Technical Directions
Pretest, Posttest Analysis Tool
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The Pretest, Posttest Analysis Tool (PPAT) is designed to allow teachers to easily and quickly analyze their student data. With just a few clicks, the PPAT will automatically read columns of data from a spreadsheet and create a summary. When used regularly, this tool will help save time and promote the use of data to improve instruction. This article will provide detailed technical directions for using the tool.
About the Tool
The PPAT is a Shiny Web Application developed by Matthew Courtney in 2020. It uses the R statistical programming language to read data off of a spreadsheet and create a summary of any column. The PPAT is hosted on the shinyapps.io server.
Preparing your Data
Attention should be paid to the proper preparation of your data before uploading it to the PPAT. The PPAT will return accurate calculations and visualizations for whatever data you upload, but it cannot account for mistakes in your original data worksheet. If you are pulling your data from a standardized gradebook or testing system, it is likely ready to go with very little preparation.
When preparing your data, you should ensure that you follow the principles of tidy data. This means that each column contains a variable (like a test score) and each row contains an observation (like a student). You should also ensure that the columns you wish to examine contain numerical values. For example, a test score of eightynine percent should be recorded in the column as 89 or 0.89 and not 89%.
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You should also ensure that each score in your column is formatted the same. If we take the previous test score example, you want to make sure that each test score is either a whole number, like 89, or a decimal point, like 0.89. The PPAT cannot tell the difference between these variables and this will cause you to have incorrect outcomes.
Finally, you should make sure that each column has a header that is easily recognizable. You will need this to be able to accurately select the correct column in the PPAT.
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The PPAT will examine whichever column you tell it to, so you do not have to remove columns with text or other variables to use the PPAT. Just ensure that the column you want to examine is properly formatted. Having said that, you should NEVER upload personally identifiable information for yourself or your students to the internet. While the information uploaded into the PPAT is not permanently stored, personally identifiable data is always vulnerable to cyberattack. Do not upload personally identifiable data for either yourself or your students to the PPAT.
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When your data is clean and ready, save the file as a .CSV file. CSV stands for comma separated values. This is a common file format for transferring large amounts of data quickly and efficiently. The PPAT will only read a .CSV file.
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Using the PPAT
Using the PPAT to analyze your data is simple. First, upload your .CSV file by selecting the “Browse” button in the grey box. This will open a window that will allow you to find the file. Select the file and click “Open”.
The PPAT will automatically upload your spreadsheet. This process is normally pretty quick, but the time it takes to upload will vary greatly depending on the size of your file. An upload progress bar will light up under the browse box.
When the PPAT has completed its upload, it will automatically display the summary information of the first column of your spreadsheet in the white space. You should select the columns with your pretest and posttest data from the dropdown menu in the grey box. The PPAT will automatically update the statistics and graphs for whichever variable you select.
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All of the information presented by the PPAT is static – meaning that you cannot change or customize it. You can, however, copy and paste the information into a document or slideshow presentation to easily share the result with your colleagues. You can also save the graphs by right clicking on the graph and selecting “Save Image As” from the menu.
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Interpreting the Results
The PPAT will return seven summary statistics and two visualizations for each test to help you interpret the meaning of your data set. It will also complete a pairedsample ttest, display two measures of effect size, and generate a scatter plot to help you compare the two tests. While the PPAT will quickly and accurately summarize your student data, it will not tell you what that data means. It is up to you to apply local context and your own background information about your students to derive meaning from the data. The PPAT will present the following outputs:
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Mean – The mean is the average of your distribution. It is a measure of central tendency that allows you to summarize a distribution.

Median – The median is the middle number in a distribution. When you compare it to the mean, the median can help you see if your data is skewed.

Mode – The mode is the number that shows up most often within a distribution.

Standard Deviation – The standard deviation is a measure that tells you how spread out your data is. The smaller the standard deviation, the closer together your students scored.

Minimum – The minimum is the lowest number in a distribution.

Maximum – The maximum is the highest number in a distribution.

Range – The range is the difference between the highest number and the lowest number.

Histogram – A histogram is a visualization that helps you see how your student data is clustered. Histograms break your students down into groups, called bins. The height of the bar tells you how many students scored within a given bin.

Boxplot – A boxplot is a visualization that helps you see how your scores fell within the distribution. The bold line in the middle is the median. The top half of the box shows the quartile of scores above the median while the bottom half shows the scores below. The whiskers show you the highest and lowest scores. Any outlier scores are shown with little dots above or below the whiskers.

tTest – A ttest is a test that compares the means of two groups of data. The PPAT uses a paired sample ttest to compare pretest and posttest data.

tstat – The tstat is the outcome of a ttest. The ttest measures the size of the difference between the two tests relative to the variation of the sample data. Alone, the tstat doesn’t tell you much, you need degrees of freedom and pvalue to interpret it.

Degrees of Freedom – Degrees of freedom represent the number of independently variable factors within a distribution.

pvalue  The pvalue tells you the probability of obtaining a given set of results in the future. This measure is commonly used to determine statistical significance. It is commonly accepted that the results of a statistical test are significant if p<0.05.

Cohen’s d – Cohen’s d is a measure of effect size. It tells you how far apart your pretest and posttest scores are.

Hedges’ g – Hedges’ g is a measure of effect size. It tells you how far apart your pretest and posttest scores are.

Scatter Plot – A scatter plot maps two variables to look for relationships. In the scatter plot created by the PPAT, each dot represents a student. The dots position represents the students’ performance on both the pretest and the posttest.