Undergraduate Dissertation Problems: An Aggregate Cross-Sectional Analysis from Five Latin American Universities
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| Title: | Undergraduate Dissertation Problems: An Aggregate Cross-Sectional Analysis from Five Latin American Universities |
|---|---|
| Language: | English |
| Authors: | Boris Christian Herbas-Torrico (ORCID |
| Source: | Studies in Higher Education. 2025 50(4):878-895. |
| Availability: | Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: | Y |
| Page Count: | 18 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research Tests/Questionnaires |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Foreign Countries, Undergraduate Students, Theses, Research Problems, Educational Resources, Student Characteristics, Supervision, Time to Degree, Influences |
| Geographic Terms: | Argentina, Bolivia, Chile, Colombia, Mexico |
| DOI: | 10.1080/03075079.2024.2359050 |
| ISSN: | 0307-5079 1470-174X |
| Abstract: | Latin America may have higher attrition rates than previously assumed. Many students in Latin America do not graduate on time because they cannot complete the traditional undergraduate dissertation. This paper examines the factors contributing to delayed graduation for a context-specific variable: dissertation problems. We designed a questionnaire survey based on multi-item scales and surveyed 480 university students from Argentina, Bolivia, Chile, Colombia, and Mexico. Aggregate-cross-sectional data and multilevel structural equation modeling show that institutional assistance and supervision quality [personal problems] reduce [increase] dissertation problems. These results may enable university authorities to identify undergraduate students with dissertation problems and thus improve their strategies for reducing attrition rates. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1498232 |
| Database: | ERIC |
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| FullText | Links: – Type: pdflink Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwFUXc5fEI8up90gkg-cQ9yfAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDAefVdYAHYZ83JZfSgIBEICBm4QbU_N8K9sAc8LmuFJf9Okgn-yQkD5ztmsvzYnp97WRasnF6YPv2m_UKiQh1K-jDayTnG3SqGPOfqWmxDkksO2T3v0sJ4_HK5xakn6FqU7ZT2u8ppJvmtjNfbiHpPtxNuKGefyTitYwU2_pmTxbKYUXEeHgHxgL6zjS95CohVWvDXJXjRbuOYSZd7oP2lDKncbmtgDuN7NtvHPG Text: Availability: 1 Value: <anid>AN0184106902;she01apr.25;2025Apr01.06:16;v2.2.500</anid> <title id="AN0184106902-1">Undergraduate dissertation problems: an aggregate cross-sectional analysis from five Latin American universities </title> <p>Latin America may have higher attrition rates than previously assumed. Many students in Latin America do not graduate on time because they cannot complete the traditional undergraduate dissertation. This paper examines the factors contributing to delayed graduation for a context-specific variable: dissertation problems. We designed a questionnaire survey based on multi-item scales and surveyed 480 university students from Argentina, Bolivia, Chile, Colombia, and Mexico. Aggregate-cross-sectional data and multilevel structural equation modeling show that institutional assistance and supervision quality [personal problems] reduce [increase] dissertation problems. These results may enable university authorities to identify undergraduate students with dissertation problems and thus improve their strategies for reducing attrition rates.</p> <p>Keywords: Dissertation problems; delayed graduation; Latin America; institutional assistance; research expertise; supervision quality</p> <hd id="AN0184106902-2">Introduction</hd> <p>What makes Latin American students continue or drop out of their university studies? This is a burning question for Latin American universities because, compared to other parts of the world, universities do not have agreements among countries to ensure comparability in the standards and capabilities to determine the quality of their attrition statistics. These questions occupy the thoughts of Latin American professors, government authorities, and university presidents. Latin American universities may consider measuring student attrition rates across countries as a potential strategy to minimize dropouts and enhance academic outcomes. Ideally, they should understand what factors, such as teaching styles, workloads, or student characteristics, affect retention, delayed graduation, and attrition. However, due to the lack of comparability standards, each university and country has its own standards to measure the most critical factors to determine attrition rates.</p> <p>Despite educational advancements, countries are still struggling with significant gaps in educational attainment among adults. As of 2022, 20.0% of adults aged 25–64 did not have an upper secondary qualification, while 40.0% held a university degree (OECD [<reflink idref="bib68" id="ref1">68</reflink>]). Notably, in Latin America, 66.0% of high school students enroll in university at 18, according to OECD data (OECD [<reflink idref="bib67" id="ref2">67</reflink>]). Chile has the highest rate of first-time university enrollment among Latin American countries, with 89.0%. However, the presence of older students may contribute to higher enrollment rates. Additionally, the data shows that only 49.0% of Latin American university students graduate by age 26, indicating a significant potential for delayed graduation, with some students graduating significantly beyond the typical four- to five-year timeframe.</p> <p>Moreover, Latin American higher education enrollment has significantly increased in recent years. Between 2000 and 2013, the proportion of 18–24-year-olds enrolled in higher education institutions in the region more than quadrupled, from 21.0% to 43.0% (Von Hippel and Hofflinger [<reflink idref="bib95" id="ref3">95</reflink>]). This rise was particularly notable for women and low-income students, who saw the largest enrollment increases, especially in countries like Chile, Peru, Bolivia, and Ecuador.</p> <p>Latin America has around 20 million students enrolled in 60,000 programs across 10,000 institutions. Universities track the effectiveness of higher education initiatives by monitoring enrollment and degree completion rates, but dropouts may hinder the desired impact of increased access to education (Gold and Albert [<reflink idref="bib38" id="ref4">38</reflink>]; Tam [<reflink idref="bib87" id="ref5">87</reflink>]).</p> <p>Moreover, universities in Latin America graduate just approximately half of their enrolled students (Ferreyra et al. [<reflink idref="bib35" id="ref6">35</reflink>]), compared to about two-thirds of registered students in the United States (Tinto [<reflink idref="bib90" id="ref7">90</reflink>]). Students who decide to abandon their studies do more than squander their own time, money, and opportunity costs. In subsidized economic systems like Latin America, they also waste taxpayers' money.</p> <p>Latin American universities may underestimate student attrition due to inconsistent methods and a focus solely on reducing it. Available regional research prioritizes lowering attrition rates (UTP [<reflink idref="bib93" id="ref8">93</reflink>]), but accurate assessment requires standardized metrics across countries. Currently, diverse methods exist, often limited to formal withdrawals. E.g. in Bolivia, no regulations exist, leading to inflated enrollment figures due to students seeking subsidies (Red Uno [<reflink idref="bib75" id="ref9">75</reflink>]). Argentina, however, uses nuanced classifications, including academic failure, career change, and transfers (Oloriz and Fernández [<reflink idref="bib69" id="ref10">69</reflink>]). These methods often overlook students enrolled beyond expected graduation timeframes, a significant predictor of eventual dropout. This lack of standardized metrics hinders comparisons and masks the true extent of the problem. Therefore, despite research efforts, actual attrition rates are likely higher than reported.</p> <p>These arguments suggest the need for studies from Latin America that analyze the higher education dropout process from other angles, such as the delayed graduation perspective. Subsequently, effective institutional policies can be developed to measure dropout rates and adequately reduce delayed graduation rates. Moreover, as our argument suggests, only focusing on measures to reduce dropout rates can be problematic when opportunities for students to complete their studies on time are not guaranteed. Hence, our research question asks: to what extent do the challenges faced by Latin American undergraduate students during their dissertation projects contribute to dissertation difficulties?</p> <p>As undergraduate programs in Latin America have contextual characteristics, our research focused on dissertation problems as a specific cause of delayed graduation. For this purpose, we surveyed 480 graduating students from five Latin American universities. Our aggregate results suggest that dissertation problems depend on institutional assistance, personal problems, and supervision quality.</p> <p>The following is how the rest of the article is structured. First, we briefly present the literature review and the context of our research. Next, we present the development of hypotheses section, providing a conceptual review of contextual, supervision, and student factors within the context of dissertation problems. The primary constructs in connection to dissertation problems are summarized in Figure 1. The methodology is then discussed, followed by an analysis of the findings. Finally, we discuss the study's contributions, limitations, future research directions, and conclusions.</p> <p>Graph: Figure 1. Conceptual framework and hypotheses.</p> <hd id="AN0184106902-3">Literature review and background</hd> <p></p> <hd id="AN0184106902-4">Determinants of delayed graduation</hd> <p>Delayed graduation is a constant subject of academic research (Aina et al. [<reflink idref="bib2" id="ref11">2</reflink>]; Aina and Pastore [<reflink idref="bib3" id="ref12">3</reflink>]). The literature has four dominant models: the undergraduate dropout model process model (Spady [<reflink idref="bib84" id="ref13">84</reflink>]), the institutional departure model (Tinto [<reflink idref="bib89" id="ref14">89</reflink>]), the student attrition model (Bean [<reflink idref="bib11" id="ref15">11</reflink>]), the student–faculty informal contact model (Pascarella [<reflink idref="bib71" id="ref16">71</reflink>]), the nontraditional undergraduate student attrition model (Bean and Metzner [<reflink idref="bib12" id="ref17">12</reflink>]), and the student retention integrated model (Cabrera, Nora, and Castaneda [<reflink idref="bib20" id="ref18">20</reflink>]). Specifically, the undergraduate dropout process model (Spady [<reflink idref="bib84" id="ref19">84</reflink>]) indicates that there are two variables, or systems, that could affect a student's permanence in an educational institution: academic factors (grades and intellectual development) and social factors (friendship and normative congruence). On the other hand, the institutional departure model (Tinto [<reflink idref="bib89" id="ref20">89</reflink>]) ultimately places greater responsibility on the student to build their relationship with the university. Through his model, Tinto ([<reflink idref="bib89" id="ref21">89</reflink>]) suggests that students bring associations and expectations in their first year at university. These associations and expectations may increase or deteriorate over time, depending on the student's capacity to integrate into the institutional community and determine their long-term permanence at the university. Next, the student attrition model (Bean [<reflink idref="bib11" id="ref22">11</reflink>]) was based on the work of his predecessors. It varied from previous studies in that it claimed that the reasons for student dropout are comparable to those for an employee unsatisfied with his job or company. Bean ([<reflink idref="bib11" id="ref23">11</reflink>]) found it advantageous to substitute similar characteristics of the college experience, such as GPA, student progress, and vocational relevance, for evident disparities between a student and an employee, such as compensation. Bean [<reflink idref="bib11" id="ref24">11</reflink>] would later incorporate a set of four instrumental variables in a revised version of his theory: background, organizational features, environment and attitudes, and outcome variables.</p> <p>On the other hand, the student–faculty contact model (Pascarella [<reflink idref="bib71" id="ref25">71</reflink>]) suggests that students' more casual interactions with faculty members would improve their institutional commitment and, as a result, reduce the likelihood of attrition. In particular, Pascarella ([<reflink idref="bib71" id="ref26">71</reflink>]) claimed that this assumption was supported, especially for students with a low level of institutional engagement. Next, the nontraditional undergraduate student attrition model was suggested by Bean and Metzner ([<reflink idref="bib12" id="ref27">12</reflink>]). They revised it and updated it for nontraditional students: commuter students. Specifically, commuter students do not reside in university housing, commute to college daily, live with their families, and might have full- or part-time jobs. Bean and Metzner ([<reflink idref="bib12" id="ref28">12</reflink>]) found that the importance of institutional integration and building a college culture do not have the same hierarchy for commuter students. For them, environmental and external factors are the primary determinants of college persistence. Lastly, the student retention integrated model (Cabrera, Nora, and Castaneda [<reflink idref="bib20" id="ref29">20</reflink>]) merged the variables of the student retention model of Tinto ([<reflink idref="bib89" id="ref30">89</reflink>]), Bean ([<reflink idref="bib11" id="ref31">11</reflink>]), and Cabrera, Nora, and Castaneda ([<reflink idref="bib20" id="ref32">20</reflink>]). This integrated model would consider both the individual student's characteristics and the institution's academic and social community. The findings suggested that integrating both models would result in a more complex interplay of personal, environmental, and institutional factors, resulting in a more comprehensive model of student persistence.</p> <hd id="AN0184106902-5">The general context of higher education in Latin America</hd> <p>Compared to developed countries, the Latin American region faces a problem of great magnitude due to the globalization of the economy, science, and technology. The rising demand for higher education to boost productivity and competitiveness in the global market forces educational systems, especially universities, to constantly improve their programs and study methods in search of academic excellence (Brunner and Labraña [<reflink idref="bib18" id="ref33">18</reflink>]). With its unequal regionalized development, each country in Latin America faces these challenges in different economic conditions with varying degrees of inequality (Murakami and Hamaguchi [<reflink idref="bib62" id="ref34">62</reflink>]).</p> <p>In particular, it is essential to highlight the importance of explaining the quantitative growth of enrollment of the articulation between the social demands for education and the political sensitivity to satisfy them. For example, in Colombia, the expansion of higher education enrollment went from less than 5.0% in 2002 to above 25.0% in 2018. Similarly, in Honduras, enrollment increased from below 3.0% between 1999 and 2003 to an average of 10.0% between 2014 and 2018. The figure rises to 35.0% in Colombia and Bolivia (UNESCO [<reflink idref="bib92" id="ref35">92</reflink>]).</p> <p>Nevertheless, despite these promising enrolling statistics, economic inequality keeps rising in Latin America. Currently, access to higher education is below 10.0% in the lowest income percentile compared with 70.0% in the highest ones. Disadvantaged ethnic groups are 15.0% less likely to access higher education. In Mexico, for example, only a quarter of indigenous students from 18 to 22 years were enrolled in universities in 2010, compared with more than a third of their non-indigenous counterparts (UNESCO [<reflink idref="bib92" id="ref36">92</reflink>]).</p> <p>Adding to this problem, university degrees in Latin America are long and tedious. The time it takes a university student in Latin America to complete his degree is 36.0% longer than in the rest of the world. For example, university students from Chile, on average, delay their graduation by about an additional 33.0% of their study plan (CINDA [<reflink idref="bib26" id="ref37">26</reflink>]). The previous statistics imply that Latin American students spend more years as university students and earn lower salaries, similar to those with secondary education. Excessive time also has a problematic side: students often need to go out to work to complete their studies, but at the same time, delay their graduation or end up dropping out because they are overwhelmed by work responsibilities and do not see the light at the end of the tunnel (The World Bank Group [<reflink idref="bib98" id="ref38">98</reflink>]).</p> <hd id="AN0184106902-6">The context of graduation delay research on higher education in Latin America</hd> <p>Available literature shows that most theoretical models to study graduation delay are from developed countries. In Latin America, most current student attrition models show the existence of a multiplicity of dimensions that depend on the country where the data was collected. For example, in Colombia, student attrition and low graduation rates are associated with conceptual and discursive difficulties in writing the dissertation and the demand for time and effort dedicated to dissertation writing (Ochoa-Sierra and Cueva-Lobelle [<reflink idref="bib66" id="ref39">66</reflink>]). In the case of Argentina, graduation delay is influenced by personal problems, working while studying, lack of dissertation completion, and late-career choices (Bondar et al. [<reflink idref="bib17" id="ref40">17</reflink>]). It is essential to mention that most graduation delay studies in Latin America are published in the Proceedings of the Latin American Conference of Attrition on Higher Education <emph>(CLABES,</emph> its acronym in Spanish<emph>)</emph> (see: UTP [<reflink idref="bib93" id="ref41">93</reflink>])<emph>.</emph> In this conference, the institutional departure model (Tinto [<reflink idref="bib89" id="ref42">89</reflink>]) dominates the study of graduation delay in Latin America. Moreover, most of these studies are qualitative, with an overrepresentation of results from specific countries: Chile, Colombia, and Mexico (Herbas-Torrico, Cabero-Villazón, and Titichoca-Santana [<reflink idref="bib41" id="ref43">41</reflink>]). These aspects make those studies challenging to reproduce or generalize to the rest of Latin America. Therefore, there is a need for quantitative cross-national graduation delay research from Latin America, which our study attempts to solve.</p> <hd id="AN0184106902-7">Determinants of graduation delay in Latin America</hd> <p>Our research aims to understand whether the factors influencing undergraduate graduation delays relate to students' challenges during their dissertation projects. Unlike studies forecasting the exact timing of degree completion, the Latin American tradition of undergraduate dissertations offers a valuable proxy: it provides insight into students' professional abilities, research skills, and problem-solving capabilities in a chosen area. We focus on the undergraduate level because it is a period of acquiring broad professional competencies rather than the specialized research focus of graduate programs.</p> <p>As suggested by our literature review, descriptions of differences in undergraduate completion rates or time to graduation can be found in different models and are frequently difficult to separate. However, due to the lack of literature on the determinants of undergraduate dissertation problems, as a proxy for graduation delay, we categorized them into three groups (van de Schoot et al. [<reflink idref="bib94" id="ref44">94</reflink>]):</p> <p></p> <ulist> <item> <emph>Contextual factors.</emph> These are institutional resources or facilities available for the dissertation project.</item> <p></p> <item> <emph>Student factors</emph>. These factors include gender, age, academic performance, research competencies, enrollment year, marital status changes, number of children, starting date of a dissertation project, and predicted dissertation date.</item> <p></p> <item> <emph>Supervision factors.</emph> These factors entail the assistance and support of the supervisor to the student.</item> </ulist> <p>Figure 1 depicts the research model. Negative drivers of student dissertation problems are predicted to be contextual and supervision factors. In particular, as we propose in the next section, institutional assistance and supervision quality are likely negatively associated with dissertation problems. In the case of student factors (i.e. insufficient research expertise and personal problems), we expect them to increase dissertation problems. Therefore, the main contribution of our research is that in addition to considering variables from previous graduation delay models available in the literature, we study the influence of those variables on the problems students faced during their undergraduate dissertation as a requirement for graduation in Latin America. Our results will add another dimension of discussion to the literature on graduation delay. Our research explicitly addresses the overrepresentation of graduation delay studies from developed countries with an original study from Latin America to reduce the harmful impact of bias in scientific reasoning since people from diverse backgrounds approach an issue from different points of view and can increase awareness of biases (Bangera and Brownell [<reflink idref="bib9" id="ref45">9</reflink>]; Intemann [<reflink idref="bib44" id="ref46">44</reflink>]).</p> <hd id="AN0184106902-8">Development of hypotheses</hd> <p></p> <hd id="AN0184106902-9">The influence of contextual factors on dissertation problems</hd> <p>As previously mentioned, contextual factors are institutional resources or the availability of facilities for the dissertation project (van de Schoot et al. [<reflink idref="bib94" id="ref47">94</reflink>]). These factors include how universities support students socially, financially, and academically, providing opportunities for professional development (Sverdlik et al. [<reflink idref="bib86" id="ref48">86</reflink>]). In particular, in the study context of delayed graduation, available research suggests the need for institutional interventions to tackle this problem. Moreover, available literature points out the importance of institutional assistance (e.g. career services, counseling services, financial aid offices, writing centers) to help students achieve timely graduation (Hermon and Hazler [<reflink idref="bib42" id="ref49">42</reflink>]; Remenick [<reflink idref="bib76" id="ref50">76</reflink>]). Moreover, institutional assistance allows helpful and beneficial tutoring in the writing quality of student dissertations (Solmaz [<reflink idref="bib83" id="ref51">83</reflink>]). Therefore, available research suggests that the availability of institutional assistance negatively impacts dissertation problems.</p> <p> <bold>H1:</bold> Institutional assistance negatively influences dissertation problems.</p> <hd id="AN0184106902-10">The influence of student factors on dissertation problems</hd> <p>Undergraduate dissertations are milestone experiences where students address a research issue within a disciplinary framework while supervised (Ashwin, Abbas, and McLean [<reflink idref="bib7" id="ref52">7</reflink>]). They are required for many undergraduate degrees to provide a bridge between coursework and independent study and may yield publishable results. Moreover, compared to doctoral students, undergraduate students have none or limited prior independent research experience (Cook [<reflink idref="bib29" id="ref53">29</reflink>]), may have a lower interest in conducting research (Cook [<reflink idref="bib29" id="ref54">29</reflink>]), and must complete their research in a shorter timeframe (Rowley and Slack [<reflink idref="bib80" id="ref55">80</reflink>]). Further, students' low research expertise causes uncertainties and obstacles in gathering data and managing research time (Todd, Bannister, and Clegg [<reflink idref="bib91" id="ref56">91</reflink>]). Therefore, insufficient research expertise should increase dissertation problems.</p> <p> <bold>H2a:</bold> Insufficient research expertise positively influences dissertation problems.</p> <p>Many undergraduate students strive to reconcile social and familial commitments with academic work, requiring strict prioritization and resource allocation decisions. As a result, compared to academic achievement, personal life goals, like physical health and partner relationships, become undervalued and lead to feelings of imbalance and disturbance (Maloney and McCormick [<reflink idref="bib55" id="ref57">55</reflink>]). Due to the rigors of their research, undergraduate students report the absence of social relationships as a causal factor in delayed graduation (Bocsi et al. [<reflink idref="bib15" id="ref58">15</reflink>]). Similarly, other researchers indicate that lack of leisure time and social connections were related to more significant burnout (Ziegelstein [<reflink idref="bib102" id="ref59">102</reflink>]) and depression (Armstrong and Oomen-Early [<reflink idref="bib6" id="ref60">6</reflink>]) among undergraduate students, thus causing problems with their research. Therefore, student personal problems should intensify dissertation problems.</p> <p> <bold>H2b:</bold> Personal problems positively influence dissertation problems.</p> <hd id="AN0184106902-11">The influence of supervision factors on dissertation problems</hd> <p>According to previous research performed with undergraduate dissertation supervisors, successful supervision depends on giving specific and clear guidance, supporting and establishing student confidence, and encouraging student independence and progress (Roberts and Seaman [<reflink idref="bib77" id="ref61">77</reflink>]; Roberts and Seaman [<reflink idref="bib78" id="ref62">78</reflink>]). Moreover, the supervisor's role in the undergraduate research process depends on the quality of supervision, including precise and timely feedback, frequent meetings, supportive and collegial relationship, and encouragement (Latona and Browne [<reflink idref="bib50" id="ref63">50</reflink>]). Also, compared to previous studies of graduation delay, the literature suggests that the relationship between students and supervisors can play a significant role in student satisfaction, persistence, and academic achievement (Roberts and Seaman [<reflink idref="bib77" id="ref64">77</reflink>]; Roberts and Seaman [<reflink idref="bib78" id="ref65">78</reflink>]). Likewise, previous research on undergraduate dissertation supervision found that supervisors believe successful supervision depends on giving specific and clear guidance, supporting and establishing student confidence, and encouraging student independence and progress (Roberts and Seaman [<reflink idref="bib77" id="ref66">77</reflink>]; Roberts and Seaman [<reflink idref="bib78" id="ref67">78</reflink>]). Therefore, available literature suggests that supervision quality should significantly reduce student dissertation problems.</p> <p> <bold>H3:</bold> Supervision quality negatively influences dissertation problems.</p> <hd id="AN0184106902-12">Method</hd> <p></p> <hd id="AN0184106902-13">Measures</hd> <p>Initially, we created a questionnaire survey based on multi-item scales to evaluate our hypothesis. All scales and the wording of their measurement items, and their literature references are included in the Appendix. Maher, Ford, and Thompson ([<reflink idref="bib53" id="ref68">53</reflink>]) provided the scales for insufficient research expertise, personal problems, and institutional assistance. We used reverse coding for the measurement items related to insufficient research expertise and institutional assistance. This approach aimed to motivate students to express their understanding of the scientific method and to assess their knowledge of the resources and support services available to senior students within their institutions (see Appendix). Moreover, reverse coding mitigates response biases, such as acquiescence (Wong, Rindfleisch, and Burroughs [<reflink idref="bib97" id="ref69">97</reflink>]) or inattention bias (Abbey and Meloy [<reflink idref="bib1" id="ref70">1</reflink>]), encouraging attentive responses and balancing the directionality of scale items.</p> <p>We measured supervision quality with van de Schoot et al. ([<reflink idref="bib94" id="ref71">94</reflink>]) scale. Furthermore, we measured dissertation problems based on Matin and Khan ([<reflink idref="bib56" id="ref72">56</reflink>]). Moreover, as control variables, we measured age, gender, marital status, work status, regular class attendance, public school, marital status change during studies, children, and study duration. We developed a questionnaire that can be used across countries. To ensure accuracy, we translated it from English to Spanish using a forward–backward approach with independent translators. Considering Latin American idiomatic expressions, we thoroughly reviewed the translation to ensure its semantic equivalence. Before the survey was launched, we conducted a pilot test in each country, which helped us make necessary adjustments.</p> <hd id="AN0184106902-14">Sample</hd> <p>To ensure accurate and diverse findings, we selected five universities from Argentina, Bolivia, Chile, Colombia, and Mexico based on specific criteria. These criteria included:</p> <p></p> <ulist> <item> - <emph>Geographic representation:</emph> universities were chosen strategically from different regions across the area to provide diverse perspectives.</item> <p></p> <item> - <emph>Student population and academic programs:</emph> we selected institutions with varied student demographics and program offerings to ensure a representative sample.</item> <p></p> <item> - <emph>Core values alignment:</emph> each university reflected values of strong teaching, research, and community service, which were essential for our research.</item> </ulist> <p>Our study targeted 9,176 undergraduate students across five universities who were working on their dissertations. We used Cochran's formula ([<reflink idref="bib28" id="ref73">28</reflink>]) with adjustments Bartlett, Kotrlik, and Chadwick ([<reflink idref="bib10" id="ref74">10</reflink>]) suggested to determine a suitable sample size. Based on similar studies conducted in Latin America regarding undergraduate attrition (McMinn et al. [<reflink idref="bib57" id="ref75">57</reflink>]; Nordin and Margareta [<reflink idref="bib65" id="ref76">65</reflink>]), we assumed a response rate of 54.0%. This allowed us to calculate an expected sample size of 690 students. This sample size complies with the recommended sample sizes of at least 100–200 participants for hierarchical structural equation models (Fan et al. [<reflink idref="bib33" id="ref77">33</reflink>]; Iacobucci [<reflink idref="bib43" id="ref78">43</reflink>]).</p> <p>We got help from university authorities and professors, spreading the link to all undergraduate programs across the universities focused on students doing their undergraduate dissertations. The survey was accessible from participating universities based on the IP distribution. The questionnaire was completed partially by 1452 of the 2204 students who accessed it. After removing data with missing responses to any variable, the final sample size is 480 respondents (Argentina, 20.6%; Bolivia, 21.1%; Chile, 18.4%; Colombia, 19.5%; Mexico, 20.4%). Notably, this reduction in sample size is due to using a control question (starting date of their dissertation project), which did not allow students who accessed it to continue answering the survey if they were not in the last year of their studies and working on their dissertations. Hence, our study achieved a response rate of 69.0%, exceeding our assumed response rate (54.0%). To address potential biases stemming from the reduction in responses within our sample, we investigated the existence of a possible self-selection bias as an indication that missing data might not be completely random (MNAR). The literature suggests that self-selection bias poses a significant challenge in studies focusing on student attrition, as non-participation reasons may correlate with unobserved factors (Maher et al. [<reflink idref="bib54" id="ref79">54</reflink>]). Moreover, for some variables, there was a high presence of missing data (&gt;50.0%). This might indicate self-selection bias or other unknown biases in our data. Consequently, we did not consider using multiple imputation techniques or model estimation methods, such as Full Information Maximum Likelihood (FIML), because they may yield biased estimators due to the violation of the underlying assumption of multi-normality for accurate imputation of missing values (Clavel, Merceron, and Escarguel [<reflink idref="bib27" id="ref80">27</reflink>]; Schminkey, von Oertzen, and Bullock [<reflink idref="bib82" id="ref81">82</reflink>]). Next, following the recommendations of Gibson and Olejnik ([<reflink idref="bib37" id="ref82">37</reflink>]), we opted for list-wise deletion to mitigate the possible influence of self-selection bias. This approach effectively estimates random effects in multilevel structural equation models (Gibson and Olejnik [<reflink idref="bib37" id="ref83">37</reflink>]), crucial for capturing country-specific variations in cross-country analyses like ours.</p> <p>In the final sample, 65.0% of the respondents are female, with an average age of 24. Moreover, 54.1% of students entered university before 2014; 46.9% attended lectures regularly, and 51.7% came from public schools. Additionally, 11.0% of students changed marital status during their undergraduate studies, 6.4% changed marital status during their dissertation projects, 6.4% changed marital status during the completion of their dissertation projects, 17.6% had children underage, 50.0% started their dissertation before the ninth semester, and 50.0% planned to finish their dissertation before the tenth semester. Table 1 presents the descriptive statistics and correlations of the variables. Due to the variables' reverse coding, institutional assistance, and insufficient research expertise, showed a reversed direction.</p> <p>Table 1. Pearson correlation coefficients and descriptive statistics of multi-item measures.</p> <p> <ephtml> &lt;table&gt;&lt;thead valign="bottom"&gt;&lt;tr&gt;&lt;td&gt;Variables&lt;/td&gt;&lt;td&gt;Correlations&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;1&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;3&lt;/td&gt;&lt;td&gt;4&lt;/td&gt;&lt;td&gt;5&lt;/td&gt;&lt;/tr&gt;&lt;/thead&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td&gt;&lt;italic&gt;Constructs:&lt;/italic&gt;&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; 1. Institutional assistance&lt;/td&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;italic&gt;(reverse coded)&lt;/italic&gt;&lt;/td&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; 2. Insufficient research expertise&lt;/td&gt;&lt;td&gt;&amp;#8722;.15&lt;/td&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;italic&gt;(reverse coded)&lt;/italic&gt;&lt;/td&gt;&lt;td&gt;(&amp;#60;.001)&lt;/td&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; 3. Personal problems&lt;/td&gt;&lt;td char="."&gt;.21&lt;/td&gt;&lt;td&gt;&amp;#8722;.20&lt;/td&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;(&amp;#60;.001)&lt;/td&gt;&lt;td&gt;(&amp;#60;.001)&lt;/td&gt;&lt;td /&gt;&lt;td /&gt;&lt;td /&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; 4. Supervision quality&lt;/td&gt;&lt;td&gt;&amp;#8722;.08&lt;/td&gt;&lt;td char="."&gt;.53&lt;/td&gt;&lt;td&gt;&amp;#8722;.13&lt;/td&gt;&lt;td /&gt;&lt;td /&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;(0.046)&lt;/td&gt;&lt;td&gt;(&amp;#60;.001)&lt;/td&gt;&lt;td&gt;(&amp;#60;.001)&lt;/td&gt;&lt;td /&gt;&lt;td /&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; 5. Dissertation problems&lt;/td&gt;&lt;td char="."&gt;.32&lt;/td&gt;&lt;td&gt;&amp;#8722;.23&lt;/td&gt;&lt;td char="."&gt;.49&lt;/td&gt;&lt;td&gt;&amp;#8722;.20&lt;/td&gt;&lt;td /&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;(&amp;#60;.001)&lt;/td&gt;&lt;td&gt;(&amp;#60;.001)&lt;/td&gt;&lt;td&gt;(&amp;#60;.001)&lt;/td&gt;&lt;td&gt;(&amp;#60;.001)&lt;/td&gt;&lt;td /&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;Descriptive statistics:&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Mean&lt;/td&gt;&lt;td char="."&gt;6.38&lt;/td&gt;&lt;td char="."&gt;5.98&lt;/td&gt;&lt;td char="."&gt;4.61&lt;/td&gt;&lt;td char="."&gt;6.49&lt;/td&gt;&lt;td char="."&gt;5.44&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Standard deviation&lt;/td&gt;&lt;td char="."&gt;3.09&lt;/td&gt;&lt;td char="."&gt;2.65&lt;/td&gt;&lt;td char="."&gt;3.28&lt;/td&gt;&lt;td char="."&gt;2.74&lt;/td&gt;&lt;td char="."&gt;2.68&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Coefficient omega&lt;/td&gt;&lt;td char="."&gt;.75&lt;/td&gt;&lt;td char="."&gt;.89&lt;/td&gt;&lt;td char="."&gt;.89&lt;/td&gt;&lt;td char="."&gt;.87&lt;/td&gt;&lt;td char="."&gt;.88&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Coefficient alpha&lt;/td&gt;&lt;td char="."&gt;.74&lt;/td&gt;&lt;td char="."&gt;.88&lt;/td&gt;&lt;td char="."&gt;.89&lt;/td&gt;&lt;td char="."&gt;.87&lt;/td&gt;&lt;td char="."&gt;.88&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Average variance extracted&lt;/td&gt;&lt;td char="."&gt;.60&lt;/td&gt;&lt;td char="."&gt;.73&lt;/td&gt;&lt;td char="."&gt;.81&lt;/td&gt;&lt;td char="."&gt;.63&lt;/td&gt;&lt;td char="."&gt;.66&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt; Number of scale items&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;3&lt;/td&gt;&lt;td&gt;2&lt;/td&gt;&lt;td&gt;4&lt;/td&gt;&lt;td&gt;4&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt; </ephtml> </p> <p>1 Note. All correlations ∣r∣ ≥.07 are significant at <emph>p</emph> &lt;.05 (two-sided). The Pearson correlation coefficient is accompanied below by specific <emph>p</emph> values in parentheses. Sample size: 480.</p> <hd id="AN0184106902-15">Data validity</hd> <p> <emph>Convergent and discriminant validity.</emph> A confirmatory factor analysis was used to assess the reflective multi-item scales' convergent and discriminant validity, obtaining the following fit measures: χ<sups>2</sups>/df = 1.31, CFI =.99, and RMSEA =.03, and upper bound of 90.0% RMSEA confidence interval =.04. These fit measures satisfy the common acceptance criteria (χ<sups>2</sups>/df &lt; 5, CFI ≥.95, RMSEA ≤.07, upper bound of 90.0% RMSEA confidence interval ≤.1; Hair et al. [<reflink idref="bib40" id="ref84">40</reflink>]). Moreover, as summarized in Table 1 and the Appendix, the reflective constructs of our survey meet the common requirements of convergent and discriminant validity (McDonald's omega &gt;.7, average variance extracted &gt;.5, and average variance extracted &gt; squared correlations with other constructs; Hair et al. [<reflink idref="bib40" id="ref85">40</reflink>]).</p> <hd id="AN0184106902-16">Results</hd> <p></p> <hd id="AN0184106902-17">Hypothesis tests</hd> <p> <emph>Model structure.</emph> To test the hypotheses from this study, we used multilevel structural equation modeling (MSEM) using Mplus (Muthén and Muthén [<reflink idref="bib63" id="ref86">63</reflink>]) as our analytical tool. MSEM integrates aspects of both structural equation modeling (SEM) and hierarchical linear modeling, as Mehta and Neale ([<reflink idref="bib60" id="ref87">60</reflink>]) indicated. Individual answers (level 1) are nested inside countries, which our MSEM model accounts for (level 2). They include intercepts at both levels and random terms to account for differences in level 1 effects across countries (level 2). We chose MSEM as it better aligns with our research focus, instead of multigroup SEM, which is more suited for group comparisons (De Roover and Vermunt [<reflink idref="bib31" id="ref88">31</reflink>]; Fisher et al. [<reflink idref="bib36" id="ref89">36</reflink>]; Yuan and Chan [<reflink idref="bib100" id="ref90">100</reflink>]). Moreover, our study analyzes a small cluster of five countries using MSEM. We focused on fixed effects to mitigate bias concerns (McNeish and Stapleton [<reflink idref="bib58" id="ref91">58</reflink>])). The nested data structure, which involves individuals in countries, aligns with MSEM's strength in capturing hierarchical relationships (Petscher and Schatschneider [<reflink idref="bib73" id="ref92">73</reflink>]). Also, we found an Intra-Class Correlation (ICC) of 12.1%, indicating some variation between countries, which further supports MSEM's suitability for small clusters (McNeish and Stapleton [<reflink idref="bib58" id="ref93">58</reflink>]).</p> <p>Figure 2 shows the results of our research model. Specifically, the model fit meets standard acceptance criteria (Hair et al. [<reflink idref="bib40" id="ref94">40</reflink>]; Kline [<reflink idref="bib47" id="ref95">47</reflink>]): χ2/df &lt; 5, CFI ≥.95, RMSEA &lt;.07, upper bound of RMSEA confidence interval [CI] ≤.1. According to Kline ([<reflink idref="bib47" id="ref96">47</reflink>]), RMSEA is the essential fit measure and should be no higher than.1. Therefore, our model shows an acceptable range of fit (Kline [<reflink idref="bib47" id="ref97">47</reflink>]).</p> <p>Graph: Figure 2. The influence of institutional, student and supervision factors on dissertation problems.</p> <p>We then looked for evidence of metric of complete configural and metric measurement invariance of all constructs across the five countries, following the criteria of Steenkamp and Baumgartner ([<reflink idref="bib85" id="ref98">85</reflink>]) and Cheung and Rensvold ([<reflink idref="bib24" id="ref99">24</reflink>]). Specifically, we assessed the possibility of the measures having the same meaning and functioning equally across groups (metric and scalar invariance) using the ΔCFI, and ΔRMSEA. Following standard common practices and similar to Chen ([<reflink idref="bib22" id="ref100">22</reflink>]), we considered changes in CFI less than or equal to.01 and changes in RMSEA less than or equal to.01 as indicators of acceptable invariance. Thus, we found full configural and partial metric invariance (Hair et al. [<reflink idref="bib40" id="ref101">40</reflink>]). We concluded that the constructs were perceived and used in the same way in all countries, allowing for valid comparisons of construct relationships across all contexts. Moreover, our aggregate study aimed to establish if the constructs in our model had the same meaning and structure across five countries, without comparing their means. Hence, we focused on configural and metric invariance to ensure that the factor structure and measurement units were the same across all groups. This lays the foundation for valid comparisons of relationships between constructs across all groups.</p> <p>Additionally, to assess our model's power, we conducted a posthoc power analysis using observed model fit statistics and our sample size (<emph>N</emph> = 480) with <emph>semPower</emph> (Moshagen and Bader [<reflink idref="bib61" id="ref102">61</reflink>]). The analysis based on our model fit (RMSEA = 0.056) indicated a close correspondence between the hypothesized model and the data. Notably, the analysis yielded a high power (&gt;0.999). This suggests that our study design can detect the effects of the observed size with the chosen significance level (<emph>α</emph> = 0.05).</p> <p> <emph>Model hypothesis testing.</emph> Our research aimed to investigate the challenges Latin American undergraduate students faced during their dissertations. We classified these challenges into institutional, student-related, and supervision-related factors. We formulated four hypotheses (H1–H3) to study specific aspects of these factors. In simple terms, when students receive greater support from their institution (H1), they are less prone to face difficulties during their dissertation process. Conversely, a deficiency in research experience (H2a) or personal challenges (H2b) may contribute to dissertation-related problems. Additionally, effective supervision plays a pivotal role in mitigating potential problems with dissertations (H3). To ensure accurate interpretation, we reversed the measurements for insufficient research expertise and institutional assistance to match the suggested hypotheses. We did not change personal problems and supervision quality measurements in the model because they aligned with the corresponding hypotheses.</p> <p>As predicted by existing literature, several challenges impact students' dissertation success and potentially lead to delayed graduation. Specifically, Figure 2 shows that almost all standardized path coefficients are significant (i.e. two-sided <emph>p </emph>&lt;.050), with only one coefficient (H2b) showing weak evidence of the hypothesized effect (i.e. two-sided <emph>p </emph>=.085). Thus, these results point out that institutional assistance negatively influences student dissertation problems (H1). Student factors (personal problems) increase student dissertation problems (H2b). However, we did not find strong evidence that insufficient research expertise increases dissertation problems (H2a). In the case of supervision factors, supervision quality reduces student dissertation problems (H3). Age, gender, marital status, work status, regular class attendance, public school, marital status change during studies, children, and study duration were added as control variables for the model in a different data analysis. However, this did not significantly modify the path coefficients.</p> <p>Additionally, our analysis of individual countries revealed that most paths were statistically significant, without altering our aggregate findings. Specifically, institutional assistance significantly reduced dissertation problems (H1) in all countries (.004 ≤ <emph>p </emph>≤.049) except Colombia (<emph>p</emph> =.230). Insufficient research expertise significantly increased these problems (H2a) in two countries (.005 ≤ <emph>p </emph>≤.048), except for Argentina, Colombia, and Chile (.078 ≤ <emph>p </emph>≤.870). Notably, personal problems consistently had a positive and significant impact (H2b) across all countries (.001 ≤ <emph>p </emph>≤.008). Finally, supervision quality significantly reduced dissertation problems (H3) in three countries (.001 ≤ <emph>p </emph>≤.008), but not in Colombia or Mexico (.231 ≤ <emph>p </emph>≤.646).</p> <hd id="AN0184106902-18">Discussion</hd> <p></p> <hd id="AN0184106902-19">Theoretical and practical implications</hd> <p>Many students in Latin America do not graduate on time because they did not successfully write their dissertations. The main contribution of our research is that, in addition to considering variables from previous graduation delay models in the literature, it examines the impact of those variables on the problems students faced while completing their undergraduate dissertation as a requirement for graduation in Latin America.</p> <p>First, the literature review suggests that intricate contextual factors, including institutional resources and facilities, play a crucial role in timely graduation in Latin America. These factors include how universities support students socially, financially, and academically. Specifically, Solmaz ([<reflink idref="bib83" id="ref103">83</reflink>]) found that institutional assistance enhances the quality of student dissertations. This finding aligns with our results (H1) and previous research highlighting the importance of institutional assistance in reducing dissertation problems (e.g. Johnson and Collins [<reflink idref="bib45" id="ref104">45</reflink>]; Patton et al. [<reflink idref="bib72" id="ref105">72</reflink>]). Studies like those by Kuh et al. ([<reflink idref="bib49" id="ref106">49</reflink>]) and Feldman and Astin ([<reflink idref="bib34" id="ref107">34</reflink>]) further emphasize the link between student dissertation success and university resources allocated to student services and personnel. Similarly, research by Patton et al. ([<reflink idref="bib72" id="ref108">72</reflink>]), Wilson, Mason, and Ewing ([<reflink idref="bib96" id="ref109">96</reflink>]), and LIU Brooklyn ([<reflink idref="bib52" id="ref110">52</reflink>]) emphasize the significant impact of counseling programs on retention rates. To empower students facing dissertation problems, universities can invest in a diverse institutional assistance ecosystem (Chen et al. [<reflink idref="bib23" id="ref111">23</reflink>]; Krizanovic [<reflink idref="bib48" id="ref112">48</reflink>]), encompassing counseling and mentoring programs for personalized guidance, learning communities that foster peer collaboration, mobile learning tools for convenient access to resources, and a student-centered design philosophy that prioritizes effective skill development in areas like intellectual discipline, critical thinking, scientific inquiry, and methodological rigor, ensuring every student has the tools and support needed to thrive and triumph in their research journeys.</p> <p>Our study also examined the impact of student factors on dissertation problems. We found weak evidence that insufficient research expertise significantly increases dissertation problems (H2a). We speculate that this result was due to our limited sample size. However, the results show that the hypothesized sign accurately showed a statistical trend (<emph>p</emph> =.085). Hence, we cannot definitely rule out that research expertise provides students with a singular opportunity to engage in supervised research within a specific discipline, bridging the gap between coursework and independent study and potentially leading to publishable outcomes. Undergraduate students are essentially novices in research, lacking prior independent experience. Research suggests that undergraduate students exhibit lower interest in research (Cook [<reflink idref="bib29" id="ref113">29</reflink>]) and are less likely to complete their research within a shorter timeframe (Rowley and Slack [<reflink idref="bib80" id="ref114">80</reflink>]). Various studies indicate that tackling dissertation problems requires a multifaceted approach (e.g. AlGhamdi et al. [<reflink idref="bib5" id="ref115">5</reflink>]; Assar et al. [<reflink idref="bib8" id="ref116">8</reflink>]). This approach should focus on (i) boosting student research knowledge through dedicated learning time, ample research opportunities, and robust mentorship programs (Carey, Kent, and Latour [<reflink idref="bib21" id="ref117">21</reflink>]); (ii) raising awareness about research benefits by integrating it into the curriculum and cultural norms (Joshi, Aikens, and Dolan [<reflink idref="bib46" id="ref118">46</reflink>]; Ng [<reflink idref="bib64" id="ref119">64</reflink>]); (iii) fostering student–faculty interaction by lowering perceived barriers (Bangera and Brownell [<reflink idref="bib9" id="ref120">9</reflink>]); and (iv) simplifying financial procedures to encourage undergraduate research engagement (Bettinger et al. [<reflink idref="bib13" id="ref121">13</reflink>]).</p> <p>Our research further revealed that personal problems significantly contribute to dissertation problems (H2b). As our theoretical framework suggested, students often face challenges balancing their academic workload with personal and family commitments, leading to imbalance and disruptions (Maloney and McCormick [<reflink idref="bib55" id="ref122">55</reflink>]). This lack of leisure time and social interactions among undergraduate students is well documented to contribute to burnout and depression (Armstrong and Oomen-Early [<reflink idref="bib6" id="ref123">6</reflink>]), ultimately impacting their research performance. Specifically, personal problems like lack of motivation (Tayebi, Gomez, and Delgado [<reflink idref="bib88" id="ref124">88</reflink>]), concentration difficulties (Bocar [<reflink idref="bib14" id="ref125">14</reflink>]), health issues (Grace [<reflink idref="bib39" id="ref126">39</reflink>]), emotional distress (Lee [<reflink idref="bib51" id="ref127">51</reflink>]), and social and financial support difficulties (Buttery, Richter, and Filho [<reflink idref="bib19" id="ref128">19</reflink>]; Samson [<reflink idref="bib81" id="ref129">81</reflink>]) can all exacerbate dissertation problems. These issues can lead to student dissatisfaction, decreased motivation, and negative self-criticism (Akparep, Jengre, and Amoah [<reflink idref="bib4" id="ref130">4</reflink>]), ultimately causing graduation delays. To ensure research success for students, we recommend fostering a supportive environment through tailored syllabi that adapt to individual needs, offering ongoing research guidance on topics, methodology, and skill development, and equipping faculty with training to recognize and support students facing personal challenges (Rossi [<reflink idref="bib79" id="ref131">79</reflink>]; Tayebi, Gomez, and Delgado [<reflink idref="bib88" id="ref132">88</reflink>]).</p> <p>Moreover, our research highlights the pivotal role of supervision in mitigating dissertation problems (H3). This aligns with existing literature emphasizing the importance of supervision quality in reducing student problems (de Kleijn et al. [<reflink idref="bib30" id="ref133">30</reflink>]; Latona and Browne [<reflink idref="bib50" id="ref134">50</reflink>]; Roberts and Seaman [<reflink idref="bib78" id="ref135">78</reflink>]). This stream of literature emphasizes that adequate research supervision depends on precise and prompt feedback, high-frequency meetings, supportive relationships, and adaptive supervision tailored to individual needs. Furthermore, supervision quality is subjective and influenced by supervisor-student relationships, interaction quality, intellectual compatibility, workload, availability, personal touch, career development opportunities, and feelings of appreciation (Zhao, Golde, and McCormick [<reflink idref="bib101" id="ref136">101</reflink>]). To ensure proper supervision of students, Latin American universities should implement student-centered supervision strategies. This can be done by providing clear guidance on choosing a supervisor (Zhao, Golde, and McCormick [<reflink idref="bib101" id="ref137">101</reflink>]), defining the roles and responsibilities of supervisors (Boehe [<reflink idref="bib16" id="ref138">16</reflink>]), offering ongoing training and support to supervisors (Zhao, Golde, and McCormick [<reflink idref="bib101" id="ref139">101</reflink>]), and reminding them of their responsibility to create a positive research environment for students.</p> <hd id="AN0184106902-20">Limitations and directions for future research</hd> <p>Most studies have limitations, and ours is no exception. The first limitation of our study is the sample size of five universities from five countries (clusters). Listwise deletion reduced our sample size, potentially affecting the statistical power of our results. While we addressed self-selection bias and thus MNAR, this does not eliminate the impact of the smaller sample size and a limited number of clusters. This effect was clearly manifested in weak evidence for H2a (<emph>p</emph> =.085), but the sign also suggested a statistical trend with a fair to moderate level of variance between countries (ICC = 12.1%). While this aspect limits the generalizability of our findings to all universities and countries in Latin America, it nonetheless allows us to analyze results across a spectrum of development levels, from Bolivia (among the least developed) to Chile (among the most developed) (CIA [<reflink idref="bib25" id="ref140">25</reflink>]; World Economic Forum [<reflink idref="bib99" id="ref141">99</reflink>]). This diversity provides valuable insights into how different levels of economic development might interact with student hardships and dropout rates, particularly among groups facing financial constraints, geographical challenges, or cultural expectations that may clash with academic demands. Further, research on a larger sample with more countries (clusters) is needed to confirm and expand upon these findings across all Latin American student groups and university contexts. However, our research found full configural and partial metric measurement invariance and aggregate and country-specific significant paths for all countries, hence predicting similar outcomes in other Latin American universities. Future studies could examine each country's specific cultural norms, educational systems, and economic disparities to provide insights for customizing interventions to address unique challenges and improve regional dissertation completion rates.</p> <p>Furthermore, a limitation of this study, as pointed out by an anonymous reviewer, is the use of frequentist MSEM with a relatively small number of clusters (<emph>n</emph> = 5). Although we have previously discussed our rationale for selecting MSEM over multigroup SEM, McNeish and Stapleton ([<reflink idref="bib59" id="ref142">59</reflink>]) have highlighted the limitations of frequentist methods for cluster sizes as low as 7–14. Although our study falls outside this range, it underscores the potential for bias due to the small cluster size. Therefore, future studies may consider using Bayesian methods as the preferred approach for assessing bias, power, and coverage intervals.</p> <p>Another limitation of our study is the large within-country/university effect (87.9%) in dissertation problems. This suggests that country/university-specific factors (e.g. programs, faculty) have a stronger influence than the national context alone. Future research can explore these factors by comparing institutions' programs, faculty expertise, or resources. Qualitative studies can further illuminate student experiences. Additionally, replicating the study across regions can broaden our understanding of this interplay between national and university contexts in shaping dissertation completion.</p> <p>Another limitation of our research is that it is cross-sectional, making it challenging to build a temporal sequence and determine causation. Longitudinal (e.g. panel) data from other universities in Latin America can be used to test the validity of our postulated correlations in future research (Ehrenberg et al. [<reflink idref="bib32" id="ref143">32</reflink>]; Ordine, Rose, and Fasano [<reflink idref="bib70" id="ref144">70</reflink>]; Pike and Robbins [<reflink idref="bib74" id="ref145">74</reflink>]).</p> <p>Another limitation of our research is that we employed self-report questionnaires, making it impossible to rule out the potential of intrinsic self-selection bias among respondents. Furthermore, respondents may desire to project an image of themselves following graduation norms and standards because they may worry that their responses would expose them and result in negative social or legal consequences. We urge future studies to alternate scales and control groups to account for these potential constraints.</p> <p>Finally, delayed graduation in universities should not be considered shameful but instead as institutional problems to be tackled systematically. Our aggregate and individual country results indicate that Latin American universities must prioritize institutional assistance, research training, student well-being, and high-quality supervision to improve dissertation support. Tailoring interventions and investigating country-specific factors can improve dissertation completion rates in the region. No student is too old to graduate.</p> <hd id="AN0184106902-21">Acknowledgments</hd> <p>The authors extend their gratitude to their institutions for the support of this research.</p> <hd id="AN0184106902-22">Disclosure statement</hd> <p>No potential conflict of interest was reported by the author(s).</p> <hd id="AN0184106902-23">Geolocation information</hd> <p>Latin America.</p> <hd id="AN0184106902-24">Appendix. Construct scales and their literature sources.</hd> <p></p> <p> <ephtml> &lt;table&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td&gt;&lt;bold&gt;Dissertation problems&lt;/bold&gt; (reflective 10-point Likert scale: completely disagree/agree; Matin and Khan &lt;xref ref-type="bibr" rid="bibr56"&gt;2017&lt;/xref&gt;)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;1. My dissertation topic was very broad or difficult. 2. I experienced problems that hindered the progress of my dissertation.&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;3. I experienced problems during the execution of my dissertation. 4. The planned time frame for my dissertation was very tight.&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;bold&gt;Institutional assistance&lt;/bold&gt; (reflective 10-point Likert scale: completely disagree/agree; Maher, Ford, and Thompson &lt;xref ref-type="bibr" rid="bibr53"&gt;2004&lt;/xref&gt;)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;1. I was unaware of the services my university provides for students who are behind in their graduation. 2. I did not use the services provided by my university for students who are behind in their graduation.&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;bold&gt;Insufficient research expertise&lt;/bold&gt; (reflective 10-point Likert scale: completely disagree/agree; Maher, Ford, and Thompson &lt;xref ref-type="bibr" rid="bibr53"&gt;2004&lt;/xref&gt;)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;1. From the beginning, I had a clear idea of my dissertation's theoretical and/or social relevance. 2. From the beginning, I knew precisely the research question that my dissertation wanted to answer. 3. From the beginning, I had a clear idea of the data required to answer my research question.&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;bold&gt;Personal problems&lt;/bold&gt; (reflective 10-point Likert scale: completely disagree/agree; Maher, Ford, and Thompson &lt;xref ref-type="bibr" rid="bibr53"&gt;2004&lt;/xref&gt;)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;1. I had personal problems during the completion of my dissertation. 2. I had family problems during the completion of my dissertation.&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;&lt;bold&gt;Supervision quality&lt;/bold&gt; (reflective 10-point Likert scale: completely disagree/agree; van de Schoot et al. &lt;xref ref-type="bibr" rid="bibr94"&gt;2013&lt;/xref&gt;)&lt;/td&gt;&lt;/tr&gt;&lt;tr&gt;&lt;td&gt;1. My supervisor was supportive or actively involved in my dissertation. 2. 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| Items | – Name: Title Label: Title Group: Ti Data: Undergraduate Dissertation Problems: An Aggregate Cross-Sectional Analysis from Five Latin American Universities – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Boris+Christian+Herbas-Torrico%22">Boris Christian Herbas-Torrico</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7764-0186">0000-0001-7764-0186</externalLink>)<br /><searchLink fieldCode="AR" term="%22Pablo+René+González-Bravo%22">Pablo René González-Bravo</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7437-6292">0000-0002-7437-6292</externalLink>)<br /><searchLink fieldCode="AR" term="%22David+Gonzalo+Romero%22">David Gonzalo Romero</searchLink><br /><searchLink fieldCode="AR" term="%22María+Lizbeth+Murillo-Ramírez%22">María Lizbeth Murillo-Ramírez</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3583-1683">0000-0003-3583-1683</externalLink>)<br /><searchLink fieldCode="AR" term="%22Job+Angulo-Rueda%22">Job Angulo-Rueda</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0003-2727-6509">0009-0003-2727-6509</externalLink>) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Studies+in+Higher+Education%22"><i>Studies in Higher Education</i></searchLink>. 2025 50(4):878-895. – Name: Avail Label: Availability Group: Avail Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 18 – Name: DatePubCY Label: Publication Date Group: Date Data: 2025 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Research<br />Tests/Questionnaires – Name: Audience Label: Education Level Group: Audnce Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink> – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Theses%22">Theses</searchLink><br /><searchLink fieldCode="DE" term="%22Research+Problems%22">Research Problems</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Resources%22">Educational Resources</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Supervision%22">Supervision</searchLink><br /><searchLink fieldCode="DE" term="%22Time+to+Degree%22">Time to Degree</searchLink><br /><searchLink fieldCode="DE" term="%22Influences%22">Influences</searchLink> – Name: Subject Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Argentina%22">Argentina</searchLink><br /><searchLink fieldCode="DE" term="%22Bolivia%22">Bolivia</searchLink><br /><searchLink fieldCode="DE" term="%22Chile%22">Chile</searchLink><br /><searchLink fieldCode="DE" term="%22Colombia%22">Colombia</searchLink><br /><searchLink fieldCode="DE" term="%22Mexico%22">Mexico</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1080/03075079.2024.2359050 – Name: ISSN Label: ISSN Group: ISSN Data: 0307-5079<br />1470-174X – Name: Abstract Label: Abstract Group: Ab Data: Latin America may have higher attrition rates than previously assumed. Many students in Latin America do not graduate on time because they cannot complete the traditional undergraduate dissertation. This paper examines the factors contributing to delayed graduation for a context-specific variable: dissertation problems. We designed a questionnaire survey based on multi-item scales and surveyed 480 university students from Argentina, Bolivia, Chile, Colombia, and Mexico. Aggregate-cross-sectional data and multilevel structural equation modeling show that institutional assistance and supervision quality [personal problems] reduce [increase] dissertation problems. These results may enable university authorities to identify undergraduate students with dissertation problems and thus improve their strategies for reducing attrition rates. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2026 – Name: AN Label: Accession Number Group: ID Data: EJ1498232 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/03075079.2024.2359050 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 878 Subjects: – SubjectFull: Foreign Countries Type: general – SubjectFull: Undergraduate Students Type: general – SubjectFull: Theses Type: general – SubjectFull: Research Problems Type: general – SubjectFull: Educational Resources Type: general – SubjectFull: Student Characteristics Type: general – SubjectFull: Supervision Type: general – SubjectFull: Time to Degree Type: general – SubjectFull: Influences Type: general – SubjectFull: Argentina Type: general – SubjectFull: Bolivia Type: general – SubjectFull: Chile Type: general – SubjectFull: Colombia Type: general – SubjectFull: Mexico Type: general Titles: – TitleFull: Undergraduate Dissertation Problems: An Aggregate Cross-Sectional Analysis from Five Latin American Universities Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Boris Christian Herbas-Torrico – PersonEntity: Name: NameFull: Pablo René González-Bravo – PersonEntity: Name: NameFull: David Gonzalo Romero – PersonEntity: Name: NameFull: María Lizbeth Murillo-Ramírez – PersonEntity: Name: NameFull: Job Angulo-Rueda IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 0307-5079 – Type: issn-electronic Value: 1470-174X Numbering: – Type: volume Value: 50 – Type: issue Value: 4 Titles: – TitleFull: Studies in Higher Education Type: main |
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