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Attitudes play an important role in students’ academic achievement and retention, yet we lack quality attitude measurement instruments in the new field of data science. This paper explains the process of creating Expectancy Value Theory-based instruments for introductory, college-level data science courses, including construct development, item creation, and refinement involving content experts. The family of instruments consist of surveys measuring student attitudes, instructor attitudes, and instructor and course characteristics. These instruments will enable data science education researchers to evaluate pedagogical innovations, create course assessments, and measure instructional effectiveness relating to student attitudes. We also present plans for pilot data collection and analyses to verify the categorization of items to constructs, as well as ways in which faculty who teach introductory data science courses can be involved. 

Attitudes matter in statistics and data science education, but previous instruments have been limited in scope, resulting in many unanswered questions. This paper discusses the Surveys of Motivational Attitudes toward Statistics and Data Science, a family of instruments designed to provide a broad understanding of university-level student and instructor attitudes as well as learning environment characteristics. Based on Expectancy Value Theory, a meta-model explains the interrelationships among the instruments, and an iterative design process is followed for survey development. Psychometric results from data collections using instruments developed thus far are presented. This is the first time a cohesive, synergistic set of instruments has been designed to work together to give a broader understanding of the state of statistics and data science education. 

The Student Survey of Motivational Attitudes toward Statistics is a new instrument designed to measure affective outcomes in statistics education. This instrument is grounded in the established Expectancy-Value Theory of motivation and is being developed using a rigorous process. This paper provides an overview of the four pilot studies that have been conducted during the survey development process. Additionally, a description of the methods used for analyzing the data and the way the results are used to holistically make decisions about revisions to the survey is included. Brief confirmatory factor analysis results are included from two pilot studies to demonstrate that substantial progress has been made on the development. Once finalized (Spring 2023), the survey will be made freely available. 

The Motivational Attitudes in Statistics and Data Science Education Research group is developing a family of validated instruments: two instruments assessing students’ attitudes toward statistics or data science, two instruments assessing instructors’ attitudes toward teaching statistics or data science, and two sets of inventories to measure the learning environment in which the students and instructor interact. The Environment Inventories measure the institutional structures, course characteristics, and enacted classroom behaviors of both the students and instructors, all of which interact with the student and instructor background. This paper will discuss our proposed theoretical framework for the learning environment and its development.