2019
DOI: 10.3102/0002831219853533
|View full text |Cite
|
Sign up to set email alerts
|

Do Relative Advantages in STEM Grades Explain the Gender Gap in Selection of a STEM Major in College? A Multimethod Answer

Abstract: Using a multimethod approach, we investigate whether gender gaps in STEM (science, technology, engineering, and mathematics) major declaration in college are explained by differences in the grades that students earn in STEM versus non-STEM subjects. With quantitative data, we find that relative advantages in college academic performance in STEM versus non-STEM subjects do not contribute to the gender gap in STEM major declaration. To explore alternative explanations for gender gaps in major declaration, we ana… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

2
9
0
1

Year Published

2020
2020
2024
2024

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 20 publications
(12 citation statements)
references
References 78 publications
2
9
0
1
Order By: Relevance
“…The need to account for the nesting of data by using hierarchical modeling oversimple regression was assessed by intraclass correlation coefficient (ICC) and likelihood ratio testing (LRT) for linear models. Smaller models were used to estimate the effect of sex or race/ethnicity in final grades, average exam grades, and other coursework grades. These models were constructed using only the data for the class (course + sequence) of interest, such as the reformed on-sequence or traditional off-sequence, for optimum estimation of that class. Larger models were used to assess differences between classes or groups of classes such as between courses or between on- and off-sequence classes.…”
Section: Methodssupporting
confidence: 90%
“…The need to account for the nesting of data by using hierarchical modeling oversimple regression was assessed by intraclass correlation coefficient (ICC) and likelihood ratio testing (LRT) for linear models. Smaller models were used to estimate the effect of sex or race/ethnicity in final grades, average exam grades, and other coursework grades. These models were constructed using only the data for the class (course + sequence) of interest, such as the reformed on-sequence or traditional off-sequence, for optimum estimation of that class. Larger models were used to assess differences between classes or groups of classes such as between courses or between on- and off-sequence classes.…”
Section: Methodssupporting
confidence: 90%
“…Our detailed explanation of our processes incidentally answers the calls in the literature for (a) qualitative researchers to offer concrete evidence of their methodologies (Aguinis et al, 2019;Glazer and Egan, 2018;Kirk and Miller, 1986;Peterson, 2019;Stearns et al, 2019); and (b) qualitative reports to be evaluated 'in terms of their own logic of inquiry' (Levitt et al, 2018: 27-28;Syed and Nelson, 2015). We both see the need for and contribute to this 'logic of inquiry' by offering a new approach to qualitative coding that yields trustworthy results, thus foregrounding the product of the research over complete control of the process (Sandelowski, 1986;Syed and Nelson, 2015).…”
supporting
confidence: 58%
“…We combine the science as White property concept with an approach that also emphasizes the intersections of race/ ethnicity and gender to frame the experiences of students in STEM classes, forming a conception of science as White male property. Further details and additional findings from these interviews are reported elsewhere (Mickelson, Parker, Stearns, Moller and Dancy, 2015;Moller et al, 2015;Rainey, Dancy, Mickelson, Stearns and Moller, 2018;Rainey, Dancy, Mickelson, Stearns and Moller, 2019;Stearns et al, 2019). In this article, we present students' views regarding racial and gender differences in experiences of students in STEM.…”
Section: Introductionmentioning
confidence: 83%