Kajian Pemahaman Mahasiswa tentang Pengumpulan dan Analisis Data melalui Evaluasi Hasil Penelitian Skripsi

Abstract

A deep understanding of data collection and analysis techniques is an essential competence for students to produce valid and high-quality research. This study employed a quantitative descriptive approach with 22 students as subjects who had studied these techniques. Each student was tasked with evaluating two quantitative theses relevant to the learning model, published within the last five years. The evaluation used a rubric covering seven key components: Data Identification and Classification, Frequency Distribution, Measures of Central Tendency and Data Variation, Coefficient of Variation, Standard Values, Inferential Statistical Procedures, and Data Integration and Synthesis. The study results revealed varying levels of student understanding across the components. Most students achieved high competence in Data Identification and Classification (95.45%) and Inferential Statistical Procedures (95.45%). However, comprehension of more complex components, such as Coefficient of Variation and Standard Values, remained weak, with only 54.55% of students fully competent. This study highlights the need for more structured teaching methods and intensive practical exercises, particularly in advanced statistical analysis and data visualization. The implications suggest that improving these areas can strengthen students' research skills, supporting better mastery of data analysis techniques in the future.