Education and Professional Choice Correlation

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Summary

The study is designed to track and analyze the experiences of the young generations in high school and post-high school education and how these experiences influence their professional choices later on. For a categorical variable, the question of whether teenagers believe science is helpful in daily life was chosen. The answers are available can then be distributed into the four standard categories for such surveys: strongly disagree, disagree, agree, and strongly agree. The metric variable of how many years the high school teacher has taught science. These two variables were selected to perform a Pearson Correlation test and a Bivariate Regression test to examine the relationship between the two variables in the data set

Pearson Correlation

Correlations
Years science teacher has taught high school science Teenager thinks science is useful for everyday life
Years science teacher has taught high school science Pearson Correlation 1 .002
Sig. (2-tailed) .898
N 3125 2713
Teenager thinks science is useful for everyday life Pearson Correlation .002 1
Sig. (2-tailed) .898
N 2713 4003

The bivariate Pearson correlation test examines the relationship between the length of a teacher’s scientific career and a teenager’s belief that science is applicable to everyday purposes. The correlation coefficient in the analysis is equal to 0.002, indicating the existence of a positive relationship between the two variables. Thus, the greater the length of the career, the more students are likely to strongly agree or agree with the claim that science is useful in everyday life. It is worthwhile to note that the correlation is not very substantial, most likely due to the presence of many other variables influencing a teenager’s opinion.

Bivariate Regression

Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 Years science teacher has taught high school scienceb . Enter
a. Dependent Variable: Teenager thinks science is useful for everyday life
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .002a .000 .000 .758
a. Predictors: (Constant), Years science teacher has taught high school science
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression .010 1 .010 .017 .898b
Residual 1558.983 2711 .575
Total 1558.992 2712
a. Dependent Variable: Teenager thinks science is useful for everyday life
b. Predictors: (Constant), Years science teacher has taught high school science
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 2.135 .024 88.822 .000
Years science teacher has taught high school science .000 .002 .002 .129 .898
a. Dependent Variable: Teenager thinks science is useful for everyday life

The existence of the same positive correlation is illustrated by the bivariate regression model. The value of R^2 as zero indicates the absence of variation in correlation with the dependent variable. Namely, although the correlation between variables is comparatively small, it is always present, without any variation.

Implications of Social Change

Longitudinal research entails several observations over a prolonged period of time. It is a crucial component of a test on people’s life expectancy in which they are monitored for over ten years or their whole life span. By studying the evolving attitudes of high school students, the researcher gets a glimpse into their relationship with science and, by extension, the everyday innovations of digital evolution (Martin et al., 2019). Most science professors teach for roughly 10 years, as shown in the metric variable. Science professors play a crucial influence in developing career interests among pupils, with many looking up to them when pursuing careers in STEM (Chan & Hume, 2019). According to the findings, more than half of the pupils are interested in science and engaged or planning to engage in acts of innovation. As a result, we are witnessing a significant shift in the pace and substance of technical evolution.

References

Chan, K., & Hume, A. (2019). . Repositioning Pedagogical Content Knowledge In Teachers’ Knowledge For Teaching Science, 3-76.

Martin, A., Mansour, M., & Malmberg, L. (2019). . Educational Psychology, 40(9), 1113-1135.

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