Statistical Thinking Ability in Solving Statistical Problems Through Adversity Quotient

Abstract

Abstrak

Statistical thinking ability menjadi kompetensi krusial bagi mahasiswa teknik industri, khususnya dalam memecahkan masalah statistik di dunia industri yang menuntut analisis data yang akurat dan pengambilan keputusan berbasis data. Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis hubungan antara Adversity Quotient (AQ) dengan statistical thinking ability mahasiswa teknik industri. Penelitian menggunakan desain deskriptif kuantitatif dengan pendekatan ex post facto, melibatkan 115 mahasiswa tingkat akhir yang dikelompokkan berdasarkan tingkat AQ menjadi tiga kategori: climber, camper dan quitter. Data dikumpulkan melalui angket Adversity Response Profile (ARP), esai, dan wawancara, serta post-test statistical thinking. Analisis dilakukan melalui perhitungan rata-rata, persentase, distribusi frekuensi, dan analisis inferensial untuk menguji perbedaan antar kelompok. Hasil penelitian menunjukkan bahwa mahasiswa dengan AQ tinggi memiliki kemampuan statistical thinking yang lebih baik dibandingkan kelompok AQ sedang dan rendah. Temuan ini menegaskan pentingnya penguatan AQ dalam proses pembelajaran statistik, sehingga dapat meningkatkan ketahanan mental, motivasi, dan pemecahan masalah statistik di kalangan mahasiswa teknik industri. Penelitian ini merekomendasikan integrasi strategi penguatan AQ dalam pembelajaran statistik untuk menghasilkan lulusan yang lebih adaptif dan kompetitif.

Abstract

Statistical thinking ability is a crucial competency for industrial engineering students, especially in solving statistical problems in the industrial world, which requires accurate data analysis and data-driven decision making. This study aims to identify and analyze the relationship between Adversity Quotient (AQ) and statistical thinking ability in industrial engineering students. The study employs a quantitative descriptive design with an ex post facto approach, involving 115 final-year students grouped into three categories based on their AQ levels: climbers, campers, and quitters. Data were collected through the Adversity Response Profile (ARP) questionnaire, essays, interviews, and a post-test on statistical thinking. Analysis was conducted using mean calculations, percentages, frequency distributions, and inferential analysis to test differences between groups. The results of the study indicate that students with high AQ have better statistical thinking abilities compared to those with moderate and low AQ. These findings emphasize the importance of strengthening AQ in the statistical learning process, thereby enhancing mental resilience, motivation, and statistical problem-solving skills among industrial engineering students. This study recommends integrating AQ-strengthening strategies into statistical education to produce more adaptive and competitive graduates.