Synthesizing data analysis methods adopted under in situ global project in Nepal along withvariables and nature of study could be guiding reference for researchers especially to those involvedin on farm research. The review work was conducted with the objective to help in utilizing andmanaging in situ database system. The objectives of the experiment, the structure of the treatmentsand the experimental design used primarily determine the type of analysis. There were 60 papers ofthis project published in Nepal. All these papers are grouped under 8 thematic groups namely 1.Agroecosystem (3 papers), 2. Agromorphological and farmers’ perception (7 papers), 3. Croppopulation structure (5 papers), 4. Gender, policy and general (15 papers), 5. Isozyme andmolecular (6 papers), 6. Seed systems and farmers’ networks (5 papers), 7. Social, cultural andeconomical (11 papers) and 8. Value addition (8 papers). All these papers were reviewed basicallyfor data type, sample size, sampling methods, statistical methods and tools, varieties and purposes.Descriptive and inferential statistics along with multivariate methods were commonly used in onfarm research. Experimental design, the most common in on station trial was least used. Study overspace and time was not adopted. There were 5 kinds of data generated, 45 statistical tools adoptedin eight different crop species. Among the 5 kinds of data under these eight subject areas,categorical type was highest followed by discrete numerical. Binary type was least in frequency.Most of the papers were related to rice followed by taro and finger millet. Cucumber and pigeonpea were studied least. Descriptive statistics along with Χ2, multivariate analysis and regressionapproaches would be appropriate tools. Similarly SPSS and MINITAB may be good software. Thebest one among a number of statistical tools should be selected and utmost care must be exercisedwhile collecting data.Key words: Data analysis methods; on farm research; on station research; subject areasDOI: 10.3126/narj.v6i0.3371Nepal Agriculture Research Journal Vol.6 2005 pp.98-108
Principles of Experimental Design for Big Data Analysis