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The design of a study defines the study type (descriptive, correlational, semi-experimental, experimental, review, meta-analytic) and sub-type (e.g., descriptive-longitudinal case study), research problem, hypotheses, independent and dependent variables, experimental design, and, if applicable, data collection methods and a statistical analysis ...
the authors disclose the existence of the preprint at submission (e.g. in the cover letter) once an article is published, the preprint should link to the published version (typically via DOI ) the preprint should not have been formally peer reviewed
The use of a sequence of experiments, where the design of each may depend on the results of previous experiments, including the possible decision to stop experimenting, is within the scope of sequential analysis, a field that was pioneered [12] by Abraham Wald in the context of sequential tests of statistical hypotheses. [13]
It was established in 1975 as an independent section of the Journal of Experimental Psychology and covers research in experimental psychology. [1] The journal has implemented the Transparency and Openness Promotion (TOP) Guidelines. [2] The TOP Guidelines provide structure to research planning and reporting and aim to make research more ...
In the standard preregistration format, researchers prepare a research protocol document prior to conducting their research. Ideally, this document indicates the research hypotheses, sampling procedure, sample size, research design, testing conditions, stimuli, measures, data coding and aggregation method, criteria for data exclusions, and statistical analyses, including potential variations ...
In the design of experiments, optimal experimental designs (or optimum designs [2]) are a class of experimental designs that are optimal with respect to some statistical criterion. The creation of this field of statistics has been credited to Danish statistician Kirstine Smith .
Difference in differences (DID [1] or DD [2]) is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. [3]
According to the Journal Citation Reports, the journal received a 2020 impact factor of 14.307, ranking it seventh out of 177 journals in the category "Immunology" [3] and fourth out of 189 journals in the category "Medicine, Research & Experimental". [4]