In the end, I guess a final element of my one wish is simply to embrace the complexity. Complex systems require complex theory and complex models to understand them. Embracing the complexity allows researchers to pierce the gauntlet of FUDSI that reviewers and panel members too often erect. Doing good science isn’t supposed to be easy and the apparent preference for easy science is tantamount to pseudo-science. In my view, good science is innovation science. The continued use of ordered categories (introduced by Likert in 1932!) as the preferred tool of measurement, for example, should become a banished practice. Instead, innovations in measurement like using electronic delivery with visual analog scales or real time intensive recording of data should be employed more readily. Similarly, innovations in design like creating planned missing protocols to save costs, increase database coverage, and reduce participant burden (among other benefits) ought to become standard practice (Little, Jorgenson, Lang, & Moore, 2014). Moreover, moving away from simple, flat representations of data to incorporating linked meta-data and integrated relational databases, opens up an amazing world of potential modeling benefits to easily allow cross-nested, multi-way relationships across various hierarchies of influence (e.g., n-Level-SEM modeling; Mehta, 2015).
Innovations in modeling complex data structures need to be planned for and then craft-fully executed (Little, Wang, and Gorrall, in press). Here, the frequently mixtured and often messy multilevel nature of multivariate data (typically made sparse by the many mechanisms of missing data) coupled with the moderating and mediating mechanisms in multiply caused outcomes makes for a massively challenging modeling process. It also makes today’s research so much fun! And, lest I forgot something: et cetera.
This text is the prepublication version of the one wish essay Dr. Little contributed to the special issue of Research on Human Development edited by Richard A. Settersten Jr. & Megan McClelland. The special issue can be down loaded at http://www.tandfonline.com/toc/hrhd20/12/3-4 and the published version of this article is available here.
Todd D. Little is founder and director his annual Stats Camp (statscamp.org) and the director of the Institute for Measurement, Methodology, Analysis, and Policy (IMMAP) at Texas Tech University. He is a professor in the Research, Evaluation, Measurement, and Statistic Program of the Educational Psychology Department at TTU.
Special thanks to Gregory R. Hancock, Katherine E. Masyn, Annegret Hannawa, Patricia H. Hawley, my team of personnel in IMMAP, and the editors for their helpful comments and suggestions. I would also like to express my gratitude to my many colleagues, such as those in the Society of Multivariate Experimental Psychology, for regularly stimulating me with methodological innovation and rigor. I’d like to also acknowledge the many methodologically oriented conferences and subprograms that provide a forum for viewing and presenting methodological innovations.