Unveiling motives for dentistry studies: psychometric validation of a comprehensive questionnaire among aspiring dental students

Global demand for healthcare professionals has risen, including dentistry, with increased enrollments and shifting gender ratios. Understanding motivations for choosing dentistry is important for recruitment, curriculum design, and informed career decisions. Regional disparities (e.g., limited inclusion of Special Needs Dentistry in South America) underscore context-specific educational needs. Prior qualitative and quantitative research suggests multiple motivation domains, including professional status, financial incentives, job security, quality of life, personal experiences, and external influences. Despite international research, Latin America lacks validated instruments assessing the construct of motivations for choosing dentistry; existing tools mostly rely on face/content validity and expert consensus. This study addresses this gap by developing and validating a comprehensive questionnaire for Ecuadorian dental students using best-practice scale development procedures.

Prior studies have identified professional and financial incentives, job security, flexibility/independence, quality of life, personal experiences, and external influences (e.g., family) as key motives for choosing dentistry. International evidence frequently emphasizes economic and vocational factors, with social influences (parents, friends) and perceived prestige also reported. Latin American evidence is scarce (limited to Brazil and Peru), and few studies have performed rigorous construct validation (factor analyses) of motivation scales. Theoretical consolidation from qualitative work (e.g., Gallagher) guided the merging of overlapping constructs: professional status with job security/flexibility under professional reasons; financial benefits with quality of life under economic reasons; personal experiences and family/friends’ influences under social reasons; and academic constraints/opportunities (e.g., entrance test scores) under academic reasons. This background informed item selection and hypothesized factor structure for the present instrument.

Design: Instrument development and validation study. Questionnaire development: A 25-item Likert-scale instrument (1=totally disagree to 5=totally agree) was constructed from prior quantitative instruments and qualitative studies, and adapted to Ecuadorian academic culture. Five initial theoretical factors were specified: economic (Q1–Q4), professional (Q5–Q9), vocational (Q10–Q14), social (Q15–Q20), academic (Q21–Q25). Ethics: Approved by Hospital Clínica Kennedy Ethics Committee (HCK-CEISH-2022-002). Samples and data collection: Three convenience samples of dental students from private universities in Ecuador were recruited via faculty distribution of an online survey. Sample A (n=201; Apr 25–May 27, 2022) was used for initial EFA of all 25 items. Sample B (n=623; May 30–Jun 4, 2022) received the reduced instrument for EFA replication/refinement. Sample C (n=596; Jun 27–Jul 31, 2022; response rate for B and C: 81%) was used for CFA model testing. Measures and analysis: Demographics described via means/SD and percentages. EFA prerequisites assessed with Bartlett’s test of sphericity and Kaiser–Meyer–Olkin (KMO). EFA used maximum likelihood extraction with oblique (Oblimin) rotation, given expected factor correlations. Inter-item correlations were evaluated with Spearman’s rho due to non-normality. Reliability was assessed with Cronbach’s alpha. CFA in sample C compared alternative models using standard goodness-of-fit and parsimony indices: CMIN/df (≤3), CFI (>0.90), GFI (>0.90), RMSEA (<0.06 with CI), SRMR (<0.08), and lower AIC/BIC indicating better parsimony. Sequential analytic plan: EFA in Sample A on 25 items to identify initial factor structure and reduce items; EFA in Sample B on the reduced set to evaluate item behavior, cross-loadings, and finalize competing models; CFA in Sample C to compare and select the best-fitting model.

Participants: Sample A n=201 (mean age 20.75, SD 2.57), Sample B n=623 (mean 21.32, SD 2.79), Sample C n=596 (mean 21.81, SD 3.2). Overall, 67.6% female; regions: Coast 31.1%, Highlands 63.8%, Other 5.1%. Year distribution skewed toward early years (first year 29.2%). EFA Sample A (25 items): KMO=0.830; Bartlett’s χ²=1301.88, p<0.001; ML extraction with Oblimin yielded five factors explaining 64.56% variance. Resulting 18-item Model A factors: vocational (Q10–Q14), economic (Q1–Q4), academic (Q21, Q23, Q24), professional (Q5, Q7, Q8), social (Q15, Q18, Q20). Factors were positively correlated; vocational, economic, and professional showed good internal consistency; academic and social were weaker. EFA Sample B (18 items): KMO=0.850; Bartlett’s χ²=2050.86, p<0.001. ML with Oblimin indicated a two-factor solution explaining 57.92% variance; item Q8 removed due to low communality (0.197). Items Q5 and Q7 exhibited cross-loadings. Based on theory and academic context, two competing three-factor models were specified: Model B (10 items, no cross-loadings): Economic (Q1–Q4); Vocational (Q10–Q12, Q14); Academic (Q21, Q23). Model C (12 items, with cross-loadings): Labor (Q1–Q5, Q7); Vocational (Q5, Q7, Q10–Q12, Q14); Academic (Q21, Q23). In both models, factors were positively correlated and reliabilities were acceptable overall. CFA Sample C: Four models compared: Model A (five-factor, 18 items); Model B (three-factor, 10 items, no cross-loadings); Model C (three-factor, 12 items, cross-loadings); Model D (three-factor, 12 items, cross-loadings plus correlated errors between Q5 and Q7). Fit indices: Model A CMIN/df=3.165, CFI=0.904, GFI=0.931, RMSEA=0.060 (0.054–0.067), SRMR=0.0763, AIC=487.671, BIC=689.622; Model B CMIN/df=2.500, CFI=0.966, GFI=0.973, RMSEA=0.050 (0.037–0.064), SRMR=0.0408, AIC=125.994, BIC=226.970; Model C CMIN/df=3.344, CFI=0.944, GFI=0.956, RMSEA=0.063 (0.052–0.074), SRMR=0.0411, AIC=221.838, BIC=498.439; Model D CMIN/df=2.126, CFI=0.974, GFI=0.972, RMSEA=0.044 (0.032–0.055), SRMR=0.0370, AIC=162.066, BIC=293.773. Best fit: Model D, supporting a 12-item, three-factor structure assessing labor, vocational, and academic motivations, with two items (Q5, Q7) cross-loading on labor and vocational and a correlated error between them. Final instrument: 12 items capturing labor, vocational, and academic reasons for studying dentistry.

The study aimed to validate a comprehensive instrument to assess motivations for studying dentistry among Ecuadorian students. Factor analyses demonstrated that motivations consolidate into three correlated dimensions—labor (economic/professional/job characteristics), vocational (altruism, interest in working with people, hands-on/artistic aspects), and academic (selection constraints/opportunities)—rather than the initial five-theory structure. The merging of professional and economic features into a labor factor reflects conceptual overlap noted in the literature and contemporary views of vocational motives encompassing job-related attributes. The validated structure addresses the research question by empirically supporting core domains influencing career choice. The best-fitting CFA model with cross-loadings and correlated errors between conceptually similar items confirms nuanced interrelations between labor and vocational motives. This validated tool fills a gap in Latin America where construct-validated instruments have been scarce, enabling more accurate assessment of student motivations to inform recruitment strategies, educational planning, and counseling.

A concise, validated 12-item questionnaire measuring labor, vocational, and academic motivations for studying dentistry was developed for Ecuadorian dental students. Labor and vocational factors appear globally relevant, while academic motives may be context-specific to national academic systems. The instrument, supported by EFA and CFA in large samples, can aid curriculum development, student guidance, and workforce planning. Future research should examine differential motivations by gender, university type, academic year, and track changes over time to understand evolving motives and generalize across regions and public institutions.

• Convenience samples from private universities in Ecuador limit generalizability to all dental students in the country or broader Latin America. - Potential unexamined heterogeneity: subgroup analyses by gender, region, and academic year were not conducted. - Cultural/academic-system specificity may affect the academic factor’s applicability in other contexts. - Cross-sectional design precludes assessment of changes in motivations over time.