Statistical Test The data analysis intends to employ SPSS in performing principal component analysis (PCA). Particularly, PCA aims to reduce the number of variables into principal components that explain most of the variation in SPSS anxiety among students. According to Field (2013), PCA eases exploration of data by establishing patterns, linear relationships, and the influence of each variable. In essence, PCA eliminates redundant variables and promotes dimensionality of variables (Jackson, 2015). For robust PCA, data ought to meet the assumptions of the multiplicity of continuous variables, the linearity of data, sample adequacy, and the absence of significant outliers. Elliott and Woodward (2015) elucidate that PCA applies orthogonal transformation in converting raw variables that highly correlate to principal components with low correlation coefficients. Thus, PCA enables the determination of principal components with major influence on the variation of data. The Basis of Data The data emanated from the questionnaire developed and utilized in collecting data regarding SPSS anxiety among 2571 students. The questionnaire comprises 23 closed-ended questions with a five-point Likert scale answers indicating the strength of agreement from 1 (strongly disagree) through strongly agree (5) (Field, 2013). Since students experience anxiety in the course of learning SPSS, the questionnaire aims to measure and ascertain the extent of SPSS anxiety. Through the interview of students with and without anxiety, the study formulated 23 questions with viability to measure SPSS anxiety. The examination of data indicates that they meet the assumptions of PCA because the questions are 23, variables have linear relationships, data do not have significant outliers, and the sample size is more than 300 (N = 2571). The use of the questionnaire would indicate the extent of anxiety in each student learning SPSS. Moreover, the questionnaire would reveal forms of SPSS anxiety existing among students. In essence, the study aims to use PCA in revealing principal variables that explain most of SPSS anxiety among students. Research Questions   * What are the principal components that account for the variation in SPSS anxiety among students?   * What are the dominant themes in the extracted components? Hypotheses   * H 0 : The 23 questions in the questionnaire are not statistically significant variables in explaining the variation in SPSS anxiety among students.   * H 1 : The 23 questions in the questionnaire are statistically significant variables in explaining the variation in SPSS anxiety among students. Statistical Outcomes Descriptive Statistics The exploration of the data using descriptive statistics indicates that there are no missing data because 2571 students answered all the Likert items in the questionnaire. Denis (2016) asserts that descriptive statistics form the basis of data analysis for they reveal patterns and trends of data. Descriptive statistics also show that students neither agree nor disagree with most Likert statements (18) because their mean scores ranged from 2.23 to 2.89 as demonstrated in Table 1. Moreover, students agree with the remaining four Likert statements for their