METHODS OF COLLECTING ANO ANALYZING EMPIRICAL MATERIALS (Bulmer, 1979). Grounded theory, schema analysis, ethnographic decision modeling, and analytic induction all include model-building phases. Once a model starts to take shape, the researcher looks for negative cases—cases that don't fit the model. Negative cases either disconfirm parts of a model or suggest new connections that need to be made. In either instance, negative cases need to be accommodated. Negative case analysis is discussed in detail by Becker, Geer, Hughes, and Strauss (1961, pp. 37-45), Strauss and Corbin (1990, pp. 10S-109), Lincoln and Guba [1985, pp. 309-313), Dey (1993, pp. 226-233), Miles and Huberman (J994, p. 271), and Becker (1998), and is used by schema analysts {Quinn, 1997), ethnographic decision modelers (Gladwin, 1989), and scholars who use analytic * induction (Bloor, 1976; Cressey, 1953/1971; Lindesmith, 1947/1968). ,_ In ethnographic decision modeling and in classical content analysis, models are built on one set of data and tested on another. In their original formulation, Glaser and Strauss (1967) emphasized that building grounded theory models is a step in the research process and that models need to be validated. Grounded theorists and schema analysts, today are more likely to validate their models by seeking confirmation from expert informants than by analyzing a second set of data. For example, Kearney, Murphy, and Rosenbaum (1994) checked the validity of their model of crack mothers' experiences by presenting it to knowledgeable respondents who were familiär with the research. Regardless of the kind of reliability and validity checks, models are simplifications of reality. They can be made more or less complicated and may capture all or only a portion of the variance in a given set of data. It is up to the investigator and his or her peers to decide how much a particular model is supposed to describe. Below we review some of the most common methods researchers use to analyze blocks or texts. These include grounded theory, schema analysis, classical content analysis, content dictionaries, analytic induction, and ethnographic decision tree analysis. Grounded Theory Of Grounded theorists want to understand people's experiences in as rigorous and detailed a manner as possible. They want to identify categories and concepts that emerge from text and link these concepts into 278 Data Management and Analysis Methods substantive and formal theories. The original formulation of the method (Glaser Sc Strauss, 1967) is still useful, but later works are easier to read and more practical (Charmaz, 1990; Lincoln 6c Guba, 1985; Lonkila, 1995; Strauss, 1987). Strauss and Corbin (1990), Dey (1993), and Becker (1998) provide especially useful guidance. (For some recent examples of grounded theory research, sec Hunt & Ropo, 1995; Irurita, 1996; Kearney et al., 1994; Kearney, Murphy, Irwin, & Rosenbaum, 1995; Sohier, 1993; Strauss & Corbin, 1997: Wilson & Hutchinson, 1996; Wright, 1997.) Grounded theory is an iterative process by which the analyst becomes more and more "grounded" in the data and develops increasingly richer concepts and models of how the phenomenon being studied really works. To do this, the grounded theorist collects verbatim transcripts of interviews and reads through a small sample of text (usually line by line). Sandelowski (1995a) observes that analysis of texts begins with proofreading the material and simply underlining key phrases "because (hey make some as yet inchoate sense" (p. 373). In a process called "open coding," the investigator identifies potential themes by pulling together real examples from the text (Agar, 1996; Bernard, 1994; Bogdan Sc Biklen, 1992; Lincoln 8c Guba, 1985; Lofland & Lofland, 1995; Strauss & Corbin, 1990; Taylor & Bogdan, 1984). Identifying the categories and terms used by informants themselves is called "in vivo coding" (Strauss 8c Corbin, 1990). As grounded theorists develop their concepts and categories, they often decide they need to gather more data from informants. As coding categories emerge, the investigator links them together in theoretical models. One technique is to compare and contrast themes and concepts. When, why, and under what conditions do these themes occur in the text? Glazer and Strauss (1967, pp. 101-116) refer to this as the "constant comparison method," and it is similar to the contrast questions Spradley (1979, pp. 160-172} suggests researchers ask informants. (For other good descriptions of the comparison method, see Glaser, 1978, pp. 56-72; Strauss Sc Corbin, 1990, pp. 84-95.) Another useful tool for building theoretical models is the conditional matrix described by Strauss and Corbin (1990, pp. 158-175). The conditional matrix is a set of concentric circles, each level corresponding to a different unit of influence. At the center are actions and interactions; the outer rings represent international and national concerns, and the inner rings represent individual and small group influences on action. The 279 METHODS OF COLLECTING AND ANALYZING EMPIRICAL MATERIALS •no' matrix is designed to help investigators to be more sensitive to conditions, actions/inreractions, and consequences of a phenomenon and to order these conditions and consequences into theories. Mcmoing is one of the principal techniques for recording relationships among themes. Strauss and Corbin (1990, pp. 18, 73-74,109-129,197-219) discuss three kinds of memos: code notes, theory notes, and operational notes. Code notes describe the concepts that are being discovered in "the discovery of grounded theory." In theory notes, the researcher tries to summarize his or her ideas about what is going on in the text. Operational notes are about practical matters. Once a model starts to take shape, the researcher uses negative case analysis to identify problems and make appropriate revisions. The end results of grounded theory are often displayed through the presentation of segments of text—verbatim quotes from informants—as exemplars of concepts and theories. These illustrations may be prototypical examples of central tendencies or they may represent exceptions to the norm. Grounded theory researchers also display their theoretical results in maps or the major categories and the relationships among themXKearney et ah, 1995; Miles &c Huberman, 1994, pp. 134-137). These "concept maps" are simitar to the personal semantic networks described by Leinhardt (1987, 1989), Strauss (1992), and D'Andrade (1991) (see below). Schemo Analysis Schema analysis combines elements of the linguistic and sociological traditions. It is based on the idea that people must use cognitive simplifications to help make sense of the complex information to which they are constantly exposed (Casson, 1983, p. 430). Schänk and Abelson (1977) postulate that schemata—or scripts, as they call them—enable culturally skifled people to fill in details of a story or event. It is, says Wodak (1992, p. 525), our schemata that lead us to interpret Mona Lisa's smile as evidence of her perplexity or her desperation. From a methodological view, schema analysis is similar to grounded theory. Both begin with a careful reading of verbatim texts and seek to discover and link themes into theoretical models. In a series of articles, Quinn {1982,1987, 1992,1996, 1997) has analyzed hundreds of hours of interviews to discover concepts underlying American marriage and to show how these concepts arc tied together. Quinn's {1997) method is CO 280 Data Management and Analysis Methods "exploit clues in ordinary discourse for what they tell us about shared cognition—to glean what people must have in mind in order to say the things they do" {p. 140). She begins by looking at patterns of speech and the repetition of key words and phrases, paying particular attention to informants' use of metaphors and the commonalities in their reasoning about marriage. Quinn found that the hundreds of metaphors in her corpus of texts fit into just eight linked classes, which she calls lastingness, sharedness, compatibility, mutual benefit, difficulty, effort, success {or failure), and risk of failure. Metaphors and proverbs are not the only linguistic features used to infer meaning from text. D'Andrade (1991) notes that "perhaps the simplest and most direct indication of schematic organization in naturalistic discourse is the repetition of associative linkages" (p. 294). He observes that "indeed, anyone who has listened to long stretches of talk—whether generated by a friend, spouse, workmate, informant, or patient—knows how frequently people circle through the same network of ideas" (p. 287). In a study of blue-collar workers in Rhode Island, Claudia Strauss {1992) refers to these ideas as "personal semantic networks." She describes such a network from one of her informants. On rereading her intensive interviews with one of the workers, Strauss found that her informant repeatedly referred to ideas associated with greed, money, businessmen, siblings, and "being different." She displays the relationships among these ideas by writing the concepts on a page of paper and connecting them with lines and explanations. Price (1987) observes that when people tell stories, they assume that their listeners share with them many assumptions about how the world works, and so they leave out information that "everyone knows." Thus she looks for what is not said in order to identify underlying cultural assumptions (p. 314). For more examples of the search for cultural schemata in texts, see Holland's {1985) study of the reasoning that Americans apply to interpersonal problems, Kempton's (1987) study of ordinary Americans' theories of home heat control, Claudia Straitss's (1997) study or what chemical plant workers and their neighbors rhink about the free enterprise system, and Agar and Hobbs's {198J) analysis of lrow an informant became a burglar. We next turn to the two other methods used across the social sciences for analyzing text: classical content analysis and content dictionaries. 281 METHODS OF COLLECTING AND ANALYZING EMPIRICAL MATERIALS Displaying Concepts and Models Visual displays are an important pare of qualitative analysis- Selecting key quotes as exemplars, building matrices or forms, and laying theories out in the ŕorm of flowcharts or maps are all potent ways to communicate ideas visually to others. Models arc typically displayed using boxes and arrows, with the boxes containing themes and the arrows representing the relationships among them. Lines can be unidirectional or bidirectional. 'For example, taxonomies are models in which the lines represent the .super- and subordinate relationships among items. Relationships can include causality, association, choices, and time, to name a few. A widely used method for describing themes is the presentation of direct quotes from respondents—quotes that lead the reader to understand quickly what it may have taken the researcher months or years to figure out. The researcher chooses segments of text—verbatim quotes from respondents—as exemplars of concepts, of theories, and of negative cases-Ryan (1999) has used multiple coders to identify typical quotes. He asks 10 coders to mark the same corpus of text for three themes. Ryan argues that the text marked by all the coders represents the central tendency or typical examples of the abstract constructs, whereas text marked by only some of the coders represents less typical examples and is more typical of the "edges" of the construct. Tables can be used to organize and display raw text or can be used to summarize qualitative data along multiple dimensions (rows and columns). The cells can be filled with verbatim quotes (Bernard Sc Ashton-Voyoucalos, 1976; LeinhardtČVc Smith, 1985, p. 254; Miles ScHubermaii, 1994, p. 130), summary statements (Yoder, 1995), or symbols (Fjellman&c Gladwin, 1985; Van Maanen, Miller, AC Johnson, 1982). (For a range of presentation formats, see Bernard, 1994; Miles & Huberman, 1994; Werner & Schoepflc, 1987.) Classical Content Analysis Whereas grounded theory is concerned with the discovery of data-induced hypotheses, classical content analysis comprises techniques for reducing texts to a unit-by-variable matrix and analyzing that matrix quantitatively to test hypotheses. The researcher can produce a matrix by applying a set of codes to a set of qualitative data (including written texts as well as audio and video media). Unlike grounded theory or schema 282 Data Management one Analysis Methods analysis, content analysis assumes that the codes of interest have already-been discovered and described. Once the researcher has selected a sample of texts, the next step in classical content analysis is to code each unit for each of the themes or variables in the codebook. This produces a unit-by-variable matrix that can be analyzed using a variety of statistical techniques. For example, Cowan and O'Brien (1990) tested whether males or females are more likely to be survivors in slasher films. Conventional wisdom about such films suggests that victims are mostly women and slashers are mostly men. Cowan and O'Brien selected a corpus of 56 slasher films and identified 474 victims. They coded each victim for gender and survival. They found that slashers are mostly men, but it turned out that victims are equally likely tö be male or female. Women who survive are less likely to be shown engaging in sexual behavior and arc less likely to be physically attractive than their nonsurviving counterparts. Male victims are cynical, egotistical, and dictatorial- Cowan and O'Brien conclude that, in slasher films, sexually pure women survive and "unmitigated masculinity" leads to death (p- 195). The coding of texts is usually assigned to multiple coders so that the researcher can see whether the constructs being investigated are shared and whether multiple coders can reliably apply the same codes. Typically, investigators first calculate the percentage of agreement among coders for each variable or theme. They then apply a correction formula to take account of the fact that some fraction of agreement will always occur by chance. The amount of that fraction depends on the number of coders and the precision of measurement for each code. If two people code a theme present or absent, they could agree, ceteris paribus, on any answer 25% of the time by chance. If a theme, such as wealth, is measured ordinally (low, medium, high), then the likelihood of chance agreement changes accordingly. Cohen's (1960) k;ippa, or K, is a popular measure for taking these chances into account. When K is zero, agreement is whar might be expected by chance. When K is negative, the observed level of agreement is less than one would expect by chance. How much intercoder agreement is enough? The standards are still ad hoc, but Krippendorf (1980, pp. 147-148) advocates agreement of at least .70 and notes that some scholars (e.g., Brouwer, Clark, Gerbner, oc Kri$pendorf, 1969) use a cutoff of. 80. Flciss (1971) and Light (1971) expand kappa to handle multiple coders. For other measures of intercoder agreement, sec Krippendorf (1980, pp. 147-154) and Craig a case is found that doesn't fit, then, under the rules of analytic induction, the alternatives are to change the explanation (so that you can include the new case) or redefine the phenomenon (so that you exclude the nuisance case). Ideally, the process continues until a universal explanation for all known cases or a phenomenon is attained. Explaining cases by declaring them all unique is a tempting but illegitimate option. Classic examples of analytic induction include Lindcsmith's (1947/1968) study of drug addicts, Cressey's (1953/1971) study of embezzlers, and McQeary's (1978) study of how parole officers decide when one oftheir charges is in violation of parole. For a particularly cleať example of the technique, see Bloor's (1976, 1978) analysis of how doctors decide whether or not to remove children's tonsils. Ragin (1987, 1994) formalized the logic of analytic induction, using a Boolean approach, and Roníme (1995) applies the approach to textual data. Boolean algebra involves just two states (true and false, present and absent), but even with such simple inputs, things can get very complicated, very quickly. With just three dichotomous causal conditions (A and not A, B and not B, and C and not C) and one outcome variable (D and not D), there are 16 possible cases: A, B, C, D; A, not B, C, D; A, B, not C, D; and so on. Boolean analysis involves setting up what is known as a truth table, or a matrix of the actual versus the possible outcomes. (For more on truth tables and how they are related to negative case analysis, see Becker, 1998, pp. 146-214.) Schweizer (1991, 1996) applied this method in his analysis of conflict and social status among residents of Chen Village, China. (For a discussion of^Schweizer's data collection and analysis methods, sec Bernard 6c Ryan, 1998.) All the data about the actors in this political drama were extracted from a historical narrative about Chen Village. Like classic content analysis and cognitive mapping, analytic induction requires that human coders 286 Data Management and Analysis Methods read and code text and then protluče an cvcnt-by-variablc matrix. The object or the analysis, however, is not to show the relationships among all codes, but to find the minimal set or logical relationships among the concepts that accounts for a single dependent variable. With more than three variables» the analysis becomes much more difficult. Computer programs such as QCA (Drass, 1980) and ANTHROPAC (Borgatti, 1992) test all possible multivariate hypotheses and find the optimal solution. (QCA is reviewed in Weinman 6c Miles, 1995.) Ethnographic Decision Models Ethnographic decision models (EDMs) are qualitative, causal analyses that predict behavioral choices under specific circumstances. An EDM, often referred to as a decision tree or flowchart, comprises a series of nested if'tben statements that link criteria (and combinations of criteria) to the behavior of interest (Figure 7.7). EDMs have been used to explain how fishermen decide where to fish (Gatewood, 1983), what prices people decide to place on their products (Gladwin, 1971; Quinn, 1978), and which treatments people choose for an illness (Mathews 6c Hill, 1990; Ryan & Martinez, 1996; Young, 1980). EDMs combine many of the techniques employed in grounded theory and classic content analysis. Gladwin (1989) lays out the fundamental steps for building an ethnographic decision tree model. (For other clear descriptions of the steps, sec Hill, 1998; Ryan tic Martinez, 1996.) EDMs require exploratory data collection, preliminary model building, and model testing. First, researchers identify the decisions they want to explore and the alternatives that are available. Typically, EDMs are done on simple yes/no types of behaviors. They can be used, however, to predict multiple behaviors (Mathews & Hill, 1990; Young, 1980) as well as rhe order of multiple behaviors (Ryan 6c Martinez, 1996). Next, the researchers conduct open-ended interviews to discover rhe criteria people use to select among alternatives. The researchers first ask people to recall the most recent example or an actual—not a hypothetical—behavior and to recall why they did or did nor do rhe behavior. Here is an example from ajtudy we've done recently: "Think about the last time you had a can ofsomething to drink in your hand— soda, juice, water, beer, whatever. Did you recycle rhe can? Why [Why not]?" This kind of question generates a list or decision criteria. To 287 1 3 .r. C "H r:. 3 ■' 3 !; S 11 ' > j H '.j Ä o *-* ■" O i ■•--. i- 5 S ( n •2 á - 1 3 1 r*. h r» « Ul p '-> ŕl b o t/l ZSö laoogemení and Analysis Methods understand how these criteria might he linked, EDM researchers ask people to compare the latest decision with other similar decisions made in the past, Some researchers have used vignettes to elicit the relationships among criteria (e.g., Weiler, Ruebush, & Klein, 1997; Young, 1980). With a list of decision criteria in hand, the researchers' next step is to systematically collect data, preferably from a new group of people, about how each criterion applies or does not apply to a recent example of the behavior. "Was a recycling bin handy?" and "Do you normally recycle cans at home?" are 2 of the 30 questions we've asked people in our study of recycling behavior. The data from this stage are used to build a preliminary model of the decision process for the behavior under scrutiny. Cases that do not fit the model are examined closely and the model is modified. Researchers tweak, or tune, the model until they achieve a satisfactory level of postdictive accuracy—understood to be at least 80% among EDM researchers. Parsimonious models arc favored over more complicated ones. (For automated ways of building and pruning decision trees, see Mingers, 1989a, 1989b.) The process doesn't end there—the same data are used in building a preliminary model and in testing its postdictive accuracy. When EDM researchers feel confident in their model, they test it on an independent sample to see if it predicts as well as it postdicts. Typically, EDMs predict more than 80% of whatever behavior is being modeled, far above what wc expect by chance. (For more detailed arguments on how to calculate accuracy in EDMs, sec Ryan ŮC Martinez, 1996; Weiler et al.> 1997.) Because of the intensive labor involved, F.DMs have been necessarily restricted to relatively simple decisions in relatively small and homogeneous populations. Recently, however, wc found wc could effectively test, on a nationally representative sample, our ethnographically derived decision models for whether or not to recycle cans and whether or not to ask for paper or plastic bags at the grocery store {Bernard, Ryan, 6c Borgatti, 1999). EDMs can be displayed as decision trees (e.g., Gladwin, 1989), as decision rabies (Mathews & Hill, 1990; Young, 1980), or as sers of rules in the form of if-then statements (Ryan &c Martinez, 1996). Like componential analysis, folk taxonomies, and schcma/nalysis, EDMs represent an aggregate decision process and do not necessarily represent what is going on inside people's heads (Garro, 1998). 289 METHODS OF COLLECTING AND ANALYZING EMPIRICAL MATERIALS ♦ Breaking Down the Boundaries Tex i analysis as a research strategy perineales die so» 1.1I sciences, .nul the range of methods for conducting text analysis ll Inspiring« Investigators examine words, sentences, paragraphs, pnges, documents, ideas, meanings, puralinguistic features, and even what is missing Iron» the text. They interpret, mark, retrieve, and count. By turns, they apply interpretive analysis and numerical analysis. They UM text analysis for exploratory and ■• confirmatory purposes. Researchers identify themes, describe them, and . compare them across cases and groups. Finally, they combine themes into conceptual models and theories to explain and predict social phenomena. ľigurc 7,1 depicrs a broad range of analysis techniques found across the suii.il sciences. To conform our presentation with the literature on qualitative methods, we have organized these techniques according to the goals of the tnVMtigfUOrt and the kinds of texts to which the techniques arc typically applied. In this chapter, wc focus on the sociological tradition 1h.11 uses text as a "window into experience™ rather than du- linguistic tradition that dc-.. 1 IbeS how lexis arc developed ami structured, lexis such as conversation',, performances, and narratives ate iiualyml by investigators from boili 1 he sociological and linguistic traditions. All hough the agendas of the investigators may differ, we see no reason why many nt the sociological techniques we describe could not be useful in the linguistic tradition and vice versa. We also distinguish between those analyses associated with systematically elicited data and those associated with free-flowing texts. We argue, however, that these data-analytic pairings are ones of convention rather than necessity. Investigators want to (a) identify the range and salience of key items and concepts, (b) discover the relationships among these items iml tomepts, .md (c) build and test models linking thetc concepts together. They use free-listing tasks, KWIC, word counts, and the exploratory phases .»I grounded theory, schema analysis, ami I-DM to discover potentially useful themes and concepts. ReeotrcherS use pile sorts, paired comparisons, triads lesis, frame substitution tasks, semantic networks, cognitive maps, content analysis and content dictionaries, and che modeling phases ol grounded theory, schema analysis, ami IÍDM to discover how abstract concepts are related to each other. They display the relationships as models or frameworks. These 290 Doto Monogement and Anofyin Atof/ior/s frameworks include form.il models that rely tin Boolean logic (componcn-iial analysis and analytic induction), hierarchical models (taxonomies and ethnographic decision models), probabilistic models (.lassie content analysis and content dictionaries), and more abstract models such as those produced by grounded theory and schema analysis, below we describe two important examples of studies in which researchers combined methods to understand their data more fully. Jehn and Doucct (1996, 1997) used word counts, classical content analysis, and mental mapping to examine conflicts among Chinese and U.S. business associates. They asked 76 U.S. managers who had worked in Sino-Amcrican joint ventures to describe recent interpersonal conflicts With business partners. Each person described a situation with a same-culture manager and a different-cultural manager. The researchers made sure that each manager interviewed included information about his or her relationship to the other person,who was Involved, what the conflict was about, what caused the conflict, and how the conflict was resolved. After collecting the narratives, Jehn and Doucct asked their informants to help identify the cmic themes in the narratives, lirsi, they generated Separate lists ol words from the intcrculimal mu\ miracultural conflict nnrratlveSi They asked three expatriate managers to QCl RS judges and to identify all the words that were related to conflict ľhey settled on .1 list of 542 conflict words from the intcrcultural lisi ami 2-12 conflict words from the intracultural list. Jehn and Doucct then asked the three judges to sort the words into piles or categories. The experts identified 1S subcategories for the intcrcultural data (things like conflict, expectations, rules, power, and volatile) and 15 categories for the mtraciiltur.il data (things like coh-flict, needs, standards, power, contentious, and lose). Taking into consideration the rotal number of words in each corpus, conflict words were used mote m intracultural interviews and resolution terms were more likely to be used in intcrcultural interviews. Jehn and Doucct also used traditional content analysis on their data. 1 he had two coders read the 152 conflict scenarios (76 intracultural and '() iiiiiiuilntr.il) and evaluate (on a 5-point scale) each on 27 different memos they had identified from the literature« This produced two 76 x 27 s.eiiario-by-thcme profile matriccs-ronc for the inltaailtural conflicts and one for the interciilmral conflicts. The first three factors from the intcrcultural matrix reflect (a) interpersonal animosity and hostility, (b) aggravation, and (c) the volatile nature of the conflict. The first two 291 417� MErHOOS OF COLLECTING AND ANALYZING EMPIRICAL MATERIALS i_______________________________^^—^^^^^^^ »no1 (aCtOM Írom the intracultural matrix reflect (-0 hatred .mil animosity with a volanie nuuitc :\nó (b) conflicts conducted calmly with little verbal tntciiMtv Finally. Jchn and Doucci identified the iO miraculuir.il and the 10 mtcrcultural scenarios that (hey felt werť the clearest and pithiest. They recruited 50 more expatriate managers to assess the similarities (on a 5-point scale) of 60-120 randomly selected pairs of scenarios. When combined across informants, the manager«' judgments produced two aggrc-ifttc, sccnario-by-scenario, similarity matrices—one for the innaculmral conflicts and one for the inrercultural conflicts. Multidimensional scaling of the inrercultural similarity data identified four dimensions: (a) open versus resistant to change, (b) situational causes versus individual traits, (c) high- versus low-resolution potential based on trust, and {d) high-vcrsus low-rcsoluiioii potential based mi patience, Scaling ol ihe intra Cultural similarity data identified lour different dimensions: (a) high versus low cooperation, (b) high versus low confrontation, (c) problem solving versus accepting, and (d) resolved versus ongoing. I In- work of Jehu mu\ I >omet is impressive because the analysis of the data from these tasks produced different Htt of themes. All three emicully induced theme sets have some iniuilivc appeal, and all three yield analviu results that are useful. The researchers could have also used the techniques of grounded theory or schema analysis to discover even more themes. Jchn and Doucet arc not the only researchers ever to combine different analytic techniques. In a series of articles on young adult "occasional" drug users, Agar (1979, 1980, 1983) used grounded theory methods to build models of behavior. He then used clinical content analysis to test his hypotheses. Agar conducted and transcribed three interviews with each of his three informants. In his 1979 article, Agar describes his initial, intuitive analysis. He pulled all the statements that pertained to informants' interactions or assessments of other people. I-lc then looked at the statements and sorted them into piles based on their content He named each pile as a theme and assessed how the themes interacted. He found that he had three piles. The first contained KamMMI In which the informant was express mg negative feelings toward a person m a dominant social position. I he second was made up of staiements emphasizing the other's knowledge or awareness. The statements in the third IBUÜI cluster emphasized thfl Im-portance of change or openness to new experiences. 293 Data Management and Anorys.* Method* From this Intuitive analysis, Agar felt that Ins informants were telling him that those m authority were only interested in displaying their authority unless they had knowledge or awareness; that knowledge or awareness comes through openness to new experience; and that mmt in authority are closed to new experience or change. To test his intuitive understanding of the data, Agar (1983) used all the statements from a single informant and coded the statements for their role type (kin, friend/acquaintance, educational, occupational, or other), power (dominant, symmetrical, subordinate, or undetermined), and affect (positive, negative, ambivalent, or absent), Agar was particularly interested in whether negative sentiments were expressed toward those in dominant social roles. For one informant. Agar found that out of 40 statements coiled as dominant, 32 were coded negative ami 8 were coded positive. For the id statements coded as symmetrical, 10 were coded positive and 16 negative, lending support to his original theory. Next, Agar looked closely at the deviant cases—thr S suteuienis where the informant expressed positive affect toward a person m a dominant role. These counterexamples suggested that tlie positive allect was expressed inward a dominant um ial Oth« when thi IO< lal othei possessed, or was communicating to the informant, knowledge that the Informant valued. Finally, Agar (I 980) developed a more systematic questionnaire to test his hypothesis further. He selected 12 statements,-I Ironi each of the control, knowledge, and change themes identified earlier. He matched these statements with eight roles from the informant's transcript (father, mother, employer, teacher, friend, wife, coworker, and teammate). Agar then returned to his informant and asked if the resulting statements were true, false, or irrelevant. (In no case did the informant report "irrelevant.") Agar then compared the informant's responses to his original hypotheses. He found that on balance his hypotheses were correct, hut discrepancies between his expectations and his results suggested areas for further research. These examples show that investigators can apply one «■< Imiquc to different kinds ol J.u.i ind they can apply multiple techniques to the nunc data set. Texi analysis is used by avowed positivism and mterprctivists alike. As we have argued elsewhere (Bernard, 1991; Bernard fk Ryan, 1998), methnď. are simply tools that belong tu everyone. 293 11913243 90 29 METHODS OF COLLECTING AND ANALYZING EMPIRICAL MATERIALS ■ ♦ What's Next? We do not want to minimize the profound intellectual differences in the epistemological positions of positivists and intcrpretivists. We think, however, that when researchers can move easily and cheaply between qualitative and quantitative data collection and analysis, the distinctions between the two epistemological positions will become of less practical importance. That is, as researchers recognize the full array of tools at their disposal, and as^these tools become easier to use, the pragmatics of research will lessen the distinction between qualitative and quantitative data and analysis. The process is under way—and is moving rast—with the development of increasingly useful software tools for qualitative data analysis. Useful tools create markets, and market needs create increasingly useful tools.; Qualitative data analysis packages (ATLAS/ti, NUD "1ST, Codc-A-'Icxt, the Ethnograph, AnSWR, and others) have improved dramatically over the past few years (Fischer, 1994; Kelle, 1995; Weitzman & Miles, 1995). These products, and others, make it easier and easier for researchers to identify themes, build codebooks, mark text, create memorand develop theoretical models. Based loosely on a grounded theory type of approach to qualitative analysis, many program suites have recently folded in techniques from classical content analysis. Several programs, for example, allow researchers to export data to matrices that they can then analyze using other programs. Investigators, however, remain constrained by program-defined units of analysis—usually marked blocks of text or informants. Researchers need the flexibility to create matrices on demand, whether they be word-by-themc or word-by-informanl matrices for word analysis and sentence-by-codc or paragraph-by-code matrices for content analysis. A series of word analysis functions would greatly enhance the automated coding features found in programs that are geared to the interests of scholars in the grounded theory school. Investigators should be able to code a section of text using grounded theory, then identify the key words associated with each t heme. They should be able to use key words to search for additional occurrences of the theme in large corpuses of text. When programs make it easy ro use multiple coders and tô identify intercoder agreements and disagreements systematically, researchers will be better able to describe themes and to train assistants. Adding a variety of measures for calculating intercoder agreement, which only some pro- 294 Data Management and Analysis Methods grams do, would also be helpful. Some programs offer researchers the option of recording the marking behavior of multiple coders, yet offer no direct way to measure intercoder agreement. The evolution of text analysis software is just beginning. Some 15 years ago, spell checkers, thesauruses, and scalable fonts were all sold separately. Today, these functions arc integrated into all full-featured word-processing packages. Just 10 years ago, graphics programs were sold separately from programs that do statistical analysis. Today, graphics functions are integrated into all full-featured packages for statistical analysis. As programmers of text analysis software compete for market share, packages will become more inclusive, incorporating methods from both sides of the epistemological divide. It can't happen too soon. ♦ Notes 1. MDS displays arc highly evocative. They beg lo be interpreted. In face, they must be interpreted. Why ate some illnesses at ihe rop of Higurc 7.3 and some at the borcom; We think (lie illnesses at the top are more of (lie eh tonic variet)', whereas those at the botcom arc more acute. We also think that the iUnesseson the left ate less serious than those o n the right. We can test ideas like these by asking key informants to help us understand the arrangement of the illnesses in the MDS plot. {For more examples of mental maps, see Albert, 1991; D'Andradcctal., 1972; Rrickson. I997.J (There is a formal method, called property fining analysis, or PROFIT, fur testing ideas about the distribution of items m .in .MDS map. This method is based on linear regression. See Kruskal & Wish, 1978.) 2. Alternatively, profile matrices (the usual ching-by-varinblc attribute matrix ubiquitous in the social sciences) can be converted to similarity matrices (thing-by-thing matrices in which the cells contain measures of similarity among paits of things) and ihcn analyzed with MDS (for stcp-by-stcp instructions, sec Bvrgattu 1999). ♦ References Agar, M. {1979). Themes revisited; Some problems in cognitive anthropology. Discourse Processes, 2, 11-31. Agar, M. (1980). Getting better quality stuff: Methodological competition in an interdisciplinary niche. Urban Life, '), 34-50. Agar, M. (1983). Microcomputers ns field Tools. Computers and the Humanities, 17, 19-26. Agar, M. (1996). Speaking of ethnography (2nd ed.). Thousand Oaks, CA: Sage. 295