advocated by John Goldthorpe, attempt to draw out regularities and specify the underlying social mecha- nisms and processes that generate these regularities.
Conversely, a wider debate within qualitative research questions the very validity of the concept of causality (e.g., by Yvonna Lincoln and Egon Guba) or chal- lenges the necessity to establish universals with vari- able-oriented approaches (e.g., by Ragin). Joseph Maxwell provided an excellent summary and discus- sion of strategies for causal explanation using qualita- tive methods.
Qualitative researchers are often interested in exam- ining differences, similarities, and associations among a variety of objects such as statements, individual meanings, and political configurations. This makes comparative research virtually inescapable. To accom- plish this, researchers need to consider vital aspects such as selecting a particular case or scale of analysis, defining constructs, and deciding whether they will focus on cases or characteristics. Comparisons can then take place on a variety of topics using many different types of qualitative methods.
Melinda C. Mills See alsoComparative Analysis
Further Readings
Butte, G. (2004).I know that you know that I know:
Narrating subjects from Moll Flanders to Marnie.
Columbus: Ohio State University Press.
Collier, D. (1993). The comparative method. In A. Finifter (Ed.),Political science: State of the discipline II (pp. 105–119). Washington, DC: American Political Science Association.
Collier, D., & Mahon, J. E. (1993). Conceptual “stretching”
revisited: Adapting categories in comparative analysis.
American Political Science Review, 87,845–855.
Ebbinghaus, B. (2005). When less is more: Selection problems in large-Nand small-Ncross-national comparisons.International Sociology, 20,133–152.
Eggan, F. (1954). Social anthropology and the method of controlled comparison.American Anthropologist, 56, 743–763.
Glaser, B. G., & Strauss, A. L. (1967).The discovery of grounded theory.Hawthorne, NY: Aldine.
Gravlee, C. C. (2005). Ethnic classification in southeastern Puerto Rico: The cultural model of “color.”Social Forces, 83,949–970.
Gunew, S. (2004).Haunted nations: The colonial dimensions of multiculturalisms.London: Routledge.
Maxwell, J. A. (2004). Using qualitative methods for causal explanation.Field Methods, 16,243–264.
Miles, M. B., & Huberman, A. B. (1994).Qualitative data analysis: An expanded sourcebook.Thousand Oaks, CA: Sage.
Przeworski, A., & Teune, H. (1970).The logic of comparative social inquiry.NewYork: John Wiley.
Van de Vijver, F. J., & Leung, K. (1997).Methods and data analysis for cross-cultural research.Thousand Oaks, CA: Sage.
with men’s documents or comparing documents in which someone either reveals or does not reveal a per- sonal trauma. Second, individual sections of data doc- uments can be organized into codes. Simple code and retrieve procedures can be used for summary by code topic. More complex procedures can be used to evolve code meanings and definitions, to explore how combi- nations of codes applied to data influence ideas that emerge during analysis, and to pursue answers to questions that preceded data collection.
Document System
Initially, researchers make decisions about what type of data they will collect and how they will manage those data within software. The document system within qualitative software is the primary tool for stor- ing each data document users work with in a qualitative software package. A document can be in the form of text, graphic, audio, or video file. Several packages allow the use of rich text or Word files, thereby main- taining the original formats (e.g., bold, italic, underline, color) present in documents when they are reviewed within a software package. In software packages where users can engage with multimedia files (e.g., pho- tographs, audio, video), the program either permits full engagement with a file or has a linking system where the users connect an entire multimedia file from within the body of a text document. This procedure works like weblinks placed in the body of an email. Direct work with a multimedia file proceeds in the same way as work with a text document. Users can write notes about all or parts of the file. Sections of the file can be marked and/or coded for later retrieval, and entire files can be organized by major categories that characterize them.
Although users do not engage directly with the document system within a software package, the doc- ument system serves to manage and track their data documents. In programs where users can edit files, the document system automatically tracks how any changes caused by editing data documents affect the placement of codes and memos. The codes and memos placed before any edits automatically adjust so that these items remain with the original text where they were first placed.
When users review each segment of text coded to a code or to which they attached a memo, they are able to view that text within the body of the document where it originates. The document system is the core
tool that maintains the integrity of users’ original data document so that users are able to view these sections of text in context.
Memo System
Qualitative software offers an opportunity to write memos and locate them in places that are easy to access. These memos vary in size and content. Memos can be simple reactions to a section of a fieldnote, an interview, or a focus group, or they can be reactions to complex theoretical treatises. Users can write a memo about any individual data document, about any section of text, about any code, or about any independent top- ics that arise in the course of their analysis. In essence, memos about documents, codes, and sections of text are the equivalent of “sticky notes” that users place on those items. Just like working within a word processor or on a notepad, memos in qualitative soft- ware are live, editable documents into which users can copy quotes from documents. Any memo that users write can be saved and opened in their word proces- sors. Reminder icons and memo lists help users to access their memos for retrieval, reaction, and adjust- ment. Memos are easily exported to users’ word processors for further editing and integration with other writing on their research topic.
Category System
There are two primary levels of categorization within qualitative software. Researchers can categorize entire data documents into attributes for sorting and fil- tering larger data sets. In addition, sections of a docu- ment can be grouped together to gather examples of topics represented in codes designated by researchers.
Attributes
If a study involves comparisons of groups of data, attribute functions of qualitative software can be helpful.
Major variables and points of comparison can be entered into a program, and researchers can identify which documents belonged to specific subcategories of each variable. For example, researchers can add infor- mation about participants’ background characteristics to compare along lines of gender, age, income, race, ethnicity, and political or religious affiliation. For mixed-methods studies, spreadsheet files that contain 104———Computer-Assisted Data Analysis
background information and/or responses to key ques- tions can be imported into qualitative software, and spe- cific responses can be associated with corresponding qualitative documents. Points of comparison that arise during review of data documents can be added as well.
For example, if researchers discover that some partici- pants experienced financial challenges, this category can be added as well and the sample can be categorized according to who did or did not experience financial problems. Organizing data by attributes of data docu- ments allows researchers to focus reviews of topics rep- resented in code categories. Discussions of religion can be read for all women who live in the northeastern United States and then for all men who live in that same region of the country. These steps help researchers to answer foundation questions that define their analysis.
Codes
Codes are used to organize sections of text into key topics defined by researchers. A review of text by codes is a key component of diagnosing patterns of discussion within qualitative data. Codes can be cre- ated, maintained, and adjusted within codebooks, which are inherently flexible. Some researchers start a project by entering deductive codes into their qualita- tive software program. These codes might arise from research questions, topic literature, and/or interview and data collection protocols. Inductive coding is also possible. New topics that arise via document review can be made and applied to the selections of data where they first appear and throughout an entire data set. Codes can be renamed, deleted, combined, and broken into smaller subcategories.
Codes can also be applied via search facilities within qualitative software. Researchers can search for instances of “health” and code results along with surrounding text to a code category. For more struc- tured data, researchers can use “autocode” functions to sort all responses to each question of an open-ended survey or a structured interview into its own code folder. Use of this feature requires minor data format- ting to enable this functionality.
Supplemental Tool: Marking and Labeling Key Sections of Text
As qualitative software evolves, efforts to simulate all tasks that qualitative researchers do off-screen
imitated inside software continue to increase. One area of focus is an early phase of analysis where researchers gain familiarity with text and their reac- tions to it via a first read. At this time, it is common to simply highlight sections of text and write notes with reaction and reflection in the margin of the document.
There are several advantages to this form of comput- erized text highlighting.
Visual Aid.As with a manual highlight pen, comput- erized highlighting results in a mark placed over or next to the text that users highlight. This visual aid allows users to easily recognize this section of text on a second review.
Gathering Tool. Sections highlighted within qualita- tive software are added to a convenient list that allows easy retrieval and examination.
Labels for Sections. The highlighted sections can be named or labeled. To distinguish this process from coding, users are not gathering examples on a topic with this feature. Instead, they are labeling and nam- ing individual sections with what can be considered
“nicknames.” All of these labels can be reviewed as a transition into shaping a codebook.
Foundation for Data Profiles.Highlighted sections of text can be imported into diagrams to create pictures and profiles for all or part of a data document or series of documents.
The Basic Toolkit:
Tools to Access and Review
Any item created within the systems introduced in the previous section can be reviewed as a means of gaining clarity of its meaning and import to the analysis at hand. Simple memo and code retrieval tools assist in this process. It is important to note that access and review of any memo, code, or combination of these items is not an isolated task. Software packages are built to invite continual evolution of ideas. The names and content of memos, codes, and attributes are easy to adjust as researchers refine their developing under- standing of each item. These processes often dictate how researchers take advantage of co-occurrence and diagramming tools found within qualitative software.
Researchers use co-occurrence tools to find instances where codes, originally applied by them, occur in Computer-Assisted Data Analysis———105
combination (or not in combination) within and across data documents. Changes to these tools now allow researchers to find an anticipated combination of codes (e.g., every time health and finances are coded together) or to assess combinations that they did not predict. This latter function encourages discovery of serendipitous connections. Researchers use diagramming tools as a way to explore potential connections or to design mod- els to portray concrete ideas for presentation.
Simple Retrieval Tools
As users label and organize data into categories and write memos, they begin to reflect on what their efforts are teaching them. Memos, coding, and catego- rization efforts can be retrieved in isolation or combi- nation to help users assess what they have learned and to help them determine next steps.
Memos
Memos that users write are available for retrieval in two ways. First, lists of memos are available for inde- pendent retrieval. As users read any individual memo, they can edit text and/or add sample quotes from their data. Memos can be saved and opened in a word pro- cessing program. This facility makes memos portable.
They can be attached to or pasted into emails to share with colleagues and research team members.
Memos that were written alongside sections of text can be retrieved as users review codes and co- occurrences. If users coded a section of text to religion and wrote a note about that text, they can opt to display their note along with that text on retrieval. This strategy is common and allows for thinking out loud and linking ideas in data with thoughts and reactions to text with knowledge of material that users bring to their data.
Memos evolve as analysis progresses. They serve as useful transition points in analysis and help to build the foundation for final written material about users’ project.
Codes
A core function within qualitative software is the retrieval of all segments of data coded to sections of text. Users can examine complete sets of text coded to any one topic for summary and reflection. Early in a project, this process allows users to determine the importance of a category and the effectiveness of cod- ing efforts. Later in a project, summaries of codes facilitate important conclusions about a data set.
Codes can be reviewed on-screen or in report form.
On-screen code review encourages adjustments to
coding. Users can remove or replace codes from text, adjust the amount of text coded to a category, and add memos during this review. They can also review all instances coded to a category in a report for a direct summary of that topic. These reports can be read in users’ word processors and/or shared with colleagues.
It is common for research teams to share information about key topics using this tool.
Filtering for Comparison
Filters can be applied prior to a review of codes and memos to narrow and focus users’ search of these items.
For example, users can filter their data set to just the women’s documents before they review all instances coded to religion. All coded instances that users review will appear with corresponding memos for just the women’s documents. This step should give users a bet- ter perspective of women’s experiences with and per- spectives on religion along with their thoughts on women’s discussions of religion. Users can then change their filter to the men’s documents to gain a better picture of gender difference. Filtering can be used for single variable comparison or for exploration of how combinations of variables, such as gender and age, affect discussions and experiences within users’ projects.
Co-Occurrence Tools
The ability to retrieve co-occurring codes is one of the major features that distinguishes qualitative soft- ware from simple code-and-retrieve programs. Rather than just seeing all quotations coded to religion or all quotations coded to health, co-occurrence tools allow users to retrieve all quotations coded to both religion and health, providing better access to the ways in which two codes interact. The existence of co-occurrence tools enables users to monitor single concepts, such as religion and health, and dynamic ways in which topics combine to potentially build thematic discussion.
Co-occurrence tools can be used to find instances where the same two codes are applied to the same text or for more specific questions of how the location of two codes falls across users’ entire data set. Users can find instances of one code inside another code, one code overlapping another code, and/or one code preceding a second code. Pursuit of options within co-occurrence tools is determined by users’ research questions and goals and their evolving analysis.
Recent innovations in these tools allow more flexible engagement with co-occurrence. Previously, these tools required researchers to know code connections, 106———Computer-Assisted Data Analysis
such as religion coded with health, before they searched for a co-occurrence. New tools monitor co- occurrences throughout a project. At any point in the coding process, users can assess all codes that over- lap. They can focus on the religion code and see every code that overlaps at least once and then move to each section that overlaps. Serendipitous connections are now more accessible because of these changes.
Diagramming Tools
Diagramming tools in qualitative software (fre- quently called maps, models, or networks) can be used for brainstorming about potential or real connec- tions researchers uncover in their analysis or for pre- senting concrete ideas to an audience. Researchers can link component parts of a project to display con- nections they are pursuing. Increasingly, researchers have the ability to link any part of their project to any other part of the project. Typically, researchers link codes to codes to show component parts of code groups or how one code might relate to another.
More dynamic connections are available in models of different programs. Users can include icons to rep- resent different sections of text that contribute to an important conversation. Clicking on icons brings users to the text of the data documents. Users can also link data documents to diagram connections between individuals within a data set. Graphic files can be added to maps as well to enhance the messages con- veyed by maps. Although currently the functionality presented in this paragraph is uneven across pro- grams, the discussion does represent what is possible and might predict what to expect as functionality con- verges across programs in the future.
Diagrams created in any program can be exported for work in word processing, presentation, and visual diagramming programs.
Supplemental Tools: Tools for Integrating Qualitative and Quantitative
Research and Facilitating Teamwork Integrating Qualitative
and Quantitative Research
If a project requires a combination of qualitative and quantitative data, researchers can use tools to import or export quantitative information to a project.
Spreadsheets that provide demographic and survey information for respondents can be imported to a qualitative software program. This information can
be linked to data about these individuals within researchers’ qualitative software projects. Importing quantitative information provides the foundation for comparisons outlined earlier in the “Filtering for Comparison” section.
Qualitative software also provides counts of coded instances by code. Counts for individual codes appear next to codes in codebook displays. In addition, tables showing code distribution across documents can be exported to spreadsheets and, in some instances, directly to SPSS software. These outputs can be linked to quantitative databases for further exploration.
Teamwork
Teamwork continues to be an area of focus for qualitative software developers. Research teams can use output reports to share information on key topics.
Log-in functions provide basic information about who works on different sections of a data set. Component projects worked on by different members of a team can also be combined via teamwork import and merge functions of qualitative software.
Raymond C. Maietta
See alsoATLAS.ti (Software); DICTION (Software);
Ethnograph (Software); Framework (Software);
HyperRESEARCH (Software); MAXqda (Software);
NVivo (Software); Qualrus (Software); Quantitative Research; SuperHyperQual (Software); TextQuest (Software); Transana (Software)
Further Readings
Creswell, J. W., & Maietta, R. C. (2002). Qualitative research. In D. C. Miller & N. J. Salkind (Eds.), Handbook of research design and social measurement (6th ed.). Thousand Oaks, CA. Sage.
Fielding, N. G., & Lee, R. M. (1998).Computer analysis and qualitative research.Thousand Oaks, CA: Sage.
Lewins, A., & Silver, C. (2007).Using software in qualitative analysis.Thousand Oaks, CA: Sage.
Maietta, R. (2006). State of the art: Integrating software with qualitative analysis. In L. Curry, R. Shield, & T. Wetle (Eds.),Improving aging and public health research:
Qualitative and mixed methods.Washington, DC:
American Public Health Association and Gerontological Society of America.
Tesch, R. (1990).Qualitative research: Analysis types and software tools.New York: Falmer.
Weitzman, E. A., & Miles, M. B. (1995).Computer programs for qualitative analysis.Thousand Oaks, CA: Sage.
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