Suppose that you have collected data throughout a semester from a large elementa

Question

Suppose that you have collected data throughout a semester from a large elementary school regarding the average number of days per week each math teacher spends in a collaborative teaching community. The average ranges from 0 to 7 days per week. You have also obtained the pre-test and post-test scores in math administered in those same classrooms in the beginning and end of the semester, and you have calculated a score that shows learning during the semester by subtracting the pre-test score from the post-test score for each student. You are interested in examining any potential relationship between the average number of days of teacher collaborative activities and student learning (i.e., post-test minus pre-test). How can you best handle the data so that you can perform group comparisons to examine the relationship between the two variables? Why is this technique the best for handling the data?

Explanation / Answer

In this case information available from the questions asked a researcher can argue that there are several elements involved when examining the relationship between these conditions. The greatest or with the highest amount of degree frequently utilized methodical approaches is group comparison and correlation. When researchers consider using a variable as an element of formation to arrange the critical information consideration must be given to the independent . The researcher is in control of the independent variable in an exploratory study however inherently occurs or exists beforehand in a quasi- or non-experimental study. The organization of the critical information demands the utilization of organizing differentiations as the premier methodical approach. The examination disclosed expresses that there may be an alternate variable, or a dependent variable. This change between the categories will vary on the independent variable. The numerical data tested permits the researcher to differentiate the categories serving to experiment the null hypothesis demonstrating that the categories do not contradict the dependent variable. The dependent variable will demonstrate that the categories will disagree notably from the alternative theory. A quasi-experimental investigation which rejects the null hypothesis argues that there is cause to the association between the independent and dependent variables. When the researcher cannot control, manipulate or even alter the predictor variable this is considered a non-experimental investigation. The rejection of the null hypothesis will only demonstrate that there is a common connection omitting cause-and-effect directionality. Therefore a group comparison will become the pre-selected option or approach for the analysis of the critical information. The researcher cannot prohibit the relationship between the two or other tests involving numerical data from being executed to assist in the analysis of the critical information

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