Klein, K. J., Knight, A. P., Ziegert, J. C., Lim, B. C., & Saltz, J. L. (2011). When team members’ values differ: The moderating effects of team leadership. Organizational Behavior and Human Decision Processes, 114, 25-36.
Abstract. Integrating theory and research on values, diversity, situational strength, and team leadership, we proposed that team leadership moderates the effects of values diversity on team conflict. In a longitudinal survey study of national service teams, we found significant, but opposite, moderating effects of task-focused and person-focused leadership. As predicted, task-focused leadership attenuated the diversity–conflict relationship, while person-focused leadership exacerbated the diversity–conflict relationship. More specifically, task-focused leadership decreased the relationship between work ethic diversity and team conflict. Person-focused leadership increased the relationship between traditionalism diversity and team conflict. Team conflict mediated the effects of the interactions of leadership and values diversity on team effectiveness.
Shteynberg, G., Leslie, L. M., Knight, A. P., & Mayer, D. M. (2011). But affirmative action hurts us! Race-related beliefs shape perceptions of White disadvantage and policy unfairness. Organizational Behavior and Human Decision Processes, 115, 1-12.
Abstract. Drawing on social identity theory, we examine how Whites’ race-related beliefs drive their reactions to race-based Affirmative Action Policies (AAPs). Across laboratory and field settings, we find that Whites with relatively high modern racism (MR) or collective relative deprivation (CRD) beliefs perceive greater White disadvantage in organizations that have race-based AAPs, than in organizations that do not. Alternatively, race-based AAPs do not lead to perceptions of White disadvantage among Whites with relatively low MR and CRD beliefs. We also find that White disadvantage mediates the relationship between the combined effects of race-based AAPs, MR beliefs, and CRD beliefs and the perceived fairness of the organization’s selection and promotion policies. Our findings suggest that race-based AAPs do not necessarily lead to perceptions of White disadvantage, but are contingent upon the interpretive lens of Whites’ MR and CRD beliefs, and also offer practical insights for preventing negative reactions to race-based AAPs.
Nundy, A., Mukherjee, A., Sexton, J. B., Pronovost, P. J., Knight, A. P., Rowen, L., Duncan, M., Syin, D., & Makary, M. (2008). Impact of preoperative briefings on operating room delays: A preliminary report. Archives of Surgery, 143, 1068-1072.
Conclusions. Preoperative briefings reduced unexpected delays in the OR by 31% and decreased the frequency of communication breakdowns that lead to delays. Preoperative briefings have the potential to increase OR efficiency and thereby improve quality of care and reduce cost.
Klein, K. J., Ziegert, J. C., Knight, A. P., & Xiao, Y. (2006). Dynamic delegation: Shared, hierarchical, and deindivididualized leadership in extreme action teams. Administrative Science Quarterly, 51, 590-621.
Abstract. This paper examines the leadership of extreme action teams—teams whose highly skilled members cooperate to perform urgent, unpredictable, interdependent, and highly consequential tasks while simultaneously coping with frequent changes in team composition and training their teams’ novice members. Our qualitative investigation of the leadership of extreme action medical teams in an emergency trauma center revealed a hierarchical, deindividualized system of shared leadership. At the heart of this system is dynamic delegation: senior leaders’ rapid and repeated delegation of the active leader- ship role to and withdrawal of the active leadership role from more junior leaders of the team. Our findings suggest that dynamic delegation enhances extreme action teams’ ability to perform reliably while also building their novice team members’ skills. We highlight the contingencies that guide senior leaders’ delegation and withdrawal of the active leadership role, as well as the values and structures that motivate and enable the shared, ongoing practice of dynamic delegation. Further, we suggest that extreme action teams and other “improvisational” organizational units may achieve swift coordination and reliable performance by melding hierarchical and bureaucratic role-based structures with flexibility-enhancing processes. The insights emerging from our findings at once extend and challenge prior leadership theory and research, paving the way for further theory development and research on team leadership in dynamic settings.
Sexton, J. B., Makary, M., Tersigni, A., Pryor, D., Hendrich, A., Thomas, E., Holzmueller, C., Knight, A. P., Wu, Y., & Pronovost, P. (2006). Teamwork in the operating room: Frontline perspectives among hospital and operating room personnel. Anesthesiology, 105, 877-884.
Background & Conclusions. The Joint Commission on Accreditation of Healthcare Organizations is proposing that hospitals measure culture beginning in 2007.However, a reliable and widely used measurement tool for the operating room (OR) setting does not currently exist. Rigorous assessment of teamwork climate is possible using this psychometrically sound teamwork climate scale. This tool and initial benchmarks allow others to compare their teamwork climate to national means, in an effort to focus more on what excellent surgical teams do well.
Sexton, J. B., Holzmueller, C., Pronovost, P. J., Thomas, E., McFerran, S., Nunes, J., Thompson, D., Knight, A. P., Penning, D., & Fox, H. (2006). Variation in caregiver perceptions of teamwork climate in labor and delivery units. Journal of Perinatology, 26, 463-470.
Objectives & Conclusions. To test the psychometric soundness of a teamwork climate survey in labor and delivery, examine differences in perceptions of teamwork, and provide benchmarking data. We demonstrate a psychometrically sound teamwork climate scale, correlate it to external teamwork-related items, and provide labor and delivery teamwork benchmarks. Further teamwork climate research should explore the links to clinical and operational outcomes.
Klein, K. J., & Knight, A. P. (2005). Innovation implementation: Overcoming the challenge. Current Directions in Psychological Science, 14, 243-246.
Abstract. In changing work environments, innovation is imperative. Yet, many teams and organizations fail to realize the expected benefits of innovations that they adopt. A key reason is not innovation failure but implementation failure—the failure to gain targeted employees’ skilled, consistent, and committed use of the innovation in question. We review research on the implementation process, outlining the reasons why implementation is so challenging for many teams and organizations. We then describe the organizational characteristics that together enhance the likelihood of successful implementation, including a strong, positive climate for implementation; management support for innovation implementation; financial resource availability; and a learning orientation.
I recently took some time to figure out how to write a new method for nlme to enable structuring the variance-covariance matrix of the random effects in a specific way. My goal here was to be able to run Dave Kenny’s social relations model (Kenny, 1994) using multilevel modeling and the approach described by Snijders and Kenny (1999). Taking this approach requires “tricking” the software in a way through the use of dummy variables and constraints on the variance-covariance matrix.
Figuring out how to write a new method was more challenging than I had initially expected. There are many twists and turns in lme and it took quite a bit of time to reverse engineer the software to figure out what was going on. Unfortunately, there isn’t great documentation on the web for this process.
As part of my process, I created my own replication of one of the existing methods–pdCompSymm. I went through and commented each part of the different functions that are called, explaining my interpretation of what is going on. As you can see, there are some places where I’m just off and don’t really know what’s going on. I also converted some of the C code in nlme for running pdCompSymm into R code (this is the pdFactor.pdCompSymm function).
In the end, I was able to figure out enough of it to succeed in my goal of creating a new method for the social relations model through multilevel modeling in R. You can find this on my github page. I‘ve called it pdSRM and it has some comments at the top that explain how to use it.
One lesson learned from this is that it is challenging–but not impossible!–to specify a structure for the variance-covariance matrix using nlme that is not already in the generic methods that are provided. I also learned a ton about how lme is working behind the scenes. This took a bunch of time, but did pay off in the end.
I’ve been a huge fan of TextWrangler for years. I use it for all of my coding (including with R), for taking notes during meetings, for taking notes on articles, and more. It’s my most frequently used application. But, I’m ready for a change and am going to give Sublime Text 3 a try. It seems very powerful, relatively light, and incredibly extensible.
I recently installed Sublime Text 3, added Package Control, and installed some R packages to try out. I’ll give it a try for the next three weeks and see what I think. It might be time to say farewell to TextWrangler.
This page has some wonderful resources for recurrence analysis. One particularly useful resource on this site is the listing of software options for conducting recurrence analysis. After a fair amount of searching, I couldn’t find an R package that computed the metrics from a recurrence quantification analysis. The tseriesChaos package provides a function for producing recurrence plots; but, I didn’t see anything for quantifying these plots.
After digging through the different software options listed on this site, I tried out and really like the Commandline Recurrence Plots script offered by Norbert Marwan himself.
The script was very easy to setup on my Mac and, by using Rscript it was easy to combine with R code to (a) draw specific chunks of data for different individuals in my dataset; (b) compute and output the recurrence quantification metrics; (c) output the recurrence plot dataset for creating the actual plot; and, (d) producing the plot and creating a dataset of metrics.
I’ll clean up, comment, and post the code that I used as soon as I can come up for air.