































   Advancements in Agricultural Development 
  Volume 5, Issue 4, 2024 
  agdevresearch.org 

 

1. Arati Joshi, Graduate Student, Department of Agricultural Education and Communication, University of Florida, 408 Rolfs 

Hall, 341 Buckman Dr, Gainesville, FL, 32611, aratijoshi@ufl.edu,  https://orcid.org/0000-0002-4946-9366 
2. John Diaz, Associate Professor & Extension Specialist, Department of Agricultural Education and Communication, 

University of Florida, 1200 N Park Road, Plant City, FL 33563, john.diaz@ufl.edu,  

 https://orcid.org/0000-0002-2787-8759 
3. Anil Kumar Chaudhary, Associate Professor of Agricultural and Extension Education, Pennsylvania State University, 386 

Shortlidge Rd, 209C Ferguson Building, University Park, PA 16802, auk259@psu.edu,  

 https://orcid.org/0000-0001-5809-8854  
4. K. S. U. Jayaratne, Professor and State Leader for Extension Evaluation, 240 Ricks Hall, Campus Box 7607, Department of 

Agricultural and Human Sciences, North Carolina State University, Raleigh, NC 27695, ksjayara@ncsu.edu,   

 https://orcid.org/0000-0001-5591-3905  
5. Sebastian Galindo, Associate Professor of Program Evaluation, Department of Agricultural Education and Communication, 

University of Florida, 113C Bryant Hall, Gainesville, FL 32611, sgalindo@ufl.edu,  

 https://orcid.org/0000-0002-4061-773X 
59 

 

Enhancing Effectiveness of Extension Program Evaluations by 
Validating the Trustworthiness of Self-Reported Measures of 

Extension Program Outcomes 
 

A. Joshi1, J. Diaz2, A. Kumar Chaudhary3, K. Jayaratne4, S. Galindo5 
 
 

Article History 
Received: July 10, 2024 
Accepted: November 12, 2024 
Published: November 15, 2024 
 
 
Keywords 
subjective knowledge; objective 
knowledge; behavior change; impact 
assessment; SDG 15: Life on Land   

Abstract 

Assessment of program outcomes in extension often relies on subjective 
measures, such as perceived or self-reported knowledge, which are criticized for 
potential bias and inaccuracy. Conversely, objective knowledge, i.e., how much 
an individual actually knows, is considered more accurate. Studies show varying 
associations between subjective and objective knowledge, ranging from no 
correlation to high correlation, and their influence on behavior change also varies. 
In this study, we aim to quantify the relationship between subjective knowledge, 
objective knowledge, and behavior change. Data were collected from Master 
Gardener Volunteer training attendees. We used Pearson correlation and 
hierarchical linear regressions to explore the relationship between subjective and 
objective knowledge and their influence on behavior, i.e., engagement in 
gardening practices. Our findings show that subjective and objective knowledge 
post-training were moderately correlated, indicating that participants' self-
assessments were not entirely accurate before training. Interestingly, only 
subjective knowledge before training predicted engagement in gardening 
practices after training, highlighting the significant role of perceived 
understanding in behavior change. Based on the findings, we suggest that 
extension programs should focus on addressing participants' existing beliefs to 
foster enduring behavior change. By designing programs that consider these pre-
existing perceptions, extension can more effectively translate knowledge into 
practical, lasting behaviors. 

 

mailto:aratijoshi@ufl.edu
https://orcid.org/0000-0002-4946-9366
mailto:john.diaz@ufl.edu
https://orcid.org/0000-0002-2787-8759
mailto:auk259@psu.edu
https://orcid.org/0000-0001-5809-8854
mailto:ksjayara@ncsu.edu
https://orcid.org/0000-0001-5591-3905
mailto:sgalindo@ufl.edu
https://orcid.org/0000-0002-4061-773X


Joshi et al.  Advancements in Agricultural Development 
 

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Introduction and Problem Statement 
 
Evaluating the impact of extension programs and measuring knowledge and behavior change is 
a complex task (Larese-Casanova, 2017). There is ongoing debate regarding the use of 
subjective versus objective measures to evaluate these changes. Subjective measures, where 
participants self-report their knowledge and awareness before and after program participation, 
are commonly used due to their practicality, ease of administration, and broad applicability 
(Aqueveque, 2018; Gonyea, 2005). However, research suggests that relying solely on subjective 
assessments may lead to cognitive bias, where participants may either underestimate or 
overestimate their actual knowledge (Han, 2019; House et al., 2004; Kruger & Dunning, 1999; 
O’Leary & Israel, 2019). 
 
Other researchers argue for the inclusion of objective measures, such as quizzes, tests, or skills 
assessments, alongside subjective assessments, as they more accurately gauge knowledge 
acquisition (Macků et al., 2020; Waters et al., 2018). The correlation between subjective and 
objective measures of knowledge varies across studies, with some showing no correlation 
(Ellen, 1994), while others report low to moderate correlations (Aqueveque, 2018; Han, 2019; 
Pieniak et al., 2010) or strong correlations (Gámbaro et al., 2013). Similarly, studies highlight 
diverse effects of subjective and objective knowledge on behavior (Han, 2019; Ienna et al., 
2022; Kim et al., 2018; Redman & Redman, 2016). However, the majority of research on this 
topic originates from health and education disciplines, leaving a gap in understanding within 
agricultural extension programs. Evaluating these measurement approaches specifically within 
agricultural contexts could yield valuable insights, particularly since current evaluations of 
extension programs predominantly rely on subjective assessments.  
 

Theoretical and Conceptual Framework 
 
Program evaluation uses social science research methods to assess the efficacy of social 
intervention programs (Rossi et al., 2004). This study adapts the targeting outcomes of 
programs (TOP) model (Rockwell & Bennett, 2004) as its conceptual framework. The TOP Model 
offers a valuable framework for assessing extension program outcomes by outlining a hierarchy 
of effects. It posits that changes in knowledge, attitudes, skills, and aspirations (KASAs) – 
considered short-term outcomes – can lead to behavioral and practice changes (medium-term 
outcomes), ultimately contributing to improvements in social, environmental, and economic 
conditions (long-term outcomes or impacts). The TOP model also contributes to two purposes 
of evaluation, (a) to evaluate the programming process for improvement and (b) to evaluate 
the program outcomes for accountability. Measuring the achievement of these outcomes 
requires indicators, which can be subjective or objective. For instance, structured observation 
of participants regarding the adoption of behavior is an objective indicator for practices, while 
using self-reported tests is a subjective measure. Similarly, for measuring knowledge, test 
scores are objective indicators, and self-assessment is a subjective indicator.   
 

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Additionally, behavior change theories, such as the theory of planned behavior (Ajzen, 1991), 
social cognitive theory (Bandura, 1977), and the knowledge-attitude-behavior model 
(Bettinghaus, 1986; Liu et al., 2016), explain the impact of perceived knowledge (or cognition) 
on behavior. These theories emphasize that attitudes, beliefs, and an individual's subjective or 
perceived understanding significantly impact behavior change. Several studies reveal a stronger 
effect of objective knowledge on an individual's behavior (Ienna et al., 2022; Klerck & Sweeney, 
2007; Sharma et al., 2008). The use of both subjective and objective outcome indicators would 
complementarily allow for triangulation of the findings, rigorously making the outcome 
evaluations more realistic and useful. However, extension educators lack resources, time, and 
skill to conduct comprehensive evaluations (Diaz et al., 2019; Kluchinski, 2014; Kumar 
Chaudhary et al., 2020) and are not able to use both types of indicators. Therefore, in this 
study, we assess whether subjective or objective indicators are the best measures of knowledge 
change and their relationship with behavior/practice change. 
 

Purpose 
 
The purpose of this research was to compare subjective and objective assessment measures 
used in agricultural extension programs, to identify the most effective approach for accurately 
evaluating changes in knowledge and subsequent behavior. To guide this study, we used the 
following objectives:  
1. Determine correlations between participants' subjective (perceived) and objective (tested) 

knowledge in an extension program. 
2. Compare the efficacy of subjective versus objective knowledge measures at predicting 

engagement in behavior following extension program participation.  
 

Methods 
 
We conducted a quasi-experimental, longitudinal quantitative survey (Creswell & Creswell, 
2017) using Google Forms™ to collect data from Master Gardener Volunteer (MGV) training 
attendees in Leon (n = 15), Orange (n = 16), Osceola (n = 10), and Palm Beach (n = 16) counties 
in Florida during spring 2023. Data were collected before (n = 58) and after (n = 41) the 50-hour 
training program. The pretest questionnaire included 122 questions on subjective knowledge, 
objective knowledge, and engagement in gardening practices covering topics like Florida 
friendly landscaping, botany, entomology, plant pathology, nematology, soil, nutrients, 
fertilizers, turfgrass, landscape plant selection and maintenance, pesticides, vegetable 
gardening, fruits, propagation, and wildlife. The post-test questionnaire included additional 
questions on retrospective subjective knowledge, i.e., participants were asked to reflect on 
their knowledge before the training. This was done to identify any differences in knowledge 
assessment by the participants. The question item was ‘How would you rate your knowledge in 
the following areas prior to the Master Gardener Training Program?’  
 
Subjective knowledge was assessed through thirteen different items using a five-point Likert 
scale (0 = not knowledgeable at all to 4 = extremely knowledgeable) (e.g., How would you rate 

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your knowledge in the following areas – ‘Plant pathology: diseases affecting plants’). Objective 
knowledge was assessed through multiple-choice questions with a correct answer. The 
maximum score that one could get on objective questions was 118. Engagement in gardening 
practices was evaluated through eleven different items (e.g., Select the response that best 
reflects the frequency for which you are using the practice – ‘I use irrigation as a supplement to 
rain’). Engagement was measured on a five-point Likert scale, based on the transtheoretical 
model for behavior change (Prochaska & Velicer, 1997), which was later condensed to four 
points for consistency in both pre and post-tests (0 = not important to me to 3 = I am doing this 
most of the time/all the time). Indices were created by averaging all items under each 
construct.  
 
Post-hoc reliability analyses for internal consistency for Likert scale questions were conducted, 
which resulted in Cronbach’s alpha, α >.85 for subjective knowledge question and α >.79 for 
behavior question.  Since all the indices exceeded the minimum threshold of .7 for Cronbach’s 
alpha measures were considered reliable for use (Nunnally & Bernstein, 1994). A panel of 
experts in program evaluation, survey methodology, and MGV training were invited to review 
the instruments and provide feedback to ensure content and face validity. The research team 
revised the instruments accordingly and resubmitted them for further review. After reaching a 
consensus, we conducted cognitive interviews with existing MGVs to ensure the instrument's 
clarity for the target audience (Kumar Chaudhary & Israel, 2014). 
 
Data Cleaning and Analysis 
After preliminary data cleaning, we found that only 34 response pairs matched in both pre-and 
post-test assessments; all other cases were discarded. Upon further cleaning through 
regression diagnostics, we identified two multivariate outliers using Mahalanobis distance (p < 
0.001), which were removed. Standardized, studentized, and studentized deleted residuals 
were checked for influential cases, but none were found (all residuals within ±3 SD). For 
research objective two, the regression assumptions were tested, and all were met. 
 
A total of 32 responses were analyzed using IBM SPSS (Version 29.0). We used descriptive 
statistics and paired t-tests for research objective one and Pearson correlation and hierarchical 
linear regression for research objective two. The effect size measure for paired t-test, i.e., 
Cohen’s d was interpreted using Cohen’s (1988) guidelines, .2, .5., and .8 as small, medium, and 
large effect size, respectively. The Pearson correlation effect size measure, i.e., correlation 
coefficient (Pearson’s r), was interpreted using Davis’s (1971) guidelines, .01 to .09, .1 to .29, .3 
to .49, .5 to .69, and .7 and higher as negligible, association, low association, moderate 
association, substantial association, and very strong association, respectively. Finally, R2 was 
used as an effect size measure for hierarchical linear regression (Kotrlik et al., 2011).  For 
hierarchical linear regression, engagement in gardening practices after the training was the 
dependent variable, and independent variables entered in the first block (model 1) were 
objective scores in the pre and post-test, and variables entered in the second block (model 2) 
were subjective knowledge scores pre and post-test.   
 

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Findings 
 
We found that the subjective knowledge index increased from 0.92 before the training to 2.41 
after the training (see Table 1). Similarly, objective scores increased from 64.47 before the 
training to 109.84 after the training. A slight change in engagement in gardening practices was 
found from 2.40 to 2.61 before and after training, respectively. The paired t-test indicated a 
significant change in subjective knowledge, objective knowledge, and engagement in gardening 
practices before and after the training (see Table 2). We found a small effect size (d = 0.28) for 
the change in mean subjective knowledge from the pre-test to the post-test. There was a 
medium effect size for the change in engagement in gardening (d = 0.55) before and after the 
training. Retrospective subjective knowledge showed a large effect size (d = 2.76), indicating a 
substantial change from pretest to posttest. Similarly, objective knowledge also exhibited a 
large effect size (d = 2.83) before and after the training. 
 
Table 1 
 
Summary of Subjective Knowledge, Objective Knowledge, and Engagement in Gardening 
Practices Before and After the Spring 2023 MGV Training for Participants (n = 32)  
Variable M SD 
Index of subjective knowledge before training 0.92 0.39 
Index of subjective knowledge after training 2.41 0.49 
Index of subjective knowledge prior training (retrospective pretest) 0.94 0.45 
Objective score before training 64.47 15.04 
Objective score after training 109.84 14.56 
Index of engagement in gardening practices before training 2.41 0.57 
Index of engagement in gardening practices after training 2.61 0.41 

Note. The response scale for subjective knowledge was: Not knowledgeable at all (0), Slightly 
knowledgeable (1), Moderately knowledgeable (2), Very knowledgeable (3), and Extremely 
knowledgeable (4). Response scales for engagement in gardening practices were: Not important 
to me (0), Considering this (1), I'm doing this occasionally (2), and I am doing this most of the 
time/all the time (3). 
 
 
 
 
 
 
 
 
 
 
 
 

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Table 2 
 
Results of Paired t-Tests for Subjective Knowledge, Objective Knowledge, and Engagement in 
Gardening Practices for Spring 2023 MGV Training Participants (n = 32)  

Variable 

 

Mean 
before 

Mean 
after 

Paired 
mean 

difference 

Confidence 
interval of the 

difference 
p-value Cohen’s d Lower Upper 

Subjective 
knowledge 

Pretest – posttest 0.92 2.41 1.49** 1.304 1.686 <0.001 0.28 
Retrospective 

pretest – posttest  
0.94 2.41 1.47** 1.282 1.666 <0.001 2.76 

Objective 
knowledge 

Pretest – posttest 64.72 109.84 45.12** 39.380 50.869 <0.001 2.83 

Engagement in 
gardening 
practices 

Pretest – posttest  2.41 2.61 0.21** 0.075  0.349 0.004 0.55 

*indicates significance at p ≤ 0.05; **indicates significance at p ≤ 0.01 
 
Objective One: Relationships Between Subjective and Objective Knowledge Assessments 
We found a significant correlation between subjective and objective knowledge after training 
with moderate association (r = 0.398, p = 0.024) (see Table 3). A very strong association (r = 
0.720, p < 0.001) between retrospective and baseline pretest was also found. No significant 
correlation was found between subjective and objective knowledge before the training, 
retrospective subjective knowledge and objective knowledge before training, and pretest 
subjective and posttest objective knowledge.      
 
Table 3 
 
Pearson Correlation Coefficients Between Subjective and Objective Knowledge at Different 
Training Stages for Spring 2023 MGV Training Participants (n = 32) 
Correlation between   

Item 1 Item 2 
Pearson 

correlation 
p-

value 
Subjective knowledge after the 

training 
Objective knowledge after the 

training 
0.398* 0.024 

Subjective knowledge before 
training 

Objective knowledge before 
training 

0.087 0.637 

Retrospective pretest subjective 
knowledge  

Pre-test subjective knowledge 0.720** <0.001 

Retrospective pretest subjective 
knowledge  

Objective knowledge before 
training  

0.159 0.386 

Pretest subjective knowledge  Post-test objective knowledge -0.025 0.892 
*indicates significance at p ≤ 0.05; **indicates significance at p ≤ 0.01 
 

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Linear regression was carried out to quantify the relationship between subjective and objective 
knowledge after training. We aimed to understand how subjective knowledge (independent 
variable) predicted objective knowledge (dependent variable) after the training. The result of 
linear regression indicated that subjective knowledge significantly predicts objective knowledge 
after the training (F (1, 30) = 5.654, p = 0.024) and explains 13.1% variability in objective 
knowledge. The unstandardized regression coefficient (B) of 11.659 indicated that every one-
unit increase in subjective knowledge after training increases objective knowledge by 11.659 
units. 
 
Objective Two: Compare the Efficacy of Subjective Versus Objective Knowledge Measures at 
Predicting Engagement in Behavior Following Extension Program Participation. 
The hierarchical linear regression analysis where engagement in gardening practices after 
training was regressed against subjective and objective knowledge before and after training 
(see Tables 4 & 5) indicates that only subjective knowledge before the training (B = 0.404, t = 
2.284, p = 0.031) significantly predicts engagement in gardening practices after training. Model 
1 which included objective knowledge before and after training was not significant (F (2, 29) = 
1.904, p = 0.167). When subjective knowledge before and after training was added, i.e., Model 
2, explained variability increased to 22% (adjusted R2) and the model was significant (F (2, 27) = 
4.475, p = 0.021). The unstandardized regression coefficient (B) of 0.404 indicated that every 
one-unit increase in subjective knowledge before training increases engagement in gardening 
practices after training by 0.404 units when other variables were held constant.   
 
Table 4 
 
Model Summary of Hierarchical Linear Regression of Engagement in Gardening Practices as 
Dependent Variable and Subjective and Objective Knowledge Before and After Training as 
Independent Variables 
    Std. 

error of 
the 

estimate 

Change statistics 

Model R R2 
Adjusted 

R2 
R2 

change 
F 

change df1 df2 
Sig. f 

change 
1 0.341 0.116 0.055 0.40117 0.116 1.904 2 29 0.167 
2 0.580 0.336 0.238 0.36030 0.220 4.475 2 27 0.021 

Note. Model 1 = Objective knowledge before and after training; Model 2 = Model 1 + Subjective 
knowledge before and after training 
 
 
 
 
 
 
 
 
 

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Table 5 
 
Hierarchical Regression Analysis of Engagement in Gardening Practices After Training as 
Dependent Variable and Subjective and Objective Knowledge Before and After Training as 
Independent Variables 

Variable 
Model 1 Model 2 

B β t p B β t p 
Objective score after training 0.008 0.276 1.436 0.162 0.006 0.227 1.177 0.249 
Objective score before training 0.003 0.114 0.593 0.558 0.002 0.085 0.486 0.631 
Subjective knowledge index 

after training 
    0.148 0.178 0.963 0.344 

Subjective knowledge index 
before training* 

    0.404 0.387 2.282 0.031 

*indicates significance at p ≤ 0.05; **indicates significance at p ≤ 0.01 
 

Discussion, Conclusions, and Recommendations 
 
Our study sheds light on the intricate interplay between subjective and objective knowledge, 
their influence on engagement, and the enduring impact of pre-existing perceptions on post-
training behaviors. We found a moderate correlation between subjective and objective 
knowledge after training, but no correlation was found before the training. Moreover, our 
study revealed that subjective knowledge before training is the sole predictor of post-training 
gardening behaviors. The lack of correlation between subjective and objective knowledge 
before training might be because of a lack of meta-cognitive skills among participants to access 
their knowledge accurately (Kruger & Dunning, 1999) or because of overprediction of their 
subjective knowledge (Karaca et al., 2023). Participants’ subjective and objective knowledge 
changed significantly after the training, leading to a more congruent alignment between the 
two after completing the training. Regarding behavior, i.e., engagement in gardening practices, 
we found an overall increase in it after the training. However, participants' original perceptions 
predicted this behavior, overshadowing the effect of newly acquired objective knowledge. 
Ajzen et al. (2011) found a similar result and described that factual information alone seldom 
directly translates into decision-making or actions; instead, self-appraisals shape behavioral 
beliefs. Similarly, the stronger impact of perceived knowledge on behavior is reported in 
various contexts (Han, 2019; Pieniak et al., 2010; Redman & Redman, 2016). Additionally, this 
finding supports the idea that altering participants’ subjective knowledge is more challenging 
than enhancing objective knowledge (Lonka et al., 1996). Consequently, addressing pre-existing 
perceptions and beliefs should be a central focus of extension programs seeking to facilitate 
enduring behavior change. 
 
There is a practical application of the insights derived from the findings of this study to the 
design and implementation of agricultural development programs and extension initiatives 
aimed at fostering behavior change. This is the major implication of this study contributing to 
the field of Extension and agricultural development. Understanding the complexities of 

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cognitive processes and the critical role of participant’s perceptions is essential for creating 
lasting behavioral shifts toward the planned direction in agricultural extension and 
development programs. By prioritizing the reshaping of perceptions and beliefs before 
engaging participants in training, extension educators can enhance the likelihood that newly 
acquired objective knowledge will transform effectively into desired agricultural behaviors.  
 
Our findings reveal a crucial area of focus for agricultural extension programs: facilitating 
participants to appraise more accurate self-assessments before training begins (e.g., doing 
reflective activities on their beliefs and assumptions), rather than solely concentrating on the 
delivery of information. This strategy could significantly improve the alignment between 
participant’s expectations and current level of knowledge and skills with the introduction of 
new knowledge and skills.  
 
Furthermore, our results highlight the value of integrating both subjective and objective 
assessments into extension program evaluations to triangulate the program outcomes 
realistically. This approach allows for a more comprehensive analysis of knowledge gains and 
better captures the perceptions most directly linked to behavior change, which is especially 
pertinent in the context of Extension education and agricultural development. 
 
Finally, our regression analyses shed light on the intricate relationships between subjective and 
objective knowledge and their impact on gardening practices within the agricultural sector. 
Recognizing these dynamics, extension training programs could benefit from placing greater 
emphasis on specific knowledge domains that have shown a more substantial influence on the 
application of new knowledge and skills following the training. By tailoring educational content 
to address these influential factors, extension programs can significantly enhance the overall 
efficacy of Master Gardener Volunteer (MGV) training initiatives and other agricultural 
education efforts, ultimately aiding in the effective dissemination of research-based 
horticultural information and supporting the broader goals of sustainable agricultural 
development. 
 

Limitations 
 
To address the limitations of this study, particularly the small sample size and one group of 
extension audience, future research should replicate this study with a diverse and larger 
participant base. Additionally, it is advisable to implement strategies such as targeted follow-up 
communication, incentives, or other engagement measures to encourage post-training 
application, which is critical for sustaining agricultural practices introduced through extension 
programs. In terms of questionnaire design, a more concise and streamlined set of questions is 
recommended to improve response rates, reduce survey fatigue, and ensure efficient data 
collection, thereby enhancing the effectiveness of agricultural education evaluations. 
 
 
 

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Acknowledgments 
 
This work was supported by funding from the Penn State Extension’s Multistate & Integrated 
Grants Program. This work was partially supported by the USDA National Institute of Food and 
Agriculture and Hatch Appropriations under Project #PEN04945 and Accession #7006518 titled 
“Applying Socio-psychological Factors to Enhance Educational Programs and Policies.” 
 
Author Contributions: A. Joshi – Data curation, formal analysis, writing-original draft, writing- 
reviewing & editing; J. Diaz – Conceptualization, methodology, validation, formal analysis, 
investigation, writing-reviewing & editing, supervision, funding acquisition; A. Kumar 
Chaudhary – Conceptualization, methodology, validation, formal analysis, investigation, 
writing-reviewing & editing, supervision, funding acquisition; K. S. U. Jayaratne – 
Conceptualization, methodology, validation, formal analysis, investigation, writing-reviewing & 
editing, supervision, funding acquisition; S. Galindo – Writing-reviewing & editing, supervision. 
 

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