































   Advancements in Agricultural Development 
  Volume 6, Issue 1, 2025 
  agdevresearch.org 

 

1. Jason D. Mc Kibben, Assistant Professor, Auburn University, 5060 Haley Center, Auburn, AL, 36849, jdm0184@auburn.edu,  

 https://orcid.org/0000-0003-2080-202X  
2. Christopher A. Clemons, Associate Professor, Auburn University, 5075 Haley Center, Auburn, AL, 36849, 

cac0132@auburn.edu,  https://orcid.org/0000-0001-9879-0888  
3. Jessica M. Blythe, Associate Professor, West Virginia University, 4432 Agricultural Sciences Building, Morgantown, WV 

26506, jmblythe@mail.wvu.edu,  https://orcid.org/0009-0004-8351-7992   
43 

 

Survey Response Rates by Requestor’s Characteristics 
 

J. D. McKibben1, C. A. Clemons2, J. M. Blythe3 

 
 

Article History 
Received: July 26, 2025 
Accepted: February 14, 2025 
Published: February 21, 2025 
 
 
Keywords 
SDG: 17 Partnerships;  
survey research methods;  
agriculture; nonresponse bias; 
response optimization   

Abstract 
Response rates are crucial for the effectiveness of survey-based 
behavioral science and the validity of research conclusions. This study 
investigates the impact of the characteristics of individuals requesting 
participation on response rates in online surveys within the agricultural 
sector. Using social exchange theory and Coleman’s social capital theory 
as guiding frameworks, we examined whether the personal 
characteristics of the requestor influence response rates. Data were 
collected from a sample of 1,452 agricultural development personnel 
using four different request formats varying by the gender and position 
of the requestor. Following Dillman’s tailored design method, 
participants received a pre-email, a request email with a link to the 
survey, and four waves of follow-up emails. Response rates were 
analyzed based on the four treatment groups, the gender of the 
requestor, and the position of the requestor. The findings indicate no 
significant differences in response rates based on the requestor's gender 
or position. Analysis of variance (ANOVA) and independent t-tests 
revealed that neither the highest level of education, years of teaching 
experience, nor the wave of response significantly affected by the 
requestor's characteristics. These results suggest that the established 
trust and social capital within the agricultural community do not 
significantly influence survey participation. The study highlights the need 
for researchers to address declining response rates in survey research. It 
recommends building and maintaining community trust by providing 
clear, concise, and accessible research findings. Researchers should also 
consider more targeted sampling methods to reduce survey fatigue and 
improve response rates. The implications of these findings extend to the 
broader field of social science research, emphasizing that the gender and 
position of the requestor do not increase response rates or reduce 
selection bias. Future research should explore alternative methods to 
enhance survey participation and address the challenges of non-response 
bias in agricultural education research. 

 

mailto:jdm0184@auburn.edu
https://orcid.org/0000-0003-2080-202X
mailto:cac0132@auburn.edu
https://orcid.org/0000-0001-9879-0888
mailto:jmblythe@mail.wvu.edu
https://orcid.org/0009-0004-8351-7992


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Introduction 
 
Understanding study participants' personal characteristics and how they affect their likelihood 
of participating in research studies could positively impact agricultural development research 
(Lindner & Lindner, 2024). Using mixed mode and internet-only questionnaire distribution has 
quickly provided researchers access to a varied diasporic population at a reasonable cost 
(Dillman et al., 2014). Error, specifically non-response error, has been a problem that has 
plagued researchers for several decades, including mail (Heberlein & Baumgartner, 1978) 
internet only (Fan & Yan, 2010) and mixed-mode survey techniques (Dillman et al., 2014). 
Stedman et al. (2019) suggested that with the increase in non-response rates and declining 
sample survey responses (Baruch, 1999; Baruch & Holtom, 2008; Eggleston, 2020), survey 
results should be under high levels of scrutiny. Previous studies have highlighted the 
importance of addressing non-response error in survey research (Groves et al., 2006; Groves & 
Peytcheva, 2008) specifically within agricultural education (Fraze et al, 2003; McKibben et al., 
2022; Roberts & Dyer, 2005). Scholars in Agricultural and Extension Education have outlined the 
standard methods for accounting for non-response error (Lindner et al., 2001; Lindner, 2002), 
suggesting that non-response error and non-response bias are primarily ignored (Dooley & 
Linder, 2003). Rather than dismiss non-response, a more appropriate alternative to address 
error would be to ensure it does not exist in the first place (Groves, 2006; Groves et al., 2006; 
Hansen et al., 1951).  
 
Don Dillman developed a process used by many researchers in agricultural development to 
obtain responses from sample surveys (Dillman, 1978; 1983; 1991; Dillman et al., 2014). 
Dillman’s method of eliciting quality responses is based on social exchange theory (Blau, 1964; 
Dillman, 1978; 1983; 1991; Homans, 1961; Thibaut & Kelly, 1986) and has proven to elicit 
higher response rates over other commonly held theories (Greenberg & Dillman, 2021). Despite 
survey respondents perceiving no personal benefit, social exchange appeared to motivate their 
participation based on societal norms where costs outweigh benefits to the community. This 
theory has historically driven survey research design methods. With response rates at their 
lowest in decades, can we leverage social exchange theory for the representation and guidance 
our communities deserve?  
 

Theoretical Framework 
 
Greenberg and Dillman (2021) noted that in social exchange theory, people are more apt to 
comply with a task if they believe and trust that the rewards of compliance will outweigh the 
costs of acting. Furthermore, we can leverage social exchange to increase the likelihood of 
participant response rates by increasing the benefits of participation while decreasing the costs 
of responding and establishing trust. To establish trust, Dillman et al. (2014) suggested that 
survey researchers address six caveats: ensuring potential respondents can assess the survey's 
authenticity and ask questions easily, establishing a relationship with a legitimate authority 
(e.g., government or university), providing a token of appreciation (incentive-based) in advance, 
assuring confidentiality, communicating professionally, and building upon relationships and 

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friendships. In response to these caveats, requests for study participation typically follow 
established guidelines. Potential participants are given contact information for the researcher 
to establish trust and ask questions. Appreciation or token incentives are offered before the 
instrument is distributed. Confidentiality and data security are discussed in letters of 
participation. The instrument is branded with university or organizational logos to convey 
legitimacy and signal shared loyalty. All communications are conducted professionally and 
emphasize belonging to a shared community, such as agriculture, through images and 
language. Despite these efforts, response rates and the potential for non-response errors 
continue to increase (Koen et al., 2018; Zahl-Thanem et al., 2021). Dillman et al. (2014) further 
explained that tactics to increase response rates have negligible effects on overcoming non-
response errors if they encourage responses from one specific sample group over another. 
 
The theoretical framework for this research integrates social exchange theory (Blau, 1964; 
Homans, 1961; Thibaut & Kelly, 1986) through Dillman’s tailored design method (2014) and is 
grounded in Coleman’s social capital theory (1988; 1990). Dillman (1991; Dillman et al., 2014) 
recommended using social exchange theory to design data collection experiences that 
encourage truthful responses to survey instruments. He emphasized that online survey 
research should focus on a social contract, where participants exchange their time and 
information for perceived benefits. In this study, we utilized Coleman’s social capital theory 
(1988), which builds on the work of Loury (1977; 1987) and Bourdieu (1986). 
 
According to social capital theory, social structures develop over time based on a network of 
constructed trust. This trust is imparted through a sense of reciprocal obligation and perceived 
benefits rather than earned through individual actions (Coleman, 1988). Trust often flows from 
positions of lower rank to positions of higher rank and services in the reverse. Trust can be 
utilized and leveraged like currency, exchanged for services, creating an aleatory contract 
between community members. This exchange of trust for services depends on the pressure 
exerted by the social structure itself (Coleman, 1990) and becomes influential only after the 
event's structure has occurred. We approached this research with the understanding that all 
members of society exist within these authentic and assumed social networks.  
 
In an agricultural cooperative, for example, an agricultural development advisor or specialist (a 
person in a higher position) imparts trust to local farmers (people in a lower position) by 
providing expert advice, resources, and training on sustainable farming practices. This trust is 
part of the specialist's obligation to support the community's agricultural development. In 
return, the farmers implement the recommended practices, share their crop data, and 
participate in cooperative activities. 
 
Over time, this trust becomes a form of social currency. For instance, when the specialist needs 
farmers to adopt a new, experimental technique, the previously established trust encourages 
farmers to comply, even if the immediate benefits are not clear. Conversely, the farmers can 
leverage this trust to request additional resources or support from the agronomist, knowing 
that their cooperative relationship and past compliance will make the specialist more likely to 

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fulfill their requests. This reciprocal exchange of trust and services strengthens the network and 
ensures mutual benefits within the agricultural community. 
Our research design aimed to determine if this social capital can be responsibly leveraged to 
obtain more robust data. 
 

Purpose 
 
This study aimed to determine if the characteristics of the individual requesting participation 
affect the number or type of respondents engaging with an online instrument. We wanted to 
establish if we could change the characteristics of the requester and elicit more responses 
when working with a population given to help a specific member of their community (i.e. 
agricultural specialists helping farmers or teachers helping students), thus lessening non-
response bias. It was additionally questioned if the gender of the requestor could affect the 
response rates.  
 
Three objectives guided this investigation: (a) Describe the response rates of the sample 
population based on who requested the subject’s participation; (b) Determine if the gender of 
the person who sends the letter to participate in the research influences response rates based 
on demographic indicators; (c) Determine whether the person who sends the letter is a faculty 
or student who influences response rates based on demographic indicators.  
 

Methods 
 
To address the objectives of this study, data were collected as part of a more extensive US-
based study of agricultural educators investigating agriculturists' experiences and motivations 
(McKibben et al., 2022).  
 
The sample was taken from the National Association of Agricultural Educators (NAAE) 
membership list (N = 6,645), the largest organization representing agricultural educators in the 
United States. The study's findings are limited to this selective population and should not be 
generalized beyond its scope and limits. An accepted limitation of this study is the implicit 
frame error that exists when organizational lists are used for sampling (Tomaskovic-Devey et 
al., 1994).   
 
Four representative random samples (n = 1,452) from the general population were obtained 
using a random number generator and Cochran’s (1977) method to ensure coverage. Over-
sampling was conducted based on a 50% response rate derived from a review of peer-reviewed 
agricultural leadership, education, and communications journals. Since this study was part of a 
larger research project, the population was sampled four times to ensure the viability of the 
broader research. 
 
Data were collected via the online survey system Qualtrics. Follow-up reminders were made 
during the five weeks of data collection, adhering to Dillman’s Tailored Design suggestions 

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(Dillman et al., 2014). Those contacts included a pre-notice, including a link to the survey, and 
four reminder emails with a one-week interval between points of contact. Each request was 
initiated from the system, and each treatment was distributed simultaneously via the system. 
Four different requests to participate were provided to participants. The content of the letters 
was consistent with treatment fidelity. The variation was in the opening line of the request and 
the salutation at the end. Example: Hi! My name is Sara, and I am a student…. One request 
presented as a female faculty member (FF), one presented as a male faculty member (MF), one 
presented as a female student (FS), and one presented as a male student (MS). The gender was 
indicated using gender-specific names, such as Jessica (FF), Jason (MF), Sara (FS), and Scott 
(MS), all of which are commonly held as being names of either female or male in the United 
States (Bauer & Coyne, 1997). Position as either a student or faculty member was explicitly 
written in the first line of each request for participation by stating: “My name is X, and I am a 
student” or “My name is X, and I am a faculty member.”  
 

Findings 
 
The first objective was to describe the response rates of the sample population based on who 
requested the subject’s participation. The four requests all had similar respondent 
characteristics across all measured variables and waves of requests. There were no significant 
differences in the reported grouping characteristics of respondents across any of the measured 
characteristics in the sample. Female respondents: FF (51.85%), FM (51.85%), SF (51.85%), SM 
(51.85%).  
 
Respondents with a bachelor’s degree make up 49.03% of the sample, with the following 
breakdown: FF (43.38%), FM (46.76%), SF (50.34%), and SM (55.10%). Respondents with a 
master’s degree constitute 49.91% of the sample, with the following distribution: FF (55.88%), 
FM (51.80%), SF (48.28%), and SM (44.22%). Respondents with less than one year of teaching 
experience account for 4.23% of the sample, distributed as follows: FF (2.21%), FM (5.71%), SF 
(5.52%), and SM (3.40%). Respondents with one to five years of teaching experience represent 
26.76% of the sample, with the following breakdown: FF (21.32%), FM (28.57%), SF (26.90%), 
and SM (29.93%). Respondents with six to ten years of teaching experience make up 18.49% of 
the sample, distributed as follows: FF (22.06%), FM (17.14%), SF (15.17%), and SM (19.73%). 
Respondents with eleven to fifteen years of teaching experience account for 14.61% of the 
sample, with the following distribution: FF (13.97%), FM (11.43%), SF (17.24%), and SM 
(15.56%). Respondents with sixteen to twenty years of teaching experience represent 12.54% 
of the sample, with the following breakdown: FF (18.06%), FM (11.56%), SF (13.90%), and SM 
(9.62%). Respondents with twenty-one to twenty-five years of teaching experience make up 
6.51% of the sample, distributed as follows: FF (6.62%), FM (7.86%), SF (6.90%), and SM 
(4.76%). Respondents with more than twenty-five years of teaching experience account for 
15.85% of the sample, with the following distribution: FF (13.24%), FM (17.14%), SF (16.55%), 
and SM (16.33%). Respondents who participated in the first wave make up 46.46% of the 
sample, with the following breakdown: FF (48.23%), FM (44.76%), SF (40.79%), and SM 
(51.90%). Respondents who participated in the second wave constitute 22.56% of the sample, 

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with the following distribution: FF (22.70%), FM (21.68%), SF (28.29%), and SM (17.72%). 
Respondents who participated in the third wave represent 15.49% of the sample, with the 
following breakdown: FF (16.31%), FM (15.38%), SF (16.45%), and SM (13.92%). Respondents 
who participated in the fourth wave make up 15.49% of the sample, with the following 
distribution: FF (12.77%), FM (18.18%), SF (14.47%), and SM (16.46%). See Table 1.  
 
Table 1  
 
Respondent characteristics by who requested their participation.  
 
  Faculty Requestor Student Requestor Total 
  Female Male Female Male  
  f % f % f % f % f % 
Gender            
 Female 70 51.5 67 47.9 74 51.0 84 57.1 295 51.9 
 Male 65 47.8 73 52.1 71 49.0 63 42.9 272 47.9 
          567  
Highest Education            
 Associates 0 0.0 2 1.44 1 0.7 1 0.6 4 0.7 
 Bachelors 59 43.4 65 46.4 73 50.3 81 55.1 278 48.9 
 Masters 76 55.9 72 51.4 70 48.3 65 44.2 283 49.8 
 Doctorate 1 0.7 0 0.0 1 0.7 0 0.0 2 0.4 
          567  
Years of Teaching            
 < 1 3 2.2 8 5.7 8 5.5 5 3.4 24 4.2 
 1-5 29 21.3 40 28.6 39 26.9 44 29.9 152 26.8 
 6-10 30 22.1 24 17.1 22 15.2 29 19.7 105 18.5 
 11-15 19 14.0 16 11.4 25 17.2 23 15.6 83 14.6 
 16-20 28 20.6 17 12.1 17 11.7 15 10.2 77 13.3 
 21-25 9 6.6 11 7.9 10 6.9 7 4.8 37 6.5 
 > 25 18 13.2 24 17.1 24 16.6 24 16.3 90 15.8 
          568  
Response Wave            
 1 68 48.5 64 45.7 62 42.8 77 52.4 269 47.4 
 2 32 23.5 31 22.1 39 26.9 27 18.4 129 22.7 
 3 20 14.7 21 15.0 24 16.6 20 13.6 85 15.0 
 4 18 13.2 24 17.1 20 13.8 23 15.6 85 15.0 
          568  

Note. Percentages may not equal 100% based on rounding errors. Non-binary and other 
response categories were included in the questionnaire and were omitted for clarity due to no 
responses. Discrepancies may exist based on empty cells and incomplete data. 
 
 

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Objective number two was to determine if the gender of the person who sends the letter to 
participate in the research influences response rates based on demographic indicators. 
Objective two was analyzed by one-way analysis of variance (ANOVA) using the requestor as a 
fixed factor with the highest level of education, years of teaching, and a wave of response as 
dependent variables. Tested at the (α = 0.05) level, no statistically significant effects were 
determined to exist and all effect sizes were measured to be small: Level of Education (F(3, 564) 
= 1.68, p = .17, ω = 0.06); Years of Teaching (F(3, 564) = .37, p = 0.77, ω = 0.06); Gender (F(3, 
565) = .87, p = 0.46, ω = 0.03); Wave of Response (F(3, 565) = 0.39, p = 0.76, ω = 0.06).  
 
Objective Three was to determine whether the person who sends the letter is a faculty or 
student who influences response rates based on demographic indicators. A similar analysis was 
conducted by calculating an independent t-test based on binary coding the requestor as either 
a faculty member or student; Level of Education (t(565) = 1.76, p = 0.08, d2 = 0.15); Years of 
Teaching (t(566) = 0.68, p = 0.49, d2 = 0.06); Gender (t(565) = 1.02, p = 0.31, d2 = 0.09); Wave of 
response (t(566) = 0.14, p = 0.89, d2 = 0.01). Coding was also conducted where the requestor 
was binary coded, presenting as either female or male; Level of Education, (t(565) = 1.41, p = 
0.16, d2 = 0.12);  Years of Teaching (t(566) = 0.80, p = 0.43, d2 = 0.01); Gender (t(565) = 0.28, p = 
0.78, d2 = 0.02); Wave of response (t(566) = 0.08, p = 0.94, d2 = 0.01).  
 
There were no statistically significant differences regardless of how the dependent variables 
were coded, and all effect sizes were deemed trivial (Cohen, 1992; Field, 2013). We 
acknowledge that repeated analyses increase the probability of committing a Type I error. If 
there had been any significant findings, an improved statistical model would need to be 
developed.    
 

Conclusions, Implications, & Discussion 
 
This study, examining survey participation among agriculturalists through the lens of social 
exchange theory, found no evidence that existing social capital or trust, as embodied in pre-
existing relationships, influences response rates. Respondents were not more likely to 
participate if the survey request came from someone they were predisposed to help. They were 
not more likely to respond to someone of a higher or lower position, and none were more likely 
to respond to someone of a specific gender. These findings suggest that the reciprocal 
obligations, perceived benefits, and established trust posited by social exchange theory did not 
significantly impact survey participation in this context. While other social factors, potentially 
offering different forms of exchange or reward, may influence response rates within this 
community, this research found no impact from the specific social obligations tested. 
 
With falling response rates, many people are attempting to make minor adjustments to their 
research methods or instrumentation to ensure appropriate levels of response. As social 
scientists, we must address the tolling of the bell, a final imperative call to our longevity and 
value as researchers; the design, distribution, and analysis of our questionnaires and attempts 
to conduct our scholarship is the livelihood of our professional.  

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We attempted to discover if we could influence a higher response rate based on the 
characteristics of the researcher requesting participation. If we can get closer to a 100% 
response rate reliably, we could ask fewer people to respond, knowing they all will. Survey 
fatigue is caused by receiving too many requests to participate. Since we need a baseline 
response rate, we over-sample. If we could influence more of a specific demographic to 
respond, we could influence or mitigate the outcomes of social pressure. The unknown 
influence of the requisitioner on the respondent fueled this investigation.  
 
If we as faculty are not negatively influencing our respondents by asking for their participation, 
we must explore other options for our non-responses. To return to Coleman (1990), there is a 
chance that we have all spent our credit slips, causing our community to stop conducting these 
favors for us no matter who asks. That is to say that, according to the social capital theory, if A 
does something for B and trusts B to reciprocate in the future, this establishes an expectation in 
A and an obligation in B. This obligation can be seen as a credit slip held by A to be redeemed, 
such as in development work. Advisors or specialists provide information or equipment to 
producers and farmers. The advisor does this with the trust that the producer will reciprocate. 
That trust the advisor has in the farmer to reciprocate places an obligation on the farmer to 
provide some benefit to the advisor. That obligation can be considered a tangible credit slip or 
an I Owe You.  
 
Two explanations for the current lack of reciprocation, in the form of responses, can be 
suggested. As a group of research faculty, we are not as part of the community network as we 
may believe. Students and faculty are simultaneously tertiary members and former members of 
that social network. Researchers are no longer part of the social exchange in which the 
respondents participate. Though the researchers in this field were almost all community 
members at one point, they are no longer seen as core members. Another explanation is that 
the trust has been broken, and there is not enough trust in the community. The participants do 
not see enough reciprocal benefits from the researchers or the research to compensate for the 
use of energy. We have overdrawn our trust balance and are now paying the fee.  
 

Recommendations 
 
When designing research studies, we do not need to worry about who the request is coming 
from; they will not respond better anyway, as the trust imbalance is too far gone to affect it in 
this way. The real work must be done to correct the trust imbalance rather than manipulate 
respondents. Researchers need to be more diligent about being succinct in their samples. 
Researchers should not try to impress the field with a large national survey but rather be 
clearer about who and what their population is. Researchers need to do the hard work of 
finding contacts for the exact sample we are looking for rather than relying on the expediency 
of a listserv, an email database, or a targeted social media group. Advisors and Chairs must be 
more diligent in advising graduate students about proper sampling methods and achieving 

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quality results rather than larger data sets.  These actions will stop the overburden on the 
system and help to diminish fatigue.  
 
However, we must also replenish trust and build back that obligation and mutual benefit. To do 
this, researchers must return the results as digestible and understandable information back to 
our population. Conclusions and recommendations must address our research population and 
be packaged to fit their ability to consume those recommendations, such as best practice 
recommendations or suggestions. Leaving it in a journal behind paywalls and in a language only 
those with doctorates speak does not help them and will ultimately further alienate us from 
them. Once, the work of agricultural researchers went into handouts and pamphlets written 
and distributed to farmers and producers. White papers based on current research written at 
the level of the producer and practitioner were once a measure used to determine impact. That 
white paper might now need to be in other forms, such as podcasts, snippet videos, or social 
media posts. We must remember our obligation and responsibility to our community and repay 
the debt of their time by returning the results. This will do much to replenish the trust we have 
lost.  
 

Acknowledgments 
 
Author contributions: J. McKibben - Conceptualization, methodology, writing - original draft, 
writing - reviewing & editing, investigation, supervision, project administration, and formal 
analysis. C. Clemons – Writing - reviewing & editing, J. Blythe - Conceptualization, 
methodology, and project administration.  
 
Grammarly, a natural language processing and machine learning artificial intelligence tool 
(Grammarly, 2025), was used during the development of this document to aid in grammar, 
spelling, and syntax.  
 
This research is linked to Dr. McKibben’s involvement with the USDA-NIFA Multistate research 
coordinating committee: NCERA197: Agricultural Safety and Health Research and Extension and 
Dr. Clemons’s involvement with the USDA-NIFA Multistate research coordinating committee 
WERA1010: Improving Data Quality from Sample Surveys to foster Agricultural and Community 
Development in Rural America.  
 

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