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Ilm Ideas Evidence and Advocacy Accelerator for Grant partners

- 1. What to research and how? Research questions, sampling and all that Faisal Bari Associate Prof. of Economics, LUMS Associate Fellow, IDEAS (With contribution from Dr. Farooq Naseer, IDEAS)

2. Outline What does the TNA tell us Framing of research issues, questions and tools. This appears to be simpler than it is.demands reflexivity Sampling and related issuesall about power 3. Ilm-Ideas TNA: Research Tools 0 2 4 6 8 10 12 14 16 18 Focus Groups Interviews HH Surveys School level survey Case studies Secondary analysis of data Statistical models Other Research tools used most often 1 2 3 4 5 4. Ilm-Ideas: Tools Required 11 13 12 15 13 11 14 14 10 15 14 17 14 13 6 4 5 2 4 6 3 3 7 2 3 0 3 4 Conducting a research needs assessment and/or defining research objectives Identifying priority research questions Selecting research sites and developing criteria for the selection Selecting and justifying the sampling strategy and target numbers Sampling selecting the research target group Conducting desk research to identify good practice examples within and outside the country of similar researches undertaken Developing research indicators Developing research tools and instruments Piloting the research instruments Conducting qualitative and quantitative research tools and instruments Management of data collection/fieldwork, including the control, supervision and debriefings of field workers/interviewers Use of data analysis software and systems Conduct data interpretation and analysis Report writing Priority Non-Priority 5. Research Capacity 8 7 7 6 7 10 7 10 12 10 10 3 9 11 8 10 8 8 8 5 8 7 3 5 5 7 5 4 1 0 2 3 2 2 2 0 2 2 2 7 3 1 0 2 4 6 8 10 12 14 16 18 Conducting research needs assessment and/or defining research objectives Identifying priority research questions Selecting research sites and developing criteria for the selection Selecting and justifying the sampling strategy and target numbers Sampling selecting the research target group Conducting desk research to identify good practice examples Developing research indicators Developing qualitative and quantitative research tools and instruments Piloting the research instruments Conducting qualitative and quantitative research tools and instruments Management of data collection/fieldwork, including the control, supervision and debriefings of field workers/interviewers Use of data analysis software and systems Conduct data interpretation and analysis Report writing Organizational Capacity - Research High Medium Low 6. Main Challenges in Policy Research Getting concerned institutions engaged and motivated Data management Interpreting data Report writing Availability of updated data Accessing policy documents Low experience/expertise in conducting policy research Access and availability of public expenditure documents Discrepancy in government data/inaccurate govt. data Dearth of qualitative research experts in the country Lack of interest within policy circles Shortage of sector experts Community based research Sometimes funding agency and govt. interests dont match 7. Framing Research Can you tell whether you are drinking Coca Cola? For a single person: coke or not For a single person: coke or other colas For many people: coke or not For many people: coke or other colas Trivial? Think of cure for cancer 10% total cure versus 50 percent improvement for 50% (but not cure) 8. Framing Research: Examples Private Public Partnerships in Education: Adopt a school programme Importance and need: 25 A and quality issues Variation in legal frameworks: Punjab and Sindh Variation in models: PEN, CARE, SEF Variations across time: do models mature. What is the exit strategy 9. Framing Research: Examples Remedial education for teachers (will come back to this at the end too) DSD reports, PEC results.content knowledge of teachers is a significant issue How to remedy that? CPD already in place Something that is scale-able also Using DTEs to reach teachers (Maths and Science) Use technology to reduce cost 10. Framing Research: Examples MFN Post fact impact evaluationone way MFN paper Introductory paras set the context and question Issue of composite effectrather than isolating contributions. Child friendly (teacher training, materials, parental involvement)better learning 11. Framing Research: Examples MFN Propensity score matching (not gold standardbut best available here) Two stage matching: Schools and then children (need both school and children/family characteristics) School level matching: geography, medium, level of school 12. Framing Research: Examples MFN Within school blocks.child matching Robustness Children joiningdroppedselection bias Treatment and control childrengood match on average 13. Framing Research: Examples MFN Mining.Item Response Theory (IRT) Possibility of leakage (teacher transfers, student transfers) No non-cognitive testing.where gains might be large too Could we check if the effect was different on the weakest/strongest students 14. Framing Research: Examples Tahir Andrabi and the recent education recovery paper. Distance from Fault Line as the independent variable How is that established? And What is its importance The results are insightful.the hey, wait a minute moment 15. Sampling Issues: Statistics Refresher: Summarizing data Sampling: Minimizing error Representativeness Hypotheses testing Power 16. Data: Summarizing Variation is what we study: variation is King Statistics helps us summarize data by using two important features of a dataset: the average (mean, center) what is the average age of participants in this room? Is it important?....not a technical issue only (The deer hunter) the variance (variability, spread) by how much does age vary across participants? Again.is it importantand when (50 or 0/100) 17. Distribution 18. Population and Sample Measuring the population gives us the truth! (assuming there is no measurement error) But we usually cannot survey the entire population Hence we must draw a sample How do we choose the sample? How large should be the sample? 19. Sample Sample must be representative of the population: Draw a random sample Jute example, skulls, Indian census But still, the sample is not some fixed subset of the population so each sample will be different! This is called sampling error. How to reduce it? Draw a larger sample. But how large? (depends on the hypothesis of interest and sampling error want to maximize the power to reject incorrect hypotheses) 20. Simple Random Sampling List every individual in the population of interest (population size: N) Decide on a sample size based on power calculations (sample size: n < N) to be discussed Randomly pick n individuals from the population such that each individual has a positive chance of being picked Examples: Toss a coin Draw lots out of a basket Use a computer software 21. Stratified Random Sampling Mark separate sub-groups (or strata) in the population list before drawing a random sample from each Stratified Sampling For adequate representation of different sub-groups (i.e. strata) in the population For a given sample size, reduces the sampling error as compared to the un-stratified simple random sampling Trade-off between the cost of doing stratification and the smaller sample size needed Fraction sampled could be different across strata; improves across-group comparisons 22. Two Nice Results Before we turn to hypothesis testing and the concept of statistical power, important to recognize that the sample average behaves well in large samples Law of Large Numbers The sample average will approach the true population average as the sample size increases Central Limit Theorem The sample average will tend to be normally distributed, around the true population average value, as the sample size increases 23. Normal Distribution : = . : = ( ) 2 24. Hypothesis Testing Suppose the average pre-training knowledge of M&E in the population is 3/10 points on a standardized test How can we empirically test whether this course improves M&E knowledge? In statistical terms, this test can be stated as follows: H0 or the null hypothesis: This hypothesis states what you would like to disprove i.e. no effect. H1 or the alternative hypothesis: The course improves M&E knowledge i.e. positive effect. 25. Hypothesis Testing Ex-post, administer the test on multiple cohorts of course participants OR use statistical theory to decide based on just one cohort When is the average test score of course participants in a cohort significantly (i.e. statistically) higher than 3? That is, allowing for sampling error, when can we be confident that we are observing a real improvement in M&E scores? Depends on the sampling error in average test score 26. Hypothesis Testing Suppose, you want to test a promising intervention designed to improve (M&E) education. Question: Is the intervention (treatment) effective? 27. Statistical Power The power of a test is the probability of correctly rejecting the null hypothesis In other words, power is the probability of correctly declaring the treatment as beneficial Hence, Statistical Power = 1 Prob(Type-II error) 28. Importance of getting power right Testing a new miracle cure for cancer Power too low; missed a large treatment effect Power too high; wasted resources in doing a large study to declare a tiny, clinically irrelevant effect as statistically significant Power just right; have a good chance of detecting reasonably sized effects, but not tiny ones 29. Power: Main Ingredients For a given significance level, power depends on the following: 1. Sample Size 2. Assumed Effect Size under H1 3. Variance of outcome in the study population 4. Proportion of sample in T vs C 5. Clustering 30. Power Samp