Bios 331: Statistical Analysis Using Vassar Stats
IF YOU ARE USING A DESIGN WITH TWO VARIABLES USE THE ANOVA INSTRUCTIONS. FOR A ONE VARIABLE SETUP, SEE THE T-TEST INSTRUCTIONS BELOW
A t-Test is equivalent to a one-way ANOVA but the Vassar Stats website will refuse a single variable ANOVA and provide you an error.
Go to www.vassarstats.net
For the foraging lab we need to analyze our data using a 2×2 way anova.
Step one: select ANOVA from the available tests on the left side of the screen.
Step two: Select Two-Way Factorial ANOVA for Independent Samples,.
Step three: you want to conduct a 2×2 way ANOVA so conduct a test with 2 rows and 2 columns then click the setup button. Click weighted. This will set up the table so that you can enter your data into it.
Step four: enter your data, in the example experiment we looked at poor and rich nutrient foods varied over 2 spatial arrangements to a focal tree (1m to tree and 5m to tree). I would enter all poor foods into column 1 and rich nutrient foods into column 2. In row 1, I would enter all corresponding 1m guds. In row 2 I would enter corresponding 5m guds, as outlined below. Click the Calculate button.
Above you will see that in Column 1 row 1 I have entered the data for the three days of GUD’s of trays that were poor nutrient foods near (1m) the tree. In Column 2, row 1 I have entered the data from the three days of high nutrient food near (1m) the focal tree. Likewise, in Column 1 row 2 I have entered the data for three days of GUD’s of trays that were poor nutrient foods far (5m) from the tree. In Column 2, row 2 I have entered the data from the three days of high nutrient food far (5m) from the tree.
Scroll down to see the above ANOVA output has all the relevant data on it. It indicates that there is a significant difference between the rows (spatial arrangement of the trays relative to the focal tree). It also suggests there is a significant difference between the Columns (high nutrient content v low nutrient content). Finally, the output indicates that there is a significant spatial arrangement by food interaction, whereby there was a change in bunny foraging of different foods as the distance from the tree increased.
FOR A SINGLE VARIABLE ANALYSIS, USE THESE INSTRUCTIONS FOR A T-TEST
Go to www.vassarstats.net
For the foraging lab we need to analyze our data using a T-Test.
Step one: select t-Tests & Procedures from the available tests on the left side of the screen.
Step two: Select Two-Sample t-Test for Independent or Correlated Samples,.
Step three: you want to conduct a Correlated Samples test, press the button for it under Setup.
Step four: enter your data, in an example experiment we looked at poor and rich nutrient foods. I would enter all poor foods into Sample A and rich nutrient foods into Sample B. It is important that the data are in the same chronological order, i.e. day 1 data, then day 2 data, etc. This takes advantage of the fact that you did both treatments as matched pairs on the same day, but that requires the data to be in the same order. Click the Calculate button.
Step five: Finally, go down to the results section to find your T value, degrees of freedom, and p value. Use the two-tailed p value unless given other instructions by your TA.
Example screenshots are below.
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