Evaluation of a Questionnaire Measuring University Students' Sense of Belonging to and Involvement in a Biology Department.

Sense of belonging and involvement has shown to be positively associated with academic achievement and retention. We argue that a focus on sense of belonging and involvement specifically at the departmental level is valuable, yet rarely discussed in the literature. In this article, we describe the development and evaluation of a questionnaire measuring university students' sense of belonging to and involvement in their "home" department (biology). The questionnaire was named the DeSBI questionnaire (Departmental Sense of Belonging and Involvement) and was administered to students attending an R1 university in the southeastern United States during Spring 2018 (n = 201) and Fall 2018 (n = 737) semesters. Factor analysis indicated a three-factor solution: one factor consisting of five items representing sense of belonging: valued competence; a second factor consisting of six items representing sense of belonging: social acceptance; and a third factor consisting of nine involvement items. Analysis of variance showed significant differences in all subscales between students included in a special biology program, biology majors, and non-biology majors. The study findings support the use of the instrument for measuring biology students' sense of belonging to and involvement in their biology department.

Defining and Measuring Students' Interest in Biology: An Analysis of the Biology Education Literature.

Understanding how students develop biology interests and the roles interest plays in biology contexts could help instructors and researchers to increase science, technology, engineering, and mathematics students' motivation and persistence. However, it is currently unclear how interest has been defined or measured in the biology education research literature. We analyzed this body of literature to determine how interest has been defined and used by the biology education research community. Specifically, we determined the extent to which previously published work drew on theories that conceptualize interest. Further, we identified studies that measured student interest in biology and characterized the types of measures used. Our findings indicate that biology education researchers typically describe interest as a relationship involving positive feelings between an individual and a physical object, activity, or topic of focus. We also found that interest is often not defined, theories involving interest are not often consulted, and the most common measures of interest only assess a single aspect of the construct. On the basis of these results, we make suggestions for future research seeking to examine biology students' interest. We hope that this analysis can serve as tool for biology educators to improve their own investigations of students' interest and measure outcomes of interest-generating educational activities.

One Size Doesn't Fit All: Using Factor Analysis to Gather Validity Evidence When Using Surveys in Your Research.

Across all sciences, the quality of measurements is important. Survey measurements are only appropriate for use when researchers have validity evidence within their particular context. Yet, this step is frequently skipped or is not reported in educational research. This article briefly reviews the aspects of validity that researchers should consider when using surveys. It then focuses on factor analysis, a statistical method that can be used to collect an important type of validity evidence. Factor analysis helps researchers explore or confirm the relationships between survey items and identify the total number of dimensions represented on the survey. The essential steps to conduct and interpret a factor analysis are described. This use of factor analysis is illustrated throughout by a validation of Diekman and colleagues' goal endorsement instrument for use with first-year undergraduate science, technology, engineering, and mathematics students. We provide example data, annotated code, and output for analyses in R, an open-source programming language and software environment for statistical computing. For education researchers using surveys, understanding the theoretical and statistical underpinnings of survey validity is fundamental for implementing rigorous education research.