Submissions

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Submission Preparation Checklist

As part of the submission process, authors are required to check off their submission's compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines.
  • The submission has not been previously published, nor is it before another journal for consideration (or an explanation has been provided in Comments to the Editor).
  • The submission file is in OpenOffice, Microsoft Word, or RTF document file format.
  • Where available, URLs for the references have been provided.
  • The text adheres to the stylistic and bibliographic requirements outlined in the Author Guidelines.

Author Guidelines

Font size

Manuscript should be clearly typewritten in font Times New Roman, 12pt, 1.5pt-spaced throughout, on A4-sized paper with approximately 2.54cm margin on both sides left and right, and 2cm for top and bottom. Use 12pt. bold for section headings, and 12pt. italics for subsection headings. Use Harvard system for citing references.

Length and Style

Authors are advised to keep the number of pages of a paper to less than 20. The preferred submission file is in Microsoft Word file format.

Layout
Manuscripts should be organized as follows: Title, names and affiliations of all authors including e-mail address, Abstract, Keywords, Introduction, Methodology, Results and Discussion, Conclusion, Acknowledgement, References. Pages should be numbered consecutively and arranged in the following order.

References

Examples of correct forms of references are given below:        

  1. Agresti A. (1992). Analysis of Ordinal Paired Comparison Data. Journal of the Royal Statistical Society, 41(2):287-297.
  2. Agresti A. (1996). Categorical Data Analysis (2nd). New York: John Wiley & Sons.
  3. Černá L. and Chytrý M. (2005). Supervised Classification of Plant Communities with Artificial Neural. Journal of Vegetation Science, 16(4):363-372. DOI: 1111/j.1654-1103.2005.tb02380.x
  4. Fahrmeir L. and Tutz G. (1994). Multivariate Statistical Modeling Based on Generalized Linear Models. New York: Springer-Verlag. DOI: 1007/978-1-4899-0010-4

 

 

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