1 Module 5 Gender Issues in Data Collection, Sampling and Analysis

2 Exercise Review Working with the questions you developed earlier, which ones would you now use to monitor and evaluate this project? How would you define and operationalize the key terms? How would you measure your key terms? What steps would you take to ensure that your data are gender sensitive? What information did you enter into the design matrix?

3 Learning Objectives At the end of this session participants will understand: data collection options sampling options data analysis options gender issues related to data collection and data analysis

4 Data Collection Strategy The strategy depends upon: What you want to know “Numbers” or “stories” Where the data resides Environment, files, people Resources available Money Time Expertise Gender issues

5 Multiple Approaches Quantitative When you want to do statistical analysis, to be precise, to know exactly what you want to measure and/or want to cover a large group Hard to develop, easy to analyze Qualitative When you want anecdotes or in-depth information, when you cannot measure what you want to measure, want to know reasons for achievements and problems and/or there is no need to quantify Easy to develop, hard to analyze

6 Common Data Collection Approaches  In-person interviews Structured or unstructured  Self-administered questionnaires  Focus group discussions  Diaries, self-administered time-use reports and income and expenditure reporting

7 Common Data Collection Approaches (Continued)  Observation Participant observation, unobtrusive observation, obtrusive observation  Secondary data Prior studies, existing reports Media Existing data

8 Why Data Collection Methods Often Are Not Gender Sensitive Managers, researchers, and technical staff are not aware of gender issues in the projects or lack experience with gender issues and methods. Surveys frequently interview only the (male) “household head.” Formal interviews are not an adequate way to capture information on sensitive topics. Women may not be able to speak freely in interviews or to attend or speak in community meetings.

9 Gender Sensitive Approaches to Data Collection  Collect data in ways that allow men and women to participate and speak freely Consider time of day, child care arrangements, safe settings  Combine quantitative and qualitative data collection methods  Collect information on priorities, constraints and opportunities of individual household members  Ask questions specific to men or women but maintain common core questions so responses can be compared

10 Checklist for Assessing Gender Sensitivity of Data Collection Situations/Issues to avoidActions to ensure methods adequately address gender issues  Sex disaggregated data is available but not used  Information is not collected from the right people  Household surveys are not the appropriate data collection method  Inadequate analysis of gender differences in control of resources within the household Assess the availability of gender- responsive data before considering the need to collect new data. Include additional questions on gender- specific topics Use special methods to analyze gender differences in household decision- making and control of resources. Use special methods to study domestic and public violence Budget time and resources for follow- up field visits to interpret and further explore statistical findings.

11 Case Discussion What are some data collection strategies that could be used in your Family Health Project Case? How can you ensure the data collection is gender-sensitive? Refer to Your Family Health Project Design Matrix

12 Sampling Samples are required because it is often not feasible or necessary to collect data on all subjects in the universe being studied. Random samples ensure that the findings are representative and can be generalized to all families, communities, etc., covered by the study. Always report sampling procedures used for selection, number of participants, and participation rate (response rate), even in qualitative monitoring and evaluation.

13 Importance of Sampling with Qualitative Methods Often necessary to generalize from qualitative studies. Focus groups, PRA techniques etc often do not pay sufficient attention to sampling issues. Conclusions and recommendations can be very misleading.

14 Types of Sampling Random: each has an equal probability of being selected (statistical sample). Results are generalizable Non-Random Accidental Judgmental/Purposive Convenience Results are not generalizable Random

15 Random Samples Level of precision required. Level of disaggregation required By region By economic group By sex of household head Type of estimates to be made Point estimate (average income) Difference between means Impacts of different project components

16 1. Sample too large Waste time and money 2. Sample too small Cannot do required analysis 3. Sample covers wrong population Wrong conclusions 4. Parts of the population not covered Wrong conclusions Four Dangers in Random Samples

17 Random Sampling Problems in Field No sampling frame [list] Difficult to define unit of analysis Important groups missing [illegal residents, squatters/renters/ landless] Some groups not accessible Some groups remote and expensive to reach

18 Non-Random Sampling The results of non-probability samples cannot be generalized. Data is reported in terms “Of the respondents….” Sample size not that important Enough so it seems reasonable Purposeful selection

19 Gender Issues in Sampling Sample should be representative of men and women in the community or population of interest. Sample should include sufficient amount of variation for making comparisons. Different age groups, different marital status, different villages

20 Gender Issues (continued) Sample designs to reach women: Separate module for women Snowball sampling Cultural problems to interview women Intersection theory: need to compare gender, race, class. Large enough sample to stratify

21 Case Discussion Micro-Credit Studies Economic Study: Why did they use a statistical sample? What are the advantages and disadvantages? Social Study: Why did they use a purposive sample? What are the advantages and disadvantages?

22 Case Discussion: Family Health Project Would you use a sample for some data collection? Why or why not? What kind of sample would you use? What are the advantages and disadvantages?

23 Data Analysis Two Basic Types: Quantitative Data Analysis Qualitative Data Analysis

24 Quantitative Data Analysis Frequencies, percentage distributions Rates of change Cross tabulations Measures of central tendency Means, medians and modes Measures of dispersion. Standard deviation Analysis of relationships between variables

25 Social Micro-credit Study Full control 18% Significant control 19% Partial control24% Very limited control17% No control 22% The study found that less than 40% had significant control.

26 Measures of Association How strong is the association between two variables? (e.g., income and education) Several different measures of association Some measures of association range from 0 to 1 Others range from -1 to +1 Perfect Relationship = 1 or –1 Closer to 0: no relationship

27 Deterministic Statistics (estimates/predictions) For every change in one variable, we expect an estimated amount of change in another variable. For example, simple linear regression: Change in education (x) results in a change (  ) in income (y)

28 Deterministic Statistics: Economic Micro-credit Study For every 10% increase in male borrowing in the Grameen Bank, per capita spending by men increases.18. So if male borrowing increases 20%, we would predict a.36 increase in per capita spending. For every 10% increase in female borrowing in the Grameen Bank, per capita spending by women increases.43. Turn to the Micro-credit Design Matrix

29 Case Discussion Micro-credit Results Are these results what you expected? What surprised you? What might explain these results? What might you want to ask in the next study? Looking at the results of this study, what conclusions would you draw about the impact of micro-credit programs?

30 Qualitative Data Analysis Data from narrative documents, open-ended interviews, focus groups, unstructured observations Conduct content analysis: Identify common words, ideas, themes Write on cards Keep track of where they are located Have a second person do the analysis Compare results Work out differences Identify “quotable quotes” Greatest Risk: Bias Hard to recognize things you don’t expect

31 Challenges in Qualitative Data Analysis Maintaining uniqueness while seeking uniformities and patterns. Is the purpose of the analysis: Exploratory and hypothesis generation? Generalization and testing of hypotheses? Avoid the trap of selecting extreme or dramatic cases and implying they are typical. !

32 Integrating Quantitative/Qualitative Analysis Exploratory studies followed by surveys. Defining key concepts Quantitative and qualitative research in parallel. Understanding the setting Follow-up qualitative research to interpret survey findings. Discuss survey results to understand why people responded as they did; context.

33 Gender Issues in Data Analysis 1. Ensure sex-disaggregated data analysis is conducted for all key variables. 2. Avoid exclusive focus on household level data: go into the household. 3. Do not rely solely on comparison of male and female headed households for analysis of gender differences. !

34 Gender Issues in Data Analysis 4. Break down female-headed households into voluntary and involuntary households. 5. Study cultural traditions and other factors limiting women’s control over productive assets and ability to take advantage of economic development projects. 6. Consider cultural traditions that would impact acceptance of family planning, education and employment. !

35 Group Exercise Each group should complete the Design Matrix for the Family Health Project Case. Select someone to present elements of the Design Matrix. Finish Design Matrix