The dependent variable for this interactive assignment is the Provincial Achievement Test (PAT) score earned by students in an Alberta high school. There are 70 observations for this data set measuring PAT scores and a number of possible causal factors have been randomly drawn out of a pool of approximately 750 students over approximately one decade.

QUESTION

Use Stata software

The dependent variable for this interactive assignment is the Provincial Achievement Test (PAT) score earned by students in an Alberta high school. There are 70 observations for this data set measuring PAT scores and a number of possible causal factors have been randomly drawn out of a pool of approximately 750 students over approximately one decade. The literature on PAT scores indicates that scores are determined not only by ability and training but also various socio-economic factors. Please see the attached article by James Fallows, ‘The Tests and the Brightest: How Fair Are the College Boards.’ for a summary of views in the literature on how SAT performance in the USA might be impacted by various socio-economic factors (PAT scores and SAT scores should be similarly determined). Measures of ability and training included here are the cumulative high school grade point average (GPA) and participation in advanced placement math and English courses (APMATH and APENG). Advanced placement courses may help students perform better on the PAT. This data set also includes a number of dummy variables measuring qualitative socio-economic factors such as a student’s gender (MALE), ethnicity (WHITE), and native language (ENG). The data set also includes a dummy variable indicating whether or not a student has attended a PAT preparation class (PREP). The data set includes a variable indicating what year (YEAR) the students PAT score and other information was recorded. Finally there are several variables created as the product of two other variables.

ANSWER

 Factors Affecting Provincial Achievement Test Scores: An Analysis Using Stata

Introduction

The Provincial Achievement Test (PAT) scores earned by students in an Alberta high school are influenced by a variety of factors, including socio-economic variables, measures of ability and training, and other qualitative factors. This essay aims to analyze the relationship between these factors and PAT scores using the statistical software Stata. By employing Stata’s tools and techniques, we can explore the dataset containing 70 observations and determine the significance of various variables on PAT scores.

Variables and Their Significance

Dependent Variable: The PAT score is the primary dependent variable of interest. It measures students’ performance on standardized tests and is influenced by multiple factors.

 Measures of Ability and Training: Cumulative high school grade point average (GPA), participation in advanced placement math (APMATH) and English (APENG) courses are indicators of students’ academic proficiency and training. These variables are expected to have a positive association with PAT scores (Richman, 1965).

Socio-Economic Factors: Socio-economic factors play a significant role in educational outcomes. The dataset includes qualitative variables such as gender (MALE), ethnicity (WHITE), and native language (ENG), which may impact PAT scores differently based on the social context. It is important to examine the relationships between these variables and PAT scores to identify any potential biases or disparities.

PAT Preparation Class: The presence of a dummy variable, PREP, indicates whether or not a student attended a PAT preparation class. This variable serves as a proxy for test preparation and may influence PAT scores positively.

Year: The YEAR variable indicates the year in which the PAT score and other information were recorded. It allows for examining temporal trends and assessing whether there have been any changes in PAT scores over time.

Data Analysis Using Stata

To analyze the dataset and explore the relationships between the variables, we can utilize various statistical techniques available in Stata. These techniques include descriptive statistics, regression analysis, and hypothesis testing.

Descriptive Statistics: Stata provides summary statistics for the variables, such as means, standard deviations, and frequency distributions. These statistics can provide an overview of the data and identify any outliers or patterns (Data Analysis Using Stata, n.d.).

Regression Analysis: By employing regression analysis in Stata, we can estimate the relationship between the dependent variable (PAT scores) and the independent variables (GPA, APMATH, APENG, MALE, WHITE, ENG, PREP, etc.). Stata’s regression commands allow us to control for confounding factors and assess the significance and direction of the relationships.

Hypothesis Testing: Stata enables us to perform hypothesis tests, such as t-tests or chi-square tests, to determine whether the relationships between variables are statistically significant. These tests can help us understand the strength and reliability of the relationships identified in the regression analysis.

Interpretation of Results

Based on the analysis conducted using Stata, we can interpret the results to gain insights into the factors affecting PAT scores. The regression analysis will provide information on the magnitude and significance of the relationships between the independent variables and the dependent variable (Zhang et al., 2017). Additionally, by examining coefficients, p-values, and confidence intervals, we can determine the relative importance and direction of these relationships.

Conclusion

In conclusion, the analysis of the dataset using Stata allows us to understand the factors influencing PAT scores in an Alberta high school. By considering measures of ability and training, socio-economic factors, and other qualitative variables, we can identify the significant determinants of PAT scores. This analysis provides valuable insights for educators, policymakers, and researchers to address disparities and improve educational outcomes. Utilizing Stata’s statistical tools facilitates evidence-based decision-making and promotes a comprehensive understanding of the factors impacting students’ performance on standardized tests like the PAT.

References

Data Analysis Using Stata. (n.d.). Google Books. https://books.google.com/books?hl=en&lr=&id=4rrsRqUSls8C&oi=fnd&pg=PR17&dq=Data+Analysis+Using+Stata&ots=y2H6C_vJck&sig=OGRene3IzW3COkzcEDQwHhu_Nss 

Richman, B. M. (1965). Significance of Cultural Variables. Academy of Management Journal, 8(4), 292–308. https://doi.org/10.5465/255345 

Zhang, Q., Yang, J., Sun, Z., & Wu, F. (2017). Analyzing the impact factors of energy-related CO 2 emissions in China: What can spatial panel regressions tell us? Journal of Cleaner Production, 161, 1085–1093. https://doi.org/10.1016/j.jclepro.2017.05.071