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Science Technology Engineering Math (STEM) Classes and Females' Career Choices

Females have been discouraged from taking science, technology, engineering, and math (STEM) classes during high school and college, resulting in limited access to high-paying STEM careers. Therefore, these females could miss opportunities for these high-paying careers. The rationale of this research was to quantify the relationship between the number of STEM classes the sampled females took, the number of female role models they had during high school and college, their career choices, and salaries. The theoretical construct was based on Erikson's social developmental theory, which postulates a relationship between earlier life events and later life events, and Acker's masculinity theory, which postulates that females in traditionally male fields may be uneasy performing functions opposite to what they naturally perform. Key questions examined the relationships between STEM classes, role models, career choices, and salaries. The sample was a stratified random sample (n = 48) of female alumnae of 4 universities, born after 1980. Data were collected from a designed online instrument, validated by a pilot. The data were analyzed with a multiple regression and an analysis of variance. The findings revealed a significant relationship between the number of STEM classes, career choices and salary. However, there was no significance found between the numbers of role models, career choices and salary The implication for social change is that by making scholars in the fields of education and management aware about the relationship between the number of STEM classes taken, career choices, and salaries, females can be more encouraged to become interested in STEM courses earlier in life, making it more likely they will choose STEM careers This can be accomplished through scholarly journals, which hopefully will improve perceptions of the STEM abilities of females.

Identiferoai:union.ndltd.org:waldenu.edu/oai:scholarworks.waldenu.edu:dissertations-4456
Date01 January 2017
CreatorsLavorata, PhD, Dr Reagan Lorraine
PublisherScholarWorks
Source SetsWalden University
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceWalden Dissertations and Doctoral Studies

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