MCOB Final Data (1).sav

Missing file

Associative analysis and a data-driven recommendation.

The SPSS file has been successfully loaded, and the first few rows of the dataset have been displayed above. The dataset contains a variety of columns, including

`StartDate`

, `EndDate`

, `Progress`

, `Duration__in_seconds_`

, and many others related to survey responses.To perform a **differential analysis**, we can compare different groups within the dataset to identify significant differences. For example, we could compare the average

`Progress`

or `Duration`

between different ethnic groups or year levels.For an **associative analysis**, we can explore relationships between variables, such as the correlation between

`Progress`

and `Duration__in_seconds_`

, or how different survey responses are associated with each other.Let's start by performing these analyses. Please specify which variables or groups you would like to focus on for the differential analysis, and any specific relationships you are interested in for the associative analysis.

The dataset contains a large number of columns, but the specific columns related to "Cost" and "Campus Environment" are not immediately clear from the column names.

The differential analysis between the

`Q14_3`

(Cost) and `Q14_9`

(Campus Environment) variables has been conducted using a t-test. Here are the results:**Mean of Cost (Q14_3):**3.67**Mean of Campus Environment (Q14_9):**3.30**T-statistic:**3.87**P-value:**0.00012

- The
**p-value**is less than 0.05, indicating that there is a statistically significant difference between the means of the two groups. - The mean score for Cost is higher than that for Campus Environment, suggesting that respondents rated Cost higher on average.

Given the significant difference, it might be beneficial to investigate further into why Cost is rated higher. Understanding the specific aspects of Cost that are valued by respondents could help in enhancing the Campus Environment to match or exceed these expectations.

Would you like to proceed with an associative analysis or explore any other aspect of the data?

The associative analysis between

`Q14_4`

and `Q14_14`

has been conducted using Pearson's correlation coefficient. Here are the results:**Correlation Coefficient:**0.225**P-value:**0.0003

- The
**correlation coefficient**of 0.225 suggests a weak positive correlation between the two variables. This means that as the value of one variable increases, the other tends to increase slightly as well. - The
**p-value**is less than 0.05, indicating that the correlation is statistically significant.

Given the weak positive correlation, it might be beneficial to explore if there are underlying factors that influence both variables similarly. Further investigation into these factors could provide insights into improving both aspects simultaneously.

If you have any more questions or need further analysis, feel free to ask!