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Abstract

The increased awareness of the importance of self-efficacy in online learning has led to a development of several scales. Of those, the Self-Efficacy Questionnaire for Online Learning (SeQoL) has served as a key instrument for researchers who examined students in online learning environments (Tsai et al., 2020). However, its utility with a diverse population has not been tested in the literature. Specifically, measurement invariance of the SeQoL has never been investigated. Ensuring measurement invariance of the SeQoL helps determine the adequacy of the measurement to diverse populations. This study explored measurement invariance of the SeQoL with people from diverse backgrounds including age, sex, and race. Study results supported strong measurement invariance for all the studied variables. These results collectively suggest validity evidence of the SeQoL and demonstrate that the target construct being assessed by the SeQoL holds the same meaning across multiple groups.

Keywords

online learning measurement invariance learning self-efficacy students with disabilities

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References

Abdous

M. H.

(2019). Well begun is half done: Using online orientation to foster online students’ academic self-efficacy. Online Learning, 23(3), 161–187. https://doi.org/10.24059/olj.v23i3.1437

Alano

Erlandez

Gonzales

Palo

M. A.

Punsalan

Celis

M. L.

Lobo

(2025). Tracing back from online learning during the pandemic: Digital literacy and online learning self-efficacy in today’s Physical Education. Digital Education Review, 47, 229–246. https://doi.org/10.1344/der.2025.47.229-246

Alqurashi

(2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148. https://doi.org/10.1080/01587919.2018.1553562

Aragon

Johnson

(2008). Factors influencing completion and noncompletion of community college online courses. American Journal of Distance Education, 22(3), 146–158. https://doi.org/10.1080/08923640802239962

Artino

A. R.

(2008). Motivational beliefs and perceptions of instructional quality: Predicting satisfaction with online training. Journal of Computer Assisted Learning, 24(3), 260–270. https://doi.org/10.1111/j.1365-2729.2007.00258.x

Bandura

(1986). Social foundation of thought and action: A social cognitive theory. Prentice-Hall.

Cheung

G. W.

Rensvold

R. B.

(2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5

Coduti

W. A.

Hayes

J. A.

Locke

B. D.

Youn

S. J.

(2016). Mental health and professional help-seeking among college students with disabilities. Rehabilitation Psychology, 61(3), 288–296. https://doi.org/10.1037/rep0000101

Colorado

Eberle

(2010). Student demographics and success in online learning environments. Emporia State Research Studies, 46(1), 4–10.

10.

Derakhshan

Fathi

(2024). Grit and foreign language enjoyment as predictors of EFL learners’ online engagement: The mediating role of online learning self-efficacy. The Asia-Pacific Education Researcher, 33(4), 759–769. https://doi.org/10.1007/s40299-023-00745-x

11.

Derakhshesh

Fathi

Hosseini

H. M.

Mehraein

(2022). An investigation of the structural model of online course satisfaction, online learning self-efficacy, and online learning climate in the EFL context. Computer-Assisted Language Learning Electronic Journal, 23(2), 261–281.

12.

Fearnley

Fabia

J. N.

Caballes

(2025). Perceptions and self-efficacy in online learning: Do students' characteristics and access to technology matter? Journal of Social and Scientific Education, 2(2), 97–115. https://doi.org/10.58230/josse.v2i2.317

13.

Fletcher

K. M.

(2005). Self-efficacy as an evaluation measure for programs in support of online learning literacies for undergraduates. The Internet and Higher Education, 8(4), 307–322. https://doi.org/10.1016/j.iheduc.2005.09.004

14.

Güçlü

H. Y.

Akça

N. K.

(2024). Relationship between self-efficacy and death anxiety of persons with chronic obstructive pulmonary disease. YOBÜ Sağlık Bilimleri Fakültesi Dergisi, 5(2), 164–172. https://dergipark.org.tr/en/download/article-file/3485459.

15.

Lim

C. L.

Ab Jalil

Ma’rof

A. M.

Saad

W. Z.

(2020a). Peer learning, self-regulated learning and academic achievement in blended learning courses: A structural equation modeling approach. International Journal of Emerging Technologies in Learning, 15(3), 110–125. https://doi.org/10.3991/ijet.v15i03.12031

16.

Lim

C. L.

Ab Jalil

Ma'rof

A. M.

Saad

W. Z.

(2020b). Self-regulated learning as a mediator in the relationship between peer learning and online learning satisfaction: A study of a private university in Malaysia. Malaysian Journal of Learning and Instruction, 17(1), 51–75. https://doi.org/10.32890/mjli2020.17.1.3

17.

Meredith

(1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525–543. https://doi.org/10.1007/BF02294825

18.

Moore

M. G.

Kearsley

(2005). Distance education: A systems view (2nd edition). Wadsworth.

19.

Mosqueira Taipe

C. M.

Mazer

L. C.

Fernandes Martins Molina

N. P.

Rossi Baldassarini

Di Donato

Do Carmo Pereira Júnior

dos Santos

P. L.

(2025). Sociodemographic predictors of difficulty in performing online academic activities among Brazilian graduate students in the COVID-19 pandemic. Journal of Latinos and Education, 24(1), 221–234. https://doi.org/10.1080/15348431.2024.2370849

20.

Murray

(2008). Internet-delivered treatments for long-term conditions: Strategies, efficiency and cost-effectiveness. Expert Review of Pharmacoeconomics & Outcomes Research, 8(3), 261–272. https://doi.org/10.1586/14737167.8.3.261

21.

National Center for Education Statistics . (2018). Number and percentage distribution of students enrolled in postsecondary institutions, by level, disability status, and selected student characteristics: 2015-16.

22.

National Student Clearinghouse Research Center . (2022). Current term enrollment estimates report series. https://nscresearchcenter.org/current-term-enrollment-estimates/

23.

Ortagus

J. C.

Yang

(2018). An examination of the influence of decreases in state appropriations on online enrollment public universities. Research in Higher Education, 59(7), 847–865. https://doi.org/10.1007/s11162-017-9490-y

24.

Özaydin Özkara

Ibili

(2021). Analysis of students’ E-learning styles and their attitudes and self-efficacy perceptions towards distance education. International Journal of Technology in Education and Science, 5(4), 550–570. https://files.eric.ed.gov/fulltext/EJ1323474.pdf.

25.

Park

Choi

(2010). Factors influencing adult learners’ decision to drop out or persist in online learning. Educational Technology & Society, 12(4), 207–210.

26.

Prior

D. D.

Mazanov

Meacheam

Heaslip

Hanson

(2016). Attitude, digital literacy and self-efficacy: Flow-on effects for online learning behavior. The Internet and Higher Education, 29(1), 91–97. https://doi.org/10.1016/j.iheduc.2016.01.001

27.

Rosseel

Oberski

Byrnes

Vanbrabant

Savalei

Merkle

Barendse

(2015). Package “Lavaan”: Latent variable analysis. R Package. https://lavaan.org

28.

Shen

Cho

M.-H.

Tsai

C.-L.

Marra

(2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. The Internet and Higher Education, 19, 10–17. https://doi.org/10.1016/j.iheduc.2013.04.001

29.

Sun

Rogers

(2020). Development and validation of the online learning Self-efficacy scale (OLSS): A structural equation modeling approach. American Journal of Distance Education, 35(3), 184–199. https://doi.org/10.1080/08923647.2020.1831357

30.

Tsai

C. L.

Cho

M. H.

Marra

Shen

(2020). The self-efficacy questionnaire for online learning (SeQoL). Distance Education, 41(4), 472–489. https://doi.org/10.1080/01587919.2020.1821604

31.

L. T. M.

Thuyet

P. T. S.

(2022). An analysis of online learning self-efficacy in the case of Vietnamese university students. Ho Chi Minh City Open University Journal of Science-Social Sciences, 12(1), 40–49. https://doi.org/10.46223/hcmcoujs.soci.en.12.1.2145.2022

32.

Westland

J. C.

(2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487. https://doi.org/10.1016/j.elerap.2010.07.003

33.

Yang

(2024). Self-regulated learning strategies, self-efficacy, and learning engagement of EFL students in smart classrooms: A structural equation modeling analysis. System, 125, Article 103451. https://doi.org/10.1016/j.system.2024.103451

34.

(2018). Examining construct validity of the student online learning readiness (SOLR) instrument using confirmatory factor analysis. Online Learning, 22(4), 277–288. https://doi.org/10.24059/olj.v22i4.1297

35.

Zimmerman

W. A.

Kulikowich

J. M.

(2016). Online learning self-efficacy in students with and without online learning experience. American Journal of Distance Education, 30(3), 180–191. https://doi.org/10.1080/08923647.2016.1193801

Online Learning Self-Efficacy Scale: Measurement Invariance Across Various Demographic Characteristics

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Keywords

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