Study designs depend greatly on the nature of the research questions one is looking for to answer. Thus, knowing the research questions will affect the way one particular research study will be carried out. Two typical study approaches are cross-sectional and longitudinal study designs. Both cross-sectional and longitudinal studies are typically observational studies in which researchers gather information from the study participants without manipulating their environment. Cross-sectional studies focus on one time point whereas longitudinal studies follow the development of a particular behavior or phenomenon over time.
Both approaches have their benefits and difficulties. For example, cross-sectional studies may easily gather information on several factors influencing students’ motivation at a given time, however, fail to tell anything about how students’ motivation may change over time and across different educational transitions. On one hand, cross-sectional studies are often less time consuming and require less resources than longitudinal studies, whereas on the other hand, longitudinal research may be able to capture much richer picture of the study interest (e.g. behavior or phenomenon), how it develops over time, and what kind of factors are related to it longitudinally.
Gender and ICT is organizing this research seminar with Katja Upadyadya to discuss these topics further, including examples of different research designs and methodological strategies.
Tuesday, 22 November 2016
15:oo – 17:30 h
Room -1A IN3 (Av. Carl Friedrich Gauss, 5 Castelldefels)
Katja Upadyaya, Ph.D. has been conducting research on various elements related to students’ academic motivation, well-being, and student-teacher and parent-child interaction using various longitudinal and multiple cohort data sets at the University of Jyväskylä and University of Helsinki (Finland), and at University of Michigan and Michigan State University (U.S.A.). Currently she is also collaborating with MilagrosSáinz Ibáñez and analyzing with her longitudinal data sets at the Universitat Oberta de Catalunya.