Marketing Letters - Data Policy

Marketing Letters’s Data Policy

It is the policy of Marketing Letters to publish manuscripts only when the data collection materials, data, and the code used in the analyses are clearly and precisely documented and accessible to the review team and the journal's readers. Data that are proprietary, contain confidential information, or are subject to NDAs may be fully or partially exempt from the above stated requirements at the Editors’ discretion. In some rare situations Editors may refer specific cases to the Marketing Letters’s Policy Board.

Authors submitting manuscripts that contain empirical work or simulations must provide sufficient information about the data collection process and materials, analyses, simulation programs, and all other details necessary to assess the rigor of the research, validity of the authors’ conclusions, and permit independent replication of the results (whereby independent means that replicators can replicate the studies, data simulations, and analyses without the need to contact the manuscript authors for details). These materials should be submitted and updated in each review round.

All data and research materials should be archived on a public repository (e.g., Open Science Framework [https://osf.io/] or Wharton Credibility Lab’s Researchbox [https://researchbox.org/]). For experimental work, the latter repository is strongly encouraged. Upon manuscript submission, the authors should provide access to the Marketing Letters review team to all data collection and analyses materials of empirical/simulation studies reported in the manuscript. The Editors should be notified at the time of the initial manuscript submission if access to the data used in the submitted manuscript is restricted or limited, or if, for some other reason, the Marketing Letters’s Data Policy requirements cannot be met. In all circumstances, authors must commit to preserving the data for a period of no less than ten years following the publication of the manuscript, and to offering reasonable assistance to requests for clarification and replication.

Scope

Authors of empirical studies should upload all of the following: (a) the raw data set(s), where only the identifying information such as participant IDs can be removed, time-stamps shall not be removed, (b) description sufficient to access all data at their original source location, (c) programs/code used to create any final analysis data sets from raw data, (d) programs/code used to run the analyses/simulations, (e) all stimuli, survey instruments, and experimental instructions (if the data were collected using Qualtrics or similar survey platform, the posting of the Qualtrics .qsf files or equivalent files from alternative survey platforms is required).

Preregistration of Experimental Studies

For experimental studies, Marketing Letters strongly encourages preregistration. For all invited revisions (but not for the initial submissions), the authors are required to explain their reasons to the review team if the studies were not preregistered. Author-blinded preregistrations should be posted together with the other materials on a research repository. Marketing Letters encourages the use of https://aspredicted.org/ for preregistrations.

Qualitative Data

For qualitative data, authors are required to post their data (transcripts of interviews, field notes, videos, audio recording, photographs, etc.) on a research repository, along with detailed description of the research design, including, description of the research process, aims of the research, selection of informants, interview protocols, and any other information useful to understanding the research process. Unlike experiments and secondary data where the aim of transparency is to enable reproducibility/replicability, the aim of transparency for qualitative research is to enable the reader to assess the rigor of the research and the validity of the researchers’ interpretations and conclusions.

Secondary Data

For secondary data, a data availability statement covering both the source data and any derivative data is required. The data availability statement should provide detailed information on how, where, and under what conditions an independent researcher can access the original source data, as well as author-generated derivative data. It must be explicit and accurate about any restrictions, requirements, payments, and processing delays. The data availability statement should provide information to assure the reader that the data are available for a sufficiently long period of time.

Proprietary Data, Non-Disclosure Agreements (NDAs), and Big Data

Full data disclosure may not be feasible when datasets or a subset of variables in a study are proprietary, are subject to NDAs, or are too large to be shared. In such situations, the authors are required to alert the Editors at the time of the initial manuscript submission, and to either provide a slice of the data containing a randomly drawn subset of observations, or provide a subset of the data that are not covered by an NDAs. When none of the data can be shared, for example because the data are confidential with personally identifying information of subjects or businesses (e.g., video), or the data are subject to copyrights that prohibit redistribution, the authors are required to alert the Editors of these restrictions at the time of initial manuscript submission.

Data Formats

The data files should be provided in any open, non-proprietary format compatible with any commonly used statistical package or software. Authors should ensure that a meaningful variable name or description (label) is used for every variable in the provided datasets. Codebooks or similar metadata should meaningfully describe each variable. It is acceptable to reference publicly available documentation for these items.