Module Directory

This module prepares students to replicate and extend published research. For this module, replication includes but it is not limited to the re-estimation of models using the data and codes provided by the authors. Additionally, students are expected to analyse the data accuracy and appropriateness of the modelling choices – i.e., consulting original sources of the data, and/or using alternative operationalization for key variables, and/or using alternative estimation techniques to assess the robustness of the findings.

For the extension component of this module, students should introduce a theory-informed modification that would allow them to build upon that study, and test it. The hypothesis could propose a conditional effect (an interaction), a differential effect for larger or different samples or subsamples, a different effect after the removal of outliers, the test for selection effects, or the inclusion of third variables that could be affecting the relationship of interest, among other innovations.

This component is not a mechanical data-mining exercise. Students should theoretically justify the inclusion of this hypothesis or of any empirical innovations.

The aims of this module are:

• To support students’ replication and extension of a recent research article.

For this module, replication includes but it is not limited to the re-estimation of models using the data and codes provided by the authors. Additionally, students are expected to consult original sources of the data, and/or use alternative operationalization for key variables, and/or use alternative estimation techniques to assess the robustness of the findings to other operationalization and/or measurement and/or modeling choices. Students need to analyze the robustness and/or limits of generalization of the replicated findings.

For the extension component of this module, students should propose an additional hypothesis grounded on the theory, and test it. The hypothesis could propose a conditional effect (an interaction), a differential effect for larger or different samples or in subsamples, a different effect after the removal of outliers, the test for selection effects, or the inclusion of third variables that could be affecting the relationship of interest, among other innovations. This component is not a mechanical data-mining exercise. Students should theoretically justify the inclusion of this hypothesis or of any empirical innovations.

By the end of this module, students will be expected to be able to:

1. understand the purpose and importance of replication in social sciences;


2. replicate and extend a recent research article;


3. evaluate critically, provide constructive feedback, and propose avenues for further research to peers and to extant research.

The module consists of five meetings to support students in the process of replicating and building upon published research.

Sequential assessments are designed to discuss and provide feedback on each of the stages of the process of replicating and extending published empirical research. This allows students to reinforce concepts of research design and to practice different forms of communicating research.