Gender Data Collection for Environment (Climate Change) & Food Security Research

1. Day 2 October 23rd, 2013 Gender Data Collection for Environment (Climate Change) & Food Security Research

2. Research process/ journey

3. Thus research process can involve a series of • Observations: (over long time or one time) • Experiments • Case studies (in-depth follow up of selected subject)

4. Research/study design manner in which the series of observationaland experimental studies are arranged in order to get all desired objectives

5. Research design & methods of data collection

6. Considerations in developing a research design • What is my research intention? • Description of gender situation? • Comparison of issue/s by gender groups? • Classification of the gender issues? • Explanation of gender issues on ground?

7. Considerations in developing a research design • Target population & research units? • gender considerations • Time dimension • Longitudinal or cross-sectional? • Prospective or retrospective? • What number of measurements • sample size implications • cost implications

8. Considerations in developing a research design • Interventions/ experiments/trials • if there, what format will they take? • Need for greater generalisation? • sample size implications • Method(s) of data collection? • What are the trade offs? – reality check

9. Types of studies

10. Cross sectional studies Researcher • does not interfere with subjects • records information about subjects at a single point in time - snapshot • can compare information of the different gender groups in the population at that time

11. Experimental studies Can be: • Field/clinical trial (deals with individuals from gender categories or groups of gender categories) • Community trial - a whole community is taken as one experimental unit

12. Case studies • In depth study of a specific individual or group of subjects • Can be in-depth study of a specific topic – effects of fish export on fish consumption by female headed households in Kisumu • Can use one or combination of observations and experiments/trials

13. Question In gender based climate change/food security /research when would it be best to use: • Observational/descriptive studies? • Experimental studies? • Case studies?

14. Exercise (30 min) Based on what we have discussed so far • Refine the objective/s if necessary • What will the target population & research units be? • Over what period will the study be • How many times will you need to measure/observations will be required • What is intervention/s will you make (if any)? • What will your findings represent? • What data collection methods will you use? • Visualise your research design on flip chart

15. 9. In view of your research design revisit the • Hypothesis • Research questions • Objective/s and make changes where necessary

16. Sampling procedures

17. Probability sampling • Simple random sampling • Systematic Sampling • Stratified random sampling • Multi-stage random sampling • Cluster sampling

18. Probability sampling used to reduce bias in sample selection and ensure gender-responsiveness

19. Determining sample size Required information in sample size calculation • Confidence level (significance level) or t-value • Minimum difference expected to be detected by the study (d) • Measure of variation expected (standard deviation, (s) • Expected power of the study in detecting significant differences See hand out pg4

20. Data & data collection procedures Checklist • What data collection methods are you going to use? • How will you collect gender disaggregated data? • Will the tools and methods used generate data on gender outcomes and impacts? • Is there need for baseline data?

21. Methods of quantitative data collection • Survey, Interview • Records review • Physiological assessments • Biological tests • ICT based data collection

22. ICT based data collection • Online questionnaires • Internet based crowd sourcing • PDAs (personal Digital assistants) • data loggers • mobile phones packages • Apple iTouch/iPhone • Handheld computer/wireless barcode scanner Wireless barcode scanner

23. Numerical / quantitative data 30 pounds 11.3 inches 14 seconds 9 correct questions Categorical / qualitative data Bus, lorry Male, female Yes, No Pass, Fail Types of data Discrete (finite values) 2 children in a household 3 rainy seasons in a year 5 people dozing now Continuous (infinite number of steps which form a continuum) 3.5 kgs body weight 05:45 hours saved on labor 6.3 KM walked to water

24. Data analysis 1. Descriptive statistics 2. Inferential statistics • Central tendency • Mean • Median • Mode • Variability/spread • Range • Variance • Standard deviation • Coefficient of variation • group diffs • chi square, • t-test • anova, manova • association • correlations • factor analysis • path analysis • Prediction • regression • logistic regression

25. Ways of data reporting Pie Graph/charts • commonly used to visualise percentages of the whole

26. Ways of data reporting Bar Graph/chart • illustrate data that does not vary in magnitude too greatly • common in presentation of gender statistics • X-axis - variable with distinct categories - sex, regions, wealth quartiles

27. Ways of data reporting Bar Graph/chart • Y-axis can represent absolute frequencies or %ges, sums, averages • often used to compare data taken over long periods • Often used on very small sets of data

28. Ways of data reporting Stacked bar charts most effective for categories adding to 100 per cent

29. Ways of data reporting Horizontal bar charts when many categories need to be presented

30. Ways of data reporting Scatter plots show relationship between 2 variables particularly patterns of their grouping used to identify & analyze outliers in the data

31. Ways of data reporting Line graphs • compare two separate variables both plotted on an axis • used only for time series (chronological) or some sequence to the dimensions on the x-axis - dates, months, sequence of stages of a project, sequence of meters along gas pipeline • used to detect trends & patterns, not to give people exact quantitative readings

32. Ways of data reporting Line graphs • useful to reveal changes from one age cohort to another, in labour force participation, employment, or literacy, mortality • forcing zero scaling is not necessary • Give clear picture of changes over time or over age cohorts that cannot easily be discovered in data tables