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This article explores the transformation in qualitative research from a “linguistic turn” to a “pictorial turn,” emphasizing the growing role of visual data (e.g., still photos, videos, emojis) in reflecting social realities. It defines Visual Research Methods (VRM) as the use of visual materials to generate evidence for social science research questions, offering a more authentic and richer insight into participants’ emotions and experiences. The paper provides a detailed presentation of Photo Elicitation and Visual Content Analysis (VCA).
Specifically, it illustrates how to effectively use the qualitative analysis software webQDA to organize, describe, interpret, and code visual data (e.g., photos, videos). Furthermore, the article discusses the importance of clearly articulating the rationale, data type, origin, and analytical approach for visual data (the 3W1H framework: Why, What, Where, How) to enhance research credibility, as well as the ethical considerations when using images. The objective is to provide a practical framework for effectively integrating visual methods into research, thereby enriching data analysis and yielding new insights.
Introduction
The modern world is witnessing a “pictorial turn,” a shift from the “linguistic turn,” where symbols and images play a central role in communication and social construction. We are living in an economy based on experiences, emotions, and sensations, in which images have become a core element. The development of the internet and social media has propelled the prevalence of new types of visual data, from still photos and videos to emojis, which have been likened to the “body language of the digital age.”
In this context, qualitative research methodologies must adapt and effectively leverage these types of visual data to capture social reality more comprehensively. The use of visual data has the potential to yield deep and authentic insights that purely textual data might miss. This article aims to explore visual methods in qualitative research and to illustrate how visual data can be analyzed systematically, with a particular focus on the use of the qualitative analysis software webQDA. The goal is to provide a practical guide for the effective integration of visual methods, thereby enhancing the quality and credibility of social science research.
Theoretical Overview and Conceptual Framework
Visual Research Methods (VRM)
Visual Research Methods (VRM) are defined as methods that utilize visual materials of some kind as part of the evidence-generation process to answer social science research questions. VRM offers a more authentic and rich perspective, helping participants express emotions and experiences more naturally and diversely.
The history of VRM began in the 1960s in the field of anthropology with the work of Collier, who used photographs in his research. Subsequently, Harper introduced the use of photos into visual sociology, particularly with the photo elicitation method. Since the 1990s and the early 21st century, with the contributions of scholars like Gillian Rose and Banks, VRM has gained wider acceptance and has been extensively studied across the social sciences.
The Importance of Visual Data in Qualitative Research
Adds Value and New Perspectives: Visual data can bring new value and fresh perspectives to qualitative research, especially in studying phenomena within their natural contexts.
Reveals Invisible Information: Images can help reveal intangible aspects of information, such as emotions and personal experiences, which words sometimes cannot fully express.
Encourages Creative Participation: The use of visual data can encourage participants to engage with the research more creatively, fostering a different approach.
Enriches Data: Combining images with textual data (e.g., interviews) can enrich the dataset, either reinforcing findings or providing novel insights.
Reflects the Visual World: We live in a world of images and visual culture, where social media is saturated with visual information. Therefore, studying these images is essential for a deeper understanding of society.
Questions to Ask When Using Visual Data (3W1H)
To ensure the transparency and credibility of research using visual data, the following questions must be clearly answered:
Why: Why is visual data being used? What new value or perspective does it bring to the research?
What: What type of images are being used (still photos, videos, drawings, emojis, cartoons)?
Where: What is the origin of the images (researcher-generated, participant-generated, or from existing sources like the internet/social media)?
How: How will the images be used and analyzed?
The Role of Visual Elements in Research
Visual elements can play various roles in qualitative research:
Image as a Stimulus: Using images to evoke thoughts and emotions from participants (e.g., photo elicitation).
Image as a Methodological Source: Creating distinct research methods such as Reflexive Photography, Photovoice, Photo Elicitation, or Photo Diary.
Image as Primary Data: Using images as the primary corpus for analysis (visual data as corpus), or to supplement textual data. When using images as data, it is necessary to identify appropriate analytical methods and techniques (e.g., content analysis, semiotic analysis).
Specific Visual Methods
Photo Elicitation
Definition: This method involves inserting a photograph into a research interview. The photo can be taken by the participant or provided by the researcher, but a clear justification for the choice of image is required.
Applications and Benefits:
Helps participants express intangible information, especially regarding emotions and experiences.
Encourages more creative engagement from participants.
When combined with interviews, this method can enrich the collected data.
Example in Tourism Research: Photo elicitation has been used to explore tourists’ experiences, where travelers are asked to take photos and are subsequently interviewed based on them.
Reflexive Photography and Photovoice
Reflexive Photography: An exercise that can be applied in a classroom setting, requiring students to take pictures of certain concepts (e.g., travel experiences, satisfaction) and then discuss them based on their photos.
Photovoice: This method “gives voice to the pictures” and was initially used extensively in the fields of health and education.
Visual Content Analysis (VCA)
Definition: VCA is the application of traditional content analysis techniques to visual data. It allows us to make valid and reliable inferences from visual data.
Steps for Conducting VCA:
1. Initial Question: Formulate a clear research question. One must ask whether analyzing visual data will be useful, offer a new perspective, or add value to the research.
2. Literature Review: Review articles that have used content analysis for visual data to justify your methodology.
3. Identify Image Source: Decide on the origin of the images (researcher, participant, or available sources like the internet/social media, e.g., TripAdvisor). Clearly explain the reason for this choice.
4. Sampling: Determine the representativeness of the source, the type of sampling (random, stratified, cluster, convenience), and the number of images. For example, in a study on lake tourism, the author sampled photos from the “Lake Lubbers” portal based on geographical and convenience criteria.
5. Coding Process: Explain in detail how you will analyze and code the images.
Approaches:
Quantitative: Focuses on quantifying elements that appear in the images (e.g., counting the number of times a blue sky appears).
Qualitative: Involves describing and interpreting the images qualitatively. Qualitative software like webQDA supports this by allowing for description, interpretation, and subsequent coding of those descriptions.
Ethical Considerations
Ethics are a critical aspect of using visual data, especially when human images are involved.
Consent and Anonymity: If faces are present in photos, consideration should be given to not showing them or blurring them to protect individuals’ identities. In some countries, like Brazil, research projects require approval from an ethics committee.
Public vs. Private Sources: There are differing opinions on the use of public images from social media (e.g., Facebook, TripAdvisor). Some argue they can be used because they are public, while others advise caution. To minimize ethical risks, it is best to obtain permission from the organizer or owner to use the images. For example, when using cartoons in research, permission should be sought from the competition organizers.
Analyzing Visual Data with webQDA Software
webQDA is a cloud-based qualitative analysis software that effectively supports the organization and analysis of visual data, especially for VCA.
Functions and Workflow
Project Creation and Management: Users can create new projects within the software. With its cloud-based collaboration feature, webQDA allows projects to be shared with colleagues, facilitating effective teamwork.
Importing Visual Data:
Still Images: Images can be imported directly from a computer or the cloud. Common formats like JPEG and PNG are supported, with a maximum size of 5MB per image.
Videos: Videos can be linked directly from platforms like YouTube or Vimeo, or downloaded and imported into the software. Downloading is recommended to prevent issues if the video is removed from its original platform.
Describing and Interpreting Images/Videos:
After import, users can view each image or video. For videos, it is possible to pause at specific segments for description.
The software allows for describing the content and interpreting its meaning through text boxes or memos directly linked to the image.
Coding:
Information Classification: After describing and interpreting, users can code this information into pre-established or data-driven categories and subcategories.
Approaches: A deductive approach (using a pre-existing coding structure based on theory) or an inductive approach (developing codes from the data itself) can be used.
Data Integration: webQDA allows for the coding of both textual data (e.g., interviews, TripAdvisor comments) and the descriptions/interpretations from images. This is highly useful for comparing and reinforcing data or for discovering new insights that visual data provides compared to textual data.
Generating Outputs: The software can generate various types of reports and visual representations:
PDF Reports: Create detailed reports from the coding system.
Code Map: Display relationships between codes, helping to visualize the coding structure.
Word Cloud: Generate a cloud from frequently occurring keywords in the image descriptions or coded text data.
Auto-coding: webQDA has an auto-coding feature; however, it primarily applies to textual data from Excel files, not directly to images or videos.
Application Examples
Lake Tourism Study: In a study on lake tourism, the author used webQDA to analyze photos and describe occurring elements such as blue skies, riparian vegetation, mountains, and water color. The images added value to the research by illustrating the relationship between the water and the sky, which tourists had mentioned in the textual data.
Iran Destination Study: Another example involved analyzing major attractions in Iran using photos and comments from TripAdvisor. Photos of museums showed musicians playing traditional instruments, providing a better understanding of the tourist experience. The coding system included categories such as emotions, sensory perception, intellectual understanding, active participation, and memory, facilitating a deep analysis of these experiences.
Conclusion
Qualitative research is undergoing a significant transformation where the integration of visual methods is not just a trend but a necessity for fully grasping the complexity of social reality. Methods such as photo elicitation and visual content analysis, when applied systematically, can yield profound and authentic insights into the emotions, experiences, and perspectives of participants.
Việc sử dụng phần mềm phân tích định tính như webQDA cung cấp một công cụ mạnh mẽ để tổ chức, mô tả, diễn giải và mã hóa dữ liệu hình ảnh một cách hiệu quả. Khả năng kết hợp phân tích dữ liệu hình ảnh và văn bản của webQDA giúp củng cố kết quả và phát hiện những khía cạnh mới của hiện tượng nghiên cứu.
Để đảm bảo tính minh bạch và độ tin cậy, các nhà nghiên cứu cần tuân thủ nguyên tắc 3W1H, giải thích rõ ràng “tại sao, loại gì, ở đâu và như thế nào” khi sử dụng dữ liệu hình ảnh. Đồng thời, các cân nhắc về đạo đức, đặc biệt là về quyền riêng tư và sự đồng ý, phải luôn được ưu tiên. Mặc dù VRM vẫn đang trong giai đoạn phát triển và tìm kiếm sự công nhận đầy đủ trong giới khoa học, tiềm năng của nó trong việc làm phong phú nghiên cứu định tính là không thể phủ nhận.
