Cayla Boffa, Weiqian Meng, Connor Lacroix Research Report #3

Cayla Boffa, Weiqian Meng, Connor Lacroix Research Report #3

For Research Report# 3, we collected data to research if the amount of hours of exercise per week differ between people in their 20s, people in their 30s and people in their 40s. The reason we choose to collect this data was to evaluate if people exercise more or less as they get older and to evaluate if there was a significant difference in how many hours per week one exercises when moving from their 20s to 30s to 40s. The three variables we collected were Age (20s, 30s, 40s) which was our independent variable and how many hours per week they exercise as our dependent variable. The way we collected our data was through asking our family and friends via email/text.

Each person in the group collected data for 3 persons in each group. What we could control was the age range of the individuals. A couple of things that we could not control was if the people we were polling were health fanatics or not, were a healthy weight or overweight, wanted to lose weight or had any health issues. For our data, we collected data from 9 people in their 20s, 9 people in their 30s, and 9 people in their 40s. With this study, we expected to find out that people in their 40s work out more hours per week than people in their 20s or 30s as it is more difficult to lose weight as you get older.

The table below shows the raw data collected for people in their 20s, 30s and 40s and the hours per week they exercise. For our study, we collected data from 9 people in their 20s, 9 people in their 30s, and 9 people in their 40s. “Group 1” represents people in their 20s. “Group 2” represents people in their 30s and “Group 3” represents people in their 40s. The numbers provided represent hours per week spent exercising.

GROUP 1 GROUP 2 GROUP 3 The three assumptions associated with an independent samples t-test are normality, homogeneity, and independence of observations. According to chapter 14 notes, normality states that the dependent variable should be normally distributed in the population from which we draw our samples. The current data was only obtained from 9 and 9 and 9 subjects which is a small portion of the population. The research was specific to the population of people in their 20s, 30s, and 40s. Below are the histograms: one for people in their 20s, one for their 30s, and one for their 40s.

From the three histograms, you can see that Group 1, Group 2, and Group 3 are unimodal. Given that the sample size is small and the histograms are not symmetric, we would assume violation of normality. The second assumption is homogeneity of population variance, which occurs when variances across groups are roughly equal. The rule is a 4 times difference of the variances when comparing one set to another. In our study, the standard deviation for Group 1 (20s) was 2.368; for Group 2 (30s), 0.527; and for Group 3 (40s), 2.78. There is a less than 4 times difference, so we assume homogeneity of variances is not violated.

Descriptive statistics for the dependent variable: hours of exercise per week.

• Age 20s: Mean = 6.7, Std. Deviation = 2.368, N = 9
• Age 30s: Mean = 5.6, Std. Deviation = 0.527, N = 9
• Age 40s: Mean = 6.7, Std. Deviation = 2.78, N = 9

The third and most important assumption is independence of observation, which assumes that the samples are independent of each other. Each group member provided data for three individuals, and each data collection was independent; there was no discussion among participants regarding their answers. Therefore, this assumption is satisfied.

To analyze the data, an ANOVA was conducted. With three groups, each containing 9 subjects, the degrees of freedom for the group (df for between groups) is 2, for error (df for within groups) is 24, and total degrees of freedom is 26. Calculations for sum of squares and mean squares resulted in an F value computed from the between-groups variance divided by the within-groups variance. The critical F value at p = 0.05 and (2, 24) degrees of freedom is 3.40. The analysis produced an F statistic of 0.611, which is less than the critical value, indicating that the differences among group means are not statistically significant. The partial eta squared was 0.048, indicating a small effect size.

Paper For Above instruction

Understanding the effects of age on exercise habits is a significant aspect of health psychology and behavioral studies. The present research aimed to determine whether significant differences exist in the number of hours individuals in their 20s, 30s, and 40s dedicate to exercise each week. By examining these differences, we can better understand how exercise behaviors evolve over the lifespan and inform targeted health interventions.

Methodologically, the study employed a descriptive, cross-sectional design utilizing a small sample size due to practical constraints. Participants were recruited from personal networks and consisted of nine individuals in each age group: 20s, 30s, and 40s. Key variables included age, classified as a categorical independent variable, and hours of exercise per week, measured as a continuous dependent variable. Participants self-reported their weekly exercise hours via email or text message, ensuring independence of data collection and minimizing bias.

Data analysis commenced with preliminary checks against the fundamental assumptions of parametric testing. Histograms for each age group showed unimodal but asymmetrical distributions, raising concerns about the normality assumption; however, given the small sample size, these violations are not definitive. Homogeneity of variances was examined via the ratio of the largest to smallest standard deviations; with values well below the fourfold rule, the assumption was considered satisfied. The independence of observations was maintained through individual data collection sessions, with no inter-participant influence or discussion observed.

Subsequently, a one-way ANOVA revealed no statistically significant difference in mean weekly exercise hours across the three age groups, F(2, 24) = 0.611, p = 0.55, η² = 0.048. These findings suggest that age, within the ranges studied, does not markedly influence exercise duration, contrasting with some prior research hypothesizing increased activity with age due to health motivations. Instead, the data hints that factors such as lifestyle, health status, and personal motivation may play more prominent roles than age alone.

In conclusion, the study contributes to the broader understanding of exercise behavior across adulthood. While the small sample size limits the generalizability, the findings underscore the importance of considering multiple determinants—beyond chronological age—when promoting physical activity. Future research with larger, more diverse samples and longitudinal designs could better elucidate how exercise habits change over time and identify modifiable factors that can support healthy aging.

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