WinS for Girls: Advocacy and Capacity Building for MHM in WinS

WinS for Girls: Advocacy and Capacity Building for MHM in WinS Module 11: Qualitative Data Analysis Photo credit: Sarah Yerian

WinS for Girls Module 11: Data Analysis Summary from Module 9 • Research Team should have qualifications that fit the needs of the research • Training for research should take a minimum of 7 days and cover the purpose of the research, ethics, child protection, qualitative research approaches, tool review, transcription and translation and piloting. • Teams should engage in active practice and review of the tools • Schools require 2 visits; one for preparation and one for the research activities. • Piloting is important to ensure that the tools are properly adapted to answer your research questions • Debriefing with your research team is invaluable and must be planned into your research activities. WinS for Girls

WinS for Girls Module 11: Data Analysis Learning Objectives At the end of the lesson, participants should be able to: • Remember the purpose of qualitative data • Understand that good analysis can only come from good data • Understand key steps of the Data Analysis Process • Recognize/identify themes in the research findings • Compare findings within schools and across schools to draw conclusions • Realize the time needed for analysis WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis Qualitative Research Brings to light human experience of a particular phenomenon We are bringing to light girls’ experiences of menstruation We are collecting multiple perspectives in order to make some generalizations Our generalizations are limited to the populations we investigate effectively Our GOAL is to design studies that let us make some generalizations about human experience, even if we are limited in our sample size. Through Analysis we use the data collected to try and make some of those generalizations. However, we can only make generalizations if we have a good study design! A strong analysis depends on quality data! WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis Qualitative Research Brings to light human experience of a particular phenomenon -We are investigating 3 schools in District 1. -We investigate each of these schools in depth. -We can compare the data from those schools to identify common or unique experiences girls face in district 1. -This allows us to make some generalizations about District 1. -We also investigate 3 schools in District 2 to identify common experiences among girls and to make some generalizations about district 2. -We can then compare Districts 1 and 2 and make some generalizations about Area 1. District 1: 3 Schools District 2: 3 Schools WinS for Girls Area 1

WinS for Girls Module 11: Data Analysis Data Analysis Qualitative Research Brings to light human experience of a particular phenomenon -We are very limited in what we can say about Country A because the activities at a single school represent an entire area in an entire country. -Our analysis is limited because we can say very little about each area. Much of girls’ experience depends on CONTEXT, and this design does not allow in depth investigation of context. Country A Area 3 Area 2 Area 1 Area 4 Area 5 Area 7 Area 8 Area 9 Area 6

WinS for Girls Module 11: Data Analysis Data Analysis Qualitative Research Brings to light human experience of a particular phenomenon It is better to concentrate effort in a few areas and to know those areas well. You can then make some generalizations about those areas, understand context, and also compare areas/ context as part of your analysis. Country A Area 3 Area 2 Area 1 Area 4 Area 5 Area 7 Area 8 Area 9 Area 6

WinS for Girls Module 11: Data Analysis Data Analysis Qualitative Research Brings to light human experience of a particular phenomenon Remember we discussed the many levels of influence on experience at the beginning of the course. Focusing in on one or even just a few areas will allow you to better understand these levels of influence as you carry out your analysis. It is this contextual understanding that will inform your basic package. Societal Factors Policy, Tradition, Cultural Beliefs, Social Norms Environmental Factors  School Water, Sanitation, Resources Interpersonal Factors  Influence of Family, Teachers, Peers Personal Factors  Girl’s Knowledge, Skills, Beliefs Biological Factors  Age, Intensity WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis What is qualitative data? • Focus on text, rather than numbers • Text can take many forms • Transcripts from recorded interviews or discussions • Notes from interviews, discussions, observations • Debrief memos or written notes taken after an activity that discusses what was learned • Lists generated by participants in activities • Stories written by participants in activities • Can also include the other components of data, including • Photos and the discussion generated from the photos • Drawings and the discussion generated from the drawing WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis What is qualitative data analysis? • An interpretation of the data that accurately reflects an individual’s experience • Allows for you to draw conclusions about girls’ experiences based on the questions you have set out to answer • Happens concurrently with data collection –not just at end—so that you can start learning along the way WinS for Girls Ayres, 2003;

WinS for Girls Module 11: Data Analysis Data Analysis Three Key Concurrent Activities As Part of Analysis 1. Data Reduction This is a means of selecting, focusing, simplifying, sorting, and organizing data. This happens: Before you begin: As you decide which research questions to focus on During data collection: Identifying themes, applying codes, writing memos After data collection: Synthesizing, summarizing, writing. You are essentially taking the huge quantity of text you have and pulling together common themes or ideas into more manageable pieces WinS for Girls Miles and Huberman, 1994

WinS for Girls Module 11: Data Analysis Data Analysis Three Key Concurrent Activities As Part of Analysis • Data Reduction • Data Display Data display is a means of organizing data into compact forms to help you see what is going on and if there are certain patterns or trajectories that influence behaviors or experiences. Creating these means of display is a part of analysis and also can help you to present or share or discuss data with other members of the team. Forms of data display include: Pictures, matrices, graphs, charts, networks, lists, etc. Some displays may become part of your final report. WinS for Girls Miles and Huberman, 1994

WinS for Girls Module 11: Data Analysis Data Analysis Data Display Example 1: Chart discussing ‘Leaks’ WinS for Girls Miles and Huberman, 1994

WinS for Girls Module 11: Data Analysis Data Analysis Data Display Example 1: Drawing displaying ‘Leaks’ (A) No School Toilets/ Not Enough School toilets Worry about Leaks Teasing Leaves school early No Access to Pads or Cloth at school (B) Good Toilets Available Comfortable participating in school Does not worry about Leaks Materials Available Knows how to change Materials WinS for Girls Miles and Huberman, 1994

WinS for Girls Module 11: Data Analysis Data Analysis Data Display Example 1: Social Ecological Model displaying ‘Leaks’ Societal Factors Norm is to not discuss talking about menstruation; Environmental Factors  Pads not available, WASH facilities not available, home far Interpersonal Factors  No one to trust and ask for help Personal Factors  No Understanding of how frequently to change Biological Factors  Need to change at school because of flow WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis Three Key Concurrent Activities As Part of Analysis • Data Reduction • Data Display • Drawing Conclusions This involves noting patterns and themes -across research activities -across schools -across regions Not everything will fit into similar patterns. It is important to see what is similar and also what is different. Dig deep to understand why there is a difference in what you see. WinS for Girls Miles and Huberman, 1994

WinS for Girls Module 11: Data Analysis Data Analysis Conclusions: Having Access to Pads and Toilets, and knowing how to use them, helps girls feel confident and participate in school. We can only make this conclusion because we compare all data. Drawing Conclusions: Example of Girls’ Experiences with their Period Trajectory 1 Commonly reported in Schools A, C, D, F, No School Toilets/ Not Enough School toilets Worry about Leaks Teasing Leaves school early No Access to Pads or Cloth at school Trajectory 2 Commonly reported in Schools B, E, G Good Toilets Available Comfortable participating in school Does not worry about Leaks Materials Available Knows how to change Materials WinS for Girls Miles and Huberman, 1994

WinS for Girls Module 11: Data Analysis Data Analysis Key Analysis Activities during data collection: • Post Data Collection Debrief Part of Data Reduction Carried out as a team at the end of each day of data collection. -Discuss each activity, key learning, what worked and what did not work. -When you get started, you will want to go through each tool and discuss each key section. This will allow you to get a sense of the tool, if the data collectors are asking the questions correctly, if they need to probe more on what is shared. -Eventually, the team will know all of the tools and will have experience to identify new ideas that had not been heard before without going through the tools in depth. WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis School Activity Sheet George: Facilitator Asiedua: Note taker Full tool FGD Y 2:30/4:25pm Grade 4 classroom Girls (6) Post Data Collection Debrief WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis Key Analysis Activities during data collection: • Post Data Collection Debrief • Activity MemosPart of Data Reduction For each activity, your research assistants should write a brief review of the activity they completed. Key points to write about may include: • Challenges, Determinants of those challenges, Voiced Impacts • Specific issues with WASH, Knowledge, Materials, Support • Recommendations • Personal perceptions of how the activity went • Suggested changes /additions to make to tool All Activity Memos should be properly labeled: • School/ Community Name and ID • Date • Activity Type and Number of Participants • Names of Research team members involved WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis Key Analysis Activities during data collection: • Post Data Collection Debrief • Activity Memos • School Report Part of Data Reduction This report will include: • Post Data Collection Debrief • Activity Memos PLUS (3) Overall synthesis of key ideas across activities. Compile all of this information in a single document. It is a valuable reference. WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis Key Analysis Activities when transcripts or other forms of text data are available : • In-Transcript MemosPart of Data Reduction Once a transcript is available, your research assistants will review the transcript of the activities they lead (or participated in) for accuracy. Make sure nothing has been omitted. Next, the lead researcher or the research team member who led the activity will “memo” or add notes to each transcript. You can write comments as a part of track changes in the margins, highlight key areas and write footnotes below. Notes can identify if a new challenge has arisen, if the challenge may be similar to other interviews, or something reported in other schools or areas. Memos should not summarize the data, or what is being said by the participant WinS for Girls

WinS for Girls Module 11: Data Analysis Memoing What is memoing? • Note (written in track changes) embedded in the transcript • Consider content and quality of data • Should be a first reaction to the data • Thoughts and potential connections • How people reacted to specific questions • Should not summarize the data WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis Key Analysis Activities when transcripts or other forms of text data are available : • In-Transcript MemosPart of Data Reduction WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis Key Analysis Activities when transcripts or other forms of text data are available : • In-Transcript Memos • Overall Activity Transcript MemosPart of Data Reduction, Data Display Once the transcript is final, the lead researcher will create a document memo to summarize key issues in the full transcript. These all may not have been remembered for the activity memo. Researcher can start compiling data in other forms—charts, lists—that help to explain the individual’s experience. WinS for Girls

WinS for Girls Module 11: Data Analysis Data Analysis Key Analysis Activities when transcripts or other forms of text data are available : • In-Transcript Memos • Overall Activity Transcript Memos • Identifying Themes • Themes may be already be clear from topics (areas of inquiry) in guides • Look for stories (What is the story telling you?) -May arise through issues identified by participants -Create “codes” based on the issues/topics that appear frequently b. You may “recognize” themes from the research literature: WASH facilities, knowledge and support, etc. c. Your knowledge may be shaped by asking: • What is happening? • Who is it happening to? • How or why is it happening? • Who is involved? WinS for Girls

WinS for Girls Module 11: Data Analysis Developing Codes for Data Creating the Codes After identifying re-occurring themes, you will want to develop a “codebook”. The codebook will have the theme listed and then a clear definition of what you will include as a part of that theme. For example: Life Changes: These changes occur at menarche and may include descriptions of girls’ relationship with others, how others may see them, how girls carry themselves and act, the way they are treated, changed feelings, and restrictions. This code is not restricted to menstruation-related issues. • "Before I could befriend boys and now I must stay away from them.“ • "I am careful with how I look now.” What are some potential themes you may anticipate using in your analysis? WinS for Girls

WinS for Girls Module 11: Data Analysis Developing Codes for Data Creating the Codes Potential codes to include in the data analysis include • Determinants • Challenges • Voiced Impacts • Potential Risks • Recommendations Each team may identify other codes as they become more familiar with their data WinS for Girls

WinS for Girls Module 11: Data Analysis Developing Codes for Data Creating the Codes In Sierra Leone we created a code for “pregnancy” because girls kept on bringing up pregnancy as an issue associated with menstruation In Bolivia, we created a code for “life course transitions”because the changes that accompanied menstruation and adolescence were varied and great In all countries, we created codes for specific places, i.e. home vs. school. Another potential code for place could include “Away” neither at home nor at school but in some kind of public place WinS for Girls

WinS for Girls Module 11: Data Analysis Applying Codes to Data Applying the Codes There are many ways in which to apply the codes to your data. One way is to highlight a section to which you are applying a code. This section is currently coded for absorbent materials. WinS for Girls

WinS for Girls Module 11: Data Analysis Applying Codes to Data Applying the Codes Another way to show the code you are applying is through using a different color of text. The same section is highlighted here in green text. This section is currently coded for absorbent materials. WinS for Girls

WinS for Girls Module 11: Data Analysis Applying Codes to Data Applying the Codes However, either of these can get complicated when you want to include more than one code on a single section of text. Can you think of any other themes that apply to this section? WinS for Girls

WinS for Girls Module 11: Data Analysis Applying Codes to Data Applying the Codes Another option is to cut and paste sections into an Excel sheet You can make different sheets for different themes or codes: WASH, support, consumables, etc. This should be done for each activity in every school WinS for Girls

WinS for Girls Module 11: Data Analysis Comparing Data for Analysis Comparing data from different sources Once you have completed coding each activity from a school, you can begin to compare the themes as revealed by different participants This is where you will use triangulation by combining what you have learned through different methods, data and researchers. Your analysis should always focus on answering your research questions/objectives. Let’s revisit the idea of triangulation from Module 7 WinS for Girls

WinS for Girls Module 11: Data Analysis Triangulation Using Triangulation in Analysis Look at the quotes from different participants under a different theme to understand the school situation s) • Area of Inquiry • Objective • Method • Participant(s) • Area of Inquiry • Key Question(s) • Key Question(s) WinS for Girls WinS for Girls

WinS for Girls Module 11: Data Analysis Triangulation Using Triangulation in analysis • Objective: To understand how knowledge, social attitudes and beliefs impact girls’ experiences of menstruation Example: Focus Group Discussion with Girls; Student 2: “We do not like answering questions when we have our period. One time I had a stain but did not know. I was at the board and all the boys teased me.” WinS for Girls Images: http://www.123rf.com/

WinS for Girls Module 11: Data Analysis Triangulation Using Triangulation in analysis • Objective: To understand how knowledge, social attitudes and beliefs impact girls’ experiences of menstruation Example: Focus Group Discussion with Boys: “The girls suddenly stop hanging out with us and being our friend. We get upset. They do not tell us why so we tease them. We once teased a girl who had her period, but we did not know what it was at the time. We would not have if we had known.” WinS for Girls Images: http://www.123rf.com/

WinS for Girls Module 11: Data Analysis Triangulation Using Triangulation in analysis • Objective: To understand how knowledge, social attitudes and beliefs impact girls’ experiences of menstruation Example: In Depth Interview with Girl Student 1: I do not face any challenges. I continue to go to school every day when I have my period. I have a bag and keep extra pads inside it.” WinS for Girls Images: http://www.123rf.com/

WinS for Girls Module 11: Data Analysis Triangulation Using Triangulation in analysis • Objective: To understand how knowledge, social attitudes and beliefs impact girls’ experiences of menstruation Example: Key Informant Interview with Female Teacher: “The girls still come to class, but I know when they have their period. They sit in the back and stop participating. Sometimes they put their head down to manage the pain.” WinS for Girls Images: http://www.123rf.com/

WinS for Girls Module 11: Data Analysis Triangulation Using Triangulation in analysis • Objective: To understand how knowledge, social attitudes and beliefs impact girls’ experiences of menstruation Example: Key Informant Interview with Male Teacher: “The boys tease the girls when they have their period. I know it is because they do not understand menstruation. They should learn about this, but what can I do? I do not think I should talk about this.” WinS for Girls Images: http://www.123rf.com/

WinS for Girls Module 11: Data Analysis Triangulation You should use all the methods you are employing, along with the different perspectives of the interviewers and note-takers to develop your analysis of the school Focus Group Discussion with Girls In Depth Interviews with Girls Focus Group Discussion with Boys Key Informant Interviews with Teachers WinS for Girls Images: http://www.123rf.com/

WinS for Girls Module 11: Data Analysis Triangulation You should use all the methods you are employing, along with the different perspectives of the interviewers and note-takers to develop your analysis of the school. You should look at each school as a cohesive unit. Analyze all activities in a school together. FGD with Girls 2 1 3 FGD with Girls FGD with Girls IDI with Girls FGD with Boys IDI with Girls IDI with Girls FGD with Boys FGD with Boys KII with Teachers KII with Teachers KII with Teachers WinS for Girls Images: http://www.123rf.com/

WinS for Girls Module 11: Data Analysis Comparing Schools Analysis beyond school level Once analysis has been developed for a school, you can compare between schools in a similar region, or schools that have a similar age group, ex. all primary schools or all secondary schools. You can continue to build a database of understanding through Excel, your word documents, etc. A B WinS for Girls Images: http://www.123rf.com/

WinS for Girls Module 11: Data Analysis Comparing Schools Analysis beyond school level Once analysis has been developed for a school, you can compare between schools in a similar region, or schools that have a similar age group, ex. all primary schools or all secondary schools. You can continue to build a database of understanding through Excel, your word documents, etc. This process of comparison between schools and regions can help you to draw conclusions about the data. You may now have a sense about what a typicalexperience may be like for a girl in a certain school, district, or region. You can make some assumptions and generalize. WinS for Girls Images: http://www.123rf.com/

WinS for Girls Module 11: Data Analysis Validation Validation Once you have developed an understanding of the situation, answering your research questions, you may choose to return to your study locations to validate your results. Validate- Checking to see if your conclusions are accurate This can be accomplished through setting up a shortened focus group discussion with key participants (at least girls) and reporting to them on your understanding or conclusions about their experience. This is an opportunity for participants to clarify any questions you may have or to correct your mistakes. WinS for Girls

WinS for Girls Module 11: Data Analysis Time for Analysis Analysis for Bolivia, Philippines, Rwanda - One person working full time on each country - Only 8-10 schools per country - Analysis took 2-3 Months to complete Analysis take a very long time An Interview - Will take 1 hour to collect - 6 to 8 hours to transcribe - Several hours to review, code, memo the first time An FGD - Will take 2 hours to collect - ~2 days to transcribe - Several hours to review, code, memo the first time How many schools are you visiting? Did you schedule enough time for transcription? Did you schedule enough time for analysis? How many people are on the analysis team? WinS for Girls

WinS for Girls Module 11: Data Analysis Questions?

WinS for Girls Module 11: Data Analysis Templates and Tools

WinS for Girls: Advocacy and Capacity Building for MHM in WinS