Department of Mathematics
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Browsing Department of Mathematics by Subject "Statistics"
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- ItemChallenges assessing statistics attitudes: Opportunities and costs(2020-03-06) Whitaker, DouglasThis presentation compares different ways to assess the Expectancy Value Theory construct, Cost, using instruments in statistics education. Empirical results and new directions are included.
- ItemChallenges assessing statistics attitudes: Opportunities and costs(2020) Whitaker, Douglasccles and colleagues’ Expectancy-value theory (EVT; 1983) has been widely-used in education research, and the cost component of this framework has recently been the subject of increased research. While advances in measuring the cost component have been made (e.g., Flake et al., 2015), an on-going instrument development project for measuring attitudes in statistics education has encountered difficulties that motivate a deeper look at this construct. An overview of cost in EVT, the current state of measuring it in statistics education, and plans for a new study are described. The first project-specific data are to be collected in Spring 2020.
- ItemChallenges to using and interpreting the SATS-36 instrument: Do you like statistics? Do your students like statistics? How do you know?(2019-05) Whitaker, Douglas; Unfried, Alana; Bond, MarjorieThe Survey of Attitudes Toward Statistics (SATS-36; Schau, 2003) is the most widely-used instrument for measuring attitude-related constructs in statistics education (e.g. Nolan, Beran, & Hecker, 2012). However, the SATS-36 is only an update to the earlier SATS-28 instrument (Schau, 1992), and in the decades since the original release, advancements in statistics education research have resulted in numerous challenges to the use of the SATS family of instruments. This poster will describe some of the challenges facing users of the SATS instruments. While some challenges have been previously discussed in the literature (e.g. research based on factor analysis and construct alignment (Cashin & Elmore, 2005; Vanhoof et al., 2011)), other challenges have not been documented thoroughly before (e.g. problems with the Effort construct and issues with alignment to the theoretical framework). This poster will describe challenges and discuss how these may impact interpretations of research and what ways they might be addressed. Additionally, a clear description of these challenges may help teachers using the SATS instruments to learn more about their students make more appropriate interpretations of the results. One way of addressing these challenges - the development of a new family of instruments for measuring students’ and instructors’ attitudes toward statistics - will also be discussed.
- ItemDesign and validation arguments for the Student Survey of Motivational Attitudes toward Statistics (S-SOMAS) instrument(Routledge, 2019) Whitaker, Douglas; Unfried, Alana; Bond, MarjorieThis chapter describes the development plan for a new survey designed to measure students’ attitudes toward statistics aligned with expectancy value theory, the S-SOMAS. Included in this description are the context and motivation for developing a new instrument, a brief overview of the theoretical framework, and the validity evidence to be collected to support the intended uses of the S-SOMAS. The S-SOMAS is currently in development and pilot data have been collected; as such, this chapter includes both planned and enacted elements of instrument development. This chapter also provides readers with an illustration about the early stages of validation work and some choices that instrument developers may encounter.
- ItemEnvironment matters: Institution, course and pedagogy(2019-05) Bond, Marjorie; Batakci, Leyla; Bolon, Wendine; Whitaker, DouglasA new survey, E-SOMAS (Environmental Survey of Motivational Attitudes towards Statistics), is a part of a family of instruments, that will measure environmental characteristics related to affective constructs in statistics education. These instruments will include student and instructor surveys (S-SOMAS and I-SOMAS, respectively). Our environmental model has two factors with three elements in each. The two factors are split by the instructor’s locus of control. The first factor, institutional and course characteristics, while influenced by the instructor, is not fully within the instructor’s control and any type of control will vary between instructors. Factor 1’s elements are (a) institutional characteristics, (b) course characteristics, and (c) learning environment. Our link between I-SOMAS and E-SOMAS is the second factor, enacted classroom behaviors, which consists of (d) general pedagogy practices, (e) statistics-specific pedagogy practices and (f) teacher-student relations. Readers will be encouraged to comment, verify, and suggest variables which we will document.
- ItemEvaluating validity evidence for instruments in statistics education(2019-05) Harrell-Williams, Leigh; Whitaker, DouglasThis Breakout Session will focus on evaluating validity evidence supporting the interpretation of scores from instruments. While specific instruments will be used as examples, this session aims to build skills for assessing validity evidence in general. The contemporary view of validity evidence is both broader and deeper than ‘an instrument measuring what it is supposed to measure.’ However, statistics education is rife with incomplete views surrounding the development and use of instruments, and the result is a myriad of interpretations and conclusions that are not supported by appropriate evidence. This Breakout Session will focus on supporting the audience in critically evaluating validity evidence supporting the use of statistics education instruments as reported in the literature. As part of this evaluation of validity evidence, attention will be given to how a lack of evidence threatens interpretations and conclusions as well as how this can be addressed.
- ItemMeasuring statistics attitudes and anxieties(2019-05) Whitaker, Douglas; White, AaronBoth attitudes and anxiety have long been studied in the statistics education literature. Recently, efforts have been taken to clarify what is meant by these constructs (e.g. Chew & Dillon, 2014). Expectancy Value Theory (EVT; Eccles, 1983) is one framework for relating affective constructs to student achievement that has seen widespread use such as in the Survey of Attitudes Toward Statistics (Ramirez et al., 2012; Schau, 2003). To clarify the relationship between statistics anxiety and attitudes, several surveys were administered to introductory statistics students including the Statistics Anxiety Rating Scale (STARS; Cruise et al., 1985), a measure of opportunity cost (Flake et al., 2015), and several scales currently in development. (The data will be collected in Spring 2020.) This poster will discuss the potential relationships between EVT and statistics anxiety.