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Data Analysis Plan

Data Analysis Post

Collecting Quantitative data:

In our experiment, the independent variable is the number of times female teenagers check Facebook in a day. There will be two groups. One group of teenagers will be the ones that check Facebook 1-10 times a day and another will be those who check Facebook more than 10 times a day. The control group will be the girls that have no Facebook. The dependent variable is the number of participants that choose a smaller ideal body size than their current body size, the number of participants that choose an ideal body size that is the same as their current body size, and the number of participants that choose an ideal body size that is bigger than their current body size on a body shape figures scale. The univariate tool that we will use to understand the variables is a histogram. The histogram will display the number of female teenagers within each category that had chosen either an ideal body size that is smaller, same, or bigger than their current size.

The level of measurement for the experiment is discrete and not continuous. The data would record the number of female students; therefore it is discrete data instead of continuous data that measures for time, weight, or distance. The type of bivariate tool used to uncover relationships between the variables is cross tabulation analysis. It is often used to analyze categorical data with a nominal measurement scale. Using the data collected, the number of females will be counted in each category and a contingency table will be created. Validity in quantitative research is whether one can draw meaningful and useful inferences from scores on the instruments. There are three forms of validity to look for, such as content validity, predictive or concurrent validity, and construct validity. Construct validity focuses on whether the scores serve a useful purpose and have positive consequences when they are used in practice. In experimental research, there are several threats to validity. In order to validate the findings, we will identify the potential internal and external threats to the experiment. We will identify any sources of error that could affect the experiment. Internal threats that could affect the validity of our findings include experimental procedures, treatments, and experiences of the participants. External validity threats arise when experimenters draw incorrect inferences from the sample data.


Creswell, J. (2013). Quantitative Methods. In Research design: Qualitative, Quantitative, and Mixed Method Approaches (4th ed., pp. 155-182). Sage Publications.



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