The Survey of Attitudes Toward Statistics (SATS) was designed for use in both research and instruction. A panel of instructors and introductory statistics students identified by consensus four facets of attitudes toward statistics: (a) Affect-positive and negative feelings concerning statistics; (b) Cognitive Competence-attitudes about intellectual knowledge and skills when applied to statistics; (c) Value-attitudes about the usefulness, relevance, and worth of statistics; and (d) Difficulty-attitudes about the difficulty of statistics as a subject. This structure was validated for a sample of undergraduate students using confirmatory factor analysis. Additional validity evidence was obtained through the correlation of the SATS with Wise's Attitudes Toward Statistics scale, which showed significant, positive relationships between the two instruments.

In addition to student learning, positive student attitudes have become an important course outcome for many introductory statistics instructors. To adequately assess changes in mean attitudes across introductory statistics courses, the attitude instruments used should be invariant by administration time. Attitudes toward statistics from 4,910 students enrolled in an introductory statistics course were measured using the Survey of Attitudes Toward Statistics (SATS) both at the beginning and at the end of the semester. Confirmatory factor analysis on the covariance structure was used to determine the gender and time invariance properties of the SATS. Results indicate that the SATS is gender, time, and Gender × Time invariant with respect to factor loadings and factor correlations. Gender was invariant with respect to 3 of the 4 factor variances; variances from these same 3 factors were larger at the end than at the beginning of the course. Having established that the SATS is factorially invariant with respect to gender, time, and Gender × Time, its component scores can be used appropriately to examine mean attitude differences for these 2 variables and their interaction.

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Students enter courses with prior knowledge of the subject area. Unfortunately, these naive notions often are misconceptions (or “folk concepts”) that hinder learning of appropriate concepts in the field.

Four causal models describing the longitudinal relationships between attitudes and achievement have been proposed in the literature. These models feature: (a) cross-effects over time between attitudes and achievement, (b) in¯uence of achievement predominant over time, (c) in¯uence of attitudes predominant over time, or (d) no cross-effects over time between attitudes and achievement. In an examination of the causal relationships over time between attitudes toward science and science achievement for White rural seventh-and eighth-grade students, the cross-effects model was the best ®tting model form for students overall. However, when examined by gender, the no cross-effects model exhibited the most accurate ®t for White rural middle-school girls, whereas a new model called the no attitudes-path model exhibited the best ®t for these boys. ß

We report on a long-term, large-scale study of a one-semester, conceptually based, introductory astronomy course with data from more than 400 students over three semesters at the University of New Mexico. Using traditional and alternative assessment tools developed for the project, we examined the pre- and postcourse results for Fall 1994, Spring 1995, and Fall 1995. We find our results are robust: novice students show large, positive gains on assessments of conceptual understanding and connected understanding of the knowledge structure of astronomy. We find no relationship between course achievement and completion of prior courses in science or math; we do find a small to moderate relationship between students’ science self-image and course achievement. Also, we detect little change over each semester in students’ mildly positive incoming attitudes about astronomy and science.

People forget what they do not use. But attitudes “stick.” Our article emphasizes the importance of students’ attitudes toward statistics. We examine 15 surveys that purport to assess these attitudes and then describe the Survey of Attitudes Toward Statistics, a commonly used attitude survey. We present our conceptual model of Students’ Attitudes Toward Statistics (SATS-M), which is congruent with Eccles and colleagues’ Expectancy-Value Theory (Eccles’ EVT), as well as others. The SATS-M includes three broad constructs that impact Statistics Course Outcomes: Student Characteristics, Previous Achievement-Related Experiences, and Statistics Attitudes. We briefly describe Eccles’ EVT and other theories that support our SATS-M. We relate findings from research using the SATS to our model and end with implications for statistics education. First published November 2012 at Statistics Education Research Journal: Archives