main types of experimental studies: (i) controlled clinical trials and (ii) community trials (1). In practice, there are particular circumstances in which the researcher goal is to demonstrate a causal relationship between an independent and dependent variable while the random allocation of the intervention may not be feasible for ethical or practical constraints (2). To response this need, Quasi-experimental designs (QEDs) are increasingly employed....

Introduction: One of the main pillars of the study is the correct choice of study design. This study aimed to give an overview of different type of study designs in medical research. Methods: In this tutorial study, all applied research designs in terms of quantitative and qualitative study were reviewed. Accordingly, terms related to "Research Designs" were searched in the online databases such as Google Scholar, PubMed, Web of Science and Scopus. Results: Based on the findings of the present study, the types of studies are generally divided into two groups: quantitative studies and qualitative studies. Quantitative studies are divided into two categories: primary and secondary studies. Preliminary studies include observational and non-observational studies. Observational studies are divided into two categories: descriptive and analytical studies. Descriptive studies include case reports, cross-sectional studies, cross-sectional correlational studies and ecological studies, and analytical studies include retrospective and group studies. Non-observational studies also include laboratory studies, clinical trials, field trials, and community trials. Conclusion: With a good understanding of the types of studies, it is easy to decide which type of study is appropriate for the research. Choosing proper study design can reduce the costs of the executive process, increase the accuracy, quality of research and give more reliable results.

Introduction: One of the main pillars of the study is the correct choice of study design. This study aimed to give an overview of different type of study designs in medical research. Methods: In this tutorial study, all applied research designs in terms of quantitative and qualitative study were reviewed. Accordingly, terms related to "Research Designs" were searched in the online databases such as Google Scholar, PubMed, Web of Science and Scopus. Results: Based on the findings of the present study, the types of studies are generally divided into two groups: quantitative studies and qualitative studies. Quantitative studies are divided into two categories: primary and secondary studies. Preliminary studies include observational and non-observational studies. Observational studies are divided into two categories: descriptive and analytical studies. Descriptive studies include case reports, cross-sectional studies, cross-sectional correlational studies and ecological studies, and analytical studies include retrospective and group studies. Non-observational studies also include laboratory studies, clinical trials, field trials, and community trials. Conclusion: With a good understanding of the types of studies, it is easy to decide which type of study is appropriate for the research. Choosing proper study design can reduce the costs of the executive process, increase the accuracy, quality of research and give more reliable results.

Choosing a response surface design to fit certain kinds of models is a difficult task. Extensive research comprising a collection of efficient second-order response surface designs from which a researcher may choose to best fit his/her needs has been conducted, which are based solely on a widely-accepted assumption of a completely randomized error structure of statistically-designed experiments. However, this assumption is not feasible in industrial experiments, which are often split-plot in nature and for which randomization of some factors have to be restricted due to certain constraints. The performance of such experimental designs depends strongly on the relative magnitude (d) of the whole-plot and sub-plot error variances. This work focuses on reduced second-order models having one, two, or all of their quadratic and/or interaction terms removed from the full models of some chosen candidate split-plot central composite designs (CCDs). It investigates the effects of model reduction on efficiency of these designs by computing the relative D-efficiencies for the formulated reduced models with respect to their corresponding full designs and assessing the efficiency losses under specific values of d. The study revealed a significant loss of D-efficiency in these designs, which depend strongly on the removed term(s) and increases, across all values of d, as the number of whole-plot factors increases.

This research investigates the relative efficiency between Neutrosophic Latin Square Design (NLSD) and Classical Latin Square Design (CLSD), with a particular focus on their use in situations where data is uncertain and ambiguous. Although CLSD is a classic experiment designed for systematic error control, its utility is limited in fields like agriculture and behavioral sciences due to its performance bottleneck regarding data imprecision. The NLSD can relatively easily be extended to incorporate neutrosophic logic to address these challenges, making it a more powerful tool for modeling uncertainty. In this paper, a systematic efficiency evaluation of NLSD against CLSD is performed for inconsistent data. It is found that the NLSD enables significant improvements in experimental efficiency while providing clearer inferences regarding treatment effects and supporting more reliable conclusions. Despite these limitations, these benefits establish NLSD as a promising candidate for overcoming environmental uncertainties, and these observations hold significant potential to further the advancement of experimental designs. The results demonstrate that NLSD conveys a 55 % chance to enhance efficiency relative to LSD, which is especially important in processes that must attain maximum resource utilization and high experimental efficiency.