I believe that one of the major purposes of psychological science is to promote that science to the public – that is, after all, one of the reasons that I write this blog. But scientists vary in the degree to which they agree to this statement. One one side, some researchers will argue that the purpose of science is to make meaningful contributions to knowledge alone, i.e. furthering the frontiers of human understanding and nothing else. To these people, research is for researchers. On the other side, we have people like those appearing in the list below, who work to actively promote their work on Facebook and elsewhere.
This is certainly not the only purpose of a lab Facebook page. I also use our lab’s page for community-building: to congratulate graduate students on major accomplishments, to announce new publications and presentations, and to share jokes that academics and graduate students would find funny. Some labs use it for recruiting undergraduate research participants.
In creating this list, I came to several surprising conclusions:
- Psychology lab Facebook pages are relatively uncommon. There are literally thousands of psychology labs, yet I could only locate 28 with Facebook pages (although there were some restrictions to my search – see below).
- Psychology lab Facebook pages don’t have a whole lot of likes. A few hundred at the high end, almost all had less than 30, and most had less than 10.
- Psychology lab Facebook pages often aren’t maintained well. I only saw pages with my purposes (community-building with graduate students, lab announcements, etc.) in two or three pages. Most post quite infrequently. Unsurprisingly, the pages with the highest numbers of posts are also those with the most likes.
Now for the list. I’ve included only pages that have at least 2 likes and 1 post, for which I can clearly identify that it’s a lab, and for which posts are in English. We’ll start with the best (wink), and the rest will be alphabetical!
| Facebook Page Name | PI | Location | Area of Psychology |
|---|---|---|---|
| Technology iN Training Laboratory – TNTLab | Richard N. Landers | Old Dominion University | industrial/organizational |
| Arcadia University: Social Psychology Lab | (unknown) | Arcadia University | social |
| Arkin Lab | Robert Arkin | Ohio State University | social |
| Carter Adolescent Interpersonal Relationships Lab | Rona Carter | University of Michigan | developmental |
| Cogpsy Lab | Maria Viggiano | University of Florence | physiological |
| CSUN Sport Psychology Lab | Mark P. Otten | California State University, Northridge | sport |
| Dr. Tom Ford’s Social Psychology Lab | Tom Ford | Western Carolina University | social |
| Early Learning Lab (ELLA) | Annette M. E. Henderson | University of Auckland | developmental |
| Environmental Psychology, Interpersonal Attachment and Relationships Lab | Kerry Anne McBain | James Cook University | social |
| Evolutionary Psychology Lab at Oakland University | Todd K. Shackelford | Oakland University | evolutionary |
| Evolutionary Psychology Lab of SUNY New Paltz | Glenn Geher | SUNY New Paltz | evolutionary |
| Exercise Psychology Lab | Edward McAuley | University of Illinois | exercise |
| Forensic Psychology Lab | Don Dutton | University of British Columbia | forensic |
| Gender, Sexuality & Critical Psychology Lab RU | Maria Gurevich | Ryerson University | social |
| Lab of Evolutionary Psychology – Research Participation (MQ) | (unknown) | Macquarie University | evolutionary |
| Loyola University Psychology Lab | (unknown) | Loyola University | (unknown) |
| NEU Psychology Laboratory | (unknown) | New Era University | (unknown) |
| Psychology of Social Justice Lab (PSJL) | Phillip Atiba Goff | University of California, Los Angeles | social |
| SIUC Dr. Yu-Wei Wang’s Psychology Lab | Yu-Wei Wang | Southern Illinois University, Carbondale | counseling |
| Social Attitudes Psychology Research Lab | (unknown) | St. Mary’s University | social |
| Social Psychology Lab – University of Innsbruck | (research group) | University of Innsbruck | social |
| Social Psychology Lab (SPLAB) | (unknown) | University of Kwa-Zulu Natal | social |
| Social Stress and Health Psychology Research Lab | Elizabeth Brondolo | St. John’s University | health |
| TCNJ Organizational Psychology Lab | Jason Dahling | The College of New Jersey | industrial/organizational |
| UGA Infant Research Lab | Janet Frick | University of Georgia | developmental |
| UMSL Multicultural Psychology Research Lab | Matthew J. Taylor | University of Missouri, St. Louis | multicultural |
| University of Queensland Evolutionary Approaches to Psychology Lab Group | (unknown) | University of Queensland | evolutionary |
| UWM Anxiety Disorders Lab | Han-Joo Lee | University of Wisconsin, Madison | clinical |
In a new article appearing in Simulation & Gaming, Bedwell and colleagues1 do what the game studies literature has generally not been able to do for games in general; they develop a taxonomy that defines what a serious game is. This effort provides a road map for researchers exploring how games can contribute to learning.
The definition of “game” is an area of surprisingly vehement debate. Many researchers and game designers have their own definition of “game,” ranging from Sid Meier’s “a game is a series of interesting choices” to researchers’ attempts to define games by exploring the largely humanities-based game studies literature. This is a challenge for the serious games researcher, because without a clear definition for “game”, there is no way to systematically and scientifically explore the aspects of games that contribute to learning. What one researcher calls “challenge”, another might call “fun.” What another researcher calls “fun”, yet another might say “contributes to the creation of flow experiences.” This leads to overlap between researchers, which often leads to seemingly contradictory results. With the above example, a study finding a positive effect of “fun” might in another researcher’s term be a positive effect of flow or a positive effect of challenge. This leads to a highly inefficient scientific process.
Bedwell and colleagues begin to solve this problem by conducting an empirical study of game attributes targeted at identifying the areas of overlap between researcher conceptualizations of game attributes. First, they identified 65 self-proclaimed “game experts” from various online sources, including the online forums of the Escapist and Penny Arcade, video game listservs, and internal distribution lists at game developers. Next, these 65 game experts (50 players and 15 developers) conducted what is called a “card sort”, in which they sorted the 19 game attributes related to learning identified by previous researchers into their own categories. Finally, they completed a post-sort survey to capture demographic information. To examine this data, Bedwell and colleagues conducted a cluster analysis, a process that groups cases by similarity of ratings provided.
Interestingly, game developers and game players did not differ in their sorting. Support was found for a 9-category system:
- Action Language. The method by which information is relayed to the game (joystick, keyboard, mouse, etc.).
- Assessment. The extent to which feedback is provided to players on their progress.
- Conflict/Challenge. The degree to which the player is challenged, and the method for manipulating that challenge.
- Control. The degree to which players can affect their environment.
- Environment. The location chosen for the game.
- Game Fiction. The extent to which the game environment represents reality.
- Human Interaction. The degree to which players interact with other people.
- Immersion. The extent to which the game uses engrossing effects (audio, video, etc.) to draw the player in.
- Rules/Goals. The degree to which the game presents clear objectives.
With this system, the authors hope that “research on game attributes and their effects on learning can progress in a purposeful and unified fashion” (p. 753). Somehow, I don’t expect it will be quite that easy, but at the very least, this provides a valuable starting point for such efforts.
- Bedwell, W., Pavlas, D., Heyne, K., Lazzara, E., & Salas, E. (2012). Toward a taxonomy linking game attributes to learning: An empirical study Simulation & Gaming, 43 (6), 729-760 DOI: 10.1177/1046878112439444 [↩]
In a recent meta-analysis appearing in the Journal of Business and Psychology, Costanza and colleagues1 compare a wide variety of attitude variables between four generations of employees: Traditionals, Boomers, Gen X, and Millenials. In a quantitative review of 20 articles on generational differences across 19,961 workers, the authors conclude that generational differences are small or near zero in virtually all cases.
Six attitude variables were examined:
- Job satisfaction, the degree to which employees enjoy and have positive attitudes towards their job
- General organizational commitment, the degree to which employees are committed to their job (a combination of the next three)
- Affective organizational commitment, the degree to which employees feel emotionally attached to their job
- Normative organizational commitment, the degree to which employees feel obligated to remain at their job
- Continuance organizational commitment, the degree to which employees feel they have invested a lot in their organization and don’t want to lose those investments (personally, not monetarily)
- Intent to turnover, the degree to which employees intend to leave their organization in the near future
Reported in standard deviation units (Cohen’s d), the authors found effect sizes ranging from .02 to .25 for satisfaction, -.22 to .46 for commitment, and -.62 to .05 for intent to turnover, which can be described as “low to effectively zero.”
Millenials are often characterized as being very different from the rest of employees, with a “drastically different outlook” and “different expectations”, are “difficult to understand” and “entitled”, “less motivated”, “high maintenance” and “silver-spoon fed”, and a variety of other colorful descriptors. In truth, it seems that they are not much different than any other generation – or at the least, they are no more different from other generations than other generations are from each other.
The article is, of course, limited by the variables it was able to meta-analyze, so it is possible that some other attitude or characteristic that was not examined does demonstrate a large difference. However, Millenials are commonly vilified as being less committed to their jobs, more likely to jump ship, and less satisfied with their work. This study demonstrates that at least in these regards, Millenials are just like everyone else.
- Costanza, D., Badger, J., Fraser, R., Severt, J., & Gade, P. (2012). Generational differences in work-related attitudes: A meta-analysis Journal of Business and Psychology, 27 (4), 375-394 DOI: 10.1007/s10869-012-9259-4 [↩]