The project uses a longitudinal and comparative research design and will generate evidence that is descriptive [observational], associative/correlational [quasi-experimental], and causal [experimental, quasi-experimental, statistical modeling]. Original data are being collected on undergraduate students, particularly underrepresented minority students, using school records, assessments of learning, and survey research [self-completion questionnaire, structured interviewer-administered questionnaire, focus groups]. Class essay assignments for students to create communal purpose orientations to science class material are being compared to essays about what they are learning in class.
The project is using a number of instruments or measures, including:
- Johnson’s (2002) ‘Work Values’ survey
- Ryff’s (1989) ‘Personal relations with others’ survey
- Diekman et al.’s (2010) ‘Communal purpose goal endorsement’ survey
- domain-specific situational interest (Linnenbrink-Garcia et al., 2010)
- individual interest (Marsh, Koller, Trautwein, Ludtke, & Baumert, 2005)
- classwork-related processing, persistence, and effort as measured by Elliot, McGregor, and Gable (1999).
- Science Identity Scale (Chemers, Zurbriggen, Syed, Goza, & Bearman, 2011)
- career motivation developed to assess perceived competence, interest, future interest, engagement and perceived career value
- A career goal affordance measure has been developed by the research team for use in ongoing projects and pilot data. The measure, adapted from a combination of Johnson’s (2002) ‘Work Values’ survey and Diekman et al.’s (2010) ‘Perceived Goal Affordance’ measure, measures the extent to which specific careers (e.g., science careers) provide opportunities to fulfill the seven types of goals measured in the work values survey (see Thoman et al., in press BioScience)
Interview and focus group data will be submitted to qualitative content analysis. Longitudinal survey data will be analyzed with structural equation models. Data from the randomized experimental study will be analyzed with regression and ANOVA.