Meninjau Ulang Praktik Analisis Kuantitatif: Kesalahan Fundamental Peneliti Pemula dan Pendekatan Metodologis Berbasis Bukti Empiris

Authors

  • Irma Rodiana Universitas Pakuan, Indonesia
  • Tommy Hendratno Universitas Pakuan, Indonesia
  • Isidora Kadarwati Universitas Pakuan, Indonesia
  • Satra Universitas Pakuan, Indonesia
  • Supandi Universitas Pakuan, Indonesia
  • Teguh Iswanto Universitas Pakuan, Indonesia
  • Nandang Hidayat Universitas Pakuan, Indonesia

Keywords:

Data Analysis, Empirical Data, Statistical Literacy, Research Misconceptions, Quantitative Research

Abstract

Metodologi penelitian kuantitatif merupakan instrumen krusial dalam pengembangan ilmu pengetahuan karena mengedepankan objektivitas dan pengujian data secara numerik. Sayangnya, rendahnya pemahaman konseptual sering menjebak peneliti pemula ke dalam berbagai miskonsepsi metodologis. Artikel ini merupakan tinjauan literatur sistematis yang bertujuan mendekonstruksi 10 kesalahpahaman utama dalam analisis data kuantitatif. Pembatasan pada 10 kesalahan esensial ini sengaja dilakukan agar evaluasi dapat dibahas secara mendalam, fokus, dan mencegah terjadinya beban kognitif (cognitive overload) bagi peneliti pemula. Berbeda dengan pandangan normatif, artikel ini menyajikan bukti empiris atas miskonsepsi yang terjadi di lapangan mulai dari temuan bahwa 90% peneliti salah mengartikan ilusi p-value, 100% dari sampel 15 skripsi terbukti melanggar uji asumsi validitas/reliabilitas, rendahnya skor empiris penalaran kritis mahasiswa (77,50), hingga kesalahan mekanis nyata dalam pembacaan tabel distribusi statistik. Melalui kajian ini, setiap kesalahan teknis dijelaskan makna konseptualnya dan diberikan mitigasinya. Hasil tinjauan menyimpulkan bahwa keabsahan penelitian tidak diukur dari kerumitan alat statistik, melainkan dari ketepatan rancangan metodologis, kepatuhan pada asumsi parametrik, dan justifikasi analisis berbasis bukti empiris.

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References

Altman, N., & Krzywinski, M. (2015). Association, correlation and causation. Nature Methods, 12(10), 899–900. https://doi.org/10.1038/nmeth.3587

Booth, A., Sutton, A., & Papaioannou, D. (2016). Systematic approaches to a successful literature review (2nd ed.). SAGE Publications.

Bradley, V. C., Kuriwaki, S., Isakov, M., Sejdinovic, D., Meng, X.-L., & Flaxman, S. (2021). Unrepresentative big surveys significantly overestimated US vaccine uptake. Nature, 600, 695–700. https://doi.org/10.1038/s41586-021-04198-4

Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376. https://doi.org/10.1038/nrn3475

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149

Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146

Gelman, A., & Carlin, J. (2014). Beyond power calculations: Assessing Type S (sign) and Type M (magnitude) errors. Perspectives on Psychological Science, 9(6), 641–651. https://doi.org/10.1177/1745691614551642

Gigerenzer, G. (2004). Mindless statistics. The Journal of Socio-Economics, 33(5), 587–606. https://doi.org/10.1016/j.socec.2004.09.033

Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations. European Journal of Epidemiology, 31(4), 337–350. https://doi.org/10.1007/s10654-016-0149-3

Hayes, A. F., & Cai, L. (2007). Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior Research Methods, 39(4), 709–722. https://doi.org/10.3758/BF03192961

Kitchenham, B., & Brereton, P. (2013). A systematic review of systematic review process research in software engineering. Information and Software Technology, 55(12), 2049–2075. doi:10.1016/j.infsof.2013.07.010.

Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131

Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, Article 863. https://doi.org/10.3389/fpsyg.2013.00863

Mauliddin, M. (2017). Analisis kesalahan penggunaan uji statistik pada skripsi mahasiswa. Matematika dan Pembelajaran, 5(2), 141–158. https://doi.org/10.33477/mp.v5i2.231

Mauliddin, M. (2017). Analisis kesalahan penggunaan uji statistik pada skripsi mahasiswa. Matematika dan Pembelajaran, 5(2), 141–158. doi:10.33477/mp.v5i2.231.

Pramita, D., Anwar, Y. S., Sirajudin, & Abdillah. (2019). Analisis kesalahan uji statistik pada skripsi mahasiswa Program Studi Pendidikan Matematika FKIP Universitas Muhammadiyah Mataram. Jurnal Riset Intervensi Pendidikan, 1(1), 39–44.

Stark, P. B., & Saltelli, A. (2018). Cargo-cult statistics and scientific crisis. Significance, 15(4), 40–43. https://doi.org/10.1111/j.1740-9713.2018.01174.x

Sudarto, S. (2024). Analisis kesalahan mahasiswa dalam membuat skripsi. Jurnal Cakrawala Ilmiah, 3(9), 2455–2462.

Sullivan, G. M., & Feinn, R. (2012). Using effect size—or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279–282. https://doi.org/10.4300/JGME-D-12-00156.1

Tejada, J. J., & Punzalan, J. R. B. (2012). On the misuse of Slovin’s formula. The Philippine Statistician, 61(1), 129–136.

Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. https://doi.org/10.1080/00031305.2016.1154108

Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 76(6), 913–934. https://doi.org/10.1177/0013164413495237

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Published

2026-08-31

How to Cite

Rodiana, I., Tommy Hendratno, Isidora Kadarwati, Satra, Supandi, Teguh Iswanto, & Nandang Hidayat. (2026). Meninjau Ulang Praktik Analisis Kuantitatif: Kesalahan Fundamental Peneliti Pemula dan Pendekatan Metodologis Berbasis Bukti Empiris. ARMADA : Jurnal Penelitian Multidisiplin, 4(8), 4263–4273. Retrieved from https://ejournal.45mataram.ac.id/index.php/armada/article/view/3518