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Lamiño Jaramillo et al.  Advancements in Agricultural Development 
  Volume 2, Issue 1, 2021 
  agdevresearch.org 

1. Pablo Lamiño Jaramillo, Graduate Assistant, Department of Agricultural Education & Communications, Texas Tech 

University, Box 42131, Lubbock, TX 79409-2131, pablo.lamino@ttu.edu,   https://orcid.org/0000-0002-3941-4935  
2. Amy Boren Alpízar, Associate Professor, Department of Agricultural Education & Communications, Texas Tech University, 

Box 42131, Lubbock, TX 79409-2131, amy.boren-alpizar@ttu.edu,  https://orcid.org/0000-0001-9002-4855  

3. Carla Millares-Forno, PhD, University of Nevada, Reno, 8050 Paradise Road, Las Vegas, NV, 89123, cmillares@unr.edu   
https://orcid.org/0000-0003-0482-9847    

4. Rafael Quijada Landaverde, Gradate Associate, Department of Agricultural Communication, Education, and Leadership,  

200 Fyffe road, Columbus, Ohio, 43210-6909, quijadalandaverde.1@buckeyemail.osu.edu   https://orcid.org/0000-
0001-6489-0477      

70 

 

Agricultural Education and Migration: The Cases of El Salvador 
and Honduras 

P. Lamiño Jaramillo1, A. Alpízar2, C. Millares-Forno3
, R. Quijada Landaverde4 

 
 

  

Abstract 

Youth is defined as a period of transition from childhood to adulthood that ranges between the ages 
of 15 and 24, and it is the most mobile social group in the world. Youth migration in El Salvador and 
Honduras is a well-known problem; however, limited research has focused on the causes of 
migration and the impact of agricultural education programs on the decision to migrate. This study 
aims to identify and compare students' migration intentions in agricultural and non-agricultural 
programs of two rural communities of El Salvador and Honduras. For this quantitative study, a two-
group model was used; the target population (N = 209) was composed of high school students with 
similar socioeconomic characteristics. Overall, the comparison between El Salvador and Honduras 
showed a significant difference between countries regarding their migration intentions (p < 0.05). 
Salvadorian students presented a stronger willingness to migrate. Moreover, students' intention of 
migration was evaluated based on their educational background. Youth who were not part of a 
formal agricultural program have a higher intention of migrating (p < 0.05). Finally, the main and 
interaction effects of intention to migrate, country of origin, and academic program based on the 
different migration drivers were analyzed. 
 
 
 

Keywords 

Youth migration, agricultural education programs, El Salvador, Honduras, rural  



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Introduction and Problem Statement 
 
According to the United Nations (UN, 2019), youth is defined as a period of transition from the 
dependence of childhood to adulthood's independence that ranges from 15 and 24 years old. It 
is expected that by 2030, the number of youth will grow from 1.2 billion to nearly 1.3 billion 
globally. Moreover, this age group is the most mobile social group in the world (Global 
Migration Group, 2014). This growth and mobility add pressure to governments because of the 
need to provide youth with the necessary services (UN, 2018). For instance, approximately 47% 
of Salvadorans and 52% of Hondurans are under the age of 25, and most of them are 
unemployed with little opportunity for gainful employment, forcing them to seek other options 
to make a living, such as migration (Congressional Research Service, 2019). 
 
In 2015, the number of international migrants reached 244 million; this number is small 
compared to the noticeably higher number of migrants (740 million) who move inside their 
countries, mainly from rural to urban areas or from one rural area to another (Food and 
Agriculture Organization [FAO], 2016). The decision to stay in their home communities or 
migrate to other areas holds great economic, social, and emotional consequences, especially 
for rural students (Theodori & Theodori, 2014). The impact of youth's decision to migrate is 
important for rural communities because of the potential loss of labor force, community 
leaders, volunteers, and parents of future generations (Demi et al., 2009).  
 
Youth migration in El Salvador and Honduras is a recognized problem, and several reasons such 
as poverty, violence, insecurity, and family reunification in the cities have been identified as 
potential drivers of this phenomenon (Warren & Kerwin, 2017). Education is often a driver of 
rural-to-urban migration; rural people who want to pursue a higher education level are more 
willing to leave their communities to seek educational opportunities (Corbettm, 2007; Kodzycki, 
2001). However, this is not the case with all the educational programs. Agricultural education 
programs teach students better agricultural practices and motivate them to replicate new 
techniques on their lands, which can reduce migration (Rhoda, 1983). Youth migration is a 
subject of significance in these countries; unfortunately, limited studies have been developed in 
Central America to determine the impact of agricultural education programs on the decision to 
migrate (Roth & Hartnett, 2018).   
 

Theoretical and Conceptual Framework 
 
The Theory of Planned Behavior (TPB) by Ajzen (1991) was used to guide this study. The TPB 
aims to predict behavioral intention based on three primary constructs: attitude toward the 
behavior, subjective norms, and perceived behavioral control. The first construct of the theory 
is the attitude toward the behavior, which is the favorable or unfavorable evaluation of a given 
behavior. The second construct is subjective norms that are social pressure to execute or not 
execute a given behavior. Perceived behavioral control is the third construct and refers to 
people's perception of the ease or difficulty of performing the behavior of interest. The 
combination of the three constructs allows predicting future intentions (Ajzen, 1991). 



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Although the TPB has been recognized for its facility of predicting behavior, the theory 
continues to evolve, and other variables might increase the model's utility (Burton, 2004; 
Whitmarsh & O'Neill, 2010). Ajzen (1991, p.179) argued the following regarding adaptations "in 
principle, open to the inclusion of additional predictors if it can be shown that they capture a 
significant proportion of the variation in intention or behavior." Therefore, for this study, the 
migration drivers were selected using previous literature and research experience, and it has 
been designed to match with the TPB (Castelli, 2018; Foroughi et al., 2001; Ramos-Vidal et al., 
2019; Yazdan-Panah et al., 2017; Yazdan-Panah & Zobeidi, 2017). 
 
Thus, subjective norms, the ideas that other community members have regarding migration, 
are composed of Subjective Expectations, Interpersonal Ties, Social Support, and Disputes.  The 
perceived migration behavioral control, the perceived level of difficulty regarding migration, 
was composed of Access to Extension Activities and Residential Satisfaction. The attitude 
toward migration, the positive or negative idea of migration, comprises environmental impacts 
(Ajzen, 1985; Yazdan-Panah et al., 2017). Figure 1 shows how selected migration drivers were 
added to TPB variables. 
 
Figure 1 
 
Theory of Planned Behavior 

 
Note. This figure shows the relationship that each component has with the predictable 
behavior. Adapted from the study (Ajzen, 1991) 
 

Attitude Toward 
Migration 

Subjective Norms 

Migration 
Behavior 

Perceived Migration 
Behavioral Control 

Actual behavioral 

Control 

Social Support 

Interpersonal Ties 

Subjective 
Expectations 

Disputes 

Access to 
extension activities 

Residential 
Satisfaction 

Environmental 
Impacts 

Intention to  

Migrate 



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Purpose 
 
The study aims to identify and compare the migration intentions of high school students in 
agricultural and non-agricultural programs from two rural communities, one from El Salvador 
and one from Honduras. The following objectives guided the study: 
1. Compare students' intention to migrate by country of origin. 
2. Compare students' intention to migrate by academic program. 
3. Determine the main and interaction effects of intention to migrate, country of origin, and 

academic program based on the different migration drivers. 
4. Calculate the results managing missing data with two techniques, pairwise deletion and 

mean imputation. 
 

Methods 
 
For this quantitative study, a two-group model was used. The target population (N = 209) was 
composed of high school students with similar economic and social characteristics from two 
communities, Chalatenango, El Salvador (n = 104), and Jesus de Otoro, Honduras (n = 105). Two 
groups from each community were recruited, one group participated in a formal Agricultural 
Education Program (AEP), and the other group was part of a Non-Agricultural Education 
Program (Non-AEP). 
 
Data were collected using a paper-pencil instrument that explored Salvadorian and Honduran 
youth's intention to migrate. The survey had five sections and 50 questions: demographic 
information, participants' academic and agricultural background, migration drivers, residential 
satisfaction, and intention to migrate. For the "Migration Drivers" section, 34 5-point Likert-
type questions were created by the researchers, ranging from 1 (completely disagree), 2 
(Disagree), 3 (Neither Agree nor Disagree Agree), 4 (Agree), 5 (completely agree). Scales 
containing multiple items were developed to measure the following migration drivers: social 
participation, social support, access to extension activities, environmental impacts, 
interpersonal ties, disputes, residential satisfaction, and subjective expectations (Ajzen, 1985 & 
Yazdan-Panah et al., 2017). The drivers were selected based on previous literature and under 
experts' criteria (Castelli, 2018; Foroughi et al., 2001; Ramos-Vidal et al., 2019; Yazdan-Panah et 
al., 2017). 
 
The instrument was pilot tested to determine its reliability and validity. A panel of experts 
confirmed the questionnaire's face validity and field-tested in a pilot study that included 20 
young people from both countries. Cronbach's alpha was calculated to measure constructs' 
reliabilities. For social participation, the reliability was .74, social support .73, access to 
extension activities .89, environmental impact .75, interpersonal ties .74, disputes .77, 
residential satisfaction .73, and subjective expectations .73.  Results ranged from .73 to .89, 
which means that they were acceptable (Rubin & Babbie, 2009).  
 



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The surveys with more than 10% of missing values (six surveys) were deleted from the data 
analysis (Raaijmakers, 1999). Missing values were between 2 and 8% for each Likert-type per 
statement. Therefore, data analyses were conducted independently, first using the Pairwise 
Deletion Technique (PDT) and then using Mean Imputation Technique (MIT) for the missing 
data. The PDT is used in missing data that are independent and all the missing cases to be used 
in the analysis (Field, 2013, p.231; Shi et al., 2020). The MIT allows using the mean value of the 
variable in place of the missing data point. Field (2013, p.231) suggested replacing the missing 
score with the average score when there is a large sample and a small number of missing 
values.  
 
The data collected were analyzed based on the objectives. Descriptive statistics were used to 
understand the participants of the study. Different independent chi-square was conducted to 
compare migration intention depending on the country of origin and academic program. 
Finally, a 2 x 3 x 2 Factorial Multiple Analysis of Variance (MANOVA) was conducted to examine 
the main effects and interactions effects of independent variables of country of origin ("El 
Salvador" vs. "Honduras"), academic program ("AEP" vs. "Non-AEP"), and intention to migrate 
("Yes" vs" I don't know" vs "No") on migration drivers, including Social Participation, Access to 
Extension Activities, Social Support, Interpersonal Ties, Disputes, Subjective Expectations, 
Environmental Impacts, and Residential Satisfaction. 
 

Findings 
 
The study aimed to identify and compare the migration intentions of high school students in 
agricultural and non-agricultural programs from El Salvador and Honduras. The sample 
consisted of 104 youth in AEP and 105 in a Non-AEP. In El Salvador, more participants were 
from AEP (n = 54, 51.92%), than Non-AEP (n = 50, 48.08%) programs. On the other hand, in 
Honduras, more participants were in a Non-AEP (n = 55, 52.38%) than an AEP (n = 50, 47.62%) 
program.  
 
In total, 106 males and 103 females participated in this study. In El Salvador, the majority of 
participants were male (n = 65, 62.50%), while in Honduras, most of the participants were 
female (n = 64, 61.95%). One hundred and twenty-three participants considered the father as 
the family head, and 86 considered the mother as the family head. In El Salvador, the majority 
considered men as the family head (n = 71, 68.27%), whereas in Honduras, women were 
considered the family head (n = 53, 50.48%). Concerning students' access to land, Honduras had 
a greater number of students from families that own land (n = 78, 74.29%) than the participants 
from El Salvador (n = 39, 37.50%). Table 1 shows the general and specific distribution by 
academic program, gender, family head, and access to land.  
 
 
 
 
 



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Table 1 
 

Summary of Sociodemographic information 
Characteristics Total (N = 209) El Salvador (n = 104) Honduras (n = 105) 
 f % f % f % 
Academic Program       
AEP 104 49.76 54 51.92 50 47.62 
Non-AEP 105 50.24 50 48.08 55 52.38 
Gender       
Male 106 50.72 65 62.50 41 39.05 
Female 103 49.28 39 37.50 64 61.95 
Family Head        
Male 123 58.85 71 68.27 52 49.52 
Female 86 41.15 33 31.73 53 50.48 
Access to Land       
Landowners 117 55.98 39 37.50 78 74.29 
Non-Landowners 87 41.63 65 62.50 22 20.95 
   Prefer not to say 5 2.39   5 4.76 

 
To answer objective one, a Chi-Square test of independence was calculated comparing the 
intention to migrate (yes = 1, I do not know = 2, and no = 3) and country of origin ("El Salvador," 
and "Honduras"). Table 2 shows the results of PDT and MIT methods. Results were the same 
and showed a significant relationship between intention to migrate and country of origin (c2 (2) 
= 17.77, p < .001). In total, 206 youth participated in the intention to migrate section. Overall, 
56.73% of Salvadorian youth considered migration as an option, while 25.96% said No, and 
17.30% were undecided. In Honduras, 36.27% of the youth have no intention to migrate, 
followed by 35.29% undecided, and 28.43% have the intention to migrate. Cramer's Value was 
used to determine the association's strength; results showed a value of .29, considered as 
medium effect size (Cohen, 1988). 
 
Table 2 
 
Chi-Square Results for Intention to Migrate Based on Country of Origin (N = 206)  
Intention to 
migrate 

El Salvador (n =104) Honduras (n = 102) 
c2 (2) Cramer's V n % n % 

Yes 59 56.73 29 28.43 17.77* .29 
I don't know 18 17.30 36 35.29   
No 27 25.96 37 36.27   

Note. *p < .05 
 
For objective two, a Chi-Square test of independence was calculated comparing the intention to 
migrate (Yes = 1, I don't know = 2, and No = 3) and academic program ("AEP," and "Non-AEP"). 
Table 3 shows the results of the Chi-square using PDT and MIT. Results show a significant 



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relationship between intention to migrate and academic program (c2 (2) = 27.67, p < .001). 
Overall, 39.81% of the youth in the agricultural program do not have the intention to migrate, 
while 30.10% said yes, and 30.10% were undecided; 55.34% of students in Non-AEP considered 
migration as a good option, followed by 23.33% undecided, and 23.33% do not have the 
intention to migrate. The Cramer's Value for the strength of the association was .37, considered 
a medium effect size (Cohen, 1988).  
 
Table 3 
 
Chi-Square Results for Intention to Migrate Based on Academic Program (N = 206)  
Intention to 
migrate 

AEP (n =104) Non-AEP (n = 102) 
c2 (2) Cramer's V n % n % 

Yes 26 25.00 62 60.80 27.67* .37 
I don't know 38 36.50 16 15.70   
No 40 38.50 24 23.50   
Note. *p < .05       

 
For objective three, a 2 x 3 x 2 Factorial MANOVA was conducted to examine the main effects 
and interactions effects of independent variables of the country of origin ("El Salvador" vs. 
"Honduras"), academic program ("AEP" vs. "Non-AEP"), and Intention to migrate ("Yes" vs." I 
don't know vs. "No") have on the migration drivers, including Social Participation, Access to 
Extension Activities, Social Support, Interpersonal Ties, Disputes, Subjective Expectations, 
Environmental Impacts, and Residential Satisfaction.  
 
The assumption of normality was met because skewness and kurtosis levels were in the range 
of +/- 2, and a histogram showed a normal distribution. According to Levene's test, the 
homogeneity of variance was no accomplished in the study. Therefore, Pillai's Trace was used 
to interpret the results to guarantee the robustness of the findings, despite the assumption 
violation. Finally, the homogeneity of variance was calculated using Box's test. Results show a 
Box's M value of 349.24 with a p-value of .03 for PDT and a Box’s M value of 469.56 with a p-
value of 0.04 for MIT, which represents a violation of the assumption. However, Field (2013) 
argues that Box's test findings are sensitive to large sample sizes, and the significant value could 
be the result of the sample. 
 
Table 4 shows the results of the sources that were significant. For intention to migrate, and 
using PDT, there was a significant medium effect, V = .26, F(16,270) = 2.47, p = .02, partial η2 
=.13; for MIT there was reported a significant small effect ,V = .19, F(16,354) = 2.29, p = .03, 
partial η2 = .09. Using PDT and MIT, a significant medium effect was found on country of origin, 
V = .28, F(8,134) = 6.44, p < .001, partial η2 = .28); V = .21, F(8,176) = 5.76, p < .001, partial η2 = 
.21). For Academic program, a significant large effect was found for both methods, PDT, V = .49, 
F(8,134) = 16.09, p < .001, partial η2 = .49, and MIT , V = .45, F(8,176) = 18.38, p < .001, partial η2 
= .46. Finally, for the interaction country of origin and academic program a significant medium 
effect size was found, using PDT, V =.32, F(8,134) = 7.93, p < .001,partial η2 = .32, and MIT, V 
=.27, F(8,176) = 8.15, p < .001, partial η2 = .27 (Cohen, 1988). 



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Table 4 
 
Multivariate Analysis of Variance for Migration Intentions  
 Method 
Source PDT MIT 

F p partial η2 F p partial η2 
Academic Program (1) 16.09 < .001* .49 18.38 < .001* .46 
Country of Origin (2) 6.44 < .001* .28 5.76 < .001* .21 
Intention to Migrate (3) 2.47 .02* .13 2.29 .03* .09 
Interaction 1 x 2  7.93 < .001* .32 8.15 < .001* .27 
Interaction 1 x 3 1.35 .16 .07 1.05 .40 .05 
Interaction 2 x 3  .85 .62 .48 1.02 .43 .04 
Interaction 1 x 2 x 3 .89 .52 .50 1.26 .26 .05 
Note. *p < .05; PDT = Pairwise Deletion Technique; MIT = Mean Imputation Technique 

 
As complementary tests for the significant MANOVA, univariate Analysis of Variance (ANOVAs) 
were implemented to understand the interaction effects of the independent variables on the 
eight migration drivers. Therefore, the ANOVA revealed a significant main effect for country of 
origin, PDT, F(1,141) = 9.97, p = .01, partial η2 = .06; MIT, F(1,183) = 9.34, p = .01, partial η2 = 
.05, on the migration driver, “Social Participation.” 
 
For the Access to Extension Activities construct, the ANOVAs revealed a significant effect, for 
country of origin, PDT, F(1,141) = 10.20, p = .01, partial η2 =.07; MIT, F(1,183) = 10.93, p = .01, 
partial η2 =.06, and academic program, PDT, F(1,141) = 89.82, p < .001, partial η2 = .39, and 
MIT, F(1,183) = 83.18, p < .001, partial η2 = .31. Besides, it was found a significant effect in the 
interaction of country of origin and academic program using PDT, F(1,141) = 4.67, p = .03, 
partial η2 = .03; MIT, F(1,183) = 3.21, p = .07, partial η2 = .01. 
 
For the Social Support construct, a significant effect was observed in country of origin, PDT, 
F(1,141) = 15.57, p < .001,partial η2 = .10; MIT, F(1,183) = 16.91, p < .001,partial η2 = .09. 
Interpersonal Ties construct had a significant effect for country of origin, using the PDT, 
F(1,141) = 6.65, p = .01, partial η2 = .05; MIT, F(1,183) = 6.25, p = .01, partial η2 = .03. 
Moreover, it was found a significant effect for intention to migrate, using MIT, F(2,183) = 3.40, 
p = .03, partial η2 = .04;  but not found significant effect for PDT, F(2,141) = 2.38, p = .09, partial 
η2 = .03. 
 
For the Dispute construct, a significant effect was found for academic program, PDT, F(1,141) = 
42.85, p < .001, partial η2 = .23; MIT, F(1,183) = 34.64, p < .001, partial η2 = .16, and the 
interaction between of country of origin and academic program, PDF, F(1,141) = 37.26, p < .001, 
partial η2 = .21; MIT, F(1,183) = 35.30, p < .001, partial η2 = .16.  Country of origin was 
significant only using the PDT method, F(1,141) = 11.67, p = .01, partial η2 = .08, MIT, F(1,183) = 
3.50, p = .06, partial η2 = .02. Finally, for Environmental Impacts construct, ANOVAs revealed a 
significant effect on intention to migrate, PDT, F(2,141) = 7.35, p = .01, partial η2 = .09, and MIT, 



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F(2,183) = 8.16, p < .001, partial η2 = .08. Table 5 summarizes the ANOVAs that are significant, 
depending on each migration driver, and using the two processes to manage missing data. 
 
Table 5 
 
Univariate Analysis of Variance for Social Participation, Access to Extension Activities, Social 
Support, Interpersonal Ties, Disputes, and Environmental Impacts as a Function of Main and 
Interaction Effects of the Intention to Migrate, Country of Origin, Academic Program 

 
Source 

Method 
PDT MIT 

F p partial η2 F p partial η2 
 Social Participation 
Country of Origin (2) 9.97 .01* .06 9.34 .01* 0.5 
 Access to Extension Activities 
Country of Origin 10.20  .01* .07 10.93  .01* .06 
Academic Program (1) 89.82 < .001* .39 83.18 < .001* .31 
Interaction 1 x 2  4.67 .03* .03 3.21 .07 .01 
 Social Support 
Country of Origin 15.57 < .001* .10 16.91 < .001* .09 
 Interpersonal Ties 
Country of origin 6.65 .01* .05 6.25 .01* .03 
Intention to migrate (3) 2.38 .09 .03 3.40 .03* .04 
 Disputes 
Academic Program 42.85 < .001* .23 34.64 < .001* .16 
Country of Origin 11.67 .01* .08 3.50 .06 .02 
Interaction 1 x 2 37.26 < .001* .21 35.30 < .001* .16 
 Environmental Impact 
Intention to migrate 7.35 .01* .09 8.16 < .001* .08 
Note. *p < .05; PDT = Pairwise Detention Technique; MIT = Mean Imputation Technique; the 
variables Subjective Expectations and Residential Satisfaction was not significant for any 
source. 

 
Post-hoc test was conducted for the intention to migrate variable because it was the only 
independent variable that had more than two levels. The researchers assumed that the sample 
sizes per condition were equal; therefore, the Bonferroni was implemented; this test is well-
known for being conservative and has strong statistical power (Field, 2011, p.374). Table 6 
shows the Post-hoc test used as a follow-up test to determine the differences between the 
means of intention to migrate. Findings from the PDT shows that for the construct of 
Environmental Impacts, the migration option “I don’t know” (M = 3.21, SD = .77) received a 
higher score than the migration options “No” (M = 2.69, SD = .77) and “Yes” (M = 2.60, SD = 
0.61). For the MIT, the results were similar for the construct, the migration option “I don’t 
know” (M = 3.23, SD = .84) received a higher score than the migration options “Yes” (M = 2.72, 
SD = 0.70) and “No” (M = 2.70, SD = .78). ANOVA result using MIT, showed significance on the 
Ties construct; however, further analysis did not confirm the previous results. 



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Conclusions, Discussion, and Recommendations 

 
Results of this research study are supported by previous studies that established an increased 
willingness to migrate from Salvadorian youth than Honduran youth (Congressional Research 
Service, 2019; Rodriguez et al., 2019). The comparison of youth from AEP and Non-AEP showed 
that investing in agricultural education programs reduces youth's intention to migrate. These 
results match with findings from Rhoda (1983), whose research indicated that agricultural 
program interventions might reduce migration intentions.  
 
The 17 % of Salvadorian and 35 % of Honduras youth are indecisive about migrating. Since this 
group is still undecided, the TPB suggests that this group's members can be influenced in one 
direction or the other. As such, it would be beneficial for programs to increase youth's 
involvement in community activities to increase their sense of belonging and social 
participation.  
 
The perception of the environment was significantly different in the intention to migrate 
variable and Bonferroni post-hoc confirmed the results. Consequently, future studies should 
explore the effect that the perception of the environment has on youth intentions to migrate 
and explore if other variables can affect this interaction.   
 
Furthermore, future studies could examine young people's perspectives in agricultural and 
other technical education programs in rural areas in other countries to create country profiles 
for rural agricultural education. Understanding the young people's ideas of their opportunities 
at home in agriculture and other areas will be important in rural development initiatives. 
 
Future research exploring rural youth's intention to migrate should examine the youth 
migration issue in more depth. Ethnographic qualitative studies would shed much needed light 
on the migration intentions of youth. Additional studies could be developed evaluating youth in 
other countries and the impact that other variables have on migration intention, such as access 
to inputs. Replicating this study in other regions besides El Salvador and Honduras could also 
strengthen the results and provide more robust data.  
 
It is important to evaluate the pros and cons of using different missing data techniques. For this 
study, the use of PDT and MIT displayed the same result for the Chi-square; however, ANOVA 
results, were different based on the missing data technique used demonstrating that results 
can be influenced, and as a result, the interpretation of the analysis can be influenced. 
Therefore, it is important to report the method used to manage missing data.  
 

 
 
 



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