Dear researchers.
I received contradictory comments from the reviewers. Initially, my manuscript was written for a single study. However, one of the reviewers considered my research model complex. He/she suggested breaking it down into multiple studies. I did so by using the same sample and experimental dataset but with different theoretical approaches and conceptual frameworks to build hypotheses for multiple studies. One reviewer accepted it. Another reviewer said that multiple studies cannot be done using the same sample and dataset and advised me to collect additional data from different samples. At the same time, the journal editor only gave me two weeks to revise the manuscript.
Please advise whether there is a prohibition or otherwise justification for using the same sample and dataset for multiple experimental studies in one paper. My information so far shows that the same sample and dataset can be used for multiple papers with different theoretical approaches and research questions/hypotheses, although some references do not allow it. Thank you in advance for helping answer my question.
Using the same sample and dataset for multiple studies within a single manuscript can be a nuanced issue in academic publishing. While it is generally acceptable to conduct various analyses or theoretical explorations on the same dataset—as long as each study addresses distinct research questions or hypotheses—clarity and transparency are key. If your studies are methodologically sound and conceptually distinct, it can enhance the understanding of your dataset's implications. However, it's crucial to ensure that the framing of each study is sufficiently differentiated to avoid redundancy and maintain the integrity of your findings. Some journals may prefer fresh data for each study to mitigate concerns about overfitting or data dredging, which could explain the mixed feedback from reviewers. To navigate this situation, consider clearly articulating the unique contributions of each study within your manuscript and citing relevant literature that supports the validity of your approach. Additionally, you may want to reach out to the journal editor for clarification on their specific policies regarding this matter, as guidelines can vary significantly between journals. This proactive communication can help you align your revisions with their expectations while also providing a solid justification for your approach.
Each study should address distinct research questions or hypotheses. You should explain why different aspects of the dataset are being used for different studies and how they contribute to a broader understanding of the subject. If the studies are part of a larger research framework or model, explain how they are related. This can make it easier to justify the use of the same sample across multiple analyses. If each study examines different variables, features, or outcomes from the dataset, it’s easier to justify the use of the same sample. If the same statistical tests or methods are applied repeatedly to the same sample, there’s a risk of inflating Type I error (false positives). Clearly state that the same dataset was used across multiple studies. Transparency is key to maintaining the credibility of the research.
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