. Every month, a selected case study will be awarded a free single-user license of ATLAS.ti Windows + Mac, or a free upgrade to version 8. To submit your case study information, please fill out our online form .

Free upgrade to ATLAS.ti 8

ATLAS.ti 8 for Windows is coming later this year. All licenses and upgrades that were purchased after April 1, 2015 qualify for a free upgrade once version 8 is released. A preview of new features will be announced soon.

Note: Support for the older Version 6 ends with the release of ATLAS.ti 8. Upgrade now to ATLAS.ti 7 and get version 8 free of charge later this year!

Newest academic articles on research using ATLAS.ti

In this issue of INSIDE ATLAS.ti, we begin listing citations of some of the latest academic articles reporting research with ATLAS.ti. Below are three of the newest articles in education and health research. To access the annotated bibliography, as well as other bibliographic resources, visit our research blog by clicking here.

Educational research

Paulus, T. M., & Lester, J. N. (2015). ATLAS.ti for conversation and discourse analysis studies. International Journal of Social Research Methodology, 1-24. doi: 10.1080/13645579.2015.1021949

Sart, G. (2015). The new leadership model of university management for innovation and entrepreneurship. Eurasian Journal of Educational Research (EJER), Winter 2015(57), 73-90.

Sherman, S., Grode, G., McCoy, T., Vander Veur, S. S., Wojtanowski, A., Sandoval, B. A., & Foster, G. D. (2015). Corner Stores: The perspective of urban youth. Journal of the Academy of Nutrition & Dietetics, 115(2), 242-248.

Health Research

Bien, C. H., Muessig, K. E., Lee, R., Lo, E. J., Yang, L. G., Yang, B., . . . Tucker, J. D. (2015). HIV and Syphilis Testing Preferences among Men Who Have Sex with Men in South China: A Qualitative Analysis to Inform Sexual Health Services. PLoS One, 10(4), 1-12.

Carmichael, N., Tsipis, J., Windmueller, G., Mandel, L., & Estrella, E. (2015). 'Is It Going to Hurt?': The impact of the diagnostic odyssey on children and their families. Journal of Genetic Counseling, 24(2), 325-335.

Cheng, F. K. (2015). A Qualitative Study of Buddhist Altruistic Behavior. Journal of Human Behavior in the Social Environment, 25(3), 2014-2213.

Presentations of ATLAS.ti at universities worldwide

In June and July, some of our consulting and reseller partners are planning visits to universities. Luis Alfredo Loaiza, certified professional ATLAS.ti trainer based in Buenos Aires, Argentina, will be visiting the following institutions to introduce the software to students and faculty:

Argentina

Universidad General Sarmiento

Universidad Abierta Interamericana

Universidad de Kennedy

Universidad Católica Argentina

Universidad de Buenos Aires

FLACSO-Buenos Aires

Universidad de Palermo

Universidad Argentina de la Empresa

Universidad del Salvador

Universidad de San Andrés

Universidad de La Plata, and

Centro de Formación e Investigación CEMET.

Colombia

Universidad Autónoma de Occidente-Cali

Universidad ICESI-Cali

Universidad de San Buenaventura-Cali

Universidad Javeriana-Cali

Universidad Libre-Cali

Universidad Cooperativa de Colombia-Cali

Universidad Católica Lumen Gentium-Cali

Instituto de Investigación CIDEIM-Cali

Universidad Nacional de Colombia Sede Manizales

Universidad Antonio Nariño, and

Universidad del Valle Sede Palmira

If you are attending any of these universities, please get in touch with Luis Alfredo at his email address: [email protected] or [email protected]

Additionally, DIPROTECNO LTDA, a software reseller company established in Chile in 2000, is inaugurating its offices in Lima, Perú, with a set of promotional activities the first week of July. Among these, are face-to-face presentations introducing ATLAS.ti to university presidents and academic staff, and to governmental, non-governmental and private research organizations working in Perú and South America in general. For more information on these events, please contact DIPROTECNO LTDA by clicking here.

The Institute for Qualitative and Multi-Methods Research (IQMR)

The Center for Qualitative and Multi-Method Inquiry at the Maxwell School of Syracuse University, USA, is hosting this year's Institute for Qualitative and Multi-Method Research (IQMR). The Institute is held annually by the Consortium on Qualitative Research Methods (CQRM). As stated on its website, the Institute "seeks to enable participants to create and critique methodologically sophisticated qualitative research designs, including case studies, tests of necessity or sufficiency, and narrative and interpretive work. It explores the techniques, uses, strengths, and limitations of these methods, while emphasizing the relationships with alternative approaches."

ATLAS.ti was the software of choice for IQMR's two-day course on computer-assisted qualitative data analysis. All participants will have access to a full version of ATLAS.ti Windows to use in class. We are grateful to IQMR for choosing ATLAS.ti and we wish the best to all participants. For more information on the Institute, visit its website by clicking here.

ATLAS.ti Sponsors the 4th Annual IPSA-NUS Summer School for Social Science Research Methods

ATLAS.ti is a proud sponsor of the 4th Annual IPSA-NUS Summer School for Social Science Research Methods, which will take place at the National University of Singapore between June 8th and 19th 2015. The Methods School is held annually in partnership between the International Political Science Association (IPSA) and the National University of Singapore. According to the event's website, "the IPSA-NUS Methods School offers rigorous training with outstanding and highly experienced international faculty in a wide range of social science research methods. Students, post-doctoral fellows, junior faculty, civil servants, professionals, and everyone with an interest in research methods can choose from a wide variety of quantitative, qualitative, and formal methods courses and benefit from intensive, hands-on coursework over a two-week period."

In addition to sponsoring the event and exhibiting, Ms. Ani Munirah, Manager of International Projects with ATLAS.ti, will be teaching an introductory workshop on the software. For more information, please contact Ms. Munirah at [email protected]. You may also visit the Research Methods School website by clicking here.

ATLAS.ti and XML

ATLAS.ti supports the open, universal data exchange format XML. This is not only good news for those wishing to manipulate and create their own output. It also creates a platform for the universal exchange, storage, and processing of QDA data across major applications. ATLAS.ti 7 comes with a number of pre-written reports that generate useful outputs directly from your XML project data. These reports serve both as tools for your work as well as examples for your own scripting endeavors. If you want to learn more about ATLAS.ti's XML format (including the official data schema), please see this page and/or the program manual. If you have questions, write to [email protected].


Learning Resources and Events

Listed below are the educational activities scheduled for June and July of this year. For more information, please contact [email protected].

Free webinar on qualitative methodology

ATLAS.ti-IIQM Master Class Webinar Series. Dr. Jude Spiers will present "Verification Strategies". Thursday June 25; 3:00 pm – 4:00 pm (EDT). Register here.

Free introductory webinars.

Register here webinars.atlasti.com/

Free group introductory webinars.

If your organization is evaluating ATLAS.ti or you want to introduce it to your students or at a professional meeting, schedule a presentation with one of our instructors. Complete this registration form.

Introductory and advanced ATLAS.ti online workshops. Register here.

Introduction to ATLAS.ti 7 Windows. English.

  • June 1st, 3rd and 5th, 1:00 pm - 3:30 pm EST each day.
  • June 22nd, 24th and 26th, 1:00 pm – 3:30 pm EST each day.
  • July 13th, 15th and 17th, 1:00 pm – 3:30 pm EST each day.

Introduction to ATLAS.ti Mac. English.

  • June 29th, July 1st and 3rd, 1:00 pm – 3:30 pm EST each day.
  • July 20th, 22nd and 24th, 1:00 pm – 3:30 pm EST each day.

Introduction to ATLAS.ti 7 Windows. Spanish.

  • June 8th, 9th and 11th, 6:00 pm – 8:00 pm CET each day.
  • July 21st, 22nd and 23th, 6:00 pm – 8:00 pm CET each day.

Introduction to ATLAS.ti Mac, Spanish.

  • June 30th, July 1st and 2nd, 6:00 pm – 8:00 pm CET each day.

Advanced Course: Building an Efficient Coding System and Beyond. English.

  • June 2nd, 3rd and 4th, 10:00 am – 12:00 pm EST each day.

Face-to-face seminars

  • Corvallis, Oregon, USA. July 30th (9:00 am to 4:00 pm), 31st (9:00 am to 4:00 pm) and August 1st (9:00 am to 12:00 pm). English.
  • Madrid, Spain. June 15th. 8 hours. Spanish.

Training from independent consultants

Conferences

  • Fourth Annual IPSA-NUS Summer School for Social Science Research Methods. National University of Singapore. June 8th-19th. For more information, visit the Summer School website here.
  • Summer Research Training Institute for American Indian and Alaska Native Health Professionals, Portland, OR, June 15- July 2, 2015. For more information, visit the Institute website here.

Innovative Approaches to Using ATLAS.ti
Mixed-Methods with ATLAS.ti 7 for Windows

Dr. Susanne Friese

Note: Some of the analysis procedures presented in this article are not yet available in the Mac version, but will be released soon. We will publish an article about Mixed-Methods for ATLAS.ti for Mac in our July newsletter.

Data Preparation

To illustrate how to approach mix-method analysis with ATLAS.ti, a data set was prepared based on a larger survey evaluating the computer game Minecraft. The survey contained two open-ended questions that were analyzed with ATLAS.ti. In addition, a few closed-ended questions were also included:

  • Status: educator / parent / teenager playing the game
  • Age recommendation: 2-5 / 6-9 / 10 -13
  • Educational value of the game: Ratings 1, 2, or 3

The two open-ended questions were the following: asking the respondents to provide a summary statement of the game in form of one sentence, and a longer more detailed evaluation. The resulting Excel table that prepared the data for import to ATLAS.ti is shown in Figure 1. The first two cases are shown. For further information on how to prepare and import survey data, see the video tutorial and documentation.

Please read the section "Data Export – Reintegrating Qualitative Data with Quantitative Data" below, if you plan to reintegrate the qualitative coding with the quantitative data later on. It contains some information on what you need to consider for matching respondents.

Approaching Analysis

You may have a deductive framework when using a mixed-method approach and thus the codes are already given and you do not need to have to develop a coding system first. It could, however, also be the case that you want to analyze the data more openly and develop codes while reading the data. In the first case, you can import the already existing code list via a memo (see section "Importing a List of Codes" below).

If you want to develop codes based on the data at hand, one strategy that I often use with open-ended questions from survey data is a combination of the word cruncher / word cloud with auto coding. This works well with survey data as each respondent has answered the exact same question and responses are not developed as part of a narrative as is the case with interview data. Based on the interesting words you see in the word cloud, you proceed with auto coding the data material, begin to organize your list of codes and explore the content that was coded. Through this process you refine the codes, adjust the length of quotations and delete inappropriately coded segments. Remember that the computer cannot do the thinking work for you. Even if auto coding allows you quick access to the data, it can never be the end result of coding. Auto coded segments always have to be checked. Consider the following two sentences:

  • The game is great.
  • The game creates great problems for parents leading to long discussions with their children about…..

If one searches for words like good / great / awesome as indicators for a positive evaluation of the game, it is correct in the first instance but not in the second. This needs to be corrected manually. You can do this during the auto coding process by stopping at every hit and deciding whether it should be coded or not. I prefer to let the auto coding process run through and to check the coded data later. This process can be facilitated by increasing the number of characters that are captured as quotation names. It is then often sufficient to just look at the quotation name to decide whether the coding is adequate or not, instead of retrieving the quotation in context.

Increasing the length of quotation names

It is suggested to do this before you code the data. You can still do it afterwards, but then you have to run an additional option (see below). From the main menu select Tools / Preferences / General Preferences . In the tab 'General' at the bottom left look for the field "increase list size for quotes." Set it to 100 characters. There are no limits in terms of number or characters you can set, but as quotation names are displayed on one line only, there is no point to enlarge it beyond what you can comfortably read on your screen without blocking everything else. Click OK and close the Preference window.

If you change the number of characters after having coded all or some of the data, you need to run one more option. After closing the Preference Window, select Quotations / Miscellaneous / Update names . Depending on the number of quotations you already have, this may take a while. Just let the process run through. Each quotation needs to be updated.

Assessing Data Content Via a Word Cloud

Next, let's get a feeling for how the respondents have evaluated the game. Open the Word Cruncher via the Analysis menu. Select the word cloud option and create word cloud for all documents. If you plan to use this option more often, you may want to spend some time to prepare a stop list so that words you are not interested in are excluded from the count. These can be words like and, but, when, while, a, the, you, I, we, etc. However, depending on your research interests words like "but," "while" or "when" might be interesting indicators. Thus, there are no standardized stop lists; rather a stop list needs to be tailored to individual project needs in most cases.

For further information on the Word Cruncher, view the video tutorial.

For a better overview of which words occur, click on the 'Count' field in the Word Cloud Window. To see how frequent a word occurs, move with the cursor over a word. To exclude a word from the cloud ( remove from cloud only ) or remove it and add it to the stop list ( exclude selected word ), right click and select the appropriate option from the context menu. The two words, evaluation and summary, are frequently occurring words, but can be ignored here as they are the titles of the two questions that were asked. For coding purposes, we need to take a look at the words used by the respondents. Based on the word cloud, the following list could be prepared:

Server

Good

Build

Great

Online

Best

Fun

Awesome

Positive

son

Parent

Protect(ing)

Monster

Multi player / multi-player / multiplayer

Mode (creative mode / survival mode)

Education(al) / learn(ing) / intellectual(ly)

Age

Addict(ive) / addiction

Skills

Challenging

Experience

Violen(ce) / violent

Child(ren) / kid

Problem

Community

Mode

Creat(ing) / creative

Recommend

Peaceful

Blood

Scare

Care(ful)

Fantastic

Destroy

Kill

Danger(rous / offensive / hazardous

boring

save(ty)

Importing a List of Codes

To import this list of codes or coding ideas into ATLAS.ti, do the following: Open the Memo Manager and create a new memo: Memos / Create free memo . As memo title enter 'Code List'. Copy the list into the memo, one expression per line. Save the Memo (Ctrl+S or Memo/Save and close it). Next, highlight the memo in the Memo Manager, right click and select Miscellaneous / Create Codes from Selected Memo .

Even though ATLAS.ti calls the words you have just imported "codes", these are, as a matter of fact, only preliminary ideas. We still have to do some work, to turn these words into proper codes and the list into a proper code list. The imported expressions provide some hints to help with auto coding. For instance, multiplayer is spelled in three different ways; if you search for 'child', ATLAS.ti will also find 'children', but you have to enter 'kid' as a search term as well using the OR operator (child|kid), and so on.

One could have sorted and organized the expressions already in terms of their meaning by adding prefixes like positive or pos in front of words such as 'good,' 'great', 'awesome', and 'fun'. But I leave this for after the auto coding process. When you can check the data behind the first initial codes, it is easier to assess the fit.

You can also use the Import function when using a predefined code list. If so, I recommend that you already set it up with prefixes so that your codes appear in the right order and underneath the category they belong to (see Figure 5).

Auto Coding Data Content

After importing or creating the list of codes you want to use, you can begin with the auto coding process. To open the tool, select Codes / Coding / Auto Coding from the main menu. The Auto Coding Dialog opens. As shown in Figure 6 select a code, enter a search expression, and select the scope: All current PDs, select what should be coded: I recommend 'sentence'. The option Single Hard Return means that the entire paragraph is coded. The option Multiple Hard Returns codes all text up to the next empty line.

The Confirm always option is not selected. As mentioned, I prefer to check the coding afterwards and not during the auto coding process.

Once all data are coded, you get a first idea of how themes are distributed in the data. A high frequency at this stage however points out that the search expression was not precise enough and the code is likely to contain a mixed bag of things. These codes are the first candidates for checking. Open the Code Manager and double click on a code that you want to check. Retrieving for instance the quotation for the code "edu: creative", one finds a number of quotations that do not fit the definition of the code (see highlighted segments in Figure 9).

Quotation 9:4 needs to be recoded with "feature: creative mode", quotation 11:6 with a code that does not exist yet – something related to issues that arise when playing online. And so on.

Another example for the necessity of double-checking auto coded segments are those segments found based on the search term "violence / violent". In most statements, the respondents say that violence is not an issue. Only a few regard it as a problematic issue. Thus, even if auto coding may have just taken half an hour of your time, you are likely to spend another few days of checking and recoding the data. How long this takes obviously depends on the size of your data set.

While going through the data and inspecting the pre-coded data, one can also begin to sort and to structure the code list. Figure 7 shows the unsorted list of coding ideas on the left hand side and the emerging structured list on the right hand side. The name in capital letters represents the title of a category; the prefix has the effect that all codes that belong to a category are sorted underneath the category title. More information on how to build a coding system can be found in the book Qualitative data analysis with ATLAS.ti, Sage Publication, which can be found here. The book also comes with a set of video tutorials, which can be accessed here.

The structured code list for the sample project looks as follows. The data are now optimally prepared for entering the next phase of analysis, which means to make further use of the analysis tools that ATLAS.ti provides.

Further Analysis - Codes-Primary Documents Table

The Codes-Primary Documents table provides an overview of all codes per document in form of an Excel table. The output can also be aggregated on document group or code group levels. To create such a table, select: Analysis / Codes-Primary Documents Table . The following window opens:

You can select items via double-click, or make a multiple selection first and click on the button with the three arrows >>> to move the items into the selection pane. Select further options on the right-hand side and click Create Report. If you select the Excel option, Excel will ask you for confirmation. Click ok.

For the results shown in Figure 8, the three document families status::educator, status::parent and status::teenager and the codes of the code category EDU were exported. The pie charts were created in Excel, which is shown below:

For further information on the Codes-PD Table, watch this video tutorial.

Combining Variables (creating Super-PD-Families)

Variables like socio-demographic characteristics are turned into what ATLAS.ti calls PD families. When importing survey data as in this example, this is done automatically from the Excel table. Thus, you have separate families for variables like gender (in ATLAS.ti gender::female and gender::male; one family per value), or as in this example three families for status (status::educator / status::parent / status::teenager). Another variable that was imported was Age recommendation: 2 to 5 year old, 6 to 9 year old and 10 to 13 year old. If you want to compare answers of parents who give different age recommendations, then you can combine these two variables by creating a Super-PD-Family. This can be done very quickly in the side panel of the PD Manager. This is how it works:

Example: Open the PD Manager. Select the two families that you want to combine by holding down the Ctrl-key and check the Venn diagram. Is it set to OR (any) or AND (all)? If it is filled in completely, it is set to OR. If only the intersection is colored, it is set to AND (see Figure 10). To change between the two operators, click on the Venn diagram. Next, right click on one of the selected families and select the option Create Super Family . ATLAS.ti automatically generates a name. You can rename the family later if you wish. You can now use this newly created variable in another query or a Codes-PD Table.

The icon for super families is colored in red like the icon for super-codes. This way you can easily distinguish them from regular codes or families. You cannot manually change the items included into a super family (or super code). If you add more documents with the characteristic 'parent' and 'age recommendation 2-5', the super families is updated automatically. The same happens if you remove documents from a project. Thus, super objects in ATLAS.ti always reflect the current state of your project.

Cross-Tabulation of Codes

In addition to relating documents, or document groups to coded content, you can also relate codes to each other. ATLAS.ti offers two tools for this: the Code Co-occurrence Explorer and the Codes Co-occurrence Table. Both options can be found under the Analysis menu. The Code Co-occurrence Explorer gives you a quick overview of the codes that co-occur with each other. This can result in long lists as a code is already listed as co-occurring if there is one co-occurring instance.

You are not quite sure what "co-occurring" means? Let me explain. Two codes co-occur if they overlap in the margin area in any way or if there is a 100% match. This is illustrated in the following graphic and screen shot:

The code SQ: question 2 encloses all other codes. The code online: internet security is embedded within for instance online: server rules, but also within feature: multiplayer and the SQ: question 2 code. The code content: violence not an issue overlaps the code feature: multiplayer and is overlapped by feature: mods. Feature: mods and feature: survival mode are an example of an AND occurrence because they code the same segment.

Example: During the process of coding, I noticed that respondents frequently mentioned that the game is addictive but this did not necessarily mean that they think Minecraft is a bad game. Let's look into this further by opening the Code-Co-occurrence Explorer and subsequently the sub-tree for the code neg: addictive. The result shows that while mentioning that the game is addictive, the respondents also described the game to be creative, educational, entertaining and fun. As I used 'sentence' as unit of coding and only modified segment size moderately when checking the data, this means in most cases both aspects have been mentioned in the same or in overlapping sentences.

To see how often this is the case, we can run the Code-Co-occurrence Table. My hypothesis is that positive aspects are mentioned together with the addictive component, and other negative aspects are not mixed with positive evaluations in the same way. Let's test this. As mentioned the Code Co-occurrence Table is be found under the Analysis menu.

After opening the table, one needs to select codes for the columns (first list on the left-hand side) and codes for the rows (second list). The results are immediately displayed. The table above only shows frequencies (number of hits). It is possible to get an indicator for the strength of the relationships between two codes, the c-coefficient. For the current example, the number of cases involved is too small in order for the c-coefficient to be interpreted in a meaningful way.

The results come up as expected: none of the other negative evaluations is related to any of the positive aspects apart from neg: addictive. If you are interested in the data behind the numbers, simply click on the cell and the quotations for the column and the row codes are displayed in the two fields at the bottom of the window.

For further information on code co-occurrences, watch this video tutorial.

Example: A further question we can explore is whether the relation comes up even stronger if the code 'neg: addictive' and any of the positive aspects have been mentioned in the overall response to question two rather than occurring in the same sentence or an overlapping sentence. In order to test this, we need to create a super-code in the query tool: Analysis / Query Tool .

In formulating a query, one first selects the codes that one wants to combine. Thus, I first selected the code for 'SQ: question 2' via a double-click, then the code 'neg: addictive'. After choosing codes, the next step is to select an operator. For this query, I selected the Co-occur operator. This resulted in 40 quotations, which means that 40 respondents felt that the game has an addictive component. As I want to use the result of this query in another query, I saved it as super-code. To do this, one just needs to click on the super-code button in the query tool. I named it 'Evaluation: game is addictive' (see Figure 18).

For further information on the query tool, watch this video tutorial.

Example continues: I can now use the super-code for creating another code co-occurrence table to further explore the question I am interested in exploring. This time I activated the button C (in the Codes Co-occurrence Table) so the c-coefficient is displayed next to the number of hits. Taking a look at the full answer of the respondents, it can be seen that the addictive component has also been mentioned by those who evaluate the game negatively. Thus, the initial hypothesis needs to be modified. The stronger relations as indicated by the c-coefficient though come up with positive aspects (see Figure 19).

The c-coefficient is a number between 0 and 1 and it can be interpreted as follows: the higher the number the closer the relationship between the two codes. This is similar to a correlation coefficient in statistics but without a p-value. If you are interested in how the c-coefficient is calculated, enter "c-coefficient" as key word into the ATLAS.ti Help file ( Help / ATLAS.ti Help ).

Creating Variables from Codes

Above I have shown how to combine variables based on already existing PD families. Another possibility is to create new variables derived from the content you have coded. For this we need the query tool ( Analysis / query tool ).

Example: I want to group respondents who have evaluated the game positively and those who evaluated it negatively. In developing the coding system, I have already created two code families – one for positive evaluation codes and one for negative evaluation codes. These now come in handy when querying the data.

In the query tool, I double clicked the code family 'Eval: positive'. Next, I right-clicked in the result pane at the bottom right of the query tool window and selected the option Create Family for found PDs . A prompt pops up asking to enter a name for the family. The resulting family can be found in the side panel of the PD Manager (see Figure 20).

Families = Variables = Filters

The main purpose of families in ATLAS.ti is their use as filters. I have shown a number of ways how to prepare just the kind of filter you need: Creating PD families, creating code families, creating Super-PD families and how to creating PD families based on coded content. By the way, Super-Code families can be created in the same way as Super-PD families. Thus, you can also aggregate your codes further as needed. We have seen PD and Super-PD families at work in creating Codes-PD tables, and code families in creating a query on an aggregated level. A further option is to use PD and Super-PD families for filtering results of a code-co-occurrence table and for queries created in the query tool.

Example: The first table in Figure 21 has been created to explore the relationships between positive and negative evaluations with the codes violent content, educational value and the online aspect of the game for all respondents. The second table at the bottom right of Figure 21 looks at the same relations, but only for respondents who recommend the game for children ages 2 to 5. The filter was set using the following menu option: Documents / Filter / Families .

From the second table it can be derived that those writing negative evaluations do not recommend it for very young children. This can be double checked with a Codes-PD table. Further those recommending it for young children highlight the educational component and do not emphasize the online aspect of the game. If you are interested in reading the data behind the numbers, click on a cell in the table. The quotations are displayed in the two panes at the bottom of the table. Another option is to prepare a report in the query tool. How this works will be explained next.

Example: PD or Super-PD families can be utilized in the query tool via the Scope button. So far we looked a lot at numbers. It is however also possible to put the focus on the data content. If this is your aim, you need to work with the query tool. Let's for example look at the responses related to 'playing on a server' by 'age recommendation'. To open the query tool, select Analysis / Query Tool .

To run the query, I double-clicked the code 'online: server'. Next, I selected the Scope button. This opens the Scope of Query window. From this window, I selected the PD family: age recommendation:: 2 to 5. This changes the results in the query tool window. NOTE: The results are always displayed in the query tool window! If you look at the Scope of Query window, you see a list of ALL quotations of the selected PD family. This is not the result you want. Taking a look at the query tool window, one can see that from the total of 52 quotations for the selected code, only 12 are written by those who recommend the game for 2 to 5 year old children (see Figure 22).

You can read the quotations in content by clicking through the list of quotations in the query tool. If you want to create a report, click on the printer icon above the result pane and select either the option Full content or Full content – No Meta (the latter option produces shorter reports).

Data Export – Reintegrating qualitative data with quantitative data

If you plan to reintegrate the results of your coding in ATLAS.ti with your quantitative data, you need to make sure that you import the cases in the same order and with the same name as they are in your statistic program. This can be achieved by adding a column to the Excel spreadsheet that you prepare for data import including the appropriate case name and number. If this column is not included, ATLAS.ti creates an automatic name for each document (PD in ATLAS.ti), which is case 1, case 2, case 3 and so on. The syntax for the name column is an exclamation mark (!). The table below shows various examples. The number for the cases may not be consecutive, or as shown in the third column, you may use different identifiers altogether.

!case

!case

!case

Case 1

1

Res 1.a.3

Case 3

3

Res 1.a.3

Case 15

15

Res 1.b.4

Case 27

27

Res 2.b.4

…..

Table 5 : Different ways to name your cases = PDs in ATLAS.ti

If you want to export an aggregated count of your coded data, use the Codes-PD table option and create a table for all codes by all documents. If you want more detail, ATLAS.ti provides an SPSS export option in form of a syntax file that needs to be run in SPSS. In the SPSS file, each quotation becomes a case. This is how it works:

Select: Project / Export / SPSS job. As output option select File . You can set various properties before creating the file. I suggest accepting the default. It is suitable for most projects. Open the resulting SPSS syntax file in SPSS, highlight it and click on the RUN button.

Figure 24 shows the resulting data matrix in SPSS. To show the various types of variables that are created I removed a number of columns. The first variable is the case number. Cases in the SPSS file created by ATLAS.ti are the quotations. Thus, to identify each respondent as case, you need to look at the variable PD. Further variables are: quotation number, start and end position of the quotation, the time of creation, and the codes (K1, K2, K3). KF stands for code families and PF for PD families. The media type (rich text, PDF, audio, video, image, geo) is also encoded.

The values for codes are 0 and 1. Zero stands for "does not apply"; one means the code "applies" to the quotation. The values for code families can be higher as the number of codes potentially applicable to a quotation is higher.

Summary

The analysis options presented here are not complete. For instance I have not yet touched on the network view function that might also be interesting for a mixed-method project. The type of queries you can run in the query tool can be much more complex. Here I only wanted to provide a flavor of what is possible. I hope that the examples presented here will inspire you to explore the potentials of ATLAS.ti for mixed-methods analysis.


Updates From The ATLAS.ti Community of Users

Welcome to the African Institute for ATLAS.ti.

In this issue of INSIDE ATLAS.ti we are introducing the African Institute for ATLAS.ti (AIA). The AIA is a service unit within the Centre for Evaluation, Science and Technology (CREST) at Stellenbosch University in South Africa. The AIA aims to support African academics and researchers carrying out qualitative data analysis with the assistance of ATLAS.ti. The intention of the AIA is to build a cadre of qualitative researchers across Africa who will produce rigorous quality research utilizing ATLAS.ti.

THE AIA grew out of the need to support PhD students from all over Africa who came to Stellenbosch University to complement their doctoral education.. In particular, they were looking for training in ATLAS.ti because their own universities could not provide the support the students needed. Dr. Lauren Wildschut together with Dr. Susanne Friese developed the idea of an ATLAS.ti training and support unit that could serve both South Africans and those who live in other countries of the African continent. The AIA was founded in June 2012 and was officially launched in January 2013, during the University's African Doctoral Academy 5th Annual Summer School in Research Methods.

The AIA provides a range of support to its members. This includes:

  • Providing updated information on training sessions - all courses offered by the AIA are advertised on the ATLAS.ti homepage at www.atlasti.com as well as on its own webpage (www.sun.ac.za/aia)
  • Assisting with the bulk buying of student licenses for Stellenbosch University students and for qualifying AIA members.
  • Assisting and accrediting introductory instructors of ATLAS.ti.
  • Providing on-line support for simple analysis queries with ATLAS.ti.

In December 2014, Dr Wildschut and Dr Friese assessed and finally accredited one student level and four introductory level trainers. They are: Mr. Richard Shambare, Dr. Diane Bell, Ms. Bronwyne Coetzee, Ms. Kathy Lawrence, and Dr. Pieter Steenkamp. These trainers have already begun working independently and by doing so have helped meet the demand for ATLAS.ti training in South Africa and beyond.

The Stellenbosch University Business School provides all MBA students with an ATLAS.ti license. All Monitoring and Evaluation (M&E) students at CREST receive an ATLAS.ti license as well. The AIA has also accredited introductory and advanced ATLAS.ti courses and students participating in these courses will receive formal accreditation from Stellenbosch University. Lorenza Williams, a PhD student who had completed both Introductory and Advanced Courses in ATLAS.ti was offered a valuable opportunity to work with Dr. Friese at the prestigious Max Planck Institute for the Study of Religious and Ethnic Diversity . After her stay in Germany, Lorenza returned to Stellenbosch and created a support group for other PhD students who are doing qualitative research. She had the following to say about her experience at the Institute:

The AIA has also motivated researchers in other parts of Africa to create similar initiatives. One of them is Dr. Justus Twesigye from the Department of Social Work, Makerere University. After attending the Introductory and Advanced ATLAS.ti courses at Stellenbosch University, he went back to Uganda and began working on the development of the East African ATLAS.ti Center, which will start training students at Makerere University and other universities in Uganda very soon. The Center will be run by other graduates from the ATLAS.ti Training Center in Stellenbosch. They are: Elizabeth Nansubuga, John Mushomi, Ambrose Ruyooka and Grace Ssekakubo. Dr. Ssekakubo and Dr Twesigye hold doctorate degrees from Wits University and Stellenbosch University respectively and the rest of the team are finalizing their doctorate degrees. "I have much hope that this incentive will meet the huge need for ATLAS.ti in Uganda," an excited Dr Twesigye reported from Makerere University.

One of our newly accredited ATLAS.ti trainers, Bronwyne Coetzee, recently offered training in ATLAS.ti to staff members of Creative Consulting Development Works , an NGO in Cape Town. Bronwyne is currently a PhD student at the department of Psychology (Stellenbosch University), and has offered a number of training opportunities to fellow and junior students within her department. See below a picture showing Bronwyne teaching a workshop.

Finally, the AIA hopes to become a virtual support network for qualitative researchers using ATLAS.ti and for Master's and Doctoral students who utilize ATLAS.ti for literature reviews or analysis of qualitative data. The future looks bright for AIA. If you would like to contact us, please do so by writing to the Institute director, Dr. Lauren Wildschut at [email protected] .



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