How to Calculate Statistics for Significant Difference Test Using SPSS: Understanding Students Comprehension on the Concept of Steam Engines as Power Plant

Meli Fiandini, Asep Bayu Dani Nandiyanto, Dwi Fitria Al Husaeni, Dwi Novia Al Husaeni, M. Mushiban


A significant difference test is used to evaluate certain treatments on the sample in two different observation periods.  One of the commonly used software is SPSS which is used to analyze data which helps researchers in calculating data so that it can be completed quickly. However, there are still many students and researchers who are not experts in calculating data using SPSS software, especially significant difference tests. This article aims to provide a step-by-step guide in calculating data using SPSS for statistical requirements and significant difference tests. To understand the calculations well using SPSS, we demonstrate the requirements tests (i.e., normality and homogeneity tests), parametric significant difference tests (i.e., One Sample t-test, Paired sample t-test, and Independent Sample t-test), and non-parametric (i.e., Wilcoxon test and Mann-Whitney test). We also added and demonstrated the steps for calculating data in the field of education with the variables analyzed being differences in student learning outcomes. We used the data when delivering the steam engine concept to students, showing how statistical calculation can understand students' comprehension. Bibliometric analysis regarding statistics was also added. This paper can be used as a guide in carrying out statistical tests using SPSS software.


Average difference test; Experimental demonstration; Bibliometric; Concept; Islamic school; Power plant energy; SPSS; Steam engine

Full Text:



Afifah, S., Mudzakir, A., and Nandiyanto, A. B. D. (2022). How to calculate paired sample t-test using SPSS software: From step-by-step processing for users to the practical examples in the analysis of the effect of application anti-fire bamboo teaching materials on student learning outcomes. Indonesian Journal of Teaching in Science, 2(1), 81-92.

Afifah, S., Mudzakir, A., Nandiyanto, A. B. D., Ragadhita, R., Maryanti, R., Al Husaeni, D. F., and Fiandini, M. (2023). Sustainability literacy to vocational students through distance learning with experimental demonstration: Ionic liquid experiment and its application as fire retardant. Journal of Technical Education and Training, 15(1), 55-72.

Agus, K. D., Elza, T., and Rahmat, P. (2021). Evaluation of the results of attitudes and self-efficacy of middle school students in science subjects. Journal of Education Research and Evaluation, 5(4), 525-535.

Al Husaeni, D.F., and Nandiyanto, A.B.D. (2022). Bibliometric using VOSviewer with publish or perish (using Google Scholar data): From step-by-step processing for users to the practical examples in the analysis of digital learning articles in pre and post covid-19 pandemic. ASEAN Journal of Science and Engineering, 2(1), 19-46.

Alfajri, M. I., Rozi, F., Azmi, M., Sariani, S., and Miladiyenti, F. (2023). Automatic measurement on learning effectiveness towards english pronunciation application using paired sample T-Test. Journal Polingua: Scientific Journal of Linguistics, Literature and Language Education, 12(1), 23-27.

Alvi, N. V., and Yerimadesi, Y. (2022). Effectiveness of the acid-base e-module based on guided discovery learning on the students learning outcomes of class XI students at SMAN 7 Padang. Pancaran Pendidikan, 11(2), 1-8.

Ananda, P. D., and Atmojo, S. E. (2022). The Impact of the discovery learning model on problem-solving ability and scientific attitude of elementary school teacher education students. International Journal of Elementary Education, 6(2), 259-267.

Arican, M., and Kuzu, O. (2020). Diagnosing preservice teachers’ understanding of statistics and probability: Developing a test for cognitive assessment. International Journal of Science and Mathematics Education, 18, 771-790.

Birt, J., and Cowling, M. (2017). Toward future'mixed reality'learning spaces for STEAM education. International Journal of Innovation in Science and Mathematics Education, 25(4), 1-16.

Cvitanovic, C., Hobday, A. J., van Kerkhoff, L., Wilson, S. K., Dobbs, K., and Marshall, N. A. (2015). Improving knowledge exchange among scientists and decision-makers to facilitate the adaptive governance of marine resources: a review of knowledge and research needs. Ocean and Coastal Management, 112, 25-35.

De Winter, J. C. (2019). Using the Student's t-test with extremely small sample sizes. Practical Assessment, Research, and Evaluation, 18(1), 10.

Delacre, M., Leys, C., Mora, Y. L., and Lakens, D. (2019). Taking parametric assumptions seriously: Arguments for the use of Welch’s F-test instead of the classical F-test in one-way ANOVA. International Review of Social Psychology, 32(1), 1-12.

Dogan, E., Altinoz, B., Madaleno, M., and Taskin, D. (2020). The impact of renewable energy consumption to economic growth: A replication and extension of. Energy Economics, 90, 104866.

Hairida, H. (2016). The effectiveness using inquiry based natural science module with authentic assessment to improve the critical thinking and inquiry skills of junior high school students. Jurnal Pendidikan IPA Indonesia, 5(2), 209-215.

Happ, M., Bathke, A. C., and Brunner, E. (2019). Optimal sample size planning for the Wilcoxon‐Mann‐Whitney test. Statistics in medicine, 38(3), 363-375.

Hayat, B. (2022). Adjustment for guessing in a basic statistics test for Indonesian undergraduate psychology students using the Rasch model. Cogent Education, 9(1), 2059044.

Herrero, A. C., Recio, T., Tolmos, P., and Vélez, M. P. (2023). From the steam engine to steam education: An experience with pre-service mathematics teachers. Mathematics, 11(2), 473.

Kilic, D. (2016). An Examination of using self-, Peer-, and teacher-assessment in higher education: A Case study in teacher education. Higher Education Studies, 6(1), 136-144.

Knief, U., and Forstmeier, W. (2021). Violating the normality assumption may be the lesser of two evils. Behavior Research Methods, 53(6), 2576-2590.

Li, X., Wu, Y., Wei, M., Guo, Y., Yu, Z., Wang, H., and Fan, H. (2021). A novel index of functional connectivity: phase lag based on Wilcoxon signed rank test. Cognitive Neurodynamics, 15, 621-636.

Malele, V., and Ramaboka, M. E. (2020). The design thinking approach to students STEAM projects. Procedia CIRP, 91, 230-236.

McGovern, E., Moreira, G., and Luna-Nevarez, C. (2020). An application of virtual reality in education: Can this technology enhance the quality of students’ learning experience?. Journal of education for business, 95(7), 490-496.

Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., and Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of cardiac anaesthesia, 22(1), 67.

Morris, T. P., White, I. R., and Crowther, M. J. (2019). Using simulation studies to evaluate statistical methods. Statistics in medicine, 38(11), 2074-2102.

Nandiyanto, A. B. D., Fiandini, M., Hofifah, S. N., Ragadhita, R., Al Husaeni, D. F., Al Husaeni, D. N., and Masek, A. (2022). Collaborative practicum with experimental demonstration for teaching the concept of production of bioplastic to vocational students to support the sustainability development goals. Journal of Technical Education and Training, 14(2), 1-13.

Nandiyanto, A. B. D., Tiyana, R., Azizah, D. N., and Hofifah, S. N. (2020). Demonstrating the biobriquettes production using variations in particle size and binder concentration using audio-visual to vocational students. Journal of Engineering Education Transformations, 34(Special Issue), 87-94.

Nwogwugwu, C. E., and Ovat, S. V. (2021). Role of gender in students’ perception of research and statistics in education: A panacea for sustainable governance. Global Journal of Educational Research, 20(2), 139-143.

Obafemi, K. E. (2019). Step-by-step deployment of Independent T-Test Using SPSS in early childhood studies. Outlook on Human Capacity Building and Development, 532-538.

Orcan, F. (2020). Parametric or non-parametric: Skewness to test normality for mean comparison. International Journal of Assessment Tools in Education, 7(2), 255-265.

Paul, J., and Jefferson, F. (2019). A comparative analysis of student performance in an online vs. face-to-face environmental science course from 2009 to 2016. Frontiers in Computer Science, 1, 1-9.

Ragadhita, R., Nandiyanto, A. B. D., Maryanti, R., Al Husaeni, D. F., Al Husaeni, D. N., Fiandini, M., and Masek, A. (2023). Best practice in distance learning with experimental demonstration on the concept of the automotive brake pad fabrication from domestic waste to vocational students for supporting education for sustainable development. Journal of Technical Education and Training, 15(1), 40-54.

Savalei, V., and Rhemtulla, M. (2013). The performance of robust test statistics with categorical data. British Journal of Mathematical and Statistical Psychology, 66(2), 201-223.

Soni, S. (2022). Cause related marketing and sales promotion: An analysis of comparative impact on consumer attitude. Nmims Management Review, 30(3), 69-87.

Sun, P., Wang, M., Song, T., Wu, Y., Luo, J., Chen, L., and Yan, L. (2021). The psychological impact of COVID-19 pandemic on health care workers: A systematic review and meta-analysis. Frontiers in psychology, 12, 626547.

Susanti, G., and Rustam, A. (2018). The effectiveness of learning models realistic mathematics education and problem based learning toward mathematical reasoning skills at students of junior high school. Journal of Mathematics Education, 3(1), 33-39.

Usman, M. (2016). On consistency and limitation of independent t-test Kolmogorov Smirnov Test and Mann Whitney U test. IOSR Journal of Mathematics, 12(4), 22-27.

Ustaoglu, A., Kursuncu, B., Alptekin, M., and Gok, M. S. (2020). Performance optimization and parametric evaluation of the cascade vapor compression refrigeration cycle using Taguchi and ANOVA methods. Applied Thermal Engineering, 180, 115816.

Vinje, H., Brovold, H., Almøy, T., Frøslie, K. F., and Sæbø, S. (2021). Adapting statistics education to a cognitively heterogeneous student population. Journal of Statistics and Data Science Education, 29(2), 183-191.

Wan, F. (2021). Statistical analysis of two arm randomized pre-post designs with one post-treatment measurement. BMC medical research methodology, 21(1), 1-16.

Zakaria, M. Y., Malmia, W., Irmawati, A., Amir, N. F., and Umanailo, M. C. B. (2019). Effect mathematics learning achievement motivation on junior high school students 1 namlea. International Journal of Scientific and Technology Research, 8(10), 1495-1498.



  • There are currently no refbacks.

Copyright (c) 2023 Universitas Pendidikan Indonesia

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

Indonesian Journal of Science and Technology is published by UPI.
StatCounter - Free Web Tracker and Counter
View My Stats