How to Analyze Student Health and Behavior Data Using SPSS

In this analytical journey, we employ the power of SPSS to analyze a multifaceted dataset encompassing the perspectives of students on physical activity, internal consistency measures, correlations with baseline physical indicators, and an evaluation of the effectiveness of participant randomization. Explore the tables and findings below for a comprehensive understanding of the student health and behavior data.

Problem Description:

The SPSS homework aimed to analyze student data related to school, gender, race, grade, and treatment homework. The goal was to assess the students' perspectives on physical activity and health, specifically their value-expectancy and self-efficacy measures. The homework also included data regarding the internal consistency of these measures and correlations with baseline physical activity and heart rate. Furthermore, it assessed whether the randomization of participants into control and treatment groups was effective in creating similar groups.

Solution

Table for Task 2: Student Perspectives on Physical Activity

This table displays the students' perspectives on physical activity, including how it may help them cope with stress, be fun, make new friends, and more. The percentages and frequencies are presented for various responses.

Table for Task 4: Internal Consistency Measures

This table presents the internal consistency measures (Cronbach's alpha) for derived value-expectancy and self-efficacy measures. The high values of Cronbach's alpha suggest strong internal consistency.

High values of Cronbach's alpha indicate excellent internal consistency.

Table for Task 12: Correlation Analysis

This table shows the correlations between various scales and baseline physical activity and heart rate. The values indicate the strength of the relationships between different variables.

Control and Treatment Groups Comparison:

The comparison of the control and treatment groups suggests that randomization worked effectively, as the frequencies are remarkably similar for variables like gender, race, grade, and more. For example, in the control group, 50% were male and 50% female, while the treatment group had 51.4% female and 48.6% male. The similarity is observed across various variables.

This evidence supports the conclusion that randomization was successful.

Descriptive Statistics for Treatment Group:

In the treatment group, descriptive statistics for the variables SEPRE, VEPRE, and baseline physical activity are provided, including the minimum, maximum, mean, and standard deviation.

These statistics help provide insights into the treatment group's data.

In summary, the homework involved analyzing data related to students' perspectives on physical activity, internal consistency measures, correlations, and the effectiveness of randomization. The provided tables and comparisons offer a comprehensive understanding of the data and its implications.