Data management and analysis

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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


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Data management and analysis

  1. 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. 2. Content • Introduction and Objectives • Data Management • Data Analysis ZT (ILRI-ICRAFJan 30, 2015 2
  3. 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. ZT (ILRI-ICRAFJan 30, 2015 3
  4. 4. Introduction and Objectives (Cont’d) Objective: • To share experience in data management and analysis The HOWs: • Managing the data • Analyzing the data ZT (ILRI-ICRAFJan 30, 2015 4
  5. 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 ZT (ILRI-ICRAFJan 30, 2015 5
  6. 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 ZT (ILRI-ICRAFJan 30, 2015 6
  7. 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) ZT (ILRI-ICRAFJan 30, 2015 7
  8. 8. Data Analysis • Choice of a Statistical Software Genstat.lnk ZT (ILRI-ICRAFJan 30, 2015 8
  9. 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! ZT (ILRI-ICRAFJan 30, 2015 9
  10. 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. ZT (ILRI-ICRAFJan 30, 2015 10
  11. 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 ZT (ILRI-ICRAFJan 30, 2015 11
  12. 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? ZT (ILRI-ICRAFJan 30, 2015 12
  13. 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? ZT (ILRI-ICRAFJan 30, 2015 13
  14. 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. ZT (ILRI-ICRAFJan 30, 2015 14
  15. 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) ZT (ILRI-ICRAFJan 30, 2015 15
  16. 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) ZT (ILRI-ICRAFJan 30, 2015 16
  17. 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. ZT (ILRI-ICRAFJan 30, 2015 17
  18. 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. ZT (ILRI-ICRAFJan 30, 2015 18
  19. 19. THANK YOU!! ZT (ILRI-ICRAFJan 30, 2015 19

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