代写SOC 20004/30004: Introduction to Quantitative Methods in Social Science帮做R编程

SOC 20004/30004:

Introduction to Quantitative Methods in Social Science

Final Project

Due December 12, 2024

This assignment is based on the Early Childhood Longitudinal Study Kindergarten Cohort, 1998 (ECLSK 98), a nationally representative sample of US children entering kindergarten in 1998.

The assignment follows from a preliminary analysis you completed for assignment 2. In that study, you looked at bivariate relationships between the social origins of a child and that child’s early mathematics skill. We chose this outcome because it is known to predict later school success – which itself predicts adult economic outcomes.

What is new in this final project is the task of modeling these relationships.

The data set name on canvas is “ECLS_98_Soc30004_revised.dta.” We’ll confine our interest to the following variables:

B_MOMEDW = mother’s education POV= family’spoverty status

ln_income (treat as interval scale)

R4MTHT_R=child’s math score in fall of kindergarten

R4MTHT_0=child’s math score in spring of kindergarten

Please write a report not to exceed 10 double-spaced pages (including tables and figures) that answers the following questions:

1.   To what extent do social origins (mothers ’ education, family poverty status, and income (log scale) predict a child’s math scale when the child enters kindergarten (typically at    age 5)?

2.    To what extent does the child’s math skill at entry to kindergarten predict that child’s math skill in the spring of kindergarten, controlling for the social origin variables?

3. All findings in social science are based on some assumptions regardless of how carefully the analysis is done. Please state any assumptions that are required to justify your answers to questions (1) and (2). Also, please evaluate the credibility of these assumptions with respect your findings in this study. (Use plots to evaluate these assumptions when possible.)

Your report should define key variables used in the analysis and provide a table of basic

descriptive statistics. Please use multiple regression to answer these questions. Please write down any models that you study. Define each term in the model. Please describe key findings with language accessible to a policy maker or practitioner. (Although pvalues are relevant, do not rely primarily on computed p values to interpret your findings. If a relationship is “statistically significant,” interpret the magnitude of the regression coefficients themselves.) In a final paragraph, briefly summarize your key conclusions.