Attitudes toward statistics-Tabulation Data
Description
This data represents students' attitudes towards statistics before and after learning, academic achievements, and student demographic background (study program (hospitality, travel, and tourism); class levels; gender; origin of high school; study program in high school; and work experience).
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Data collection was carried out using a survey method among students from the leading and oldest state tourism universities in Indonesia who took statistics courses for the 2020–2021 academic year. A total of 447 students were asked to fill out the Survey of Attitude Towards Statistics (SATS) questionnaire adapted from Schau et al. (1995). The pre-SATS questionnaire, completed online during the first week of classes, is self-administered and takes 20–30 minutes. After one semester of statistics study, students must complete a post-SATS questionnaire and report their academic performance. After the study, pre- and post-SATS answers were collected, and the questionnaire was checked for completeness. 435 respondents completed the questionnaire, providing a demographic profile for the study sample. The questionnaires that students filled out were based on instruments from the Survey of Attitude Towards Statistics (SATS) (Berndt et al., 2021; Chew & Dillon, 2014; Fairlamb et al., 2022; Lavasani et al., 2014; Prayoga & Abraham, 2017). There are 28 items, which are grouped into four attitudinal components: cognitive (6 items), affective (6 items), and difficulty (7 items). The SATS uses a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) and has been tested for reliability and validity The study analyzed SATS data using descriptive statistics to compare achievement scores. The t-test was applied to determine the significance of the relationship between students' attitudes towards statistics before and after learning and their academic achievement. ANCOVA was used to analyze the relationship based on student demographics. The univariate analysis of variance test removed factors like student background, focusing on the impact on student scores and removing its influence on the model.