































   Advancements in Agricultural Development 
  Volume 5, Issue 2, 2024 
  agdevresearch.org 

 

1. Richie Roberts, Associate Professor and Graduate Coordinator, Louisiana State University, 131 JC Miller Hall, Louisiana 

State University, Baton Rouge, LA 70803, roberts@lsu.edu,  https://orcid.org/0000-0002-2993-4945 
 

119 

 

Visual Q Methodology: A Methodological Approach to 
Empower Marginalized Populations in Agriculture Throughout 

the Global South 
 

R. Roberts1  
 

 
Article History 
Received: June 2, 2023 
Accepted: July 31, 2023 
Published: January 31, 2024 
 
 
Keywords 
factor analysis; images; research 
methodology; visual analysis 
  

Abstract 
Ensuring that marginalized populations become empowered in 
agriculture is vital to the success of global agricultural development goals. 
However, these populations have reported lacking access, power, and 
voice. Perhaps one strategy researchers can use to address this issue is 
through visual Q methodology. In this methodological paper, I argue that 
by combining the tenets of Q and visual methodologies, researchers can 
offer empirically grounded findings that evoke powerful, rich insight into 
the perspectives of marginalized populations in agriculture who may lack 
the communication skills to articulate their perspectives through words. 
To this point, however, the approach has lacked clear guidance, which 
has led to diminished quality in the published literature on visual Q 
methodology. In response, I offer six principles to guide visual Q 
methodological studies moving forward: (a) relationship-building with 
participants, (b) participant training, (c) concourse development, (d) Q set 
sampling, (e) data collection, and (f) data analysis and interpretation.  

 

mailto:roberts@lsu.edu
https://orcid.org/0000-0002-2993-4945


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   120 
 

Introduction, Rationale, and Statement of Purpose 
 
The literature on global agricultural development has been dominated by research that has 
focused primarily on using quantitative data to demonstrate the impact of various strategies 
and interventions (Meinzen-Dick et al., 2019). However, change regarding the methodological 
approaches used to address research issues and problems in agricultural development has 
begun to emerge. For example, Rubin (2016) argued that methodological transformation was 
underway as researchers increasingly began to use non-traditional data sources such as 
drawings, images, and other visual representations to demonstrate the outcomes and impacts 
of development approaches in agriculture. In this article, I aim to contribute to the new wave of 
methodological innovation occurring in the agricultural development literature by providing a 
rationale for using visual Q methodology to empower marginalized populations in the Global 
South who often lack the communication skills to express their attitudes, beliefs, and 
perspectives.  

On this point, researchers in neuroscience (Azizan et al., 2006; Schlochtermeier et al., 2013) 
have reported that humans can process and comprehend image-based information better than 
textual-based. Further, images elicited a statistically significant difference regarding individuals’ 
emotional responses compared to when the information was presented in a textual-based 
format (Azizan et al., 2006; Schlochtermeier et al., 2013). After reporting similar findings, Kiefer 
and Pulvermüller (2012) concluded that image-based information helped stimulate the 
affective domain of learning, which helped the participants relate to the material more 
intimately. Therefore, the use of images in research has the potential to stimulate greater 
understanding— a key rationale for the use of visual Q methodology for research on global 
agricultural development.  

Individuals often interpret images based on what they know (Machin & Ledin, 2018). As such, 
images can serve as a way to amplify the lived experiences of marginalized populations in 
agriculture by providing a glimpse into the ways their voices, sociocultural complexities, and 
indigenous ways of knowing have been muted (Rose, 2022). Therefore, images can evoke 
understanding that may be lost in textual-based communication. Images can also be used to 
allow individuals to reframe their experiences in ways that can challenge, disrupt, and create 
change (Machin & Ledin, 2018). Or, as Weber (2008) explained, “images provoke action for 
social justice” (p. 46).  

Although visual Q methodology has not been used extensively, with greater exposure, it could 
be used to answer research questions relevant to global agricultural development. To begin, I 
provide a description of Q methodology followed by an overview of the use of visual 
approaches in social science research. Thereafter, I provide a rationale for combining the two 
methodologies and describe the product of its application through examples. Finally, I address 
the inherent limitations of visual Q methodology while charting possibilities for the approach’s 
use in research on global agricultural development. 

 

https://doi.org/10.37433/aad.v5i2.359


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   121 
 

Q Methodology 
 
William Stephenson (1953) introduced the idea of Q methodology, often referred to as simply 
Q, in the 1930s and later provided a more nuanced perspective of the approach in his seminal 
work The Study of Behavior: Q Technique and its Methodology. Similar to other approaches, Q 
has been defined differently based on scholars’ epistemological and ontological worldviews 
(Watts & Stenner, 2013). However, at its most basic level, Q can be understood as the study of 
individuals’ attitudes, beliefs, opinions, or perspectives. Therefore, Q’s best applications occur 
when researchers desire to understand a group’s collective points of view, articulate similarities 
and differences in perspectives, or reject the mind-body dualism, i.e., that an individual’s 
internal sphere can be accessed by measuring their behavior (Brown, 1980).  

Although traditional quantitative research collects and analyzes data about a phenomenon 
through numeric data, Q challenges participants to communicate their subjective perspectives 
using self-referent meanings (Brown, 2000). To accomplish such, Q draws on the philosophical 
underpinnings of both quantitative and qualitative paradigms (Watts & Stenner, 2013). 
However, it should be noted that Q is not purely either; instead, it is a paradigm in its own right 
(Stephenson, 1953). It is through this conceptualization that global agricultural development 
scholars should engage with Q and begin to understand its possibilities.  

A major tenet of Q methodology is concourse development (Brown, 1980). A concourse in Q is 
the full range of perspectives on a phenomenon in the form of statements. Each Q investigation 
will generate a unique number of statements during this phase. For example, Rampold et al. 
(2020) reported creating 235 unique statements during the concourse development phase, 
while Roberts et al. (2020a) generated 154 statements. The number of statements in both 
studies was then reduced to 36 to mitigate redundancy and participant fatigue during the 
sorting process (Rampold et al., 2020; Roberts et al., 2020a). 

To operationalize the concourse, Q methodology can be facilitated through a novel procedure, 
called a Q-sort, which allows individuals to arrange their views in the form of statements onto a 
forced distribution printed on a physical board (McKeown & Thomas, 2013). This process begins 
when the researcher poses a condition of instruction, or a framing question, by which 
participants sort statements reflecting their views onto a forced distribution. After collecting 
each participant’s Q-sort, researchers can generate a correlation matrix based on each 
participant’s ranking of the statements, which helps facilitate a factor analysis. A key difference 
between traditional factor analysis used in quantitative research and the process used in Q is 
that researchers do not correlate an instrument’s items. Instead, individual sorts are correlated 
(Brown, 1980). Through the use of this approach, factors emerge that allow researchers to 
examine similar and disagreeing perspectives. As such, Q can promote the expression of a 
sample’s views on a phenomenon while providing a flexible approach to allow findings to 
emerge.  

It should be noted that a central tenet of Q is the use of purposeful sampling to identify 
individuals who hold diverse perspectives (Watts & Stenner, 2013). Consequently, small sample 

https://doi.org/10.37433/aad.v5i2.359


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   122 
 

sizes are appropriate in Q because participants’ “observational perspectives are their own” 
(Roberts & Montgomery, 2017, p. 154). Other unique aspects of Q are that validity and 
reliability have not historically been significant concerns for this methodological approach 
(McKeown & Thomas, 2013). As an illustration, in quantitative research, Creswell (2012) 
explained that reliability represented the accuracy of instruments to produce consistent results. 
However, in Q methodology, researchers place more importance on the replication of similar 
findings. For example, could participants in a similar context sort the same statements, 
resulting in similar factors? Because of this, Brown (1980) argued that Q only provides insight 
into participants’ views at a particular point in time instead of being able to generalize to a 
population of interest. Therefore, this innovative research approach allows researchers to gain 
unique insights into participants’ perspectives on a phenomenon.  

Visual Methodologies 
 
Research on global agricultural development has begun using spoken and written word to 
illuminate challenges over the past few decades more prominently (Meinzen-Dick et al., 2019). 
However, words can fail to capture critical forms of communication in individuals’ lives, such as 
body movement, gestures, sounds, and the visual (Riessman, 2008). As a result, a visual turn 
has begun to emerge in the literature on global agricultural development as scholars have 
chosen to use photography and images to help tell the stories of marginalized populations who 
may lack the skills to communicate their experiences through spoken and written word 
(Meinzen-Dick et al., 2019). At the heart of visual methodologies is merging textual-based data 
and images to evoke a thick, rich description of the phenomenon (Pink, 2007).  

Photovoice, photo elicitation, and visual narratives are recent examples of this new 
methodological wave in social science research (Riessman, 2008). At their core, visual 
methodologies require researchers to engage intimately with their participants to identify 
issues and problems in a given context (Roberts et al., 2020b; Rose, 2012). Consequently, using 
visual methodologies can generate opportunities for marginalized populations to actively 
participate and critically reflect on how they have been excluded, diminished, and silenced 
(Emmison et al., 2012). Through these conscious-raising experiences, visual methodologies can 
empower participants by ensuring their voices have been heard while also strategizing realistic 
ways they could create change in their social context (Mitchell, 2011).  

To accomplish this, researchers using a visual methodological approach provide their 
participants with cameras to allow them to capture images and symbolic representations to 
allow others to “see through their eyes” (Pink, 2007, p. 5). Through this insight, visual 
methodologists (Emmison et al., 2012; Mitchell, 2011; Rose, 2012) have argued that other 
individuals can begin to understand marginalized populations’ experiences and advocate for 
change. As such, visual methodologies require participants to undertake multiple roles, such as 
change agent, investigator, and photographer (Riessman, 2008).    

 

https://doi.org/10.37433/aad.v5i2.359


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   123 
 

Visual Q Methodology 
 
Ensuring that marginalized populations become empowered in agriculture is vital to the success 
of global agricultural development (Agard & Roberts, 2020; Richardson & Roberts, 2020). 
However, these populations have reported lacking access, power, and voice in the industry 
(Richardson et al., 2023; Roberts & Edwards, 2017). Perhaps one strategy researchers can use 
to address this issue is through visual Q methodology. By combining the tenets of Q and visual 
methodologies, researchers can offer empirically-grounded findings that evoke powerful, rich 
insight into the perspectives of marginalized populations in agriculture who may lack the 
communication skills to articulate their perspectives through words.  

Although visual Q has not been widely used, some researchers have advanced the methodology 
(Brown, 1980; Kinsey, 1993; Naspetti et al., 2016; Simpson, 1989; Stephenson, 1980). Despite 
this, the literature on this approach has lacked clear guidance, which has led to a lack of quality 
in the published literature on visual Q methodology. In response, I offer six principles to guide 
visual Q methodological studies moving forward: (a) relationship-building with participants, (b) 
participant training, (c) concourse development, (d) Q set sampling, (e) data collection, and (f) 
data analysis and interpretation.  

Principle #1: Relationship-Building with Participants  
Building relationships with participants, the first principle in visual Q research is a complex and 
multifaceted process that requires careful planning, ethical considerations, and ongoing effort. 
Effective relationship-building enhances the quality of data collected and contributes to the 
overall success of the research project. Researchers must prioritize transparency, respect, and 
ethical conduct to ensure that participants' rights and well-being are upheld. 

Relationship-building with participants is perhaps the most critical phase since visual 
methodologies require a deep connection and commitment between the researcher, 
participants, and the broader community (Pink, 2007). This phase can require considerable time 
and follow-through (Mannay, 2015). When researchers have completed the appropriate work 
in this phase to build relationships, the participants will often be more open and honest about 
their experiences and perspectives (Machin & Ledin, 2018). These strong relationships can set 
the tone for the remainder of the research project (Rose, 2012). In this phase, researchers 
should use purposeful sampling to identify participants who hold a unique perspective on the 
phenomenon of interest.  

Principle #2: Participant Training   
The second principle, participant training, involves establishing group sessions by which the 
researcher explains the intent of their approach while also providing guidance to participants 
about fundamental photography and ethical considerations. This process can begin with an 
initial meeting with participants in which the researchers discuss the project’s intent and how 
they intend to cultivate an encouraging culture in which all participants can contribute and ask 
questions. These group sessions should be established early in the process to build rapport.  

https://doi.org/10.37433/aad.v5i2.359


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   124 
 

If possible, it can also be helpful to recruit a professional photographer to attend sessions so 
that they can provide insights into how to capture powerful, evocative images. The researchers 
should also be clear that all images that participants submit should be accompanied by a 
corresponding caption to help accurately convey the image’s story. Regarding ethics, the 
researchers should be clear about what should be included in photographs and what not to 
include. For example, Rose (2012) noted that many researchers using a visual methodological 
approach ask that participants not capture photographs involving human subjects. However, 
Riessman (2008) argued that including human subjects can bring deeper meaning to the study. 
Ultimately, whether or not to include human subjects, especially the faces of the subject 
depicted, will be the decision of the Institutional Review Board (IRB) and the human subject 
present in the image.  

By establishing a positive rapport with participants through regular interactions, researchers 
can create a culture of encouragement. During the relationship-building phase, researchers can 
inspire participants by sharing stories of visual Q can be used to empower marginalized 
populations. Researchers can also create a culture of encouragement by sharing 
recommendations about ways participants can tell their stories through images more 
evocatively. 

Principle #3: Concourse Development  
The most appropriate way to develop a concourse has long been debated in the Q literature 
(Lee, 2019). For example, some scholars have advocated using naturalistic approaches to 
understand a population’s view on a topic. This approach involves collecting qualitative data 
through interviews, documents, and observations to create relevant statements that represent 
the concourse. Meanwhile, other scholars (Brown, 1980; McKeown & Thomas, 2013) have 
argued that a concourse can be generated from literature reviews, existing quantitative 
instruments, social media, blogs, and the popular press. More recently, researchers have begun 
to use a blend of naturalistic and other sources to create the concourse of a study.  

In visual Q methodology, however, the participants should be asked to explore their local 
context to capture images based on the researcher’s instructions during training sessions. As 
participants capture photographs, they should be asked to journal to record their perceptions 
of the experience. Researchers should specify the number of photographs and accompanying 
captions they wish the participants to submit during this phase. Some visual Q methodological 
studies published in the literature have not used this approach; instead, the researcher(s) 
selected images they perceived were relevant to achieve the study’s purpose. If possible, I 
recommend avoiding this practice because the resulting images will likely lack context and may 
not be relevant to the participants. Therefore, participant-captured images should be 
considered the gold standard during concourse development for visual Q studies.  

Principle #4: Q Set Sampling  
Q set sampling involves the researcher refining the concourse by establishing boundaries to 
define what matters. Stephenson (1953) argued there was no single way to create a Q set: “[it] 
may be designed purely on theoretical grounds, or from naturally occurring (ecological) 

https://doi.org/10.37433/aad.v5i2.359


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   125 
 

conditions, or as required for experimental purposes, to suit the particular requirements of an 
investigation” (Stephenson, 1953, p. 223). Despite the lack of structure inherent in 
Stephenson’s (1952) position, Watts and Stenner (2012) suggested that researchers examine 
the concourse against their research question(s) to determine their relative fit. Therefore, a 
clearly defined research question can help set parameters regarding what images from the 
concourse should be mobilized for further use.  

One contemporary technique used to create the Q set is using a naturalistic approach and 
operationalizing the resulting categories as dimensions (Richardson & Ramlo, 2020). However, 
Stephenson (1978) and Sylvester (2020) advocated for using Fisher’s balanced block design (see 
Table 1) to classify the Q set—a more quantitative-oriented approach. To accomplish this, 
Stephenson (1978) opined that the components of the concourse could be categorized in the 
following way: (a) feelings—ranging from pleasure to unpleasant, (b) morality—ranging from 
positive to negative, and (c) reality—ranging from realistic to unrealistic. Creating the Q set 
through this approach would be similar to quota sampling in quantitative research by which 
researchers seeks to obtain a diverse sample regarding demographic variables. A visual Q set, 
however, is a sampling of images, and no population exists by which it could be designed or 
benchmarked. Because of this limitation, researchers could also use pre-existing theories as a 
basis for the categories by which to generate the visual Q set (Watts & Stenner, 2012).  

Table 1  
 
Fisher’s Balanced Block Design  
Causes Effects 

A. Feeling  
B. Morality  
C. Reality  

Pleasure  
(a)  
 
Positive 
(c) 
 
Realistic  
(e) 

Unpleasure  
(b)  
 
Negative  
(d)  
 
Unrealistic  
(f) 

Note. Table adapted from Stephenson (1978). 
 
One major issue that researchers often struggle with is how to know when the concourse is 
complete. On this point, Eden et al. (2005) explained that concourse development should be 
treated similarly to a qualitative analysis by which researchers stop once saturation has been 
achieved, i.e., when images and captions become repetitive and do not add new insights. 
Therefore, this element of concourse development has often been considered a judgment call 
by researchers influenced by experience and resource constraints (Eden et al., 2005). Although 
the number of images to include in the visual Q set can vary considerably, most will have 
between 20 to 60. Time and other potential challenges should also be considered when 
determining the visual Q set. Therefore, conducting a pilot study could help refine the Q set.  

https://doi.org/10.37433/aad.v5i2.359


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   126 
 

Principle #5: Data Collection  
After establishing the Q set, researchers should ask the participants to sort the images on a 
forced distribution of negative to positive integers. Brown (1980) described this process as “the 
technical means whereby data are obtained for factoring” (p. 7). The images should be sorted 
based on the condition of instruction, or a framing question the researcher poses. Table 2 
provides instructions for facilitating this process through seven steps.  

Table 2  
 
Seven Steps to Facilitate the Collecting of Data in a Visual Q Methodological Study 

Step Example Instructions 
1. Present the Condition of Instruction 

and Pre-Sorting  
• “Read each caption and sort the images into three 

piles using the question: ‘What are your thoughts on 
women’s roles in agriculture in Sub-Saharan Africa?’” 

• “Pile the images most like you on your right and least 
like you to your left. Then, put images you do not feel 
strongly about in the middle pile.” 

 
2. Initial Positive Rankings  • “Now review your most like you pile. Begin to place 

the images onto the far right columns of the forced 
distribution. Fill in the rest of the right side of the 
forced distribution with the images that are most like 
you.”  

 
3. Initial Negative Rankings  • “Now review your least like you pile. Begin to place 

the images onto the far left columns of the forced 
distribution. Fill in the rest of the left side of the forced 
distribution with the images that are “most like you.” 

 
4. Complete Rankings  • “Now that most of your images are placed on the left 

and right columns use the remaining images to fill out 
all spaces on the forced distribution.” 

 
5. Reconsider Rankings  • “Now that you have placed all images on the forced 

distribution, go ahead and rearrange any images until 
you feel the forced distribution accurately represents 
your view.” 

 
6. Capture Rankings  • Record the positions of the images on the forced 

distribution. 
 

7. Post-Sort Interview  • “Would you mind walking me through your thought 
process for why you placed the images the way you 
did?” 

 
An example condition of instruction for a study on global agricultural development could be: 
“What are your thoughts on women’s role in agriculture throughout Sub-Saharan Africa?” The 
participants could then sort the images using a rank order of their preference, from most unlike 

https://doi.org/10.37433/aad.v5i2.359


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   127 
 

me to most like me, on a forced distribution. The use of forced versus free distributions has 
long been a point of discussion in Q methodology (Watts & Stenner, 2013). Despite this, the use 
of a forced distribution in Q has emerged as the most prominent approach because “it permits 
a fully commensurate and less ambiguous comparison of Q sorts… [or in other words]… it 
provides data in a more convenient and processed form” (Watts & Stenner, 2013, p. 51). Figure 
1 provides an example of a forced distribution for a study using a 36-image Q set, with array 
positions ranging from -4 to +4. 

Figure 1  
 
Example of a Forced Distribution Used for a Visual Q Investigation 
 
     

 
    

    
 

     

   
 

      

  
 

       

  
 

       

  
 

       

-4 -3 -2 -1 0 1 2 3 4 
 
Most UNLIKE Me                    Most LIKE Me 
 
It should also be noted that after completion of the sort, Q researchers should conduct a post-
sort interview to gain deeper insight into why participants ranked images the way they did. The 
type of interview approach used can vary. For example, focus group interviews could help 
participants make sense of and assign meaning to how they sorted the images collectively 
(Rose, 2012). However, researchers could also employ one-on-one interviews to allow meaning-
making to occur independently (Riessman, 2008).  

Principle #6: Data Analysis and Interpretation 
The final stage, data analysis and interpretation, begins after the participants have completed 
sorting images. The product of the process, the Q sort, can then be analyzed using a 
combination of computer software and naturalistic processes to emerge an interpretation of 
the data. Multiple computer software programs, e.g., KenQ, PQMethod, and Q Factor, exist to 
assist researchers with analyzing Q sorts. For example, all major Q analysis programs can input 
and analyze data through factor analysis. However, it should be noted that factor analysis does 
introduce a level of subjectivity into the analytic process because decisions must be made 
regarding how to rotate the factors and which to retain to emerge relevant findings.  

https://doi.org/10.37433/aad.v5i2.359


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   128 
 

Because of this, some scholars (McKeown & Thomas, 2013; Stephenson, 1953) have made a 
case for selecting factors on a theoretical basis rather than a statistical one. As an illustration, 
through analysis of factor loadings, a researcher may discover an individual would have 
provided a theoretically important sort but loaded significantly on another factor. Through this 
discovery, the researcher could facilitate a manual rotation of the factors to examine whether 
their initial hunch could be explained differently. It should also be noted that an automated 
factor rotation function exists within most Q statistical programs, called Varimax. Varimax 
rotates factors on a statistical basis; however, describing the factor may prove difficult because 
the resulting product often lacks a theoretical basis. Nevertheless, because of its ease of use, 
Varimax rotation is often a good entry point into Q analysis for beginning researchers (Watts & 
Stenner, 2012). Table 2 provides an example of a factor matrix using a varimax rotation from a 
study published by Roberts and Montgomery (2017) that had a three-factor solution regarding 
school-based agricultural education teachers’ epistemological positions.  

Despite the rotational approach used, factor analysis in visual Q allows researchers to group 
sorters through their shared viewpoints based on their sort’s correlation to factors. As a result, 
sorters that correlate highly and significantly with a factor, but do not load significantly on 
another factor, are described as defining that particular factor. Using sorters who define the 
factor, an average score can be computed, and researchers can understand the dimensions that 
constitute each factor. At this point in the analytic process, however, the factors exhibit no 
meaning besides grouping individual sorters, and as a result, they persist as numerical 
abstractions until further interpretation.  

Q data analysis programs provide various statistical outputs that can help researchers at this 
stage with additional interpretation. Here are a few statistical outputs that Mauldin (2012) 
recommended considering to aid in the interpretation of factors: (a) correlations between 
factors, (b) Z-scores differences, (c) distinguishing and consensus images for each array, and (d) 
participant demographics. Despite such recommendations, the Q literature provides little 
guidance on how much weight each statistical output should be given when making a final 
interpretation of factors. Typically, through deeper analysis, the researchers emerge a name for 
each factor and describe it through a narrative that weaves images along with direct quotes 
from participants’ post-sort interviews and journals. Table 3 offers practical recommendations 
to facilitate the interpretation of visual Q findings. 

 

 

 

 

 

 

https://doi.org/10.37433/aad.v5i2.359


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   129 
 

Table 3  
 
Facilitating the Interpretation of Visual Q Findings 
Stages Explanation 
Examine the Highest and Lowest Z-scores 
(positive and negative)  

• Although some researchers only 
examine Z-scores that are 1.0 or 
above, z-scores within three columns 
(positive and negative) should be 
considered 

Examine Consensus and Distinguishing 
Images for Arrays 

• Ensure all consensus and 
distinguishing images that were 
statistically significant are considered. 
These outputs can be beneficial when 
understanding how factors are 
different yet similar 

Assign Provisional Factor Names  • Trying out provisional factor names at 
this stage can help you describe what 
you observe in the data. You can 
always revise the names later, but this 
practice can aid in sensemaking 

Consider Post-Sort Interviews, Journals, and 
Participant Demographics 

• By considering emergent qualitative 
findings in concert with Q output data 
and participant demographics, 
similarities and discrepancies can 
become more visible  

Create Factor Mock-ups with Data Support • Use factor array positions and Z-
scores to create an ideal sort. This 
process can help you understand how 
the images fit together holistically for 
each factor 

• Consider potential themes or 
concepts that can help you describe 
each factor  

Finalize Factor Names and Interpretation • Based on the data available and your 
interpretation of such, finalize the 
names of the factors and how you will 
describe each with the support of 
data 

Note. Interpretation recommendations used to create this table were adapted from Mauldin 
(2012). 
 
 
 

https://doi.org/10.37433/aad.v5i2.359


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   130 
 

Conclusions, Discussion, and Recommendations 
 
Ensuring a safe and inclusive food system throughout the world will be critical to furthering 
global agricultural development (World Bank, 2023). However, the ability to achieve such has 
been compounded by issues such as extreme poverty, disease, water scarcity, and other wicked 
problems that oppress already marginalized populations even more (Fuglie et al., 2021).  
Consequently, it has become critical to understand the perspectives of these marginalized 
populations in agriculture so the industry can respond to their needs better (International Fund 
for Agricultural Development, 2023). Visual Q methodology could be one approach used to 
achieve this aim. As such, visual Q methodology has exciting possibilities for social scientists 
focused on global agricultural development issues.  

However, the methodological approach also has several complexities that researchers will have 
to learn to navigate. For example, visual Q will take considerable time and resources in the field 
to complete effectively. Because many marginalized populations in the Global South lack access 
to technology and the internet, web-based options for data collection will likely not be an 
option for the foreseeable future. Consequently, I encourage greater debate about visual Q’s 
utility for global agricultural development. Further, I recommend that researchers focused on 
global agricultural development explore whether their line of inquiry could support the use of 
this approach.  

Although the method has inherent limitations, it allows researchers to be flexible enough to 
adapt to contextual-based challenges while employing a creative, thought-provoking 
methodology. Despite the intensive nature of visual Q, it can be accomplished with relatively 
small numbers; for instance, 15 to 30 participants would be enough to achieve quality results. 
As such, the method could be adapted to be more cost-effective. Also, because multiple forms 
of data (e.g., Q-sorts, photographs with captions, interviews, and journals) are collected during 
a visual Q study to triangulate findings, the qualitative data sources could serve a dual purpose 
by providing data for additional publications.   

Finally, it is important to note that visual Q studies could also help enact policy change. On this 
point, Steelman and Maguire (1999) explained that because images can be powerful tools that 
can change individuals’ perspectives, the findings of visual Q studies could help open 
policymakers’ eyes to the realities of the challenges that marginalized populations in the Global 
South face. 

References 
 
Agard, A. C., & Roberts, R. (2020). A re-envisioned agricultural system in Thailand: The growth 

in human capital experienced by agriculturalists after adoption of the sufficiency 
economic philosophy. Advancements in Agricultural Development, 1(3), 14-26. 
http://agdevresearch.org/index.php/aad/article/view/67/43  

https://doi.org/10.37433/aad.v5i2.359
http://agdevresearch.org/index.php/aad/article/view/67/43


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   131 
 

Azizian, A., Watson, T. D., Parvaz, M. A., & Squires, N. K. (2006). Time course of processes 
underlying picture and word evaluation: An event-related potential approach. Brain 
Topography, 18(3), 213-232. 
https://www.proquest.com/openview/66daf1b9f0aeeb4fcdbab358a2d82f11/1?cbl=372
96&pq-
origsite=gscholar&parentSessionId=zqk70B5zkplJPKSvAFUPBrt8Iw3%2Bop%2FxkGRTXax
z24s%3D 

Brown, S. R. (1980). Political subjectivity: Applications of Q methodology in political science. Yale 
University Press.  

Brown, S. R. (2000). Q methodology in assessment and research. Kent State University.   

Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative 
and qualitative research (4th ed.). Pearson.  

Eden, S., Donaldson, A., & Walker, G. (2005). Structuring subjectivities? Using Q methodology in 
human geography. Area, 37(4), 413-422. https://doi.org/10.1111/j.1475-
4762.2005.00641.x 

Emmison, M., Smith, P., & Mayall, M. (2012). Researching the visual. Sage. 

Fuglie, K., Jelliffe, J., & Morgan, S. (2021). Slowing productivity reduces growth in global 
agricultural output. United State Department of Agriculture. 
https://www.ers.usda.gov/amber-waves/2021/december/slowing-productivity-reduces-
growth-in-global-agricultural-output/  

International Fund for Agricultural Development. (2023). Let’s raise a glass to South Asia’s dairy 
farmers. IFAD. https://www.ifad.org/en/web/latest/-/let-s-raise-a-glass-to-south-asia-s-
dairy-farmers 

Kiefer, M., & Pulvermüller, F. (2012). Conceptual representations in mind and brain: Theoretical 
developments, current evidence and future directions. Cortex, 48(7), 805-825. 
https://doi.org/10.1016/j.cortex.2011.04.006 

Lee, B. (2019). Tools for collecting a concourse and selecting a Q sample. Operant 
Subjectivity, 41, 17-47. https://doi.org/10.15133/j.os.2019.010  

Machin, D., & Ledin, P. (2018). Doing visual analysis: From theory to practice. Sage.  

Mannay, D. (2015). Visual, narrative and creative research methods: Application, reflection and 
ethics. Routledge. 

Mauldin, C. (2012). Overview of the Q interpretation process. Media Marketing Inc.  

McKeown, B., & Thomas, D. (2013). Q methodology (2nd ed.). Sage. 

https://doi.org/10.37433/aad.v5i2.359
https://www.proquest.com/openview/66daf1b9f0aeeb4fcdbab358a2d82f11/1?cbl=37296&pq-origsite=gscholar&parentSessionId=zqk70B5zkplJPKSvAFUPBrt8Iw3%2Bop%2FxkGRTXaxz24s%3D
https://www.proquest.com/openview/66daf1b9f0aeeb4fcdbab358a2d82f11/1?cbl=37296&pq-origsite=gscholar&parentSessionId=zqk70B5zkplJPKSvAFUPBrt8Iw3%2Bop%2FxkGRTXaxz24s%3D
https://www.proquest.com/openview/66daf1b9f0aeeb4fcdbab358a2d82f11/1?cbl=37296&pq-origsite=gscholar&parentSessionId=zqk70B5zkplJPKSvAFUPBrt8Iw3%2Bop%2FxkGRTXaxz24s%3D
https://www.proquest.com/openview/66daf1b9f0aeeb4fcdbab358a2d82f11/1?cbl=37296&pq-origsite=gscholar&parentSessionId=zqk70B5zkplJPKSvAFUPBrt8Iw3%2Bop%2FxkGRTXaxz24s%3D
https://doi.org/10.1111/j.1475-4762.2005.00641.x
https://doi.org/10.1111/j.1475-4762.2005.00641.x
https://www.ers.usda.gov/amber-waves/2021/december/slowing-productivity-reduces-growth-in-global-agricultural-output/
https://www.ers.usda.gov/amber-waves/2021/december/slowing-productivity-reduces-growth-in-global-agricultural-output/
https://www.ifad.org/en/web/latest/-/let-s-raise-a-glass-to-south-asia-s-dairy-farmers
https://www.ifad.org/en/web/latest/-/let-s-raise-a-glass-to-south-asia-s-dairy-farmers
https://doi.org/10.1016/j.cortex.2011.04.006
https://doi.org/10.15133/j.os.2019.010


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   132 
 

Meinzen-Dick, R. S., Rubin, D., Elias, M., Mulema, A. A., & Myers, E. (2019). Women’s 
empowerment in agriculture: Lessons from qualitative research. International Food 
Policy Research Institute. https://doi.org/10.2499/p15738coll2.133060  

Mitchell, C. (2011). Doing visual research. Sage. 

Naspetti, S., Mandolesi, S., & Zanoli, R. (2016). Using visual Q sorting to determine the impact of 
photovoltaic applications on the landscape. Land Use Policy, 57, 564-573. 
https://doi.org/10.1016/j.landusepol.2016.06.021     

Pink, S. (2007). Doing visual ethnography. Sage. 

Rampold, S. D., Coleman, B. M., Bunch, J. C., & Roberts, R. (2020). Exploring students’ cultural 
competence development during a short-term international experience: A Q-sort study. 
Advancements in Agricultural Development, 1(2), 65-78. 
http://agdevresearch.org/index.php/aad/article/view/45 

Roberts, R., & Edwards, M. C. (2017). Challenges to sustaining university-community 
partnerships in war-torn, northern Uganda: Investigating resistance, negative 
stereotyping, and gender bias in agricultural students’ attachments. Journal of 
International Agricultural and Extension Education, 24(2), 4-21. 
https://web.archive.org/web/20201210174540id_/https://www.aiaee.org/attachments
/article/1679/1%20Challenges%20to%20Sustaining%20University-
Community%20Partnerships%20in%20War-Torn.pdf 

Roberts, R., & Montgomery, D. (2017). Using epistemological positions and orientations to 
instruction to explore school-based, agricultural educators’ perceptual identities: A Q-
sort study. Journal of Agricultural Education, 58(1), 151-171. 
https://doi.org/10.5032/jae.2017.01151  

Roberts, R., Rampold, S. D., Ramage, R., & Komunjeru, B. (2020a). A typology of university 
agriculture students’ projected motivations to study abroad: An application of Q 
methodology. Journal of International Agricultural and Extension Education, 27(3), 59-
74. https://doi.org/10.4148/2831-5960.1105  

Roberts, R., Stair, K. S., & Granberry, T. (2020b). Images from the trenches: A visual narrative of 
the concerns of preservice agricultural education teachers. Journal of Agricultural 
Education, 61(2), 324-338. https://doi.org/10.5032/jae.2020.02324 

Rose, G. (2022). Visual methodologies: An introduction to researching with visual materials (3rd 
ed.). Sage. 

Richardson, L. A., & Ramlo, S. (2019). Generating a concourse using anecdote circles: Exploring 
students’ views of obesity. Operant Subjectivity, 41, 3-16. 
https://doi.org/10.15133/j.os.2019.003  

https://doi.org/10.37433/aad.v5i2.359
https://doi.org/10.2499/p15738coll2.133060
https://doi.org/10.1016/j.landusepol.2016.06.021
http://agdevresearch.org/index.php/aad/article/view/45
https://doi.org/10.5032/jae.2017.01151
https://doi.org/10.4148/2831-5960.1105
https://doi.org/10.5032/jae.2020.02324
https://doi.org/10.15133/j.os.2019.003


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   133 
 

Richardson, M. A., & Roberts, R. (2020). Modern women and traditional gender stereotypes: An 
examination of the roles women assume in Thailand’s agricultural system. Journal of 
International Agricultural and Extension Education, 27(4), 7-21. 
https://web.archive.org/web/20201228005408id_/https://www.aiaee.org/attachments
/article/1807/3%20Roberts.pdf 

Richardson Gilley, M. A., Roberts, R., Blackburn, J.J., & Stair, K.S. (2023). The intersection of 
gender, media, and policy: A qualitative analysis on Thai newspaper coverage of women 
in agriculture. Journal of International Agricultural and Extension Education, 30(1), 41-
58. https://doi.org/10.4148/2831-5960.1079 

Riessman, C. K. (2008). Narrative methods for the human sciences. Sage.  

Rubin, D. (2016). Qualitative methods for gender research in agricultural development. 
International Food Policy Research Institute.  

Schlochtermeier, L. H., Kuchinke, L., Pehrs, C., Urton, K., Kappelhoff, H., & Jacobs, A. M. (2013). 
Emotional picture and word processing: An fMRI study on effects of stimulus 
complexity. PLOS ONE, 8(2), 596-619. https://doi.org/10.1371/journal.pone.0055619  

Simpson, S. H. (1989). Use of Q-sort methodology in cross-cultural nutrition and health 
research. Nursing Research, 38(5), 289-300. 
https://journals.lww.com/nursingresearchonline/Citation/1989/09000/Use_of_Q_Sort_
Methodology_In_Cross_Cultural.8.aspx  

Steelman, T. A. & Maguire, L. A. (1999). Understanding participant perspectives: Q-
methodology in national forest management.  Journal of Policy Analysis and 
Management, 18(3), 361–88. https://doi.org/10.1002/(SICI)1520-
6688(199922)18:3%3C361::AID-PAM3%3E3.0.CO;2-K 

Stephenson, W. (1953). The study of behavior: Q-technique and its methodology. University of 
Chicago Press. https://psycnet.apa.org/record/1954-06810-000  

Stephenson, W. (1978). Q-methodology, quantum theory, and Newton’s fifth rule. State 
Historical Society of Missouri.  

Stephenson, W. (1980). Newton’s fifth rule and Q methodology: Application to educational 
psychology. American Psychologist, 35(10), 882-889. 
https://psycnet.apa.org/doi/10.1037/0003-066X.35.10.882  

Sylvester, J. (2020). Stephenson: Defining concourses and selecting statements using Fisher’s 
balanced block design and Newton’s fifth rule. Operant Subjectivity, 41, 48-61. 
https://doi.org/10.15133/j.os.2019.008  

Watts, S., & Stenner, P. (2013). Doing Q methodological research: Theory, method, and 
interpretations (2nd ed.). Sage. 

https://doi.org/10.37433/aad.v5i2.359
https://doi.org/10.4148/2831-5960.1079
https://doi.org/10.1371/journal.pone.0055619
https://journals.lww.com/nursingresearchonline/Citation/1989/09000/Use_of_Q_Sort_Methodology_In_Cross_Cultural.8.aspx
https://journals.lww.com/nursingresearchonline/Citation/1989/09000/Use_of_Q_Sort_Methodology_In_Cross_Cultural.8.aspx
https://doi.org/10.1002/(SICI)1520-6688(199922)18:3%3C361::AID-PAM3%3E3.0.CO;2-K
https://doi.org/10.1002/(SICI)1520-6688(199922)18:3%3C361::AID-PAM3%3E3.0.CO;2-K
https://psycnet.apa.org/record/1954-06810-000
https://psycnet.apa.org/doi/10.1037/0003-066X.35.10.882
https://doi.org/10.15133/j.os.2019.008


Roberts  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v5i2.359   134 
 

Weber, S. (2008). Visual images in research. In J. G. Knowles & A. L. Cole (Eds.), Handbook of the 
Arts in Qualitative Research (pp. 41–53). Sage Publications. 

World Bank. (2023, September 19). Understanding poverty: Agriculture and food. The World 
Bank. https://www.worldbank.org/en/topic/agriculture/overview  

 
 
 
 
© 2024 by authors. This article is an open access article distributed under the terms and conditions of 
the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). 
 

https://doi.org/10.37433/aad.v5i2.359
https://www.worldbank.org/en/topic/agriculture/overview

