Home<\/a> > Research Design<\/p>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row el_class=”multile-research”][vc_column][vc_column_text css_animation=”bottom-to-top” el_class=”research-programme”]<\/p>\n
This Research Programme Draws On
\nMultiple Research Designs.<\/h3>\n
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Longitudinal:<\/h2>\n
Cross-sectional designs are useful for explaining the variability and relations amongst variables of interest at a particular time periods. But longitudinal designs provide more powerful mechanisms that allow measurements of change (using repeated measures) and direct inferences regarding causal pathways underlying inter and intra-level relationships (using longitudinal measures), and are therefore pivotal for the study of life course transitions and development trajectories.[\/vc_column_text][\/vc_column][\/vc_row][vc_row css_animation=”bottom-to-top” css=”.vc_custom_1648036653005{background-color: #005bc5 !important;}” el_class=”multi-cohort”][vc_column][vc_column_text el_class=”research-content”]<\/p>\n
Multi\u2013level:<\/h2>\n
Multi-level designs are based on nested analytical frameworks and are well-suited for evaluating factors and processes at the individual (micro) and social context (macro) levels simultaneously. In education for example, the nesting or clustering of students within classrooms, classrooms within schools, and schools within communities and\/or systems needs to be accounted for due to the lack of independence between variables that operate at different levels. Multilevel data also differs along the degree to which within-group units correlate with one another, depending on the degree of dependency. For example, patterns of relationships among students nested within classrooms or courses are likely to be more correlated than within schools and schools more than neighbourhoods and so on.[\/vc_column_text][\/vc_column][\/vc_row][vc_row css_animation=”bottom-to-top” css=”.vc_custom_1648036662134{background-color: #000000 !important;}” el_class=”multi-cohort”][vc_column][vc_column_text el_class=”research-content”]<\/p>\n
Longitudinal Multilevel:<\/h2>\n
As educational pathways and trajectories of individual development are shaped by life course transitions and social contexts that are inherently group-level constructs, longitudinal multilevel designs are needed to specify and evaluate their consequent effects on subsequent outcomes and for evaluating the nesting of time within students, students within schools, and schools within systems.[\/vc_column_text][\/vc_column][\/vc_row][vc_row css_animation=”bottom-to-top” css=”.vc_custom_1648036677419{background-color: #005bc5 !important;}” el_class=”multi-cohort”][vc_column][vc_column_text el_class=”research-content”]<\/p>\n
Cross classified:<\/h2>\n
This research design permits the identification and control for the impact of simultaneous multiple membership effects such as neighbourhoods and schools. Additionally, complex school effects can also be efficiently decomposed into main and crossed effects that allow addressing complex research questions. For example, the persistence of primary on higher education outcomes or choices after adjusting for secondary schools. Misleading inferences can result when such effects are present but ignored, thus leading researchers to overlook important features group-level variation.[\/vc_column_text][\/vc_column][\/vc_row][vc_row css_animation=”bottom-to-top” css=”.vc_custom_1648036686793{background-color: #000000 !important;}” el_class=”multi-cohort ecological”][vc_column][vc_column_text el_class=”research-content”]<\/p>\n
Ecological:<\/h2>\n
The research can draw on an \u201cecological\u201d human development approach to understand youth and adulthood development, taking into account the variety of micro and macro social or institutional processes and interactions.[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"
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