Critically Analyzing a Research StudyShaleontyne PaytonSouth UniversityBUS-7100November 7, 2018This article discusses how women and girls are underrepresented in the STEM (science, technology, engineering, and math) fields. The number of men obtaining degrees and employment in math is more than double of women, although the number of women getting graduate degrees has increased in science disciplines over 40 years (Su, Rounds, & Armstrong, 2009). These upper level degrees pertain to biological science, social science and psychology, with the lack of interest in STEM fields being the top reason for females opting out. The authors note that “interests are vital to one’s identity and is an expression of an individual’s attempt to adjust to the work and academic environment by seeking opportunities that match” (Su, Rounds, & Armstrong, 2009, p.860). A person’s role model, education, and even expectations from parents can also greatly influence the changing interest of girls and boys that lead into the future .
This leads people to seek an environment that can let their differences function and compliment effectively. The educational tradition of interest focuses on relationships between motivation, attention, and interest while primarily examining these functions in academic achievement and learning in children’s classroom settings (Krapp, 1999; Renninger, Hidi & Krapp, 1992; Su et al., 2009). Despite the interest and educational importance of career choices, these hasn’t been any inclusive review that qualifies the magnitude and inspects the nature of interest in sex differences (Su et al., 2009).The studies design was made by using a meta-analytic review to examine sex difference sizes in work and interest dimensions using John Holland’s most adopted RIASEC framework.
This framework for vocational interest falls into six categories: realistic, investigative, artistic, social, enterprising, and conventional. The manuals of vocational interest inventory was used as the source of data as this will generally contain large sample results of various ethnic and age groupings which also controls any sampling error. The design is expected to show that men and women prefer working in different categories, with men preferring “working with things” while women prefer “working with people” (Su et al., 2009, p. 862). This study can offer a review in sex difference interest that can provide groundwork for gender disproportion in the future. Furthermore, the sex difference sizes were calculated for all interest inventory that’s in print as well as assessing the “effect sizes for inventories that are highly regarded in the field of vocational assessment” (Su et al.
, 2009, p. 863). A moderator analysis was used in the study to view a mixed-effects model, which has been known to be the best meta-analysis that involves system influence from facilitators. This model allows the examination of moderator influence without assuming all variances in the systematic factors can be accounted for due to random error (Su et al., 2009). Three accompanying analyses using math, engineering, and science related areas were conducted in order to observe sex differences in STEM fields. This analysis not only studied sex difference sizes and moderators but also looked at the variance ratio of female and male interests with the size difference comparative within respondents comparison in male and female differences (Su et al.
, 2009). In evaluating the meta-analysis for inventory inclusion, several steps were taken. Samples were published from the U.S. or merged in samples from Canada with tests that are intended to determine the education interests being excluded. The intention is to measure the vocational interest and use female and male respondent equally.
Additionally, standard deviation and mean for both genders were listed to make it feasible in calculating the effects of six difference sizes. This included a sample total of 81 individuals that consisted of 259,518 women and 243,670 males, with the year of birth ranging from 1939 to 1987 (Su, Rounds, ; Armstrong, 2009). Similarly, effect sizes were also calculated for the dimension of Things-People and Data-Ideas using formulas from the UNIACT-Revised Manual (America College Testing Program, 1995, p. 126; Su, Rounds, ; Armstrong, 2009).In evaluating the findings of this study, important suggestions for inventory interest and guidance in career development were obtained. The field of engineering was shown to have a large effect size while math and science showed small effects, all favoring men (Su et al., 2009).
More so, only the interest in engineering seem to be obstructed by the variable samples of group and age. The analysis of variance ratios exhibited men had more variability with interest in the engineering areas than women (Su et al., 2009).
For these men and women the intragroup variances were significantly bigger than the intergroup STEM field. This indicates how important considering differences in individual vocational interests are. The present study also revealed differences with the largest change between genders seen with men gravitating to things-oriented careers and women to people-oriented careers (Su et al., 2009). These results showed that the low number of females in engineering and science fields may result from people orientated preferences in careers. This indicates how important the interest role in gender disparity and choices in STEM fields are and how future measures of interest are developed.
Men seem to show interest in the realistic and investigative category and more interest in the areas of STEM. When occupational and educational choices are made, the comparison of ones interest with the areas of others are bought to light. It is of importance for educators, counselors and parents to get involved in interests early on. Increasing effects to get females interested in STEM fields and bridge gender gaps will also need initiating when kids are starting to acquire perceptions and gender roles in careers (Su et al., 2009).Just as the authors note the studies findings, they also note criticisms as well. One common argument about the meta-analysis is that studies get combined. This includes the good and not good which dilutes how the accuracy of the effect size estimates (Su et al.
, 2009). According to Su (2009), “the use of raw scores in interest inventories has been criticized for producing dramatically different score distributions for male and female respondents and leading to divergent, sex-stereotypic occupational guidance” (p. 863). Theoretical framework could have impact on gender difference size in the scores of inventory due to construct variations and interest measured by scales.
Lastly, this study revealed results of development strategy applications decreasing the gender contrast in vocational interest. Looking at the technology application and gender differences in interest inventories needs to be studied under the construct validity issue (Messick, 1989; Su, Rounds ; Armstrong, 2009).In conclusion, there has been great debate as to why females aren’t highly represented in STEM fields. Findings from the study on the sex differences show that women favor working with people while men favor working with things. More so, this study proposes interests have a crucial role in occupational selections and gender disproportion. Role models, education background, and even expectations from parents can greatly influence the changing interest of people leading one to seek an environment that can let differences function and contribute efficiently. Counselors and educators have to pay special attention to interpreting results and choosing tools for assessment as not to limit the occupation choices of men and women (Su et al., 2009).
The studies design was analyzed using a meta-analytic review that examined the gender difference sizes in work and interest using the RIASEC framework. This study offers a review in sex difference interest, providing groundwork for gender imbalance in the future. In evaluating the findings of this study, results showed the low amount of females in engineering and science fields result from preferences in careers and indicates the importance of interest role in gender disparity and STEM choices.
Also revealed were the largest change between genders seen with men gravitating to things-oriented careers and women to people-oriented careers (Su et al., 2009). This change showed that people oriented career preferences are resulting from the lower number of females in engineering and science fields. Criticism on the findings displayed that studies get combined with using the meta-analysis which can dilute the accuracy of estimated effect sizes. ReferenceSu, R.
, Rounds, J., & Armstrong, P. I. (2009). Men and things, women and people: A meta-analysis of sex differences in interests.
Psychological Bulletin, 135(6), 859–884.
