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Experimental designs and data analysis in the field of Agronomy science by making use of free software available online
1. Experimental designs and data
analysis in the field of Agronomy
science by making use of free
software available online
By
Dr Jatinder Manan
2. Principles of experimental design:
There are three basic principles of design
(i) Randomization
(ii) Replication
(iii) Local control
(i) Randomization
Every experimental unit has an equal chance of receiving each treatment
(ii) Replication
Replication provides more observations when the same treatment is
used, so it increases precision
(iii) Local control (error control)
The replication is used with local control to reduce the experimental error
3. Types of trials mainly used by the
extension workers
1. Completely randomized design
2. Randomized block design
3. Repeated trials
4. Surveys
4. Completely randomized design (CRD)
•All experimental units are considered the same and no division or grouping
among them exist.
Example: Suppose there are 4 treatments and 20 experimental units,
Treatment 1 is replicated, say 3 times and is given to 3 experimental units
Treatment 2 is replicated, say 5 times and is given to 5 experimental units
Treatment 3 is replicated, say 6 times and is given to 6 experimental units
Treatment 4 is replicated [20-(6+5+3)=]6 times and is given to the remaining 6
experimental units.
No local control measure is provided
5. Randomized Block Design
Example: Suppose there are 7 treatments corresponding to 7 levels of a factor to be
included in 4 blocks.
So one possible layout of the assignment of 7 treatments to 4 different blocks in an
RBD is as follows
Block 1 T2 T7 T3 T5 T1 T4 T6
Block 2 T1 T6 T7 T4 T5 T3 T2
Block 3 T7 T5 T1 T6 T4 T2 T3
Block 4 T4 T1 T6 T5 T2 T3 T7
6. Repeated trials
Tests done with the same conditions and parameters as a previous one by the
same researcher(s). When an individual (or team) runs a test again for more data
to improve the statistical measures, it is a “repeated trial”.
When a study is undertaken by a different individual to verify the results of the
first one it is called a “replication”.
Repeated trials are used within a study to verify the results and enhance the
statistical measures.
Replication is used to verify the findings of the original group by researchers
outside of the original group.
7. Repeated trials:
In general, we had repeated trials and not replicated trials, so scientists use repeated
plots as their replications and do the analysis as “one factor analysis” and reach to
certain conclusion, than wasting data without analyzing.
8. Surveys
There is a logical sequence to producing a good questionnaire. The process of
designing good questionnaires can be divided into six steps as follows:
•Determining Data Needs. It is first important to determine why the survey is required
(i.e., justification) and, therefore, the objectives of the survey,
•Determining Question Content. Three important issues to address with respect to
question content are:
A. Appropriate identification information and variables
B. Variables need to be included that enable the sample to be classified or
stratified appropriately.
C. Questions must use terms and units of measure (e.g., for weight, area,
distance, etc.) with which the farmers in the area are familiar
9. •Determining Question Format
• Determining the Wording of Questions
•Deciding on Question Sequence
•Physical Layout and Length. If it takes longer, the questionnaire should be
divided and administered on separate days.
•Pretesting and Revision. After the initial design of the survey, it is worthwhile to
ask interested and knowledgeable individuals for comments and suggestions for
improvements.
10. Online tool for analysis
- OPSTAT
The link for this software is:
http://14.139.232.166/opstat/
15. Example:
3 replications
4 treatments
Parameter to analyze: Yield
Things to remember:
1. Don’t use serial numbers
2. Do one parameter at a time to decrease confusion
3. Enter data in excel and copy from there
4. Don’t go for alignment of data in the space given for data entry (online)
16. Data entry in excel sheet
Replications : A to C
Treatments:
1 to 4
22. Critical Difference
It is used to compare means of different treatments that have
an equal number of replications.
Generally, we take means and CD for interpretation of our data
with above data, if CD is 1.1 that means:
Treatment 1 is significantly superior to all other treatments
because difference between treatment 1 and other treatments
is more than 1.1
23. Surveys
It can be worked with the idea that, computer analyze only the numeric values.
•Convert the survey collected from the farmers into numeric values as 1, 2, 3 …
•Take one theme of positive values with higher values
Can use excel sheet for the analysis of data as correlation values.
24. Analysis can be done in excel
Age values given Education values given
25-35 0 10 0
35-45 1 12 1
above 45 2 bsc 2
Farmer response
Age Education
0 2
1 1
2 0
0 2
2 0
1 1
2 0
1 2
2 0
1 1
2 0
25. To apply any formula in excel
Go to Formulas bar and then click insert function
In search - write Correlation and click Go
Below you see correl, select it and click Ok
26. New window will open,
select all values of 1st parameter for array 1
and similarly for 2nd and click Ok.
27. The result shows the value of correlation: -0.939
means
age and education were highly negatively correlated
with each other
The values of correlation varies between -1 to +1, as,
highly negatively and positively correlated.
28. To apply Regression online
Again we need to go to OPSTAT software
Regression shows dependency of one factor on the
other given factors:
age values given Education values given Adoption values given
25-35 0 10 0 no 0
35-45 1 12 1 yes 1
above 45 2 bsc 2
Farmer response
Age Education
adoption of
new variety
0 2 0
1 1 1
2 0 0
0 2 0
2 0 1
1 1 0
2 0 0
1 2 1
2 0 0
1 1 1
2 0 1
29. Go to OPSTAT
Select correlation and regression analysis
Insert all three parameters in space given and
click submit, New window will open
30. Result window will open as:
The correlation values of variable 2 is same as in excel
The correlation values of variable 3 is non significant
R square value is 0.8825 for variable 1 against variable 2 & 3
The variable 2 is significant at 0.00002 i.e. more than 0.005
31. Site to undergo basic knowledge of statistics:
http://apps.iasri.res.in/design/
Login to IASRI site for online survey analysis:
http://cabgrid.res.in/ssda2/Default.aspx
