Parsimonious in Factor Identification Using Exploratory Factor Analysis

Statistics is one of the most important quantitative subjects in higher education. It is acknowledged learning statistics subject is difficult for students with varying background and abilities. By the nature of the statistics subject which tended to focused more on formula memorization and heavy calculation will perceive students to had negative experience with statistics subject. Perhaps most critical is the fact that statistics course is taught and dry subject. Within these problems, it is important to educators with a need for significant improvement for their student's better learning. This study is aimed primarily to highlight the factors that help educators to implement time series teaching and learning manual aids because the idea of prediction and forecasting are difficult for students from other disciplines to understand. Thus, students need to be statistically literate in their routine life. This paper presents a parsimonious method of exploratory factor analysis. Exploratory factor analysis suggests out of 19 items included, there are five components or factors which account for 72.61% of the total variance with considerably reduce the complexity of the data set with 27.39% loss of information. Kaiser-Meyer- Olkin value is 0.7960 and Bartlett's test p-value = 0.000 < 0.05 indicate factor analysis is feasible for this data set. There is no item removed considering the communalities values are all above moderate (min = 0.62, max = 0.82). In association to reduce the difficulty faced by those students as well as their expectation towards the product, the principal component analysis with Varimax rotation method was revealed five factors in term of challenges and difficulty, price, design, content and benefit to students. These five factors are important in getting attention of the student towards the product. This will hopefully lead and boost to a good result at the end.