CoChat
Sign Up
Cited by 21Teaching and Learning ProgrammingGender and Technology in EducationAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes

The DOI matches its Crossref record; 70 of the 71 references it cites were found in a registry or an index.

Development and Validation of the Programming Anxiety Scale

Open access
YILDIRIM, Osman Gazi, OZDENER, Nesrin·2022·International Journal of Computer Science Education in Schools
DOI 10.21585/ijcses.v5i3.140
Open this paper in CoChat — free accountOpen PDF

Abstract

The main goal of the current study is to develop a reliable instrument to measure programming anxiety in university students. A pool of 33 items based on extensive literature review and experts' opinions were created by researchers. The draft scale comprised three factors applied to 392 university students from two different universities in Turkey. Exploratory and confirmatory factor analysis was conducted on the draft scale. According to analysis results, the Programing Anxiety Scale comprised of two factors and 14 items. In addition, factor loadings for the 14-item scale range from .633 to .918. Internal factor reliability for the whole scale and the subscales was estimated as Cronbach's alpha values of .95, .90, and .94, respectively. In the light of these results, the Programming Anxiety Scale is an appropriate and dependable tool.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.21585/ijcses.v5i3.140

Cites

70 of 70 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 71 references

Research works printed but not linked: 16

  1. Ab Hamid, M. R., Sami, W., & Sidek, M. M. (2017, September). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. In Journal of Physics: Conference Series (Vol. 890, No. 1, p. 012163). IOP Publishing.
  2. Bergin, S., & Reilly, R. (2005). Programming: factors that influence success. In Proceedings of the 36th SIGCSE technical symposium on Computer science education (pp. 411-415).
  3. Demirer, V. & Sak, N. (2016). Dünyada ve Türkiye'de programlama eğitimi ve yeni yaklaşımlar. Eğitimde Kuram ve Uygulama, 12(3), 521-546.
  4. Dijkstra, E. W. (1989). On the cruelty of really teaching computing science. Communications of the ACM, 32(12), 1398-1404.
  5. Falkner, K., Falkner, N. J., & Vivian, R. (2013, March). Collaborative Learning and Anxiety: A International Journal of Computer Science Education in Schools, April 2022, Vol. 5, No. 3 ISSN 2513-8359 phenomenographic study of collaborative learning activities. In Proceeding of the 44th ACM technical symposium on Computer science education (pp. 227-232).
  6. Figueroa Jr, R. B., & Amoloza, E. M. (2015). Addressing Programming Anxiety among Non-Computer Science Distance Learners: A UPOU Case Study. International Journal, 9(1), 56-67.
  7. Gorsuch, R. L. (1983). Factor analysis (2nd Edition). Hillsdale, NJ: Erlbaum.
  8. Huggard, M. (2004). Programming trauma: can it be avoided. Proceedings of the BCS Grand Challenges in Computing: Education, 50-51.
  9. Jenkins, T. (2002). On the difficulty of learning to program. In Proceedings of the 3rd Annual Conference of the LTSN Centre for Information and Computer Sciences (Vol. 4, No. 2002, pp. 53-58).
  10. Nolan, K., & Bergin, S. (2016, November). The role of anxiety when learning to program: a systematic review of the literature. In Proceedings of the 16th koli calling international conference on computing education research (pp. 61-70).
  11. Nunnally, J. C. (1994). Psychometric theory 3E. Tata McGraw-Hill Education.
  12. Pallant, J. (2007). SPSS survival manual: A step by step guide to data analysis using SPSS version 15 (3rd ed.). New York: Open University Press.
  13. Scott, M. (2015). Self-Beliefs in the Introductory Programming Lab and Games-based Fantasy Role-Play (Doctoral dissertation, Brunel University).
  14. Scott, M. J., & Ghinea, G. (2014, July). Measuring enrichment: the assembly and validation of an instrument to assess student self-beliefs in CS1. In Proceedings of the tenth annual conference on International computing education research (pp. 123-130).
  15. Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon
  16. Wilson, B. C., & Shrock, S. (2001). Contributing to success in an introductory computer science course: a study of twelve factors. ACM SIGCSE Bulletin, 33(1), 184-188.

Cited by

Sorted by citations

See all 21

Related

See all

Metadata

Authors
YILDIRIM, O. G., & OZDENER, N.
ORCID
Venue
International Journal of Computer Science Education in Schools
Publisher
ICT in Practice
Published
2022-05-02
Volume
5
Issue
3
Pages
17-34
ISSN
2513-8359 (ISSN-L)
DOI
10.21585/ijcses.v5i3.140
OpenAlex
W4225281101
Cited by
18
References
71
Citations by year
2026: 5 · 2025: 8 · 2024: 3 · 2023: 2
FWCI
2.51
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Computer Science Education in Schools (publishedVersion, cc-by)
  • Dspace Repository (Marmara Üniversitesi) (submittedVersion, other-oa)
Funders
Marmara Üniversitesi
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Social Sciences › Gender Studies › Gender and Technology in Education
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Keywords
scale development, psychometric validation, university students, computer science education, measurement instruments, factor structure

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Development and Validation of the Programming Anxiety Scale. https://app.cochat.ai/papers/10.21585/ijcses.v5i3.140
The paper
YILDIRIM, O. G., & OZDENER, N. (2022). Development and Validation of the Programming Anxiety Scale. International Journal of Computer Science Education in Schools. https://doi.org/10.21585/ijcses.v5i3.140