Validasi Psikometrik Instrumen Kecemasan Statistik untuk Mahasiswa Psikologi di Indonesia Menggunakan Model Rasch

Siti Munawarah, Bahrul Hayat

Abstract


Kecemasan statistik merupakan secara signifikan mempengaruhi kinerja akademik, terutama di kalangan mahasiswa psikologi yang diharapkan dapat menguasai metode penelitian kuantitatif. Penelitian ini bertujuan untuk mengevaluasi validitas dan keandalan instrumen untuk mengukur kecemasan statistik, berdasarkan skala Statistical Self-Efficacy for Psychologists (SES-Psy). Sebanyak 373 mahasiswa psikologi dari enam perguruan tinggi di Jabodetabek yang telah menyelesaikan mata kuliah  statistika dasar berpartisipasi dalam penelitian ini. Proses adaptasi mengikuti pedoman Beaton dengan empat orang ahli yang menilai validasi konten. Data dikumpulkan secara online dan dianalisis menggunakan Confirmatory Factor Analysis (CFA) dan model Rasch. Hasil CFA mengkonfirmasi beban faktor yang kuat dan indeks kesesuaian model yang sangat baik. Analisis Rasch memverifikasi bahwa instrumen tersebut memenuhi asumsi unidimensionalitas, independensi lokal, dan kesesuaian statistik. Semua kategori skala ditafsirkan dengan benar oleh responden dan tidak menunjukkan bias gender berdasarkan analisis DIF. Temuan ini menunjukkan bahwa instrumen yang diadaptasi secara psikometris valid untuk mengidentifikasi tingkat kecemasan statistik di kalangan mahasiswa psikologi di Indonesia. Dengan standard setting berbasis Rasch, diketahui bahwa mayoritas responden penelitian ini diklasifikasikan mengalami kecemasan statistik sedang hingga tinggi. Instrumen ini dapat digunakan pada lingkungan pendidikan maupun dimanfaatkan untuk pengembangan intervensi kecemasan statistik.


Keywords


Kecemasan statistik; Model Rasch; Adaptasi instrument

Full Text:

PDF

References


Arslan, D., & Sak, U. (2023). Factorial Invariance of The Anadolu Sak Intelligence Scale Across Gender. Journal of Psychoeducational Assessment, 41(5), 542–555. https://doi.org/10.1177/07342829231158834

Aziz, A. A., Zaharim, A., Fuaad, N. F. A., & Nopiah, Z. M. (2013). Students’ Performance on Engineering Mathematics: Applying Rasch Measurement Model. 2013 12th International Conference on Information Technology Based Higher Education and Training (ITHET). https://doi.org/10.1109/ITHET.2013.6671040

Beaton, D. E., Bombardier, C., Guillemin, F., & Ferraz, M. B. (2000). Guidelines for The Process of Cross-Cultural Adaptation of Self-Report Measures. Spine, 25(24), 3186–3191. https://journals.lww.com/spinejournal/fulltext/2000/12150/Guidelines_for_the_Process_of_Cros_Cultural.14.aspx

Bond, T. G., & Fox, C. M. (2007). Applying The Rasch Model: Fundamental Measurement in The Human Sciences (2nd ed.). Routledge. https://doi.org/10.4324/9781410614575

Brentani, E., & Golia, S. (2007). Unidimensionality in The Rasch Model: How to Detect and Interpret. Statistica, 67(3), 253–261. https://doi.org/10.6092/ISSN.1973-2201/3508

Condron, D. J., Becker, J. H., & Bzhetaj, L. (2018). Sources of Students’ Anxiety in A Multidisciplinary Social Statistics Course. Teaching Sociology, 46(4), 315–326. https://doi.org/10.1177/0092055X18780501

Dabaghi, S., Esmaielzadeh, F., & Rohani, C. (2020). Application of Rasch Analysis for Development and Psychometric Properties of Adolescents’ Quality of Life Instruments: A Systematic Review. Adolescent Health, Medicine and Therapeutics, 11, 91–104. https://doi.org/10.2147/ahmt.s265413

Dempster, M., & McCorry, N. K. (2009). The Role of Previous Experience and Attitudes Toward Statistics in Statistics Assessment Outcomes Among Undergraduate Psychology Students. Journal of Statistics Education, 17(2). https://doi.org/10.1080/10691898.2009.11889515

Do?an, M. (2015). Influence of Sample Size, Estimation Method and Normality on Fit Indices in Confirmatory Factor Analysis. Journal of Social Science Research, 9(2), 75–91.

Edwards, M. C., & Houts, C. R. (2018). A Diagnostic Procedure to Detect Departures From Local Independence in Item Response Theory Models. Psychological Methods, 23(1), 138–149. https://doi.org/10.1037/met0000121

Fernanda, J. W., & Hidayah, N. (2020). Analisis Kualitas Soal Ujian Statistika Menggunakan Classical Test Theory dan Rasch Model. Square: Journal of Mathematics and Mathematics Education, 2(1), 35–42. https://doi.org/10.21580/square.2020.2.1.5363

Garfield, J. (2002). The Challenge of Developing Statistical Reasoning. Journal of Statistics Education, 10(3). https://doi.org/10.1080/10691898.2002.11910676

Griffith, J. D., Mathna, B., Sappington, M., Turner, R., Evans, J., Gu, L., Adams, L. T., & Morin, S. (2014). The Development and Validation of The Statistics Comprehensive Anxiety Response Evaluation. International Journal of Advances in Psychology, 3(2), 21–29.

Hoegler, S., & Nelson, M. (2018). The Influence of Anxiety and Self-Efficacy on Statistics Performance: A Path Analysis. Psi Chi Journal of Psychological Research, 23(5), 364–371. https://doi.org/10.24839/2325-7342.jn23.5.364

Holster, T. A., & Lake, J. (2016). Guessing and The Rasch Model. Language Assessment Quarterly, 13(2), 124–141. https://doi.org/10.1080/15434303.2016.1160096

Howard, G. S. (2019). The Present and Future of Methodology and Statistics in Psychology. The Humanistic Psychologist, 47(1), 1–12. https://doi.org/10.1037/hum0000111

Hu, L. T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Hunt, B. W., Mari, T., Knibb, G., Christiansen, P., & Jones, A. (2023). Statistics Anxiety and Predictions of Exam Performance in UK Psychology Students. PLoS ONE, 18(8), e0290467. https://doi.org/10.1371/journal.pone.0290467

Ismail, M. S., Din, M. H., & Jusoh, M. S. (2021, July). Predictive Modelling Using Rasch’s Person-Item Map: Intrepreting and Assessing in Malaysia Manufacturing Firms. In AIP Conference Proceedings (Vol. 2347, No. 1, p. 020288). AIP Publishing LLC. https://pubs.aip.org/aip/acp/article-abstract/2347/1/020288/614840

Jiao, H., Lissitz, R. W., Macready, G., Wang, S., & Liang, S. (2011). Exploring Levels of Performance Using The Mixture Rasch Model for Standard Setting. Psychological Test and Assessment Modeling, 53(4), 499–522. https://www.psychologie-aktuell.com/fileadmin/download/ptam/4-2011_20111217/06_Jiao.pdf

Kabic, M., & Alexandrowicz, R. W. (2023). RMX/PIccc: An Extended Person–Item Map and A Unified IRT Output for eRm, Psychotools, ltm, mirt, and TAM. Psych, 5(3), 265–280. https://doi.org/10.3390/psych5030062

Kaufmann, L., Ninaus, M., Weiss, E. M., Gruber, W., & Wood, G. (2022). Self-Efficacy Matters: Influence of Students’ Perceived Self-Efficacy on Statistics Anxiety. Annals of the New York Academy of Sciences, 1514(1), 41–52. https://doi.org/10.1111/nyas.14797

Kyriazos, T. A. (2018). Applied psychometrics: The Application of CFA to Multitrait-Multimethod Matrices (CFA-MTMM). Psychology, 9(12), 2933–2970. https://doi.org/10.4236/psych.2018.912150

Lee, H. S., & Geisinger, K. F. (2014). The Effect of Propensity Scores on DIF Analysis: Inference on The Potential Cause of DIF. International Journal of Testing, 14(4), 345–368. https://doi.org/10.1080/15305058.2014.922567

Linacre, J. M. (2002). Optimizing Rating Scale Category Effectiveness. Journal of Applied Measurement, 3(1), 85–106.

Linacre, J. M. (2012). A user’s guide to Winsteps/Ministep: Rasch-Model Computer Programs. Winsteps.

Ibrahim, N. L., Ekonomi da Bisnis, F., & Gorontalo, P. (2025). Analisis Literatur Penggunaan Model Rasch untuk Validasi Instrumen Asesmen Pendidikan: Tren, Temuan, dan Implikasi. Lencana: Jurnal Inovasi Ilmu Pendidikan, 3(2), 46–60. https://doi.org/10.55606/lencana.v3i2.5012

Liu, L. (2021). Assessing Statistics Anxiety in An Online or Hybrid Setting: The Adaption and Development of A New Instrument SASOH. Statistics Education Research Journal, 20(1). https://doi.org/10.52041/SERJ.V20I1.96

MacCann, R. (2009). Standard Setting with Dichotomous and Constructed Response Items: Some Rasch Model Approaches. Journal of Applied Measurement, 10(4), 438–454. https://www.kingarthursvoyagetoamerica.com/wp-content/uploads/2016/08/01-MacCann-Oxford-University-Journal-of-Applied-Measurement-10-4.pdf

Macher, D., Papousek, I., Ruggeri, K., & Paechter, M. (2015). Statistics Anxiety and Performance: Blessings In Disguise. Frontiers in Psychology, 6, 1116. https://doi.org/10.3389/fpsyg.2015.01116

Mcintee, S. E., Goulet-Pelletier, J. C., Williot, A., Deck-Léger, E., Lalande, D., Cantinotti, M., & Cousineau, D. (2022). (Mal)adaptive Cognitions as Predictors of Statistics Anxiety. Statistics Education Research Journal, 21(1). https://doi.org/10.52041/serj.v21i1.364

Ogbonnaya, K. E., Okechi, B. C., & Nwankwo, B. C. (2019). Impact of Statistics Anxiety and Self-Efficacy On Statistical Performance of Psychology Students. Nigerian Journal of Social Psychology, 2(2), 15–23. https://www.nigerianjsp.com/index.php/NJSP/article/view/33

Padgett, R. N., & Morgan, G. B. (2019). An Examination of Sensitivity to Measurement Error in Rasch Residual-Based Fit Statistics. Journal of Applied Measurement, 20(4), 358–371.

Prayoga, K., Suryana, D., & Suhendar, M. S. (2024). Penggunaan Rasch model untuk Menganalisis Konstruk Instrumen Kontrol Diri pada Siswa Sekolah Menengah. Guidance: Jurnal Bimbingan dan Konseling, 7(1), 35–44. https://journal.upy.ac.id/index.php/bk/article/view/4459

Qudratuddarsi, H., Hidayat, R., Shah, R. L. Z. R. M., Nasir, N., Imami, M. K. W., & Mat Nor, R. (2022). Rasch Validation of Instrument Measuring Gen-Z Science, Technology, Engineering, and Mathematics (STEM) Application in Teaching During The Pandemic. International Journal of Learning, Teaching and Educational Research, 21(6), 104–121

Smith, A. B., Cocks, K., Parry, D., & Taylor, M. (2016). A Differential Item Functioning Analysis of The EQ-5D in cancer. Value in Health, 19(8), 1063–1067. https://doi.org/10.1016/j.jval.2016.06.005

Smith, E. V. (2002). Detecting and Evaluating The Impact of Multidimensionality Using Item Fit Statistics and Principal Component Analysis of Residuals. Journal of Applied Measurement, 3(2), 205–231. https://europepmc.org/article/med/12011501

Sumintono, B., & Widhiarso, W. (2015). Aplikasi Pemodelan Rasch pada Assessment Pendidikan. Trim Komunikata.

Tanujaya, L., & Yudiarso, A. (2023). Uji Validitas dan Reliabilitas Multicultural Attitude Scale Questionnaire (MASQUE) Versi Indonesia dengan Model Rasch. Humanitas (Jurnal Psikologi), 7(2), 161–174. https://doi.org/10.28932/humanitas.v7i2.7420

Tunç, E. B. (2023). A Review of Measurement Tools Developed and Adapted Based on the Rasch Model. Journal of Human and Social Sciences, 6(2), 249-275.

Umar, J., & Nisa, Y. F. (2020). Uji Validitas Konstruk dengan CFA dan Pelaporannya. Jurnal Pengukuran Psikologi dan Pendidikan Indonesia, 9(2), 1–11. https://doi.org/10.15408/jp3i.v9i2.16964

Vigil-Colet, A., Lorenzo-Seva, U., & Condon, L. (2008). Development and Validation of The Statistical Anxiety Scale. Psicothema, 20(1), 174–180.

Yen, W. M. (1984). Effects of Local Item Dependence on The Fit and Equating Performance of The Three-Parameter Logistic Model. Applied Psychological Measurement, 8(2), 125–145. https://doi.org/10.1177/014662168400800201

Yu, C. H. (2020). Objective Measurement: How Rasch Modeling Can Simplify and Enhance Your Assessment. Rasch measurement: Applications in quantitative educational research, 47-7




DOI: https://doi.org/10.31004/edukatif.v7i4.8404

Article Metrics

Abstract view : 47 times
PDF - 43 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Siti Munawarah, Bahrul Hayat

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.