Presented by Zerihun Taddese at the IPMS Workshop on Alternatives for Improving Field AI Delivery System to Enhance Beef and Dairy Production in Ethiopia, ILRI, Addis Ababa, 24-25 August 2011

1. Data Management and
Analysis
IPMS Workshop on Alternatives for Improving Field AI Delivery
System to Enhance Beef and Dairy Production in Ethiopia
ILRI, Addis Ababa, 24-25 August 2011
Zerihun Taddese
ILRI/ICRAF Research Methods Group

2. Content
• Introduction and Objectives
• Data Management
• Data Analysis
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3. Introduction and Objectives
Introduction:
• IPMS experience in mass insemination of cows
– Lack of record keeping and reporting by AI service providers!!
– Lack of confidence in believing the results reported!!
• Four regional states are selected (viz., Tigray, Amhara,
Oromia and SNNPRS)
• Results of intervention work is promising
• Simulated data are used to demonstrate this success.
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4. Introduction and Objectives (Cont’d)
Objective:
• To share experience in data management and
analysis
The HOWs:
• Managing the data
• Analyzing the data
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5. Data Management (Cont’d)
Refers to any activity concerned with
• Planning data management,
– objectives
– outputs
– resources and
– skills available.
• Designing data recording format
• Collection of data, with appropriate quality control
• Checking of raw data
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6. Data Management (Cont’d)
Refers to any activity concerned with (Cont’d)
• Cleaning data
• Keep back up of the data
• Preparing for analysis
• Maintaining records of the processing steps
• Archiving the data for future use
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7. Data Management (Cont’d)
Some Examples:
• Mostly refers to collecting data. DATA
• Designing the data capturing format: FORM.doc
• The design and organization of our computer
files: AI Record Sheet.xls
• One of the regions data: Tigrai.xls
• Store all of the relevant information required with
maximum care (Quality assurance)
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8. Data Analysis
• Choice of a Statistical Software
Genstat.lnk
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9. Data Analysis (Cont’d)
• What statistical procedures do you need?
• What platform – Windows, Macintosh, Unix?
• Balance among
– Ease of learning and use
– Power, expandability, flexibility
– Data management and sharing data with other
statistical packages.
– Innovativeness
– Graphical capabilities
• Cost!
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10. Data Analysis (Cont’d)
Study Design
• What was the question that prompted the research?
• The research question must be articulated clearly,
concisely, and accurately.
How will relations between factors be quantified?
• What parameter are to be estimated?
• How large was the sample to ensure a sufficiently
precise answer?
• Was the study Experimental or Survey?
Start with DUMMY tables.
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11. Data Analysis (Cont’d)
Choosing Statistical Techniques:
• Descriptive Statistics
• Inferential Statistics
– Nominal – Χ2
– test of association
– Ordinal – methods based on ranks
– Interval
– Ratio
– Modeling Logistic Multiple Linear
Regression
Non-parametric
Parametric
DISCRETE & CONTINUOUS
Nominal Interval
Ordinal Ratio
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12. Data Analysis (Cont’d)
SAS was used to analyze the simulated data.
– Importing the four Excel data files from the regions
– Merging the data sets from the regions
• A few examples of questions answered from
analysis.
– WHAT % OF PREGNANCY RESPONDED TO OESTRUS AMONG
TREATED?
– WHAT PROPORTION OF COWS RESPONDED TO HORMON
TREATMENT?
– AMONG THOSE WHO RESPONDED, WHAT IS THE AVERAGE
RESPONSE INTERVAL?
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13. Data Analysis (Cont’d)
• A few examples of questions answered from
analysis (Cont’d).
– WHAT IS THE PERCENTAGE OF PREGNANCY RESULT
BY DIFFERENT FACTORS?
– COMPARISON OF PREGNANCY RESULT AMONG THE
BULLS, AI TECHNICIAN, BREED, and PARITY
respectively
– WHAT ARE THE FACTORS INFLUENCING OESTRUS
RESPONSE?
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14. Data Analysis (Cont’d)
SAS program leading to the following results
– Pregnancy result was 86.1% .
– Oestrus response was 86.5%.
– The mean Oestrus response was 4.36 days with
SD = 1.41 days.
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15. Data Analysis (Cont’d)Table 1: Oestrus Response for some selected characteristics
Number (%) X2
P-value
Breed 1.320 0.2506
Local 226 (66.18)
Cross 117(33.82)
Body Condition Score 8.5856 0.0353
3 152 (43.93)
4 74 (21.39)
5 76 (21.97)
6 44 (12.72)
Lactation Status 8.3583 0.0038
No 219 (63.29)
Yes 127 (36.71)
Parity 11.5754 0.0031
Heifer (0) 88 (25.43)
Young (1,2,3) 202 (58.38)
Old (> 3) 56(16.18)
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16. Data Analysis (Cont’d)Table 2: Pregnancy Results for some selected characteristics
Number (%) X2
P-value
Breed 1.128 0.2881
Local 194 (65.1)
Cross 104 (34.9)
Body Condition Score 4.9590 0.1748
3 137 (45.97)
4 64 (21.48)
5 62 (20.81)
6 35 (11.74)
Lactation Status 1.9988 0.1574
No 193 (64.47)
Yes 105 (35.23)
Parity 36.5492 0.0001
Heifer (0) 71(23.83)
Young (1,2,3) 191 (64.09)
Old (> 3) 36(12.08)
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17. Data Analysis (Cont’d)Table 3: Binary Logit estimates for the Odds Ratios associated with
the selected variables affecting Oestrus Response.
Selected OR 95.0% C.I.
Variables for OR*
Breed 1.134 0.566-2.27
Lactation Status 4.050 1.789-9.167
BCS 3 vs 6 3.297 1.355-8.020
BCS 4 vs 6 2.058 0.817-5.187
BCS 5 vs 6 1.494 0.600-3.723
Heifer vs Old 2.700 1.163-6.268
Young vs Old 3.750 1.769-7.951
*CIs including ‘1’ are not significant at p = 0.05.
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18. Data Analysis (Cont’d)Table 4: Binary Logit estimates for the Odds Ratios associated with
the selected variables affecting Pregnancy Results .
Selected OR 95.0% C.I.
Variables for OR*
Breed 1.396 0.665-2.933
Lactation Status 0.716 0.357-1.434
BCS 3 vs 6 1.456 0.527-4.021
BCS 4 vs 6 1.616 0.539-4.846
BCS 5 vs 6 0.881 0.297-2.614
Heifer vs Old 2.319 0.991-5.429
Young vs Old 9.201 3.964-21.358
*CIs including ‘1’ are not significant at p = 0.05.
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19. THANK YOU!!
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