Complex health problems require access to knowledge and technologies that span beyond one discipline. Establishing and maintaining collaborative scientific environments that can cultivate and benefit from a full range of talents is essential for the quality and influence of science. Furthermore, interdisciplinary collaboration and team science have the promise to result in innovative solutions to pressing health-related issues and speed up the pipeline in nursing education to meet increasing demands for complex nursing care and research; it also requires the development of a workforce that is versed in collaborative knowledge, skills, and attitudes. The call for interdisciplinarity in nursing research training is further amplified by the aging nursing workforce and the shortage of experienced nursing researchers who can provide mentoring to a new cadre of nursing scholars.
Inclusion of research training and career development interventions to expose nursing PhD students, postdocs, and junior faculty to team science stands to prepare graduates to effectively engage with interdisciplinary colleagues to conduct cutting-edge nursing research and compete successfully for precious research resources (American Association of Colleges of Nursing, 2010). However, this workforce development challenge requires developing a shared culture of interdisciplinary collaboration and presents a set of difficult questions. What are the ways to integrate interdisciplinary collaboration and disciplinary excellence in nursing research? What are the existing shared beliefs, behaviors, and culture of interdisciplinary collaboration in nursing research? What are the priorities for interdisciplinarity training as viewed by trainees and experienced mentors? How can the culture of interdisciplinary collaboration be measured at the level of individual interactions and training programs?
This project aims to develop, validate, and disseminate a theoretically grounded and methodologically rigorous tool for a cultural consensus analysis (CCA) of the culture of interdisciplinary collaboration in nursing research.
Most definitions of culture describe it as shared values, norms, customs, and beliefs. If a group of people defined by a researcher does not share these qualities, they are not part of a single culture. Conversely, the agreement of a group of people about domain-specific values and norms indicates the presence of a shared culture. More formally, the culture and cultural model can be defined as “the set of learned and shared beliefs and behaviors, and cultural beliefs are the normative beliefs of a group” (Weller, 2007, p. 339). The existence of shared culture can be investigated and measured through cultural consensus or “the degree to which individuals, in their own beliefs and behaviors, approximate the prototypes for belief and behavior encoded in cultural models” (Dressler et al., 2018, p. 1). The cognitive theory of culture rests on three fundamental assumptions:
- There is a set of cultural values that a group shares (i.e., there is cultural consensus).
- Each group member is more or less familiar with this set (i.e., group members have cultural knowledge that can be assessed as cultural competence).
- The more matches that two group members share, the higher their cultural competence values (in a precise mathematical relationship; Batchelder & Romney, 1988; Dressler et al., 2015).
The design for this mixed-methods study is CCA. CCA is a cognitive, cultural anthropological approach that was rigorously developed and validated (Batchelder & Anders, 2012; Batchelder et al., 1997; Dressler et al., 2005). CCA views culture as shared cognitive structures and consensus around culturally correct values, attitudes, and normative behaviors (cultural domains; Anders & Batchelder, 2012; Weller, 2007). The main objectives of CCA are to operationalize a cultural domain, discover a set of statements that a group of respondents consistently ranks as true or false (i.e., a “cultural answer key”; Oravecz et al., 2015), and measure whether there is a consistent domain of knowledge within a group (Romney et al., 1986; Weller 2007). The CCA provides an empirical approach to assess construct validity and empirically determine whether there is more than one set of underlying socially learned and shared notions about the correct answers to the cultural domain items (whether more than one answer key exists; Weller, 2007). Although individuals from different groups can agree on the general cultural domain, the presence of subcultures can explain variances in cultural competence (Dressler et al., 2005). CCA is different from standard approaches to instrument development in that it assumes the presence of an “answer key” that is correct for a specific culture but does not prescribe what that key should be. Although rigorous from the statistical and psychometric perspectives (Batchelder & Romney, 1988; Oravecz et al., 2015), the CCA tool does not “prescribe” nor assume the presence of one “correct” answer. Instead, the analysis aims to identify groups that share consensus around a set of coherent beliefs or values, or importance of values or practices, and measure agreement in group responses rather than measure an agreement with constructs or a current response predetermined by researchers.
The study will include three phases:
- define the cultural domain of interdisciplinary collaborations in nursing research and formulate cultural knowledge statements by using qualitative data collection and analysis;
- validate the CCA tool using cultural knowledge statements and a different subset of nursing researchers; and
- apply the CCA tool to assess cultural differences among nursing trainees, junior faculty, and training directors.
Phase 1 data will be collected through individual interviews. Interviews will be conducted remotely, audio-recorded, and transcribed with the use of Zoom technology. Participants will receive informed consent at the time when they are invited to participate in the study and will sign it electronically at the beginning of the interview session. Phases 2 and 3 data will be collected through an online survey. Similarly, participants will receive an informed consent at the time when they are invited to participate and will sign it electronically at the beginning of the survey.
Participants and Sample Size Estimation
The study participant pool includes training directors of National Institutes of Health (NIH)–National Institute of Nursing Research (NINR) institutional training grants (T32 mechanism, n = 38), predoctoral (n = 73) and postdoctoral (n = 7) trainees awarded with individual NIH–NINR training grants (F31/32 mechanisms), and junior faculty awarded with career development awards (K99, K01, and K23 mechanisms, n = 42). Contact information of the awardees is publicly available through the federally supported NIH’s Research Portfolio Online Reporting Tools portal and has been captured in an Excel worksheet.
The sample size necessary and sufficient for Phase 1, cultural domain definition, is estimated at 30 participants based on previous qualitative investigations and the information power of this study (Malterud et al., 2016). The sample size for Phases 2 and 3 of the CCA validation and application rests on two parameters: the degree of concordance among respondents (the average Pearson correlation coefficient; Dressler et al., 2015) and the designed level of validity (the correlation between the answers obtained from the sample and the “true” answers; see Table 1; Dressler et al., 2005). Low expected levels of agreement increase the number of participants needed to maintain a specified validity level. Given the novelty of the construct, we assume a conservative, low level of agreement about the behaviors that constitute interdisciplinary collaboration. With the low expected competency score of .50 and stringent criteria for proportion of items ordered correctly (95%), the minimum number of participants for a validation survey is 17. We hypothesize that consensus regarding values and behaviors that promote interdisciplinary collaboration may differ among trainees, junior faculty, and training directors. Therefore, we will recruit a minimum of 17 participants from each of these groups for the CCA tool validation and application.
TABLE 1 -
Minimal Number of Informants Needed to Classify a Desired Proportion of Questions With a Specified Confidence Level for Different Levels of Cultural Competence
|Proportion of questions
||Average level of cultural competence
|.95 confidence level
|.99 confidence level
In line with the methodological guidelines, we will engage in two data collection steps to develop, validate, and apply the CCA tool for interdisciplinary collaboration in nursing research. First, we will recruit a subset of eligible NIH–NINR-funded trainees, junior faculty, and training directors to identify the cultural domain of interdisciplinary collaboration culture as a shared phenomenon through focused in-person interviews with a free listing activity (Borgatti & Halgin, 1999; Schensul et al., 1999). During the interviews, we will collect basic demographic information and academic status (e.g., trainee, junior faculty, and senior faculty). The interview’s primary focus will be on getting and discussing responses to free listing prompts (e.g., Please list all activities that promote academic diversity, inclusion, equity, accessibility, and research excellence; Borgatti, 1994; Weller, 2007; Weller et al., 2018).
Second, we will conduct a CCA tool validation and application survey using another subsample of trainees, junior faculty, and training directors. To assess the consensus around the interdisciplinary collaboration culture model, participants will be prompted to agree or disagree with a set of CCA statements (e.g., Please rate whether the statements below support the environment of interdisciplinary collaboration in nursing research). To apply the CCA tool, the same set of participants will be asked to rank CCA statements in the order of importance to them (e.g., Statements below represent actions and attitudes that support the environment of interdisciplinary collaboration in nursing research. Please rank them in the order of importance for you). We will also conduct qualitative interviews with a small set of respondents from each group representing those with high competence and low competence to gain an in-depth understanding of the differences in values and cultural knowledge about interdisciplinary collaboration in nursing research.
CCA is a mixed-method approach that allows to perform quantitative analyses and apply a factor analytic-type technique to what begins as qualitative data to examine the level of agreement among respondents in a set of cultural knowledge statements. For Phase 1, to define the cultural domain of interdisciplinary collaboration in nursing research, interview data will be transcribed, and a discrete thought will be used as the unit of analysis. Data will be organized in tables to correspond with interview questions and reduced to eliminate filler words and information unrelated to the focus of this study (Watkins, 2017). Next, we will employ qualitative thematic analysis techniques (Owen, 1984), which have been successfully used in previous cultural consensus studies (e.g., Collins & Dressler, 2008; Gulbas, 2013; Kalra et al., 2018). The analysis will focus on identifying thematic repetition, recurrence, and forcefulness, and emerging themes will be presented for verification and discussed at the qualitative research colloquium—an active group of experienced qualitative researchers who meet biweekly to review and provide feedback for qualitative studies. The analysis will develop statements that represent shared cultural knowledge about the values and behaviors that promote interdisciplinary collaboration in nursing research (Romney et al., 1986). Final statements will be simplified and reworded to achieve a Flesch–Kincaid reading level of 10th grade or less to avoid ambiguous interpretation.
For Phase 2, we will employ matrix algebra and principal component analysis to examine the intersection of agreements among respondents as measures of cultural competence (individual agreement with cultural domain; Romney, 1999; Romney et al., 1996). Statistically, cultural competence is measured as a correlation coefficient that varies between 0 and 1, with higher values indicating a respondent’s greater knowledge of or behavior within the cultural domain (Batchelder & Romney, 1988). Although individuals from different groups can agree on the general cultural domain, the presence of subcultures can explain variances in cultural competence (Dressler et al., 2018). Principle component analysis and the ratio between the first and second eigenvalues will be used to determine whether a single-factor solution exists—indicating a single, shared cultural belief system. The 3:1 ratio will be used as a threshold for a single-factor solution (Oravecz et al., 2015; Romney et al., 1986; Weller, 2007). To evaluate cultural differences and variation in the rank order of the CCA statements for these three groups, we will apply the Stuart–Maxwell Marginal Homogeneity Test (Maxwell, 1970; Stuart, 1955).
The study was approved by the University of Florida Institutional Review Board and was designated as exempt from continuous review. Prospective research participants have no prior relationship with the research team, and no risk for coercion exists.
The development and validation of a CCA tool is a novel approach to assess, support, and systematically examine interdisciplinary collaboration and team science in nursing research and training. Although there is general acceptance of culture change as a long-term training outcome, there is a lack of methodologically rigorous and practically feasible ways to assess and explore the culture of interdisciplinarity. It remains critical to integrate the evaluation emerging research culture with its shared values and knowledge in the training program development. However, the investigation of culture needs to remain value neutral, refrain from being prescriptive, and be sensitive to the emergence and dominance of one “right” culture.
The results of this study will provide crucial preliminary knowledge and validation of an instrument to measure the culture of interdisciplinary collaboration in nursing research. This knowledge will inform future nursing research training program development, evaluation, and large-scale policy efforts. Given the statistical properties of CCA instruments and their ability to identify the presence of a shared culture among a small number of respondents, the tool developed by this study will also have broad application at the local level of the individual program to assess cultural competence and consonance of training directions, mentors, and trainees.
Yulia A. Levites Strekalova https://orcid.org/0000-0002-6060-1233
American Association of Colleges of Nursing. (2010, November 1). The research-focused doctoral program in nursing: Pathways to excellence
Anders R., Batchelder W. H. (2012). Cultural consensus theory for multiple consensus truths. Journal of Mathematical Psychology
, 56, 452–469. 10.1016/j.jmp.2013.01.004
Batchelder W. H., Anders R. (2012). Cultural consensus theory: Comparing different concepts of cultural truth. Journal of Mathematical Psychology
, 56, 316–332. 10.1016/j.jmp.2012.06.002
Batchelder W. H., Kumbasar E., Boyd J. P. (1997). Consensus analysis of three-way social network data. Journal of Mathematical Sociology
, 22, 29–58. 10.1080/0022250X.1997.9990193
Batchelder W. H., Romney A. K. (1988). Test theory without an answer key. Psychometrika
, 53, 71–92. 10.1007/BF02294195
Borgatti S. P. (1994). Cultural domain analysis. Journal of Quantitative Anthropology
, 4, 261–278.
Borgatti S. P., Halgin D. S. (1999). Elicitation techniques for cultural domain analysis. Ethnographer’s Toolkit
, 3, 115–151.
Collins C. C., Dressler W. W. (2008). Cultural consensus and cultural diversity: A mixed methods investigation of human service providers’ models of domestic violence. Journal of Mixed Methods Research
, 2, 362–387. 10.1177/1558689808322766
Dressler W. W., Balieiro M. C., dos Santos J. E. (2015). Finding culture change in the second factor: Stability and change in cultural consensus and residual agreement. Field Methods
, 27, 22–38. 10.1177/1525822X14542755
Dressler W. W., Balieiro M. C., dos Santos J. E. (2018). What you know, what you do, and how you feel: Cultural competence, cultural consonance, and psychological distress. Frontiers in Psychology
, 8, 2355. 10.3389/fpsyg.2017.02355
Dressler W. W., Borges C. D., Balieiro M. C., dos Santos J. E. (2005). Measuring cultural consonance: Examples with special reference to measurement theory in anthropology. Field Methods
, 17, 331–355. 10.1177/1525822X05279899
Gulbas L. E. (2013). Embodying racism: Race, rhinoplasty, and self-esteem in Venezuela. Qualitative Health Research
, 23, 326–335. 10.1177/1049732312468335
Kalra N., Pelto G., Tawiah C., Zobrist S., Milani P., Manu G., Laar A., Parker M. (2018). Patterns of cultural consensus and intracultural diversity in Ghanaian complementary feeding practices. Maternal & Child Nutrition
, 14, e12445. 10.1111/mcn.12445
Malterud K., Siersma V. D., Guassora A. D. (2016). Sample size in qualitative interview studies: Guided by information power. Qualitative Health Research
, 26, 1753–1760. 10.1177/1049732315617444
Maxwell A. E. (1970). Comparing the classification of subjects by two independent judges. British Journal of Psychiatry
, 116, 651–655. 10.1192/bjp.116.535.651
Oravecz Z., Anders R., Batchelder W. H. (2015). Hierarchical Bayesian modeling for test theory without an answer key. Psychometrika
, 80, 341–364. 10.1007/s11336-013-9379-4
Owen W. F. (1984). Interpretive themes in relational communication. Quarterly Journal of Speech
, 70, 274–287. 10.1080/00335638409383697
Romney A. K. (1999). Cultural consensus as a statistical model. Current Anthropology
, 40, S93–S115. 10.1086/200062
Romney A. K., Boyd J. P., Moore C. C., Batchelder W. H., Brazill T. J. (1996). Culture as shared cognitive representations. Proceedings of the National Academy of Sciences
, 93, 4699–4705. 10.1073/pnas.93.10.4699
Romney A. K., Weller S. C., Batchelder W. H. (1986). Culture as consensus: A theory of culture and informant accuracy. American Anthropologist
, 88, 313–338. 10.1525/aa.1986.88.2.02a00020
Schensul S. L., Schensul J. J., LeCompte M. D. (1999). Essential ethnographic methods: Observations, interviews, and questionnaires
. Alta Mira Press.
Stuart A. (1955). A test for homogeneity of the marginal distributions in a two-way classification. Biometrika
, 42, 412–416. 10.2307/2333387
Watkins D. C. (2017). Rapid and rigorous qualitative data analysis: The “RADaR” technique for applied research. International Journal of Qualitative Methods
, 16, 1609406917712131. 10.1177/1609406917712131
Weller S. C. (2007). Cultural consensus theory: Applications and frequently asked questions. Field Methods
, 19, 339–368. 10.1177/1525822X07303502
Weller S. C., Vickers B., Bernard H. R., Blackburn A. M., Borgatti S. P., Gravlee C. C., Johnson J. C. (2018). Open-ended interview questions and saturation. PLOS ONE
, 13, e0198606. 10.1371/journal.pone.0198606