Gender-Responsive Crop Breeding Course, Part 1

GREAT Gender-Responsive Crop Breeding Course, Part 1

Gender responsive qualitative data analysis: Principles and practices

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Session learning outcomes

By the end of the session, participants should be able to:

• Explain the key principles underpinning qualitative data analysis and apply them to a selected case study and own data sets
• Identify the key steps in qualitative data analysis and apply them in a selected case study and own data sets

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Session contribution to the course learning objectives

• Able to articulate the concepts and principles of gender responsive research
• Demonstrated positive practice and value for gender responsive research
• Able to conceptualise, design and plan appropriate gender-responsive research
• Able to collect, analyze, interpret and integrate qualitative and quantitative sex disaggregated data

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Session outline

• Introduction
• Process of qualitative data analysis
• Preliminary analysis in the field 
• Examples of good and poor qualitative data sets
• Analysis after the field: Step-by-step process using own data or case study data provided  
• Watching a video clip on qualitative data analysis
• Take home message

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GREAT Gender-Responsive Crop Breeding Course, Part 1

DAY 1

GREAT Gender-Responsive Crop Breeding Course, Part 1

Introduction

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• Qualitative data analysis: “a process of systematically arranging and searching the interview transcripts, field notes, and other data to enable you come up with findings” (Bogdan and Biklen 2007, p.159)

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Introduction…cont’d

• The qualitative data analysis process:

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• Entails transcribing, organizing data, breaking it up into manageable units, coding and synthesizing them, and searching for:

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• Themes
• Patterns
• Connections
• Explanations

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• Involves interpreting data to capture and reveal people’s experiences and meanings attached to those experiences in a particular context

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Introduction… cont’d

• Data interpretation involves:
• Developing ideas about your findings and relating them to literature and broader context, concepts

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• Explaining and framing your ideas in relation to theory, other scholarship, action—making your findings understandable

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Introduction… cont’d

• A gender responsive study requires a gender analysis
• Gender analysis refers to critical examination (qualitatively and/or quantitatively) of:
• Gender roles, needs, constraints, opportunities, rights and entitlements for women, men, girls and boys in particular situations or contexts (bear in mind relevant intersecting variables).
• Understanding these through lit review or preliminary data collection

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GREAT Gender-Responsive Crop Breeding Course, Part 1

• Gender-based Opportunities

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• = gender relations (in different domains) that facilitate men’s or women’s access to resources or opportunities of any type.

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• Gender-based Constraints

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• = gender relations (in different domains) that inhibit men’s or women’s access to resources or opportunities of any type.

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Introduction… cont’d

Gender Analysis reveals …

GREAT Gender-Responsive Crop Breeding Course, Part 1

Introduction…cont’d

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• While quantitative data analysis may reveal “how much,” or “magnitude” of the issue being investigated,

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• It does not bring out the deeper meaning of events and experiences regarding the issue: i.e “how things happen” and “why they happen the way they do”

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Example 1: A study by Olaosebikan et al.(2017) on cassava trait preferences of men and women farmers in Nigeria quotes:

• “Women dominate (75%) the food-processing and marketing sectors; men dominate (95%) the commercial sale of cassava stems” (Ilona et al. 2017).

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• While this is good, it masks the realities (of men and women) behind the percentages.

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• One should be able to ask why men dominate marketing and women food processing-and other variables that intersect with gender.

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Example 2 : A study by Isaac et. el. (2017) on farmer culinary testing and grain (sorghum) quality in Mali

• Found that:
• “local varieties such as ‘Seguetana’, or the new variety ‘Lampe’, had lower amounts of bran (14% and 11%, respectively), whereas new varieties ‘Peke’ and ‘Kossa’ had over 31%”. These varieties with higher amounts of bran were not appreciated by women...”

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Why did women not appreciate varieties with higher amounts of bran?

“...because they consider the actual yield of the grain after decortication, a different measure than the yield data collected at harvest time and after threshing”.

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Example 2… cont’d

• “Even varieties with a midrange of yield loss from bran, such as ‘Djeleba’ (21%), were not liked by women”.

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• Why did women not like these varieties ?

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• “Of ‘Djeleba’, one group said, “It is easy to decorticate, but it breaks and gives a lot of bran.” Discussions also revealed that if the grain breaks too much during decortification, it is not appreciated because the chafe sticks to the broken grain, making it difficult to remove or produce flour, thus reducing the final food yield”.

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• This examples brings a clear reality of women’s experiences for breeders to consider.

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Caution!

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• Unlike the analysis of quantitative data, there are few well-established and widely accepted rules for analysing qualitative data.

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Qualitative Data Analysis Process: Key Steps

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Introduction

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• This presentation focuses on the key steps applicable to common social science analytical approaches e.g content analysis, thematic analysis and grounded theory

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GREAT Gender-Responsive Crop Breeding Course, Part 1

The process of qualitative data analysis

• Starts in the field and continues after the field

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• Iterative and reflexive process - not linear

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Analysis during the field: Essentials

• Read the field notes to identify what is emerging from the data: Identifying points that relate to the research question as they emerge (confirming or surprising ideas) and following up on those points with subsequent data collection

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• Organising data and labeling: Check whether appropriate labeling has been done - audio recordings or field notes appropriately (names, age, sex and geographical location)

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• Data cleaning: Follow-up of unclear responses, concepts to fill the gaps; check spellings, incomplete statements

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Analysis during the field… cont’d

• Documentation: Documenting audio recordings and any typed field notes using agreed format for easy retrieval
• Memoing/keeping a journal
• Memoing is the act of recording reflective notes (comments and reflections) about what the researcher is learning from the data.
• Can be a few sentences or pages
• One is able to capture emerging idea on possible interesting answers to your research question; pointers to the literature that informs the study; probing areas; insights and surprises

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Analysis during the field… cont’d

• Memoing is a critical aspect for understanding one’s data, and a foundation for effectively analyzing qualitative data and writing your results
• Refining tools where necessary

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Exercise 1: Preliminary analysis in the field

• Select one of your field transcripts and assume they are your field notes.
• Carry out preliminary analysis in the field by addressing the following:
1. Read the field notes to identify what is emerging from the data
2. What changes would you make to the tools to inform subsequent data collection
3. Organise and label the field notes
4. Clean the notes (Time 30 min)

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Remember!

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• The principles of qualitative data collection discussed during week one: In-depth/rich data, iterative process, reflexivity, multiple views and meanings, context etc must have been applied for the analysis to be meaningful!

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• Good data facilitates good analysis & credibility

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Exercise 2: Characteristics of good qualitative data

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• Reflect on your qualitative data sets and comment on whether:

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• Your data is of good quality or not and why?

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• Time: 30 min

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Recap: Characteristics of good qualitative data… cont’d

• Detailed data
• Collected verbatim: as said by participants without paraphrasing
• Relevant to the research question/adequately answers the question
• Sex disaggregated
• Well labeled to enable tracking source
• Different sources e.g women, men, key informants, opinion leaders etc. to enable triangulation

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Analysis after the field: Step-by-step process

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Hypotheses or new research questions

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Findings

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Reports and publications

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From “raw” to “cooked” data: An Overview of the 4Cs

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• Cleaning up, copying, and cataloging

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• Coding in categories

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• Classifying categories into clusters (themes)

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• Constructing relationships

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GREAT Gender-Responsive Crop Breeding Course, Part 1

The 4 C's focus on the following steps

A step-by-step process to qualitative data analysis

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• Transcription
• Data cleaning
• Reviewing
• Organizing
• Coding
• Categorising/themes
• Patterns/relationships
• Interpretation
• Reporting

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Iterative and reflexive analytical process

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Dey (2005) summaries qualitative data analysis as describing, connecting and classifying which is done in a circular process (see figure below)

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Step 1: Transcription

• Transcribe the interviews verbatim. Do not paraphrase responses.
• Include all background details of the respondent and interviewer, e.g.:
• Location: Maulkon Boma
• Payam: Awiel East
• State: Northern Bahr el Ghazal
• Names of respondents: Halima Yuan
• Interviewer and note taker: Peace Musiimenta
• Date: 17^th June 2015
• Time: 9:00 AM

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GREAT Gender-Responsive Crop Breeding Course, Part 1

A sample Transcript

I: Among men, women, boys and girls, in a household who owns the resources?

R: In terms of controlling the granary and foodstuffs, the women are in charge but [laughs] if the man finds that his wife is careless or a drunkard the man takes full charge including the granary. [Phone rings and respondents stops talking…. 5 minutes]. Where was I? Oh, in terms of big resources such as cows, goats, houses, produce in large amounts are controlled by men [laughs]. In some cases, a man can give a cow or two to his wife to take care of, look after and feed it, however, the man still has a right to ask for the same cow and its offspring in case he wants to marry more wives. For me I encouraged my wife to have goats and I have let her control and she can sell [them] as hers; even when she sells sorghum I do not ask her to bring the money but she can use it for the home. So women have access to and not control ……and…… But on land, it is still hard to convince older men. However, there are some exceptions for example if women inherit lands from her maiden home she can be in charge

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Step 2: Data cleaning

• Review and finalize your field notes from interviews and observations

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• Responses from participants in their own words
• Record information about the context, including location, social status of respondents, behaviors during the interviewing process

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• Label and organize any voice recordings on the recorder, e.g., according to the data source, respondent name or title - like 'KII - Agricultural Extension Officer'

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Step 3: Reviewing

Get to know your data

• This involves reading and re-reading your transcripts to understand your data

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• The more you understand the data, the easier they become for analysis

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Exercise 3: Review interview excerpts of your transcripts or shared case study data

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Read some interview extracts from three of your transcripts (based on different methods used e.g FGDs, KIIs, IDI) or sample extracts provided to familiarize with the data.

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Time: 30min

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Step 4: Organising data

Data can be organised in different ways

• By question: Putting together all data under a particular question, by participant category
• By topic: Putting together all data under a particular topic, by participant category
• By case: E.g., one family, individual, or group

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Organising data…cont’d

Based on the reviewed extracts in exercise 3 and the above example, organise the data by question or topic

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1hr 20min

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Template for organising data

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End of day task: Analysis of own data sets

Use your qualitative data sets (transcripts) to apply the analytical steps covered in the session

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(Teams continue to analyse their data beyond the session time)

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DAY 2

GREAT Gender-Responsive Crop Breeding Course, Part 1

Step 5: Coding

Definition of coding

• Entails assigning labels using symbols, words, abbreviations, phrases, or colors to the data/text
• “Coding categories are the means of sorting the descriptive data so that the material bearing on a given topic can be physically separated from other data”. (Bogdan and Biklen 2007, p.173)
• Coding is time consuming and labour-intensive but it is the heart of qualitative analysis!

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Coding… cont’d

Coding in qualitative research means:

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• The analyst keeps assigning labels to text until all the data is assigned & no new labels emerge

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• Reading and re-reading the text helps to ensure that all the data is well coded

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Coding considerations

• Code as soon as possible e.g collect, transcribe, code your data as you go instead of leaving it till all data is in (grounded theory suggests).

Why?

• It sharpens understanding of your data & helps with theoretical sampling—the new concepts guide selection of interviewees to expound/confirm them
• To alleviate the feeling of being swamped by your data if you defer analysis entirely until the end

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Coding process

Step 1: Read through your initial set of transcripts, field notes, documents—(read and re-read data at least twice, uninterrupted)

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Why? Interruptions hinder you from getting a sense of the totality of your data

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“As you read through your data, certain words, phrases, patterns of behavior, respondents’ way of thinking, and events repeat and stand out”. (Bogdan and Biklen 2007, p. 173)

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Coding process… cont’d

• While you are reading search for regularities & patterns; topics that your data cover(Jot down)
• Words and phrases to represent these topics and patterns. These are the preliminary coding categories.
• Ideas and diagrams that sketch out relationships you notice
• Unfamiliar phrases or words—this special vocabulary may specify aspects unique to this setting which you need to explore.
• What struck you as especially interesting, important, or significant, e.g key words used by your respondents, names that you give to themes in the data.
• Begin to generate an index of terms that will help you to interpret and theorize in relation to your data.

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Coding process… cont’d

• Step 2: Review your list of preliminary codes.
• Are you using two or more words or phrases to describe the same phenomenon? If so, remove one of them.
• Do some of your codes relate to concepts and categories in the existing literature? If so, might it be sensible to use these instead?
• Can you see any connections between the codes? Is there some evidence that respondents believe that one thing tends to be associated with or caused by something else? If so, how do you characterize and therefore code these connections?

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Coding process… cont’d

Step 3: Reflect on connections between codes and known theory.

• Consider more general theoretical ideas in relation to codes and data.
• Begin to generate some general theoretical ideas about your data.
• Try to outline connections between concepts and categories you are developing.
• Consider in more detail how they relate to the existing literature.
• Develop hypotheses about the linkages you are making and go back to your data to see if they can be confirmed.

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Remember!

• Any one item or slice of data can and often should be coded in more than one way.

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• Keep on assigning labels to text until all the data is assigned & no new labels emerge

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Exercise 5: Practicing coding

• After organising your data by question or topic, follow the coding steps above and code the 2 case study excerpts provided or selected from your own data set.

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• Assign relevant labels/codes to the text – You can colour or underline texts & assign labels/codes etc...

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• As you create codes, develop a code sheet (a list of words/labels attached to the text and their meanings)
• Write at least one memo
• Note any insights on which you will collect more data
• Anything else?...(Be creative)

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• Time: 1 hour 20 min

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Coding example

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GREAT Gender-Responsive Crop Breeding Course, Part 1

An example of a code sheet

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After coding my data: what next?

• Look for patterns or regularities that occur
• Within each code, look for data units that illustrate or describe the situation you are interested in.
• Look for statements that not only support your theories, but also refute them.
• Try to build a comprehensive picture of the topic.

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Step 6: Creating categories/themes

• What is a theme?
• It is a category identified by the analyst in his/her data:
• relates to his/her research focus/research questions

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• emanates from codes identified in transcripts and/or field notes

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Themes/subthemes/categories/sub categories

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• Different codes form categories/themes & subcategories/sub-themes

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• Grouped themes/subthemes form super themes or categories

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• The entire analysis should be meaning focused and related to the research question/s

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Exercise 6: Identifying categories/themes from the coded data

• Review your codes in exercise 4 and identify some emerging themes
• Reflect on the initial codes that have been generated to gain a sense of the continuities and linkages between them.

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Time: 1 hr 20 min

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Example: Emerging categories/themes

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Step 7: Identifying patterns & connections

Key themes lead to identification of patterns and connections

• Patterns: The story-line developing in the data/what the identified themes allude to

E.g.: One may start to see that women tend not to be invited for variety dissemination meetings due to cultural barriers associated with women’s mobility, husband permissions and perceptions and stereotypes among extension workers about women not often attending meetings

• NB: Look out for variations in the patterns for different geographical contexts!)
• Connections: Show how emerging themes are interlinked to one another e.g., men’s authority over land explains women’s limited rights to own resources

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Step 8: Interpretation

• Bringing out the ‘big picture’ and creating meaning out of the patterns and connections

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Note:

• Avoid ‘data dumping’ – Scattering verbatim quotes in the report without a thorough analysis.

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Interpretation… cont’d

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Quotations should:

• Align with relevant themes to give meaning to the theme
• Support/illuminate relevant themes making them more authentic/alive /credible using the voices of the respondents.
• Show cases or instances that are unusual/unexpected.

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Interpretation… cont’d

• Identify key lessons, new insights (interesting & surprising findings) & implications
• Reflect on whether your findings answer your research question
• Reflect on the significance of your themes, patterns for the lives of the people you are studying
• Forge interconnections between themes and theory
• Reflect on the overall importance of your findings for the research questions and the research literature that have driven your data collection

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Step 9: Reporting the findings

• Have a format for reporting the findings e.g. visual display like matrices, boxes, description etc. - be innovative

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• Include verbatim quotes to illustrate the points and bring data to life (but use quotes selectively – use only those that support an argument)

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• Confidentiality and anonymity are important when using quotes - can use pseudonyms
• Get people's permission to use their words (informed consent)

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Reporting the findings… cont’d

• Qualitative findings are context specific - but can inform studies in contexts with similar characteristics and lead to new inquiries
• Read Bogdan and Biklen 2007, p.198-218 for more information on this

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Ensuring trustworthiness of the analysis

• Holloway & Wheeler (2009) summarise the means by which you can try to ensure the trustworthiness of your data including:

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• Member Validation − asking those being investigated to judge the analysis and interpretation themselves, by providing them with a summary of the analysis & asking them to critically comment upon the adequacy of the findings.

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Ensuring trustworthiness of the analysis…cont’d

• Searching for negative cases and alternative explanations – Interpretation should not focus on identifying only cases to support the researcher’s ideas or explanations, but to also identify and explain cases that contradict.

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• Triangulation – Combining the analysis with findings from different data sources is useful as a means to demonstrate trustworthiness in the analysis.

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Ensuring trustworthiness of the analysis…cont’d

• The audit trail – To ensure reliability all research should have an audit trail by which others are able to judge the process through which the research has been conducted, and the key decisions that have informed the research process.

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• Reflexivity – Means that researchers critically reflect on their own role within the whole of the data collection process, and demonstrate an awareness of this, and how it may have influenced findings, to the reader.

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Video clip: A summary of qual. analytical process

Lofgren, K. Qualitative Analysis of Interview Data: A Step by Step Guide. https://www.youtube.com/watch?v=DRL4PF2u9XA

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10 min

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Computer packages for qualitative data analysis: An overview

• There are computer packages that aid qualitative data analysis - esp. large chunks of data. Examples of the software packages:
• Atlas.ti
• NVivo
• NUdist
• Quirkos
• Computers may assist in organising…data but only the intelligence, creativity, and reflexivity of the researcher bring meaning to the data (Hatch 2002).
• Read Bogdan and Biklen 2007, P.187-191

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Tips

A good qualitative researcher should have:

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• Analytical skills – ability to capture emerging ideas and attach meaning

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• Creativity and innovation – e.g., having a clear format for organising raw data, format for data display, writing style of the findings

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• Integrative skills – how to make sense of data from different sources, e.g., KII, FGD, personal interviews and harmonise into a coherent piece.

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Take home message

• Good data is important for good analysis
• Expertise is important – this comes with practice.
• Data analysis is time consuming – allocate enough time for the process
• Know your data deeply
• Allocate enough resources for the activity – expertise an field work

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End of day task: Analysis of own data sets

Continue to practice coding and identifying themes during your free time and do the following:

• Code your transcripts and identify key themes emerging from the data.
• Share with us the products (coded data and identified themes)
• Share your products with Trainers for feedback.

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Thank you!

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GREAT Gender-Responsive Crop Breeding Course, Part 1

Citations

Basit, N.T. (2003) Manual or electronic? The role of coding in qualitative data analysis. Educational Research, Vol. 45 No. 2 pp. 143–154.

Bogdan, R.C. and Biklen, S.K.(2007) Qualitative Research for Education: An Introduction to Theory and Methods. 5th Edition, Allyn & Bacon, Boston, USA

Bryman, A. (2012) Social Research methods. Oxford University Press, Oxford, UK

Dey, I. (2005) Qualitative Data Analysis: A user-friendly guide for social scientists Routledge, Taylor and Francis group. London and New York.

Gibbs, G. (2009) Analysing Qualitative Data [16paragraphs]. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 11(3), Art. 4, Available at: (https://www.zapmeta.ws/ws?q=analysis%20of%20qualitative%20data&asid=ws_gc11_10&mt=b&nw=g&de=c&ap=1o1) (accessed 10th June, 2016)

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Citations

Hutch J.M. (2002) Doing qualitative research in education settings. State University of New York. New York

Lecompte, M. and Schensul, J (1999) Analyzing and Interpreting Ethnographic Data. Walnut Creek: Sage Publications: Walnut Creek

Lofgren, K. (undated). Qualitative Analysis of Interview Data: A Step by Step Guide. Available at https://www.youtube.com/watch?v=DRL4PF2u9XA (accessed 10th July, 2017)

Taylor-Powell, E and Renner, M. (2003) Analysing qualitative data. University of Wisconsin-Madison, USA, available at: https://learningstore.uwex.edu/assets/pdfs/g3658-12.pdf, (accessed 10th June, 2016)

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GREAT Gender-Responsive Crop Breeding Course, Part 1