This data description was often obtained from the Principles of Econometrics textbook in the database, and the data of this model is nels. Def. Blaikie, N. (2003) states that the research objective is to explore, describe, understand, explain, predict, change, and evaluate aspects of the variables. Thus, the total range in the sample is 6649 observations, with the means and standard deviations shown for each variable expected to be used in the empirical research paper. For example, FAMINC has a mean of 50.7952 and a standard deviation of 40.6019, PSECHOICE’s mean, and deviation are 2.3095 and 0.7959, GRADES has a mean and deviation of 6.4357 and 2.2616, PARSOME’s mean, and deviation are 0.4620 and 0.4986, and last but not least, PARCOLL has a mean and deviation of 0.3286 and 0.4697 respectively. In this model, the variables for interest mentioned above, along with their means and standard deviations, are included in the regression model. The following variables, FAMINC, PSECHOICE, GRADES, PARSOME, and PARCOLL, can predict and determine whether high school graduates’ choice to attend or not attend college has a relationship between the variables.

      This empirical research paper intends to emphasize high school graduates’ choices and whether the abovementioned variables would have a marginal effect on restricting a student who graduated from high school from college. For example, family income (FAMINC) is considered a factor in attending college because when a family income is low, it can have a ripple effect on a student’s interest in attending college. In this case, a PSECHOICE mainly depends on whether the family has well above income to support tuition payments and other expenses for college that a student needs to attend college. Based on the first and second parts of this empirical research paper, family income and social changes have become the key ingredients most authors talked about in journals concerning high school graduates’ choice of whether to attend college or not. Additionally, the authors attempted to compare the influences of high-income and low-income families when predicting the probability of going to college. Thus, it is essential to emphasize the sequence of part 1 and part 2 in the description data in part 3.

       In addition, GRADES can be another issue that could undermine a student’s future because if the performance does not meet the standard required by the institution, a student will not be accepted to any college. I am also interested in PARSOME because if the parents have some college or two-year college, there is a possibility that their children would also have opportunities of at least going to college. Most importantly, the PARCOLL determines the most educated parents who have graduated from college or attained advanced degrees. If parents have achieved advanced education, they tend to be well-off in earning high income that can provide more opportunities for their children to follow in their footsteps. Since education has become more expensive, parents with lower education sometimes try to pay part of education tuition for their children and let the rest be covered by either loans or grants provided by the Federal government. Sometimes, splitting education tuition may work and may not work for some students, which can implicate their education choice in the long run at the end of their academic because students and parents may end up in debt.

      In the data description, I excluded many variables, such as HSCATH, HSRURUL, FAMSIZ and FAMALE, to name a few. Though they may have contributed to the overall choice of high school graduates, they will make the model redundant in the empirical analysis. Therefore, depending on what matters most in the description data, the family income will be a dependent variable used as zero. On the other hand, the FAMSIZ does not have an overall binary option because size may be a reason, but it has been a parcel of FAMINC, even if it is true that it has been ignored to be part of the description data. Also, a few variables that made up the dataset could be substantial. However, they are omitted for a reason, as the paragraph explains. After all, the regression model will specifically focus on the following variables: the range, means, and standard deviations given in the first paragraph. If there is a need for a correction in this data description, it will be corrected based on some guidelines that might have been missed or ignored. In conclusion, all these variables are primarily related to the empirical research paper about high school graduates’ choice to attend or not to attend any college and higher institutions in this current situation.

References

Blaikie, N. (2003). Analyzing quantitative data: from description and explanation. SAGE Publication Limited. https://ebookcentral.proquest.com/lib/cochristuniv-ebooks/reader.action?docID=354948

Hill, R., Griffiths, W. and Lim, G. (2018). Principles of econometrics 5^th edition. Wiley. http://www.principlesofeconometrics.com/poe5/poe5excel.html. Nels. Def.