DATA ENTRY AND CLEANING

1. DATA ENTRY AND CLEANING Zerubabel Ogom Ojoo stalight@africaonline.co.ug

2. DATA ENTRY AND CLEANING • Objective is to help prepare effective data entry programs that minimize the post cleaning period

3. DATA ENTRY AND CLEANING • Elements of clean data • Consistent and logical • All variables are consistent. • Expenditure of continuous variables realistic • Consistency in coding • All missing values justified and documented

4. DATA ENTRY AND CLEANING • Good questionnaire layout facilitates the data entry design and subsequent data entry and cleaning • Involve a Data Management Specialist from the beginning • Delineate the questionnaire into sections • Pre-code all variables directly on the questionnaires • Enumerate each variable clearly • Create entry boxes for response fields • Integrate logical skips and test it during pilot

5. DATA ENTRY AND CLEANING Quality Controls • Design an effective data entry program • Data entry screens must match questionnaire. • Concurrent controls (Real time controls) • Range checks.

6. DATA ENTRY AND CLEANING

7. DATA ENTRY AND CLEANING • Real time Controls • Checks are done at data entry time e.g. If S1Q1 = 2 then skip to S1Q3 Endif e.g If S1Q1 = 1 and S1Q3 <0 then reject (if s1q1 =1 then amount must exist) endif

8. DATA ENTRY AND CLEANING • Range checks • Limits all out of range values • Most out of range values come from data entry miskeys and carelessness • Soft checks • Warns data entry operators but can override • Hard check • Cannot override

9. DATA ENTRY AND CLEANING • Use controls or reference tables whenever available • Sample codes • 1, 24, 45, 60, 90, 154, 766, 980 • Other identification variables

10. DATA ENTRY AND CLEANING • Simple consistency checks • Q1: when did you start teaching in this school = 2000 • Q2: when did you start teaching = 2002 Inconsistent Consistency check: If S4Q1>S4Q2 then reject Message (S4Q1 must be >= S4Q2) Endif

11. DATA ENTRY AND CLEANING Conclusion • Data Entry screens must match questionnaire • Real time Controls • Range checks • Simple Consistency checks

12. Software The data analyst has to make a selection of the software to be used for statistical analysis e.g. SPSS, SAS, MS Access, MS Excel The analyst will be guided by the researchers on the sort of tables to be generated.

13. ANALYSIS OF PETS Primary analysis should focus on your PET objectives: Measure leakage of funds tracked on their way to schools and analyze the causes • Use simple parameters (simple average percentages and standard deviations) but complex relationship can be developed. • Analyze equity in leakage distribution(urban/rural divide etc) • Comparing resources disbursed at various levels: central, district, subdistrict, subcounty, schools against entitlements. • Calculating average differences between levels. • Determine how these differences vary over time and space. • What are the explanations. • What are the proposed interventions.

14. LEAKAGES Amount actual received/Amount entitled Calculated by administrative level. Region,District, School. Explanations for the leakages.

15. Analysis beyond leakages Leakage is the primary focus but there are many aspects of service delivery that are equally important. Textbooks procured and at school but not used or stored outside school. Teachers are hired and paid for but not teaching. Students enrolled but absent most of the time. Students at school but parents not providing for uniform and lunches. Health workers paid for but not attending to patients when drugs are out of stock. Rude nurses scare pregnant women who want to deliver at health center. Health centers opened in time but waiting time to see health worker too long. Most patients opt for traditional healers instead of public health centers. All these call for a combination of research methodologies and interventions to investigate and resolve the above.