Development And Validation of Questionnaire to Assess Factors of Student Intention to Pursue Higher Education: An Integration of the Theory of Planned Behaviour

Development And Validation of Questionnaire to Assess Factors of Student Intention to Pursue Higher Education: An Integration of the Theory of Planned Behaviour

Eylia Nadiah Drahman, Nurul Rafeah Mohamed Yusuf, Davina Sharenya Chendang, Monie Ramba

School of Foundation Studies, University of Technology Sarawak, Sibu, Sarawak, Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2025.907000289

Received: 11 July 2025; Accepted: 19 July 2025; Published: 14 August 2025

ABSTRACT

Understanding the factors influencing students’ intention to pursue higher education is essential for shaping effective educational policy and interventions. Guided by the Theory of Planned Behaviour, this study aimed to develop and validate a comprehensive questionnaire that integrates six key constructs, which are i) motivation, ii) self-efficacy, iii) family influence, iv) peer influence, v) financial aid and vi) role of school counsellors. A pilot study involving 75 respondents aged 18 and above was conducted, using an instrument developed from extensive literature review and expert validation. Content validity was confirmed by academic and practitioner review. Descriptive statistics showed strong educational intentions, with 84% of participants planning to further their studies. Based on the finding of the study, a correlation matrix indicated significant positive relationships between all variable and the intention to pursue higher education, with motivation (r=.781), self-efficacy (r=.633) and financial aid (r=.675) as the strongest predictors. The Kaiser-Meyer-Olkin value of .846 and Bartlett’s Test Sphericity (χ² = 275.585, df = 21, p < .001), confirming the data’s suitability for factor analysis. Meanwhile, Cronbach’s Alpha demonstrated high internal consistency across all constructs (α = .804 to .947), confirming the questionnaire’s reliability and validity. The questionnaire provides a valuable tool for educators, researchers and policymakers to gain deeper insights into student aspirations and to inform the development of evidence-based outreach and recruitment strategies aimed at increasing higher education enrolment.

Keywords: Higher Education Intentions, Malaysian Students, Questionnaire Development, Contextual Questionnaire, Theory of Planned Behaviour, Student Motivation, Post-secondary Education

INTRODUCTION

Understanding the factors that influence students’ intentions to pursue higher education has become increasingly important in educational planning and policy development. Over the years, various validated questionnaires have been developed to explore individual constructs such as motivation (Ryan & Deci, 2000), self-efficacy (Bandura, 1997), family and peer influence (Wang & Eccles, 2021), financial aid (Castleman & Page, 2014), and support from school counsellors (Kraft, Brundage, & Schultheiss, 2021). However, most existing instruments focus on a single or limited number of these factors. Few comprehensive, validated tools exist that integrate all six variables into a single framework to assess their combined effect on students’ educational intentions.

In recent research trends, questionnaires have often been discipline-specific, theory-bound (e.g., Self-Determination Theory, Social Cognitive Career Theory), or culturally limited. While these tools provide valuable insights, they sometimes lack adaptability for local educational contexts or holistic analysis. This study addresses this gap by combining multiple key factors into a single, easy-to-administer, and validated questionnaire.

An effective instrument must be theoretically grounded, contextually relevant, and empirically tested for reliability and validity. Therefore, the present study aims to develop and validate a questionnaire to measure the factors influencing students’ intention to pursue higher education. By integrating multiple theoretical perspectives and assessing the tool’s psychometric properties, this study contributes a reliable and validated questionnaire to assess the multidimensional factors influencing student intentions. This tool not only aids researchers and educators in program development and policy planning but also provides insight into potential intervention areas to support student transitions into higher education.

METHODS

A literature-based approach was taken in developing items for the Factors Influencing Students’ Intentions to Pursue Higher Education Questionnaire. The items gathered information on variables that other researchers have identified as having an impact on a person’s decision to pursue higher education. A variety of research and models have been established to better understand them (Harris and Halpin, 2002; Islam and Jahan, 2014; Ng et al., 2011; Nyaribo et al., 2012). To determine the content validity of the questionnaire, several administrators and university academicians with experience in the areas of student recruiting, admissions, and retention were asked to review the items. The validator said that the items pertained to variables that, based on their expertise, represent the relevant area. Considering that content validity is often non-quantifiable (Fink & Kosecoff, 1985), the assessment by the reviewing authority was deemed indicative of the questionnaire’s content validity. A systematic two-phase approach was employed: Phase 1 (qualitative) for questionnaire preparation and Phase 2 (quantitative) for validation.

Phase 1: Questionnaire development

The questionnaire was created using a systematic technique that included literature research, expert evaluation, and pretesting (Table 1).

LITERATURE RESEARCH

A thorough literature analysis was conducted using search engines such as Google Scholar and Pubmed to comprehend the current information on the knowledge, attitudes, practices, and concerns of individuals about their plans to seek higher education. The keyword string (“STUDENT  INTENTATION”  OR  “FACTOR  INFLUENCING*”  OR  SPM  Leavers

Intention*) AND (Pursue* OR Continue*) AND (Higher Education* OR University* OR Private University*) AND (Theory of Planned Behaviour* OR TPB*) was used. The preliminary search yielded 8,680 relevant articles. Upon reviewing the titles, abstracts, and complete texts, 13 articles were deemed relevant, leading to the creation of 34 items.

Expert evaluation for face and content validity of questionnaire

The developed questionnaire was subjected to rigorous scrutiny and content validity assessment by a panel of six experts: four academics specializing in higher education and two industry practitioners. As a result, all items were maintained, while two items were rephrased for enhanced clarity.

Table 1: Steps involved in questionnaire development and validation

Phase 2: Questionnaire validation

Data was collected from 28 May 2025 to 6 June 2025 using a web-based questionnaire conducted via Google Forms. The survey was sent to 75 people aged 18 and above. Participants were chosen by convenience sampling from several demographic groups to provide optimum diversity.

Statistical analysis

Before data reduction, a descriptive analysis was performed to assess the mean and standard deviation of each item. Descriptive statistics were used to analyze age, gender, education, employment, and socioeconomic position. Mean, standard deviation, median, quartile, and range were computed for quantitative approaches. Content validity was determined by expert assessment and a correlation matrix was used to assess the concept validity of connected variables. Correlation matrix values over 0.3 are deemed optimum (Tabachnick & Fidell, 2014; Ahmad, 2024). Factor loadings of 0.3 are deemed mild, 0.4 considerable, and 0.5 essential, according to Hair et al. (2010). Prior to doing factor analysis, it is essential to perform the Kaiser-Meyer-Olkin (KMO) and Bartlett’s sphericity tests to assess sample adequacy and assure data quality. Principal Component Analysis (PCA) with varimax rotation was used as the dimensionality reduction method. Principal Component Analysis was used instead of Exploratory Factor Analysis (EFA) because it effectively condenses several observable variables into a reduced set of uncorrelated components based on total variance (Jolliffe & Cadima, 2016). Cronbach’s alpha evaluated internal consistency. Cronbach’s alpha of 0.7 or above indicates substantial internal consistency (Ahmad et al., 2024). The data was analyzed using SPSS Version 28.

RESULTS AND DISCUSSION

Following the systematic methodology, the final version of the factors influencing students’ intention to pursue higher education questionnaire, comprising 34 items (Appendix 1) is freely available for use. The first section of the questionnaire consists of the items related to the demographic profile and a question regarding their intention to pursue higher education. The second section of the questionnaire consists of the items to test the six factors influencing student’s intention to pursue higher education which include motivation, self-efficacy, family influence, peer influence, financial aid and role of counsellor.

Descriptive Analysis

A total of 75 respondents participated in the study. The demographic breakdown is presented in Table 2. In terms of age, the majority of respondents (88%) were between 18 and 20 years old. Smaller proportions were in the age ranges of 21–23 years (1.33%), 24–26 years (4.00%), and over 26 years (1.33%). A small group (5.33%) did not specify their age. Regarding gender, most respondents were male (70.7%), while females accounted for 29.3% of the sample. For ethnicity, the majority of respondents identified as Chinese (61.3%), followed by Iban (25.3%), Malay (10.7%), and Melanau (1.3%). In terms of education level, the vast majority were enrolled in pre-university, foundation, or matriculation programs (93.3%). A small number of respondents had completed post-SPM studies (4.0%) or were still in secondary school (1.3%).

Table 2: Demographic characteristics of respondents (n= 75)

Intention to Pursue Higher Education

As shown in Table 3 a significant majority of respondents (84.0%) indicated that they plan to pursue higher education after their current level of study. None of the respondents answered “No,” while 16.0% were undecided and responded with “Maybe.” These results suggest a strong overall intention among the respondents to continue their studies at a higher level.

Table 3: Student’s intention to pursue higher education

Validity of the Questionnaire

Correlation Matrix

The correlation matrix in Table 4 shows statistically significant and positive relationships between all independent variables (Motivation, Self-Efficacy, Family Influence, Peer Influence, Financial Aids, and Role of School Counsellor) and the dependent variable Intention to Pursue Higher Education. Motivation, financial assistance, and self-efficacy have the most significant relationships with the intention to pursue, highlighting their considerable impact on individuals’ decisions. Moreover, all variables exhibit significant correlations at the p < .05 level, indicating substantial linkages. The absence of multicollinearity is validated by the correlation matrix determinant of .020, significantly exceeding the threshold of .00001, affirming the data’s suitability for further investigation.

Table 4: Correlation matrix between independent variables and student’s intention to pursue

higher education.

KMO and Bartlett’s Test

The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy produced a value of .846, signifying excellent appropriateness of the data for factor analysis. Furthermore, Bartlett’s Test of Sphericity yielded significant results (χ² = 275.585, df = 21, p < .001), indicating that the correlation matrix is not an identity matrix. These results, shown in Table 5, collectively suggest that the data is suitable for factor analysis.

Table 5: Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test for data suitability.

Principal Component Analysis (PCA)

Total Variance Explained

The total variance in Table 6 explained reveals that the first component possesses an eigenvalue of 4.133, representing 59.045% of the overall variance. The second component possesses an eigenvalue of 0.951 and provides an extra 13.587%, resulting in a cumulative percentage of 72.632%. As only the initial component possesses an eigenvalue exceeding 1, it is deemed the most significant, indicating that a singular dominant factor underpins the data structure.

Table 6: Total variance explained by Principal Component Analysis

Extraction Sums of Squared Loadings

The extraction sums of squared loadings show that there is one main factor that explains a lot of the variation between the variables that were looked at. This backs up the idea that the six different factors which are Motivation, Self-Efficacy, Financial Aids, Family Influence, Peer Influence, and Role of School Counsellor might add to a single latent construct. This latent component may signify a comprehensive idea, such as general preparedness or perceived assistance in pursuing higher education. The significant variance explained by this single factor highlights the consistency of the evaluated constructs, suggesting that they can be viewed as components of a unified theoretical framework.

Reliability of the Questionnaire

A reliability analysis was conducted to examine the internal consistency of items within each concept. The analysis employed Cronbach’s Alpha, with a threshold of .70 deemed acceptable for research objectives. The value of Cronbach’s Alpha in Table 7 for all independent variables is significant ranging from 0.947 to 0.804.

However, for the Cronbach’s Alpha for Financial Aid constructs in its initial form is only .658. Hence, the analysis of the item-total data revealed that FA4 was problematic, with a notably low corrected item- total correlation of .020. Upon the removal of FA4, the Alpha coefficient rose to .804, signifying acceptable reliability.

Finally, the Intention to Pursue Higher Education construct attained a Cronbach’s Alpha of .918, signifying exceptional internal consistency. The item ranged from 4.17 to 4.51, indicating a strong intention among respondents to pursue additional studies.

This indicates that all constructs are acceptable to high reliability values, affirming the consistency and appropriateness of the items for further studies.

Table 7: Reliability analysis using Cronbach’s Alpha

DISCUSSION

Summary of Instrument Development

The questionnaire was developed using pertinent literature and theoretical frameworks, including the Social Cognitive Career Theory (Lent, Brown, & Hackett, 1994) and the Self- Determination Theory (Ryan & Deci, 2000). The following constructs were determined to be measured: financial aid, peer and family impact, self-efficacy, motivation, and Role of school counsellors. It was hypothesized that these factors would affect students’ choices about additional coursework. The final instrument also included items assessing the intention to pursue higher education as the dependent construct.

Validity of the Questionnaire

The correlation matrix confirmed that all six independent constructs had statistically significant and positive relationships with the intention to pursue higher education. This suggests that the selected constructs were appropriate and relevant to the study. Motivation (r = .781), self- efficacy (r = .633), and financial aid (r = .675) demonstrated the strongest correlations, indicating they are critical factors of educational intention. These results are supported by previous research highlighting the impact of internal motivation and perceived academic ability (Basileo, Otto, Lyons, Vanini, & Toth, 2024; Zhao, Liu, & Tan, 2024).

Similarly, the good reliability of family influence (α = .818) and peer influence (α = .815) supports Eccles et al.’s (2020) expectancy-value model, which emphasizes the role of social environments in shaping academic aspirations. The exceptionally high reliability for the role of school counselors (α = .947) though less frequently discussed in the literature indicates that this construct can be measured with high precision, opening avenues for further research on institutional support systems.

Factorability and Dimensionality

The dataset was deemed appropriate for factor analysis based on the Kaiser-Meyer-Olkin

(KMO) value of.846 and a significant Bartlett’s Test of Sphericity (χ² = 275.585, df = 21, p

<.001). This confirms the adequacy of the sampling and the existence of significant correlations among items to justify dimensional reduction.

The Principal Component Analysis (PCA) identified a single dominant factor with an eigenvalue greater than 1, which accounted for 59.045% of the total variance. This implies that the instrument assesses a cohesive underlying construct, which is likely to represent perceived readiness and support for pursuing higher education. Hair et al. (2019) also indicate that the cumulative variance of 72.63% with two components is consistent with psychometric standards, indicating acceptable construct coverage and dimensional strength.

Reliability of the Constructs

According to Cronbach’s Alpha reliability analysis, all constructs demonstrated satisfactory to exceptional internal consistency, except for the financial aid construct in its initial form (α=.658). Upon the removal of the problematic item (FA4), the reliability increased to α =.804, which substantiates its inclusion in the final version of the questionnaire.

The high reliability scores for motivation (α = .884), self-efficacy (α = .917), and intention to pursue higher education (α = .918) align with established theories such as Self-Determination Theory (Ryan & Deci, 2017) and Social Cognitive Theory (Bandura, 1997), reinforcing the stability of these psychological constructs in educational research. The strong consistency in these measures suggests that students’ goal-directed behaviors and belief systems can be reliably assessed using this instrument, which is crucial for future studies on educational decision-making.

Similarly, the good reliability of family influence (α = .818) and peer influence (α = .815) supports Eccles et al.’s (2020) expectancy-value model, which emphasizes the role of social environments in shaping academic aspirations. The exceptionally high reliability for the role of school counselors (α = .947), though less frequently discussed in the literature indicates that this construct can be measured with high precision, opening avenues for further research on institutional support systems.

These results confirm that the instrument that was devised is reliable and psychometrically sound to assess the factors that influence the intentions of students.

Alignment with Existing Theories and Literature

The significant relationships between educational intention and motivational and self-efficacy constructs are consistent with Self-Determination Theory (Ryan & Deci, 2000), which underscores the importance of internal motivation and autonomy in goal-directed behaviors. In the same vein, Bandura’s (1997) theory of self-efficacy and Social Cognitive Career Theory underscore the impact of social support and confidence in one’s abilities on educational decisions.

Castleman and Page (2014) and Nguyen et al. (2019) have both reported that financial aid has a positive impact on both access and persistence in higher education, which is consistent with the significance of financial aid. The moderate impact of family and peer influence is consistent with the findings of Wang and Eccles (2021), while the lower effect of role of school counsellor, indicates potential areas for further research and intervention (Kraft, Brundage, & Schultheiss, 2021).

Contribution to Research and Practice

This research makes a valuable contribution to the academic community by developing a validated, multi-dimensional questionnaire that can be implemented in a variety of educational environments to evaluate the perceived support and preparedness of students for higher education. The instrument offers researchers and practitioners a structured, empirically supported tool for program evaluation, intervention planning, and diagnosis. It also presents opportunities for cross-cultural adaptation, particularly in situations where socioeconomic or institutional factors influence higher education participation.

CONCLUSION

The study successfully developed and validated a comprehensive questionnaire based on the Theory of Planned Behaviour to measure the key factors on students’ intention to pursue higher education. The instrument incorporated six core constructs which were motivation, self- efficacy, family influence, peer influence, financial aid and the role of school counsellors; the instrument demonstrated strong content validity through expert review and high internal consistency. Key finding highlighted motivation, self-efficacy and financial aid as the strongest predictors of educational intention. Meanwhile, Principal Component Analysis showed that one main factor on students’ overall readiness and support explains most of the variation in the data. Hence, it is hoped that the questionnaire as well as the study’s findings provide researchers, educators and policymakers with a valuable tool to gain deeper insights into the factors that motivate students to pursue higher education. In addition, the tool is expected to support the design of more effective support programs, outreach initiatives and strategies to increase student enrolment and align educational opportunities with students’ academic goals and aspirations. Future research may focus on refining the instrument for application across more diverse populations and evaluating its predictive validity through longitudinal studies of actual enrolment outcomes.

REFERENCES

1. Ahmad, M. A. (2024). Assessing the Factors Influencing Students Satisfaction in Nigerian Tertiary Institution: A Principal Component Analysis Opportunities and Risks in AI for Business Development: Volume 2, 401-410.
2. Ahmad, N., Alias, F. A., Hamat, M., & Mohamed, S. A. (2024). Reliability Analysis: Application of Cronbach’s Alpha in Research Instruments. Pioneering the Future: Delving Into E‐Learning’s Landscape, 114-119.
3. Bandura, (1997). Self-efficacy: The exercise of control. W. H. Freeman.
4. Baum, S., & Ma, J. (2022). Trends in student aid 2022. College Board. https://research.collegeboard.org
5. Castleman, L., & Page, L. C. (2014). A trickle or a torrent? Understanding the extent of summer “melt” among college-intending high school graduates. Social Science Quarterly, 95(1), 202–220. https://doi.org/10.1111/ssqu.12032
6. Fink, , & Kosecoff, J. (1985). How to conduct surveys: A step-by-step guide. Beverly Hills, CA: Sage.
7. Hair, J. F. J., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed). Pearson Prentice Hall.
8. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
9. Harris, S.M., Halpin, G. (2002), Development and validation of the factors influencing pursuit of higher education Educational and Psychological Measurement, 62(1), 79-96.
10. Islam, S., Jahan, N. (2014), Factors leading to growing interest among MBA aspirants at Bangladesh open university: An empirical analysis Malaysian Journal of Distance Education, 16(1), 59-78.
11. Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical transactions of the royal society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202.
12. Kraft, M. A., Brundage, M., & Schultheiss, H. (2021). Supporting college match and persistence through personalized Center for Education Policy Research,
13. Harvard https://cepr.harvard.edu
14. Kraft, M. A., Brundage, S. C., & Schultheiss, D. E. (2021). Can professional development improve school counselors’ use of data? Journal of Counseling & Development, 99(3), 322–333.
15. Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122. https://doi.org/10.1006/jvbe.1994.1027
16. Lent, R. W., et al. (2018). Social Cognitive Career Theory at 25. Career Development
17. Ng, F., Muhd, N.M.N., Ab Rahman, K., Ismail, N. (2011), Influential factors to pursue doctorate degree in Malaysia. Procedia Social and Behavioral Sciences, 15, 2028-2032.
18. Nguyen, D., Kramer, J. W., & Evans, B. J. (2019). The effects of grant aid on student persistence and degree attainment: A systematic review and meta-analysis of the causal evidence. Review of educational research, 89(6), 831-874.
19. Nyaribo, M., Prakash, A., Edward, O. (2012), Motivators of choosing a management course: A comparative study of Kenya and India. The International Journal of Management Education, 10(3), 201-214.
20. Ryan, M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020
21. Ryan, M. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications.
22. Tabachnick, B., Fidell, L.: Using multivariate statistics (Pearson custom library). Pearson, Harlow (2014)
23. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International journal of medical education, 2, 53.
24. Wang, M. T., & Eccles, J. S. (2021). Adolescent behavioral, emotional, and cognitive engagement trajectories in school and their differential relations to educational success.
25. Journal of Research on Adolescence, 31(1), 192–207. https://doi.org/10.1111/jora.12577
26. Wang, M. T., et al. (2023). Parent and peer educational expectations and adolescent STEM aspirations. Developmental Psychology.
27. Basileo, D., Otto, B., Lyons, M., Vannini, N., & Toth, M. D. (2024). The role of self- efficacy, motivation, and perceived support of students’ basic psychological needs in academic achievement. Frontiers in Education,  9. https://doi.org/ 10.3389/ feduc.2024.1385442
28. Zhao, X., Liu, Y., & Tan, J. (2024). Academic self-efficacy and its impact on higher education enrollment decisions: A longitudinal analysis. Journal of Educational Psychology, 116(2), 215–231. https://doi.org/10.1037/edu0000756

APPENDIX 1

Factors Influencing Students’ Intentions to Pursue Higher Education Questionnaire

Factors Influencing Students’ Intention to Pursue Higher Education

Section A: Demographic Profile

1. Name:
2. Age:
3. Gender:
4. Ethnicity
5. Current Education Level:
6. Residence:
7. Are you planning to pursue higher education after your current level? Yes

2. No Maybe

Section B: Motivation, Self-Efficacy, Family Influence, Peer Influence, Financial Aid, Role of School Counsellor

Please read the given questions carefully and indicate how much you agree with the following statements.

Scale: 1 = Strongly Disagree, 5= Strongly Agree

Section C: Intention to Pursue Higher Education

Please read the given questions carefully and indicate how much you agree with the following statements.

Scale: 1 = Strongly Disagree, 5= Strongly Agree