Conceptual ordering is a method of organizing data into discrete categories by assessing the data’s proper- ties or underlying meanings and then using these properties to categorize the data into groups. At times it can be helpful to use ratings when organizing the data, for example, rating the level of importance of each of the categories. Conceptual ordering is a first step in developing themes. After conceptual ordering, the data can be grouped into similar categories and then themes can be developed.
There are multiple methods for accomplishing con- ceptual ordering. Concepts can be the basis for ordering the data, as can other schemas such as time or roles of Conceptual Ordering———109
the participants. An example of conceptual ordering is an ethnographic account. Ethnographers work to pre- sent the actions and beliefs of participants in an ordered fashion. Another example of conceptual ordering is when data are ordered according to time or stages. A final example of conceptual ordering occurring is when data are organized according to actors or actions.
When data have been ordered conceptually, it can be helpful to depict the ordering in a display. There are two common methods of displaying conceptually ordered data: within-case and cross-case. Conceptually ordered within-case displays present information for one case (i.e., a person or a group of people), whereas conceptu- ally ordered cross-case displays present information for comparing two or more cases. The multiple types of within-case and cross-case displays are outlined in what follows.
There are many types of conceptually ordered within-case displays, including conceptually clus- tered matrices, thematic conceptual matrices, effects matrices, folk taxonomies, and cognitive maps. The first type of conceptually ordered within-case dis- play, conceptually clustered matrices, connects data so that there is conceptual coherence. The matrix is created in table format with multiple research ques- tions included. The responses to these research ques- tions are placed in the body of the table. Ordering the data in this matrix assists the researcher in seeing possible connections among the concepts under investigation.
The second type of conceptually ordered within- case display, thematic conceptual matrices, has a foundation based on themes. Specifically, a thematic conceptual matrix reflects an ordering of themes. To create a thematic conceptual matrix, the researcher starts by clustering those data, in other words, putting similar data together and reading through them to identify underlying issues or problems. These under- lying issues then are used as headings in the matrix to assist the researcher in identifying similarities and dif- ferences in the data.
The third type of conceptually ordered within-case display is an effect matrix. When researchers have complex data with multiple cases and are interested in relationships, developing an effect matrix can be beneficial. Effect matrices are appropriate when there are “ultimate” outcomes. Effect matrices help the researcher to identify occurrences of change, for example, displaying the “before” and “after” impres- sions of a new teaching strategy.
The fourth type of conceptually ordered within-case display is a case dynamics matrix. Here the qualitative researcher displays a set of elements for change and attempts to link consequential processes and outcomes for the purpose of initial explanation. As such, case dynamics matrices help the researcher to examine cause and effect.
Not all conceptually ordered displays are in matrix format. Network formats, including hierarchical tree diagrams, can be used. These are commonly referred to as folk taxonomies. Folk taxonomies tend to be idiosyncratic aspects that are not labeled and that can have overlapping categories. More specifically, folk taxonomies typically represent a hierarchical tree dia- gram that displays how a person classifies important phenomena.
When data are not hierarchical, the fifth type of conceptually ordered within-case display, a cognitive map, can be developed. Frequently cognitive maps contain data for one person—his or her thoughts, per- ceptions, and/or beliefs. To create a cognitive map, the researcher identifies concepts and nodes and the rela- tionships among each.
When the researcher is interested in comparing across cases, a conceptually ordered cross-case dis- play can be useful. The main type of conceptually ordered cross-case display is a content analytic sum- mary table. The data in a content analytic summary table can be organized by concepts or by demographic information (e.g., job position, gender, level of abil- ity). The foundation of a content analytic summary table is building a matrix that allows the researcher to examine the data without referencing specific cases.
Matrices or decision trees commonly are used to rep- resent the table. When generating a matrix, the researcher can use substruction or dimensionalizing, which refers to identifying underlying themes or dimensions systematically. Cross-case content ana- lytic summary tables can illuminate how concepts play out in different cases. Other conceptually ordered cross-case displays include variable-by-variable matrices (i.e., tables that display two major variables in the rows and columns ordered by intensity with the cell entries representing the cases), causal models (i.e., networks of variables with causal connections among them to provide a stable set of propositions or hunches about the complete network of variables and their interrelationships), causal networks (i.e., com- parative analyses of all cases using variables deemed to be the most influential in explaining the outcome or 110———Conceptual Ordering
criterion), and antecedent matrices (i.e., displays ordered by the outcome variable that display all of the variables that appear to change the outcome variable).
Thus, the qualitative researcher has numerous ways of conceptually ordering data.
Nancy L. Leech and Anthony J. Onwuegbuzie
See alsoCategories; Comparative Analysis; Content Analysis; Ethnography
Further Readings
Miles, M. B., & Huberman, A. M. (1994).Qualitative data analysis: An expanded sourcebook(2nd ed.). Thousand Oaks, CA: Sage.
Strauss, A., & Corbin, J. (1998).Basics of qualitative research: Techniques and procedures for developing grounded theory.Thousand Oaks, CA: Sage.
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ONFIDENTIALITYRespect for confidentiality is an established principle in research ethics codes and professional codes of conduct. More broadly, in many cultures confidential- ity is also considered as fundamental to human dignity. Researchers often give assurances of confi- dentiality to protect the privacy of research partici- pants. This means that information shared with researchers will not be disclosed in a way that can publicly identify a participant or source.
There are many reasons for respecting confiden- tiality. It can protect people from embarrassment or save them from harm or stigma. Promises of confiden- tiality are usually necessary when researchers seek sensitive data such as information about health, sexual behaviors, drug use, tax evasion, and other personal secrets. Without confidentiality, many people either would refuse to take part in sensitive research or would be less forthcoming with the information that they share with researchers. Therefore, confidentiality helps to enhance both the quality and validity of data.
Confidentiality can be protected in various ways.
Sometimes participants are truly anonymous and cannot be identified in any way, for example, when people use pseudonyms in secure internet chat rooms.
Researchers may also remove identifying information from coding sheets or interview transcripts so that no
particular response can be linked to a specific person.
Identifying information is sometimes stored in a secure location separate from the data that will be used for analysis. This allows researchers to keep track of participants without compromising their confidentiality.
A participant’s confidential relationship with a researcher can depend heavily on the commitment the researcher makes to guarantee confidentiality. In Canada and the United States, researchers have faced legal threats to compel disclosure of confidential data.
In 1993, Rik Scarce, a Washington State University graduate student, was jailed for 159 days for contempt of court when he refused to disclose information to a grand jury about animal rights activists. In 1994, Russel Ogden, a graduate student at Simon Fraser University, was subpoenaed to a coroner’s inquest for his research into assisted suicides among persons with HIV and AIDS. He refused to violate a promise to his partici- pants of “absolute confidentiality” and eventually established a common law privilege to protect against disclosure of identifying information. Since then, Ogden has resisted two more subpoenas from Crown prosecutors to a criminal trial on assisted suicide.
Although the experiences of Scarce and Ogden are relatively rare, they highlight the conflict between researchers’ ethical responsibility to participants and competing obligations to law. In Canada, there have been calls for the development of law that will allow researchers to promise confidentiality without fearing a legal challenge to such promises.
In the United States, some criminological and health research can receive statute-based protections.
Researchers funded by the National Institute of Justice can apply for “privacy certificates.” Regardless of the funding body, health researchers can make applications to the National Institutes of Health for
“certificates of confidentiality.”
Russel Ogden
See alsoAnonymity; Harm; Privacy; Pseudonym;
Sensitive Topics
Further Readings
Palys, T., & Lowman, J. (2006). Protecting research confidentiality in Canada: Towards a research participant shield law.Canadian Journal of Law and Society, 21,163–185.
Confidentiality———111
Scarce, R. (1994). (No) trial (but) tribulations: When courts and ethnography conflict.Journal of Contemporary Ethnography, 23,123–149.
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ONFIRMABILITYIn qualitative research, the actions and perceptions of participants are analyzed for their expressions of meaning within a given context. Consistent with the practices of the selected qualitative methodology used, the researcher then interprets the participant expressions through a coding or meaning-making process. In this coding process, the researcher is looking for messages that are consistent with, con- firm, or expand on current knowledge and theory.
From these insights, the researcher is then able to make statements about the context under study. In so doing, additional processes must be incorporated into the research design that verifies the truthfulness or meaning being asserted in the study. This is called confirmability.
Confirmability is often equated with reliability and objectivity in quantitative research. Reliability and objectivity are measures of the accuracy of the truth or meaning being expressed in the study. The epistemo- logical function of this process is to suggest that truth and meaning are reliable only to the point where they can be verified as more than just a singular event peculiar to that specific research endeavor and researcher. This is essential because it is an academic process that moves the research beyond a one-time event into a framework where meaning and truth can be used to build on, expand, or create theory.
Confirmability is an accurate means through which to verify the two basic goals of qualitative research:
(1) to understand a phenomenon from the perspective of the research participants and (2) to understand the meanings people give to their experiences. Confirma- bility is concerned with providing evidence that the researcher’s interpretations of participants’ construc- tions are rooted in the participants’ constructions and also that data analysis and the resulting findings and conclusions can be verified as reflective of and grounded in the participants’ perceptions. In essence, confirmability can be expressed as the degree to which the results of the study are based on the research pur- pose and not altered due to researcher bias.
Although confirmability does not deny that each researcher will bring a unique perspective to the study, it requires that the researcher account for any
biases by being up front and open about them and use the appropriate qualitative methodological prac- tices to respond to those biases. For example, a researcher using discourse analysis can have multi- ple coders of the same data to establish a measure of the consistency in the coding of themes. The researcher can also make the research process as transparent as possible by clearly describing how data were collected and analyzed and possibly offer- ing examples of the coding process in the final doc- ument. Confirmability can also be expressed through an audit trail where an independent reviewer is allowed to verify the research process and interpreta- tions of the data as consistent on both the literature and methodological levels. Selected participants can also be asked to review some of the coding and meaning-making process to determine whether the researcher’s interpretations are consistent with their perceptions.
Devon Jensen
See alsoAudit Trail; Bias; Codes and Coding; Reliability;
Research Design
Further Readings
Lincoln, Y. S., & Guba, E. G. (1985).Naturalistic inquiry.
Newbury Park, CA: Sage.