First Year Employment Outcomes of US Psychology Graduates Revisited: Need for a Degree, Salary, and Relatedness to the Major

2009 ◽  
Vol 8 (2) ◽  
pp. 23-29 ◽  
Author(s):  
D. W. Rajecki ◽  
Victor M. H. Borden

Alumni survey responses from a multi-major (multi-course), United States university sample (c. 2003–06, N = 1760) provided a replication and extension of previous research on patterns of graduates' first-year employment outcomes. Compared with graduates from the fields of nursing/allied health, business, engineering/technology, and education, new psychology and humanities/social sciences alumni tended to have jobs that locate individuals in low tiers of features including need for a college degree, salary, and relatedness to one's major program of study (course). These patterns of employment outcomes for US psychology graduates are generally similar to those on record for liberal education (nonvocational) graduates in the United Kingdom. Results are discussed in the context of established occupational constructs such as person-job ft, congruence, career compromise, and education mismatch.

2021 ◽  
Vol 20 (1) ◽  
pp. 4
Author(s):  
TALIA RANDA ESNARD ◽  
FAREENA MARYAM ALLADIN ◽  
KEISHA CHANDRA SAMLAL

The objectives of this study were to examine (i) the expectation for performance (EFP) in statistics of first year university students, (ii) the relative effect of previous mathematics performance (PMP), perceived statistical self-efficacy (SSE), and statistics anxiety (SA) for understanding these expectations, and (iii) whether students’ EFP scores differ based on sex and academic discipline. Findings point to average to high EFP in statistics, with no differences in these levels based on sex or academic discipline. PMP had little effect on students’ EFP, but, moderate effects on their levels of SA. While SSE positively affected both students’ SA, this measure produced a negative effect on their EFP in statistics. Both SSE and SA negatively affected students’ EFP in statistics, but with minimally higher levels for the latter.


2018 ◽  
Vol 22 (3) ◽  
pp. 497-521 ◽  
Author(s):  
Yu (April) Chen ◽  
Sylvester Upah

Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success. This study took the first step to investigate the influence of using predictive analytics on academic advising in engineering majors. Specifically, we examined the effects of predictive analytics-informed academic advising among undeclared first-year engineering student with regard to changing a major and selecting a program of study. We utilized the propensity score matching technique to compare students who received predictive analytics-informed advising with those who did not. Results indicated that students who received predictive analytics-informed advising were more likely to change a major than their counterparts. No significant effects was detected regarding selecting a program of study. Implications of the findings for policy, practice, and future research were discussed.


2016 ◽  
Vol 12 (25) ◽  
pp. 111
Author(s):  
Miguel Ángel Broc

This paper studies the assessment during the first year of baccalaureate in seventy-six students. In a public educational institution of Saragossa (Spain), no differences in academic achievement between men (forty) and women (thirty-six) were found, but between two main modalities (Sciences, thirty-five students, versus Humanities and social Sciences, with forty-one students), finding differences outstrip students Sciences throughout the year, equaling to end with their counterparts. This leads us to study the hypothesis of whether the ratings could be “inflated” by teachers in Humanities students. For this they were conducted by a retrospective design “ex post facto” an multiple lineal regression analysis, using as a dependent variable grades in the third final evaluation, and as independent, performance in the first and second assessment and all subjects, not including any other variables. The results show different prediction equations that explaining 87% and 88% of the variance in the final performance, in the two types of modalities respectively. The values predicted by the model resulting in a significant percentage of “inflation” in the ratings of 23% in the group of Sciences and 37% in the Humanities and Social Sciences. The mean scores of the subjects discussed in three different moments are analyzed and significant difference was observed in the three evaluations, emerging some evaluation patterns in the teachers. It intuits that the “rating inflation” could begin much earlier than in the preuniversity entrance course.


2014 ◽  
Vol 78 (2) ◽  
pp. 36 ◽  
Author(s):  
Julie Prescott ◽  
Gordon Becket ◽  
Sarah Ellen Wilson

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