On Power Motive Imagery

Power motivation is associated with conflict escalation, according to David Winter—a thesis that Jason Quinn and Laurel Stone agree with. Nevertheless, Winter has some corrections for Quinn and Stone.

Can Motive-Related Imagery Make School Tasks More Appealing?

According to recent research ( Eccles, 1999 ; GEOlino-UNICEF-Kinderwertemonitor, 2014), young people are particularly interested in issues related to affiliation, achievement, or power. We suggest that tasks that raise these issues should be more motivating than tasks that do not raise these topics. To test this hypothesis, we enriched the tasks of common mathematics and German textbooks with affiliation, achievement, or power issues. In four experiments, fifth-graders rated how much they would like to work on the tasks (n = 31 for essay tasks, n = 76 for math tasks) and how confident they were about solving them (n = 56 for essay tasks, n = 60 for math tasks). Motive-related issues were within-subject variables. Participants were more attracted to tasks that included motive imagery than to neutral tasks, and they were more confident that they could solve the former than the latter. These effects were true particularly for tasks containing affiliation motive imagery.

Developing a computer coding scheme for the implicit achievement motive

Implicit motives are measured using a projective assessment, the Picture Story Exercise (PSE), involving labor-intensive coding of participant-generated writing. The present research uses insights from previous attempts to automate coding, as well as advances in natural language processing and machine learning, to create a new method of automated coding for the achievement motive (NAch). In part 1, I collected coded PSE sentences from implicit motive researchers. Two models were generated using multilayer perceptron neural networks to predict achievement motive imagery, one using the Linguistic Inquiry and Word Count (LIWC; Pennebaker, 2001) software, and one using a novel text processing system, called Maximum Synset-to-Sentence Relatedness (MSSR). Part 2 sought to experimentally manipulate NAch, and produce 2 more neural network models similar to those of part 1, except that the models in this case predicted experimental condition. Further, human generated NAch scores from the PSEs collected in this part were compared against computer generated NAch scores produced by the models from part 1, to provide another test of the magnitude of the relation between human and computer generated NAch scores. Part 3 tested all 4 models to predict achievement motive imagery in archival data collected by Ratliff (1979). Because these data were coded using a different NAch coding scheme, and also included other variables theoretically related to NAch, these tests were used to search for evidence of convergent and predictive validity. Findings were promising for both models developed in part 1, but further improvements will be necessary before they can replace human coders."