Centering Central American Students in Higher Education Research: A Proposal for Central American Student Self-Report (CASSR)

Blanca E. Vega and Elizabeth Iris Rivera Rodas

On November 15, 2019, the Department of Chicano Studies at the University of California, Los Angeles (UCLA) voted to expand its name to include Central American Studies (Chavez-Martinez, 2019). While many celebrated this move, there were also a few detractors. Among them included writers who believed that such a move would contribute to the erasure of Chicanos (Maxwell, 2019). On both sides of this issue remained the agreement that Central Americans cannot be ignored; their relationship to the United States is distinct from other Latin*1 groups. Yet, the question was (and still remains): How should the story of Central Americans be included in a larger pan-ethnic Latin* context and, even more broadly, in the story of the United States?

There are seven Central American countries: Guatemala, Belize, Honduras, El Salvador, Nicaragua, Costa Rica, and Panama. Today, the Central American population consists of over 3.5 million people, with the largest populations of Central Americans from El Salvador, Guatemala, and Honduras; this is followed in sequence by Nicaragua, Panama, Costa Rica, and Belize (O’Connor et al., 2019).

The purpose of this chapter is to review current literature concerning the retention and persistence of Central American students in higher education and to propose a QuantCrit approach to understanding the post-secondary experiences of Central American students. The authors propose a study called the CASSR that focuses specifically on Central Americans, in a shift away from the study of Latin*s as a pan-ethnic group. The purpose of the self-study is to collect primary data about Central American students in post-secondary education. Using the literature that currently exists on Central Americans in higher education, the authors discuss the measures that should be used to develop instruments to further understand their persistence in higher education and assess interventions for Central American student success. Using a QuantCrit approach, the authors explore how primary data can be used to inform higher education practices and policies focused on this population of students.

Studying Central American Students Within a Latin* Student Context

At almost 20% of the United States population, Latin*s continue to be the largest racial/ethnic group in the country. Despite their long history in the United States, Latin*s continue to be studied as one large pan-ethnic group. Historically, Latin*s have experienced various aspects of the United States differently, including but not limited to immigration, education, health, and business. Disaggregating the Latin* population by country of origin reveals a deeper story about the relationship between the United States and the descendants of Latin American countries. This relationship has often been shaped by specific immigration and foreign aid policies for certain regions of Latin America. While the growth of the population may tell the story of resilience, it also reveals obstacles in the pursuit of education that Latin*s experience in the United States.

Interestingly, the term Latino was originally rejected over the use of the term Hispanic (Flores-Hughes, 2006). The term Hispanic was created to ensure the inclusion of various countries whose primary language is Spanish (p. 83). The originators of the term Hispanic chose this over Latino because the root “Latin” denoted descendants of European origin (p. 82). In either case, the Hispanic/ Latino category was designed to create a political group that would assert the need to address issues specific to this group in the United States. Thus, a panethnic label served dual purposes of bringing people together under one large umbrella group while simultaneously erasing the specific needs of groups that fall within the category Latin*.

Central Americans in the United States comprise the third-largest Latin* group and, as of 2017, this includes over 3.5 million people (see Table 11.1; O’Connor et al., 2019). The Central American population experienced its greatest growth between 1980 and 2017 with the greater Los Angeles, New York, Washington, DC, Miami, and Houston metropolitan areas being home to the majority of Central Americans (O’Connor et al., 2019). Additionally, three states are home to almost 50% of Central American immigrants: California (26%), Texas (12%), and Florida (11%). Four counties in the United States are home to the majority of Central American immigrants:

  • 1. Los Angeles County, California: Salvadorans (263,700) and Guatemalans (173,700);
  • 2. Hanis County, Texas: Salvadorans (105,000) and Hondurans (51,600);
  • 3. Miami-Dade County, Florida: Nicaraguans (78,700) and Costa Ricans (5,500); and
  • 4. Prince George’s County, Maryland: Salvadorans (43,500) and Guatemalans (14,400).

Unfortunately, despite the exponential growth of the population over the last 40 years, the experiences of Central Americans have not been centered in educational research (O’Connor et al., 2019). The education of Central American students continues to be homogenized within a larger Latin* group (Maldonado Dominguez, 2019), despite very distinct experiences with immigration, raciali-zation, and representation in the United States. Moreover, Central American student persistence in higher education is still relatively under-published in the field of higher education (Lau & Gordon, 2015). Another reason for this is that this group is often conflated with the Mexican population in the United States— further rendering Central Americans invisible within higher education (Maldonado Dominguez, 2019). In fact, Ek (2009) found that not only do Central Americans endure a process of Americanization, but they also undergo a process of “Mexicanization” (p. 405). By “Mexicanzation” Ek (2009) means that Central American students are often incorporated into Mexican American culture, a process that ignores Central American culture.

According to O’Connor et al. (2019), the following is known about the education of Central Americans:

• 47% of Central American immigrant adults over 25 years of age had less than

a high school diploma in 2017

TABLE 11.1 Country of Origin for Central American Immigrants in the United States, 2017

Region and Country

Number of Immigrants

Share (percent)

Central American (total)

3,527,000

100.0

El Salvador

1,402,000

39.7

Guatemala

959,000

27.2

Honduras

655,000

18.6

Nicaragua

263,000

7.5

Panama

107,000

3.0

Costa Rica

83,000

2.4

Belize

49,000

1.4

‘Other Central America

10,000

0.3

Source: Migration Policy Institute (MPI) tabulation of data from the U.S. Census Bureau 2017 American Community Survey (ACS).

'Other Central America includes individuals of more than one Central American origin.

  • • 26% of Central American immigrants had a high school degree
  • • 10% of Central American immigrants had received a bachelor’s degree or higher
  • • 55% of Guatemalans lack a high school diploma and tend to have lower educational attainment, as compared to other Central American groups
  • • 32% of Panamanians had a bachelor’s degree and have the highest educational attainment among all Central American groups

The educational experiences of Central Americans in the United States remain an area that deserves more visibility and research (Coronado & Paredes, 2018). Several studies have begun to address this gap. Coronado and Paredes (2018), for example, used testimonios to understand the educational experiences of first- and second-generation Central American youth. Abrego (2006) similarly studied the experiences of undocumented Central American and Mexican American students with regard to their educational aspirations. Abrego (2006) found that among participants in her study, learning about their immigration status early-on affected their motivation to persist. Menjivar (2008) more specifically focused on El Salvadoran and Guatemalan immigrants and educational aspirations. Menjivar (2008) also found that immigration status affects students’ educational motivations and aspirations. Finally, Linares and Maffini (2018) studied the impact of historical trauma on the educational experiences of 13 El Salvadoran students in higher education. They found that being identified as Latin* left them feeling misunderstood because of the challenges they face that are specific to Central American students and separate from other Latin* groups.

Researchers who have focused on Central American students in higher education have typically tried to understand success factors in their persistence in post-secondary education. For example, in seeking to understand how respondents’ families influenced academic achievement, Ong et al. (2006) found three important factors: ethnic identity, family interdependence, and parental support. Lau and Gordon (2019) similarly explored factors such as motives, aspirations, and difficulties that Central American students faced upon arriving to the United States. Specifically, for El Salvadorians and other Central American families entering Washington, DC, since the 1980s, their arrival to the United States has been found to shape their educational experiences (DeCapula & Marshall, 2011). Lau and Gordon (2019) argued that individuals who identify as students with limited or interrupted formal education (SLIFE) “receive inadequate support systems that hinder their success in American schools” (p. 106) and concluded that “it is uncommon for Central American families and students to receive the proper accommodations to succeed in schooling” (p. 109). For this reason, more studies are necessary to understand the unique experiences of Central American students in higher education.

Positionality

Aside from our roles as researchers of educational equity, we are also mothers of Central American children. As two self-identified Latina scholars, we are very aware of our privileged, yet hard-earned, positions in higher education. Dr. Blanca E. Vega is a native New Yorker and daughter of Ecuadorian immigrants who is partnered with an AfroCosta Rican teacher, from Brooklyn, New York. Dr. Elizabeth Iris Rivera Rodas is from The Bronx, New York, a granddaughter of Puerto Ricans who came to New York City in the 1950s, and is married to a Guatemalan American attorney who was raised in Trenton, New Jersey. Aside from our desire for educational justice for Central American people, our connection to Central America is through our partners and our children, who are undoubtedly affected by the policies enacted and implemented by the United States.

Quantitative Methods and Critical Race Theory

We began this chapter with many questions. We wondered: In what ways should higher education research focus on the educational experiences of Central Americans in higher education? How can we measure these experiences to better support this generation and future generations of Central American students? What measures should be used to study the effectiveness of practices and programs that wish to provide better support for Central American students in higher education? Finally, what should be included in a study about Central Americans that would honor the multiple identities embodied by Central American people? To explore these questions further, we operationalized QuantCrit.

QuantCrit uses descriptive and inferential statistics to document racial inequity and demonstrate assumptions embedded within Critical Race Theory (CRT) (Milkman et al., 2015). CRT suggests that inequities in education access, achievement, and success are due to the racist structure of school systems (Ladson-Billings & Tate, 1995). Traditionally, CRT utilizes qualitative approaches (Delgado, 1995; Dixson & Rousseau, 2006; Parker & Lynn, 2006; Yosso & Solorzano, 2006). There are relatively few examples of scholars adopting quantitative approaches to advance core CRT claims. However, some studies have provided evidence that CRT and quantitative methods can be used together to examine critical issues (e.g., Covarrubias & Velez, 2013; Garcia et al., 2018; Gillborn et al., 2018; Sablan, 2019; Solorzano et al., 2005; Zuberi, 2001).

Because quantitative research has often produced racial knowledge that functions to advantage white interests, education scholars have argued for the blending of CRT and quantitative research (Covarrubias, 2011; Covarrubias & Velez, 2013; Gillborn, 2010; Garcia et al., 2018; Teranishi, 2007; Zuberi & Bonilla-Silva, 2008). Gillborn et al. (2018) have theorized, defined, and coined the term QuantCrit with five guiding principles that can be used to guide quantitative critical race scholarship. QuantCrit builds on CritQuant, which describes an approach to quantitative policy analyses that utilizes two CRT principles: “permanence of racism and critique of liberalism” (Sullivan et al., 2010, p. 77).

The first guiding principle of QuantCrit looks at the centrality of racism. Racism cannot be measured concretely and is not easily quantifiable. As such, racism will not be obvious in statistical analyses. Instead of trying to find a race effect in statistical analyses, QuantCrit assumes that race and racism are important and complex aspects, and it attempts to find other ways of understanding this (Gillborn et al., 2018).

The second guiding principle of QuantCrit is that quantitative data are not neutral. Rather, they are gathered and analyzed in ways that reflect the interests of those in power. As a result, all statistical analyses must be examined for how these interests have shaped both collection and analyses. In the same vein, the third guiding principle of QuantCrit states that categories are not neutral either. Categories used within quantitative research must be critically evaluated to ensure that they are not used to disguise race inequity (Gillborn et al., 2018).

The fourth guiding principle of QuantCrit states that “data cannot speak for itself’ (Gillborn et al., 2018, p. 173). There is no single correct interpretation of statistical analyses. Therefore, the narrative of minoritized ethnic groups should be used to triangulate the analysis of quantitative data. The final QuantCrit guiding principle focuses on understanding that statistical analyses should be used to challenge dominant narratives and support social justice.

Lopez et al. (2018) used CRT to study the achievement gaps at a large, public university in the southwest region of the United States. They argued that QuantCrit paired with intersectional information can help reshape how we look at data (Lopez et al., 2018). In particular, Lopez et al. (2018) examined unique characteristics such as race, gender, and socioeconomic class as categories of life experience to reframe how one might study data. In doing so, they found substantial achievement gaps that remain ignored in traditional models because factors such as race/ethnicity, gender, and socioeconomic class have historically been ignored. Nearly every group had a much lower chance of graduating from college, compared to the reference group of high-income women (Lopez et al., 2018). This study identified several limitations with the data that lent itself to the principles of QuantCrit. In particular, the third guiding principle of QuantCrit indicates that categories are not neutral. The authors came across several limitations when analyzing the data due to categories within the data (Lopez et al., 2018). For instance, it was not possible to identify people as being mixed race until 2010. Because of this, the authors were not certain if those who were graduating were mixed race or single race. Such data is important as research has revealed various social inequalities in education and employment for Latin*s who identify as single race versus multi [mixed]-race (Lopez et al., 2018).

Another important limitation of the categories is that Hispanic origin and race are conflated. As such, it is impossible to find different outcomes for Latin*s across the color line. Along the same lines, researchers were also not able to recognize the difference among Latin*s by specific country of origin (Lopez et al., 2018). These limitations are important to consider when looking at the retention and persistence of Central American students. The majority of large databases do not differentiate Central Americans by their country of origin and, in many cases, race and ethnicity are the same category.

In QuantCrit, descriptive and demographic statistics can be used to demonstrate CRT assumptions (Lopez et al., 2018). While descriptive statistics can indicate important outcome differences between groups, they cannot be used to establish underlying causes that can guide implementation of interventions, such as programs that could produce effective results for Central American students in higher education. For example, Milkman et al. (2015) used experimental designs to examine racial discrimination in professors’ perceptions of prospective doctoral students. The authors recommended a similar study that looks at Central Americans in higher education using the CRT framework.

Predictive regression models, exploratory factor analysis, causal inference, and structural equation modeling are all areas that are yet to be fully developed in terms of using CRT. While there are some concerns that causal modeling may misinterpret racial issues (Holland, 2008), predictive and evaluative modeling is under-developed with regard to CRT. In order for QuantCrit to expand, it is necessary to move beyond descriptive statistics and move toward more causal modeling.

Recommendations: A Proposal for CASSR

We propose a QuantCrit approach to understanding access and persistence among Central American students in higher education. We believe that doing so involves using an intersectional approach that suggests how immigration factors, generational status, socioeconomic conditions, and family structure impact postsecondary success. Finally, any approach to studying the persistence and retention of Central Americans must disaggregate the groups within Central America to determine how different groups are affected. For too long, higher education research has focused on the homogenization of Latin*s—even though the data have suggested that regional, geographical, ethnic, racial, and immigration status matter in how Latin* students persist in higher education. In the next section, the authors outline and introduce the CASSR Model.

Self-Report Surveys

Self-report surveys are an effective way to collect information about the cultural and affective perspectives of students (Gonyea, 2005). Individual self-reports of CRT constructs, combined with an appropriate theoretical framework, can contribute to critical race dialogues if they are viewed with the appropriate lens

(Padilla, 2004). Our study recommends using self-report surveys that focus on issues of campus climate and racial/ethnic identity.

We recommend focusing on a large metropolitan area where there is a large concentration of Central American students who attend college. For instance, the City University of New York (CUNY) system enrolled approximately 5,000 Central American undergraduate students in the fall of 2017 (Office of Institutional Research and Assessment, 2018). While this is approximately 2 percent of the total CUNY undergraduate enrollment, CUNY is located in the New York-Newark—Jersey City, NY-NJ—PA metropolitan area, which had the second-largest concentration of Central American immigrants at approximately 389,000 Central American people (O’Connor et al., 2019).

CUNY has played a major role in educating Latin*s in New York City. We recommend doing a census survey of all Central American students in CUNY. This would help in attaining a goal sample size of 1,500 Central American students after a 30 percent response rate. We recommend oversampling the three smallest groups of Central American students in the CUNY system—Belizeans, Nicaraguans, and Costa Ricans. Each of these groups had less than 500 students in CUNY enrolled in the fall of 2017 (Office of Institutional Research and Assessment, 2018). By oversampling these three subgroups of Central Americans, we can ensure more reliable estimates from the survey results of these groups. In addition, the oversampling of immigrant Central Americans will be necessary because Latin* immigrants are under-represented throughout CUNY. The sample weights will be included in the final dataset so that results are weighted to their actual proportion of the population.

The self-report survey should also collect sociodemographic data on Central American student respondents, including race/ethnicity, ancestry, gender, and age. These variables are similar to what is collected by CUNY’s Office of Institutional Research and Assessment. By asking these questions, it will serve as a check to ensure that the survey is completed by Central American students and provides a basis for calculating oversampling weights. In addition, these demographic data would allow us as researchers to disaggregate by race, ethnicity, and ancestry.

However, in order to avoid some of the limitations of previous QuantCrit studies (e.g., Lopez et al., 2018), we recommend that the categories for the sociodemographic data are all-inclusive and allow for students to identify as multiple races and multiple national origins. It is recommended that the survey categories are mutually exclusive and exhaustive, include the option of “other” and self-selection, and allow for multiple choices to be selected.

Kiyama-Marquez et al. (2015) provided a useful model to help the authors advance our recommendations for a proposal for a QuantCrit approach to the study of retention and persistence in higher education among Central American students. The importance of this model is the suggestion that higher education institutions must be culturally relevant and responsive to the needs of its students.

However, the model and other research on Latin* student success does not yet include an indicator for culturally sustaining (see Paris, 2012) practices for Latin* student success in higher education. We posit that this proposal for the CASSR is one way to ensure that we are adhering to a culturally sustaining practice in higher education administration and research for Central American students.

We propose that this self-report survey should include indicators of campus climate and campus culture that will help researchers understand the ways that individual institutions are responding to the specific needs of Central American students. Museus et al. (2016) found that the Culturally Engaging Campus Environments (CECE) scale exhibits a high level of content and construct validity. Therefore, the use of the CECE model as the basis for the CASSR would prove to be a useful tool for measuring campus environments and their impacts on Central Americans. Questions would also focus on how aware higher education administrators are of the national climate surrounding Central American people. Additionally, how are the institutions ensuring that their campuses are not silent on these issues so that Central American students can feel visible and cared for?

Part of the CASSR would also include generational status and information about year of entry for the students’ families. Unlike requests for information about citizenship, this scale would allow researchers and administrators to understand the history of immigration patterns that could reveal a history of oppressive immigration and foreign policies to the region of Central America. Research has clearly determined that such policies have had negative consequences for Central American people’s life chances, especially in health, employment, and education.

In order to analyze the latent constructs of campus climate and ethnic identity— two unobservable constructs that are linked to college persistence and retention— reliability tests and factor analyses are needed. Factor analysis determines how underlying factors predict variance in the scale items. Once factor analyses are conducted, latent variables for campus climate and ethnic identity can be calculated using the scales from the survey. By triangulating survey data with administrative data from various universities, it is possible to conduct multi-level logistic regressions to predict the retention and persistence of Central American students in higher education. More importantly, by collecting specific data on national origin, generational status, and race, these logistic regressions could identify differences across subgroups of Central American higher education students.

Recommendations for Practice

In addition to the proposed CASSR model, higher education administrators or researchers should seek to demonstrate a culturally sustainable practice to ensure higher education persistence among Central American students by engaging in the following:

  • 1. Partner with student organizations focused on Central American students. In so doing, higher education administrators would demonstrate a deeper commitment to understanding the needs of Central American students.
  • 2. Consider doing an audit on the post-secondary curriculum to assess how Central American studies are included or not.
  • 3. Rethink collecting post-secondary institutional data on Central American students and consider how these data may reveal immigration status. A review of how these data are collected could reveal important findings about how the university treats immigration status.
  • 4. Connect and become familiar with local organizations that work with precollege and post-secondary Central American students. Ensuring that the university provides ample opportunities for Central American students and accomplices to dedicate their time to supporting community needs will send a direct message to the Central American community that the university is committed to supporting them.
  • 5. Intentionally include Central American students in research and other professional opportunities. It is vital to ensure that the future of Central American Studies be authored and directed by Central American people and their descendants.
  • 6. Explore how the processes of Americanization and Mexicanization possibly affect Central American colleges students and their families.

Note

1 The term Latin* (pronounced Latin) is used to align with members of the Latin* community who seek to ensure more gender and racial inclusiveness. More recently LatinX has been used to infer more gender inclusivity; the X replaces “o” or “a” as a way to break from colonial grammar which assigns gender to o and a in the Spanish language. Salinas (2020) suggested that the variations in understandings among Latin* identified people mean that, as scholars, we should use Latin*, as many Latin*s still do not accept LatinX. Latin* aims to be an inclusive term to encompass existing labels such as Latinx, Latine, Latini, Latinu, Latino, Latina, Latina/o, and Latin@, and is a place holder for other emerging terms (Salinas, 2020).

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