Study Background
Statistical studies on collected data contribute to a deeper understanding of the trends and patterns that characterize the distributions of variables, identify potential relationships among them, and develop a comprehensive view of the data set. For this report, the HS Long Study Dataset C, which contains 68 variables related to quantitative and qualitative dimensions of higher education, was used. Only a few of the variables included measures of gender, socioeconomic status, self-efficacy, distributions of time to fulfill various life scenarios, and student opinions on several proposed statements.
The survey’s overall context was predominantly in the mathematical and exact sciences. Additionally, it was worth clarifying that the dataset included records for 5,925 students for whom information was collected in the baseline (T1) and follow-up (T2) years, thereby classifying this survey as a longitudinal survey (Ingels et al., 2013). Thus, this report aims to comprehensively analyze the collected data using a variety of statistical tools.
Basic Descriptive Statistics
Before proceeding to perform a more conceptual analysis, it is necessary to conduct a descriptive statistical study. Specifically, for the socioeconomic status of a student in the basic year of study, the mean was 0.056 (SD = 0.774). For this study, two additional variables of particular interest were selected, namely S1HRFRIENDS and X1MTHUTI.
On the one hand, S1HRFRIENDS estimates the total time, in hours, that a student spends on a typical school day with friends. On the other hand, X1MTHUTI is an indicator of students’ self-reported assessments of the usefulness of math. The majority of students reported spending 1 to 2 hours with friends (24.1%, n = 1264), while the fewest spent 4 to 5 hours (7.8%, n = 408), as shown in Figure 1. To assess the usefulness of math, the mean was -0.013 (SD = 1.007). Considering that the distribution of this variable ranges from -3.51 to +1.31, the obtained figure indicates an above-average arithmetic mean (0.100) for the usefulness score of math.
Conceptualization of Variables
Two variables were of increased interest for this paper, namely S1HRFRIENDS and X1MTHUTI. As previously shown, S1HRFRIENDS was an ordinal numeric variable that assessed the time individuals spent with friends on an ordinary school day. S1HRFRIENDS had 6 levels, as shown in Figure 1, ranging from “less than 1 hour” to “5 hours or more”. The unit of measurement for this variable was hours, although the specific choice the questionnaire filler had to make was based on categorical levels. Thus, students could choose the time measure that best described their social behavior on a typical day.
The second research variable, X1MTHUTI, was continuous and was assessed at the number level. Operationally, this variable measured the subjective degree of usefulness the student assigned to math: the higher the number, the greater the importance the student assigned to math. The variable was not direct, meaning the student was not required to provide their evaluation; instead, X1MTHUTI was calculated as a weighted factor based on several related variables.
Interpretation from a Social Change Perspective
The resulting variables can be used to explain the relationship with social change. Since socializing with friends is an integral part of the school day, this variable can be a key factor in explaining how students spend an ordinary day. This variable can be used in further inferential analyses to determine how students from different socioeconomic, gender, and demographic groups allocate their time to socializing with peers (Kenton, 2023). On the other hand, the variable X1MTHUTI could be used to assess students’ general attitudes toward math (specifically, the usefulness of the course) and to conduct inferential analyses to detect group differences. In addition, it may be helpful to conduct a correlation analysis to examine the potential relationship between the two variables and identify pedagogical strategies to increase student engagement in math.
References
Ingels, S. J., Pratt, D. J., Herget, D. R., Dever, J. A., Fritch, L. B., Ottem, R., & Rogers, J E. (2013) High school longitudinal study of 2009 (HSLS:09) base year to first follow-up: Data file documentation.
Kenton, W. (2023). Analysis of variance (ANOVA) explanation, formula, and applications. Investopedia.