Organizing and Interpreting Unstructured Qualitative Data

Transcription

1 The Sport Psychologist, 1993,7, Copyright O 1993 Human Kinetics Publishers, Inc. Organizing and Interpreting Unstructured Qualitative Data Jean C6t6, John H. Salmela, and Abderrahim Baria University of Ottawa Storm J. Russell Canadian Fitness and Lifestyle Research Institute In the last several years there has been an increase in the amount of qualitative research using in-depth interviews and comprehensive content analyses in sport psychology. However, no explicit method has been provided to deal with the large amount of unstructured data. This article provides common guidelines for organizing and interpreting unstructured data. Two main operations are suggested and discussed: first, coding meaningful text segments, or creating tags, and second, regrouping similar text segments, or creating categories. Furthermore, software programs for the microcomputer are presented as a way to facilitate the organization and interpretation of qualitative data. A qualitative research movement composed of a variety of approaches drawn from social sciences has recently gained popularity in sport and physical education (Bain, 1989; Locke, 1989; Sage, 1989). In sport psychology, Martens (1987) and Dewar and Horn (1992) questioned the methods of orthodox science as the most appropriate way of understanding human behavior. They proposed a heuristic paradigm where the researcher has a central position in the research process, the main objective being to seek patterns of intact organisms or groups and to look for synthesis rather than for reductionistic explanations. The heuristic paradigm stresses the importance of studying the whole, subjective experience of individuals by examining the way people perceive, create, and interpret their world. This approach requires an active role of the researcher, who generates situations in which the subjects reveal detailed information about a specific domain. Throughout the heuristic process of investigation, the researcher does J. CBtC, J.H. Salmela, and A. Baria are with the School of Human Kinetics at the University of Ottawa, 125 University, Ottawa, ON Canada KIN 6N5. S.J. Russell is with the Canadian Fitness and Lifestyle Research Institute, 1600 Naismith Dr., Gloucester, ON Canada KIB 5N4.

2 128 Ceti, Salmela, Baria, and Russell not have to be objective and impartial as in orthodox science, but instead remains bound to the object of study and contest in which the study takes place. This kind of research differs fundamentally in conception and procedure from orthodox science in which the variables are operationalized and predetermined hypotheses are tested to fulfill the ultimate objective of generalization. The methods of orthodox science treat the social world as if it were hard. external, and objective, focusing upon an analysis of relationships and regularities between various concepts of elements of the individuals under study and using different quantitative techniques for analysis. In the methods of orthodox science, the researcher's role is different from the heuristic paradigm in that it is to remain detached from the investigation process (Burrell & Morgan, 1979; Dewar & Horn, 1992; Martens, 1987). For further discussion on the methodological and epistemological differences between orthodox and heuristic methods of scientific inquiry in sport psychology, see Martens (1987) and Dewar and Horn (1992). It has been recently suggested that more heuristic methods of investigations, such as idiographic approaches, are needed in sport psychology to gain a better understanding of human behaviors in sport and exercise settings (Dewar & Horn, 1992; Martens, 1987; Strean & Roberts, 1992). These authors asserted that research in sport psychology should put more emphasis on experiential knowledge and recommended different idiographic approaches of investigation. The idiographic approach, which includes in-depth interviews and comprehensive content analysis of a person's oral or written records, has been extensively used by sport psychologists in the last several years (Cohn, 1990, 1991; Orlick & Partington, 1988; Partington & Orlick, 1991; Russell, 1990; Russell & Salmela, 1992; Salmela, Russell, C6t6, & Baria, 1991; Scanlan, Stein, & Ravizza, 1989, 1991; Weiss, Barber, Sisley, & Ebbeck, 1991). Although these authors provide adequate methodological descriptions in their studies, there is often a lack of clarity and precision in the procedure employed for the data organization and analysis. Keeping in mind that qualitative analysis remains a flexible process, the guidelines which will be presented in this paper are aimed at structuring the procedures of qualitative analysis, thus increasing the trustworthiness of the qualitative research process. Although there is no one correct way of analyzing qualitative data, it is essential that qualitative researchers provide a detailed description of the procedures, decision criteria, and data manipulation that allow them to present the final results of a study. This paper will try to systematize a procedure used to analyze unstructured qualitative data. In addition, software tools for microcomputers will be presented as a way to facilitate the organization and intrepretation of qualitative data. In an extensive review of the different kinds of qualitative analysis, Tesch (1990) distinguishes between two main approaches: interpretational qualitative analysis and structural qualitative analysis. Using Tesch's words, "The interpretational researcher overlays a structure of her or his own making on the data as a device for rendering the phenomenon under study easier to grasp" (p. 103). On the other hand, "Structural analysts assume that the structure is actually inherent or contained in the data and the researcher's job is to uncover it" (p. 103). Structural analysis means that there are words, categories, or patterns that are determined before the analysis; the function of the researcher is to retrieve and make sense of that information throughout the data by working with a set of relationships whose nature is well established. unlike-the episodes or text

3 Unstructured Qualitative Data 129 segments with which interpretational researchers work, structural analysts deal with more discrete units such as words, phrases, or sentences, whose boundaries rarely overlap. Structural analysis has been mostly used in discourse analysis, ethnography of communication, ethnoscience, and structural ethnography (Tesch, 1 990). Still, for interpretational analysis, elements, categories, patterns, and relations between properties emerge from the analysis of the data and are not predetermined. Interpretational researchers deal with fuzzily bounded categories of text, the goal being to develop the best classification system that fits the data with minimal overlapping between categories. This paper will focus primarily on providing guidelines for interpretational analysis since it has been one of the most popular methods of qualitative analysis in sport psychology. The research procedure involved in interpretational analysis was perhaps best systematized by Glaser and Strauss (1967) for the development of empirically grounded theory. The general principles of grounded theory are based on the constant comparative method and involve an inductive process of reasoning. According to Goetz and LeCompte (1981), "Purely inductive research begins with a collection of data, empirical observations or measurements of some kind, and builds theoretical categories and propositions from relationships discovered among the data" (p. 52). In sport psychology, most of the qualitative research using an idiographic approach has been aimed at providing similarities and dissimilarities between elements inductively derived from the data. Interpretational Qualitative Analysis in Sport Psychology Scanlan et al. (1989) used Glaser and Strauss's theoretical framework to investigate the sources of enjoyment of elite figure skaters. They interviewed 26 former national championship figure skaters and inductively content analyzed the interview transcripts. A four-level hierarchical synthesis emerged from the interview data. The more abstract and general level was composed of five categories that included the data of the lower order categories. Scanlan et al. (1991) also examined the sources of stress for elite figure skaters which resulted in categories and a four-level hierarchical synthesis. A similar research method was used by Weiss et al. (1991) to assess perceptions of ability and effective experiences of 28 female coaches. Interviews with coaches focused on positive and negative aspects of coaching and perceived strengths and weaknesses of each coach's ability. Twenty-one categories emerged from the interview transcripts and represented the highest level of a four-level hierarchical diagram. Cohn (1990) used a similar method of investigation to examine sources of stress among 10 high school competitive golfers. Thirty-nine sources of competitive stress were identified and regrouped into four main categories that best described the data. Furthermore, Cohn (1991) interviewed 19 competitive golfers on their personal peak performances in golf. The results provided 25 subcategories which were compared and assembled into nine main psychological categories. An idiographic approach was also used by Orlick and Partington (1988) who interviewed 75 elite athletes to assess their mental readiness and mental control. Seven mental factors and situational factors were associated with success.

4 130 CM, Salmela, Baria, and Russell Each of these "mental links to excellence" was supported by quotations from different athletes. Partington and Orlick (1991) used a similar method of investigation to evaluate the best consulting experience of sport psychology consultants. Nineteen sport psychology consultants were asked to elaborate on their best consulting experience. The analysis resulted in five general elements. Russell (1990) also used open-ended interview and inductive content analyses to examine how 20 expert athletes from four different sports defrned and classified sport task situations which represented a complex and demanding experience. Thirtythree categories were derived from the interview transcripts. These categories were regrouped into 15 and then 6 higher order categories of athletes' perception of difficult situations. In the same way, Salmela et al. (1991) elicited the knowledge of eight expert gymnastic coaches whose knowledge domains were structured into a hierarchical synthesis composed of a four-level hierarchical structure. The higher level orders represented seven abstract levels of knowledge. A comparison of these studies shows two main commonalities in the analysis. First, the interview data were divided into meaningful segments of information, and second, the data segments were categorized according to an organizing system principally extracted from the data themselves. Several of the studies presented the results of their analyses in a hierarchical diagram of categories (Russell, 1990; Salmela et al., 1991; Scanlan et al., 1989, 1991; Weiss et al., 1991). In other studies the authors provided a description of themes or elements that best described the data (Cohn, 1990, 1991; Orlick & Partington, 1988; Partington & Orlick, 1991). Although the presentation of results varied between these studies, the main goal of each study was to reduce the amount of data and obtain a unified picture of the phenomena under study. While a detailed conceptual description of the methodology is provided in some cases (Scanlan et al., 1989, 1991; Weiss et al. 1991), there is little available information on the explicit procedures, decision criteria, or data manipulation in the data analysis. Little reference is made to procedures and operations used between the first stage, in which the researcher has to deal with numerous pages of unstructured data, and the final stage, which represents the organizing system of categories or elements. One can see that as qualitative research is becoming more popular, the need for setting common guidelines for qualitative data analysis is more essential. Guidelines for Interpretational Qualitative Analysis Tesch (1990) suggested that two main operations play important roles in the development of an organizing system from unstructured data. First, there is the detailed examination of the data to identify topics which best describe particular segments of text. Secondly, there is the determination of common features which characterize the text segments in order to create and understand the relationship between topics. These two operations are typical of interpretational qualitative analysis and are usually undertaken in two separate phases: data organization and data interpretation, which can also be seen as creating tags and creating categories. Creating Tags The first part of interpretational analysis, creating tags, aims to produce a set of concepts which adequately represent the information included in the interview

5 Unstructured Qualitative Data 131 transcripts. An open coding strategy identifies meaningful pieces of information (Strauss, 1987). This procedure involves dividing the text of each interview into text segments called "meaning units." Tesch (1990) defined a meaning unit as a "segment of text that is comprehensible by itself and contains one idea, episode or piece of information" (p. 116). In open coding, the coder looks for in vivo tags, that is, terms used by the individuals who are being studied. At this point, the coder is not concerned with the aptness of the tag, because the tag can be changed in the analysis process, or can be combined later with other tags containing similar meaning. Therefore, each meaning unit is tagged with a provisional name describing the topic of the text segments. For example, in the study with expert gymnastic coaches (Salmela et al., 1991) the tag "good coaches are disciplined and goal-oriented," was given to the following meaning unit from one coach's interview: Good coaches are very disciplined and very goal-oriented. They totally understand the process that produces a high-performance athlete. They focus constantly on that process. There is not one minute in the gymnasium when they don't focus on that process. Everything they do has a reason. Sometimes you watch and you don't understand the reason, but there is a reason. In summary, creating tags separates relevant portions of data from their context, or "de-contextualizes" the information (Tesch, 1990). Further, to enhance the validity of the study, the attribution of a tag for a piece of information can be discussed between two coders until the term that best describes the text is agreed upon. This procedure assures that the validity of the terms chosen is guarded against one coder's own ethnocentrisms and perceptual biases (LeCompte & Goetz, 1982). Creating Categories The second step of interpretational analysis, creating categories, involves listing and comparing the tags derived in the first phase. Tags with similar meanings are gathered togther, and a label that captures the substance of the topic is created to identify the cluster of tags (Miles & Huberman, 1984). In the example mentioned in the previous section, the tag "good coaches are disciplined and goaloriented" was inserted with other similar tags into a category entitled "coaches' qualities." In the same study (Salmela et al., 1991), the following meaning unit was first tagged "working with young coaches" and then was put into the category "training other coaches": What happened before is that I was always practicing the swing. What 1 do with the young coaches now is practice the swing until they have it. Once they have it reasonably we will work from there. Another example of interpretational analysis is the following meaning unit which was tagged "learning through personal experience" and classified into the category "sources of acquiring knowledge": I think the experience alone outweighs the knowledge because you learn from experience. Certainly in the last three years I've learned more about gymnastics than 1 did in my whole life. I'm learning through my experience some of the problems my athletes have technically and preparation-wise.

6 132 Chft!, Salmela, Baria, and Russell The purpose of the second step of interpretational analysis is, therefore, to "re-contextualize" the information into distinct categories, resulting in a set of categories which serves as a preliminary organizing system (Tesch, 1990). This initial classification system is built according to three critical characteristics of categorization: (a) coding experience, (b) inductive inference, and (c) similarity (Smith, 1990). First, the coding or tagging experience, which is essential to categorizing a large amount of qualitative data, is used to rearrange the text into manageable and organized units. Second, inductive inference is used to create categories. In this process, there are no predetermined categories or patterns before data collection: The important dimensions of the interviews emerge from the analysis. In other words, tags and categories are generated from the data (Miles & Huberman, 1984; Patton, 1980). Finally, the categories are judged by their similarity, so that the data in each category are similar to each other yet distinct from the other categories of data (Smith, 1990). This characteristic of a category can be referred to as its internal homogeneity and external heterogeneity (Patton, 1980). According to Tesch (1990) categories for sorting the segments must remain flexible during the analysis process. Because categories are developed mostly from the data, they can be modified and refined until a satisfactory system is established. For example, in the Salmela et al. (1991) study, the three categories mentioned before ("qualities of a good coach," "training other coaches," and "sources of acquiring knowledge") were regrouped into a higher level category entitled "coach development." An important factor which limits the list of categories is that the organizing system becomes theoretically saturated (Glaser & Strauss, 1967). Theoretical saturation is reached when the categorizing of new data fits adequately into the existing organizing system without the emergence of new themes or categories. The Mechanics of Qualitative Data Analysis Somehow researchers who want to organize and interpret text segments need to rely on methods or procedures that will allow them to systematically classify the data. There are many ways to handle qualitative data; however, few studies in sport psychology have given a description of structured steps and procedures. The next section provides some examples of manual and electronic procedures to handle qualitative data which could increase the validity of the qualitative research process. Manual Manipulation of Unstructured Qualitative Data Manually handling many pages of unstructured data is laborious and time consuming. To help in the organization and interpretation of a large amount of qualitative data, Bodgan and Biklen (1982) proposed two methods: the cut-up-and-put-infolders approach and the file-card system. Essentially, what these methods entail is working with numerous copies of the transcripts and making sure that they are all well identified. Creating tags manually can be done directly on a copy of the transcript by dividing the text into meaning units and giving a tag to the text segment. For this process, one must keep an inventory of the tags by numbering them or using different colors to identify them.

7 Unstructured Qualitative Data 133 The second part of the analysis, creating categories, can also be accomplished manually by giving numbers to the categories and assigning the appropriate number to each meaning unit on the transcript. The process of recontextualizing each meaning unit into the organizing system can be done by cutting and pasting each meaning unit into different piles representing categories. Another method of recontextualizing meaning units into categories is to use a file-card system (Bodgan & Biklen, 1982; Tesch, 1990). The first task of the coder in this process is to number each data document and each line of the interview transcripts page by page. Then, a set of file cards representing each major category can be used when the coder reads through the data. The operation of categorization would then be reduced to writing the data document number with the corresponding line on the appropriate file cards. A method similar to the above is essential for manually organizing and interpreting a large amount of qualitative data. However, because previous sport psychology studies have never referred to the mechanics of analysis of qualitative data, one can only assume that some sort of manual procedure such as those described previously were used to obtain categories or elements which represent the data. Nevertheless, even with the appropriate method of manual organization, the chances of error increase with the quantity of data manipulated. Large-scale studies such as those done by Orlick and Partington (1988) with 75 elite athletes, Scanlan et al. (1989) with 26 figure skaters, and Weiss et al. (1991) with 28 coaches, require discipline and careful, tedious manipulation by the coders. Errors in tagging or categorizing are potentially frequent and are often difficult to detect. Furthermore, the researcher who analyzes large sets of complex qualitative data by hand may encounter time-consuming problems such as (a) losing the source of a meaning unit, (b) facing a decontextualized meaning unit that does not have any meaning out of its original context, (c) putting the same meaning unit in more than one category, (d) making changes on the content of a category by moving meaning units from one category to the other, and (e) renaming or redefining a category. These examples, which are not uncommon in interpretational qualitative analyses, can be attenuated with content analysis computer programs. Electronic data handling allows the analyst to use some functions such as searching and replacing which facilitate the operations involved in organizing and interpreting large sets of qualitative data. Software Programs for Interpretational Qualitative Analysis Most of the software programs specifically designed for qualitative analysis have two main functions in common: First, after tags have been created, they allow the coder to attach tags to meaning units, and second, they allow the coder to search for tags and regroup them (Tesch, 1990). Therefore, the two main operations of creating tags and creating categories involved in interpretational analysis can be performed and facilitated by computer manipulations (CBt6, 1991). Several software programs for qualitative analysis are available for use on IBM, IBM compatible, and Macintosh machines. The main qualitative analysis software on IBM and IBM compatible computers are Ethnograph, Qualpro, and Textbase Alpha; on the Macintosh, two software programs are available, HyperQual and HyperResearch. For a detailed description of these software

8 134 * C6t6, Salmela, Baria, and Russell programs and where to purchase them, see Tesch (1990) and Hess-Biber, Dupuis, and Kinder (1990). Although these programs share similar procedures in the analysis of qualitative data, HyperQual (Padilla, 1989) and its application with interpretational qualitative analysis will be discussed in more detail. Creating Tags With HyperQual. With HyperQual, two operations are involved in the creation of tags: The coder first must select a meaningful text segment and, second, assign an appropriate tag. HyperQual, as well as most of the other qualitative analysis programs, permits the user to apply more than one tag to a text segment. This feature is important because a text segment can be relevant to more than one category. After these operations, the meaning units with their corresponding tags are sent automatically to an output file for later examination or printing. Since the organizing system is subject to change during the process of analysis, an important feature of the software is to be able to change, delete, or add tags. With HyperQual, individual tags can be changed, added, or deleted, and it is possible to replace or delete all occurrences of a tag in a single operation. Creating Categories With HyperQual. The aim of the second part of interpretational analysis is to create categories by clustering together meaning units and tags with similar meanings. With HyperQual, the task of the coder is to recontextualize the data by dumping each meaning unit into separate folders or categories. For this process, all meaning units are sorted and grouped according to the similarity of their tags. When a meaning unit is selected, the coder has two options: Send the meaning unit into a new category, or put the meaning unit into an existing category. If the meaning unit is sent to a new category, the coder must provide a category name, and HyperQual automatically creates and inserts the meaning unit into a new folder. If the existing category option is selected, the coder then supplies the exact name of the existing category and inserts the meaning unit. Therefore, this operation serves to recontextualize the data; that is, each segment of text from each interviewee is placed automatically in the context of one category in the organizing system. Other Features of Software Programs and HyperQual. The qualitative analysis programs never alter the original data document; they simply store the information about the meaning units and tags in separately created folders. In fact, with a simple operation, it is always possible during any step of the analysis to return to the original transcript from which the meaning unit was extracted. Furthermore, the source information is always attached to single segments of text elicited from the original data. These features allow the coder to easily add or eliminate information in a meaning unit and to reverify the context from which the meaning unit originated. A useful function in HyperQual is the capability to automatically sort the same tag attached to more than one meaning unit. It allows the coder to retrieve in one operation all identical tags with their corresponding meaning units and create a new folder. In addition, HyperQual provides a memo and schema folder, analagous to field notes in ethnographic research, which can always be activated during the analyses; this feature may be useful for certain kinds of research projects in which some form of contextual data might be added, such as events occuring during the interview, for later use in interpreting the meaning unit. Finally, to make sure that each meaning unit belongs to the appropriate category, each folder can be printed and examined individually. Different options

9 Unstructured Qualitative Data 135 are available when printing a document; for example, the source, tags, meaning units and card number can all appear in or be left out of the document. Qualitative analysis programs like HyperQual can be very practical for interpretational qualitative analysis, as well as for structural qualitative analysis. For instance, HyperQual has a function to search for a particular idea or concept. The coder needs to provide key words and the program automatically filters the data, finds the word and dumps the key word and two lines that precede and follow into an existing or new folder. This recontextualization feature is convenient when certain emerging patterns have been previously established in the data analysis process. Software Programs for Structural Qualitative Analysis Structural qualitative analysis involves a list of categories, words or patterns which are determined before data analysis. The duty of the coder is to search, retrieve, and organize the data into predetermined categories. Although most of the qualitative analysis programs previously mentioned would be appropriate for this kind of analysis, other software programs such as data base managers and text retrievers are also helpful. Data base managers give the opportunity to code individual data units with key words and retrieve these specific data units across a large data base. Data base programs have already been used as a qualitative research tool both in psychology (Todd, 1987) and sociology (Brent, Scott, & Spencer, 1987). However, these kinds of programs do not provide easy ways to divide or move meaning units freely from one category to the other. Text retrievers are another type of software tool used for structural qualitative analysis. Text retrievers allow the coder to deal with individual words and phrases as opposed to longer meaning units or chunks of data. According to Tesch (1990), "Text retrievers make lists of the occurrence of words in a document, or of specified words, count how often the words appear, create indices, and often provide a list of words plus a specified number of the words that surround them in the document" (p. 109). Text retrievers can be of use to researchers who are interested in language or in studies where specific words or phrases regularly appear in the text. Summary This paper outlines the increasing importance of the heuristic paradigm in sport psychology by focusing on a methodological aspect: qualitative analysis. Two main operations for analyzing unstructured qualitative data have been suggested and discussed: First creating tags or coding meaningful text segments, and second, creating categories or regrouping similar text segments. Although these guidelines can systematize the interpretational qualitative analysis procedures, they do not rigidly standardize qualitative analysis methods. Thus, qualitative analysis remains a flexible process which can be adapted to the individual under study and to various research problems. The use of the computer to facilitate the qualitative research process along with some examples of software tools that help in the organization and interpretation of unstructured qualitative data have been provided. Although the assistance

10 136 C6t6, Salmeh, Baria, and Russell of the computer is becoming an indispensable tool for the organization of numerous pages of interview transcript, the major intellectual task of tagging and categorizing the data still lies with the researcher. To conclude, because there are many ways of analyzing qualitative data, it is important that researchers provide a detailed description of the methods used to organize and interpret qualitative data. This process increases the credibility of a study and indicates that the researchers took necessary precautionary measures for providing results that best describe the phenomena under study. References Bain, L.L. (1989). Interpretive and critical research in sport and physical education. Research Quarterly for Exercise and Sport, 60, Bodgan, R.C., & Biklen, S.N. (1982). Qualitative research for education: An introduction to theory and methods. Toronto: Allyn & Bacon. Brent, E., Scott, J., & Spencer, J. (1987). The use of computers by qualitative researchers. Qualitative Sociology, 10, Burrell, G., & Morgan, G. (1979). Sociological paradigms and organisational analysis. London: Heinemann. Cohn, P.J. (1990). An exploratory study on sources of stress and athlete burnout in youth golf. The Sport Psychologist, 4, Cohn, P.J. (1991). An exploratory study on peak performance in golf. The Sport Psychologist, 5, C6t6, J. (1991, November). HyperQual: A software program to analyze qualitative data. Paper presented at the meeting of the Canadian Society for Psychomotor Learning and Sport Psychology, London, Ontario, Canada. Dewar, A., & Horn, T.S. (1992). A critical analysis of knowledge construction in sport psychology. In T.S. Horn (Ed.), Advances in sport psychology (pp ). Champaign, IL: Human Kinetics. Glaser, B.G., & Strauss, A.L. (1967). The discovery of grounded theory: Strategies for qualitative research. New York: Aldine. Goetz, J.P., & LeCompte, M.D. (1981). Ethnographic research and the problem of data reduction. Anthropology and Educational Quarterly, 12, Hess-Biber, S., Dupuis, P., & Kinder, S. (1990). HyperResearch: A computer program for the analysis of qualitative data using the Macintosh. Qualitative Studies in Education, 3, LeCompte, M.D., & Goetz, J.P. (1982). Problems of reliability and validity in ethnographic research. Review of Educational Research, 52, Locke, L.F. (1989). Qualitative research as a form of scientific inquiry in sport and physical education. Research Quarterly for Exercise and Sport, 60, Martens, R. (1987). Science, knowledge, and sport psychology. The Sport Psychologist, 1, Miles, M.B., & Huberman, A.M. (1984). Qualitative data analysis. Beverley Hills, CA: Sage. Orlick, T., & Partington, J. (1988). Mental links to excellence. The Sport Psychologist, 2, Padilla, R.V. (1989). HyperQual user's guide (version 2.0). (Available from Raymond V. Padilla, 3327 N. Dakota, Chandler, AZ, 85224, U.S.A.) Partington, J., & Orlick, T. (1991). An analysis of Olympic sport psychology consultants' best-ever consulting experiences. The Sport Psychologist, 5,

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