Working with Prism 6.0

Basics

When you launch Prism 6, you’ll see the following window:

Choose whether you want to create a new file, or open an existing file. If creating a new file, you’ll need to choose the appropriate format for your data table. Note on the left navigation bar, there are multiple options (XY, column, grouped, etc). For each of these options, the main window describes the type of data to be entered, and also displays the format of the data table. The radio buttons on this page allow you to indicate the nature of the data to be entered. If you are unsure how the table should be formatted, you can choose one of the sample data sets to see various examples. When a sample data table is selected, there will be further information displayed about the sample data and step-by-step instructions for performing the selected analysis.

Data table setup

For now, set the parameters as in the figure above (“replicate values stacked into columns): this would be appropriate for an unpaired t-test where individual data points are entered.

The data table will look like the image below.

2_ColumnDataWindow.png

Next, Enter the Blood Pressure data from the Wiki, placing each data set into a different column, starting with the left-most column. If control data exist, put it in the first column, which will place it in the leftmost portion of any graph. The rationale here is that graphs (and tables) are read left to right, and baseline (control) information is the benchmark needed to interpret other experimental groups. Label the columns (click header). Also, on the left sidebar, label the data table “e.g., “Blood Pressure”. Save your data file (and do this regularly as you work).

Your data table should now look like the image below.

3_DataTable_entries.png

Note several other items in this window:

NOTE: There are times you’ll want to enter data as mean, SD (or SEM), n. These options are given in the Data Table setup window. In the current example, if you select Change>Format Data Table, you could reformat the table to receive group statistics rather than individual data points. Just be careful when choosing this format: if you choose mean/SD format but enter mean/SEM data (or vice versa), your statistics will be incorrect!

Graphs

As soon as you create a data table, Prism also creates a graph.

height="229",width="242"

For the Blood Pressure data entered in this tutorial, the default graph might looks like the figure above. You can modify the format by clicking on CHANGE>Graph type. You can modify virtually all other elements of the graph by clicking on the axis, text, etc, or by selecting an option available in the CHANGE menu. Don’t forget to appropriately label the Y axis.

Note that if you choose NEW>graph of existing data, you can create another graph of the data rather than modify the format of an existing graph. If you do this, you should title the graphs appropriately so they are easily identified in the sidebar.

Analysis setup

Once a Data Table is filled and labeled, click the “Analyze” button. The following window will appear: 5_ttestAnalysisWind.png

Under Column Analysis select “t-tests (and nonparametric tests).” Click OK.

Next, set the parameters for your analysis. First, the “Experimental Design” window will open for selecting the type of analysis to complete. For now, select unpaired, parametric test. Note that parametric tests assume equal variance between groups. If variance is not equal, nonparametric statistics should be chosen.

6_ttestExpldesign.png

Next, click on “Options” (at the top of the window). Most often, you would use a 2-tailed test (make sure you understand why!). For “Report difference as”, make sure the order is left then right columns so as to avoid confusion. Finally, set the confidence level to 95% (e.g., p<.05). For now, ignore the other options (though you might want to select “Descriptive Statistics”, which will generate a secondary results page with some useful information).

7_ttestOptionsWind.png

Results

You should now see the tabular results (shown below) of the analysis selected. This window describes which data table was analyzed, what type of analysis was run, and the results of that analysis. Note that in this example, t=0.4542, df=18. The p = .6551, therefore the means are NOT significantly different from one another. Some additional statistics are also reported, including means +/- SEM. Always check these values against your original data set (usually from an Excel spreadsheet) to catch any errors in data entry. Also shown in the analysis window are the results of an F test to compare whether the variance in the two groups differs significantly. If the variance is significantly different between groups, a nonparametric test should be used. To do this, click on the blue text at the top of the results window (it now shows “Unpaired t-test with equal SD”). This will reopen the Parameters window so you can choose a nonparametric test (under the experimental design tab).

8_ttestResutls.png

NOTE: If you realize that a paired t-test should have been used, this is easily corrected. Click on the blue text that tells you what test results are being displayed (or click on CHANGE>Analysis Parameters up in the top menus); choose the Experimental Design tab, and then select a paired t-test. The results window will now specify that the results reflect a paired t-test. If you do this for the Blood Pressure Data, you’ll find t=2.024, df = 9. The p=.0737, a value that approaches statistical significance (and is MUCH smaller than that obtained from an unpaired t-test). The results window also shows a statistically significant effect of pairing. Finally, for a paired t-test, the data are best depicted by a line graph that shows the paired values (see below). Go to the Blood Pressure graph, and use the “Change” function to make that change.

height="229",width="242"

One-way ANOVA

Description: A one-way ANOVA compares the means (or medians, if nonparametric) of three or more groups that differ on only one dimension (the independent variable). The test determines if there is a significant main effect of that variable. In order to determine which groups differ, you must perform post-hoc t-tests.

Data Table setup:

11_1wayNewDataTable.png

Analysis setup:

12_1wayExplDesign.png

Results:

You should now see the tabular results (shown below) of the analysis selected. This window describes which data table was analyzed, what type of analysis was run, and the results of that analysis. Note that for the data on TV watching, the overall F=.9188 and df = 2,12 (df for treatment=2, df for residual=12). The p = .4253, therefore there is no significant main effect of academic major on # TV hours watched per week. Also reported in this table are the results of Bartlett’s test, which evaluates whether there are significant differences in variance across groups (if there are, nonparametric statistics should be used – use the CHANGE tab to select a different test). IMPORTANT: The results of the overall ANOVA don’t tell you which (if any) groups differ from one another; only posthoc tests can evaluate specific group differences. If multiple comparisons were selected from the Analysis parameter options, there will be a second page of results reporting those results (look for this in the sidebar). In the present example, none of the posthoc comparisons are statistically significant.

Results_Anova unmatched.png

NOTE: If you realize that repeated measures ANOVA should have been used, this is easily corrected. From the ANOVA results page, click on the blue text that specifies which test was run (or, go to CHANGE> analysis parameters. The Analysis Parameters box will reopen, and in the Experimental Design tab you can select repeated measures. The results window will now specify that the statistics reported are from a RM ANOVA.

Two-way ANOVA

Description: A two-way ANOVA is used when the experimental design investigates the effects of two different independent variables (e.g., sex AND age). The test reveals if there is a significant main effect of each variable, and if there is a significant interaction between the two independent variables.

Data Table setup:

14_2wayNewDataTable.png

There are many ways to set up a table for a two-way ANOVA. The easiest, especially with large amounts of data, is to enter the data as mean/SD (or SEM)/n (see third radio button option in the image above). CAUTION! Double-check your selection: if you choose mean/SD format but enter mean/SEM data (or vice versa), your statistical calculations will be incorrect!

For the Spine Density data from the Wiki, the Data Table of means/SEM/n (after labeling rows and columns) should look as follows:

15_2wayMeansDataTable.png

However, realize that data entry using summary statistics (such as mean, etc) does not permit a repeated measures analysis because summary data do not retain information about individual data points. If we assume the spine data are from repeated measures taken at 2 different ages (e.g., based on 2-photon imaging data), replicate measures need to be entered (in this case, 5 subjects in each treatment group). To set up the data table for that format, choose the second radio button option (for replicate values in side by side subcolumns) and enter "5" to specify the number of subjects in each treatment group.

Enter the spine density data from the Wiki.

The Data Table (after labeling rows and columns) should look as follows:

16_2wayIndividDataTable.png

After creating the data table, be sure to label the table in the sidebar (e.g., Spine Density), and look at the graph that was created to be sure the format is optimized and all axes are labeled.

Analysis setup (continue assuming repeated measures design:

17_2wayAnalysis.png

18.png

19_2wayMultComp.png

Results:

Under the tabular results (shown below) you should find there is a significant interaction between age and treatment (F (1,8) =6.2, p<.05) and a main effect of treatment (F(1,8 = 44.8, =.0002), and an effect of age that approaches significance.

20_2wayResults.png

Interpreting two-way ANOVAs:

Posthoc tests: The results of the posthoc tests are reported on a separate results page. In the present example, they reveal that a significant developmental increase in spine density is evident in controls (t=3.3, p<.05) but not in cocaine-exposed animals (see below).

21_2wayPosthocAge.png

To evaluate group differences at each age, you’ll need to change the analysis parameters (Click Change>Analysis Parameters>Multiple Comparisons, and select compare each cell mean with the other cell mean in that row. These results (see below) reveal that there is a significant group difference at the earliest age, and this difference is magnified with age (4 wks: t=2.7, p<.05; 12 wks: t=6.4, p<.0001).

22_2wayPosthocTreatment.png

Taken together, the posthoc tests show that prenatal cocaine exposure prevents a developmental increase in spine density that normally occurs between 4-12 weeks of age, and results in a lower density of spines at both 4 and 12 weeks after birth.

Exporting Data/Graphs from Prism

Prism6 saves files in the .pzfx format, which is meaningless to anything but Prism.

There are several ways to get your files to a readable format elsewhere

Go back to the Homepage

StatsWiki: Using-Prism6 (last edited 2013-10-27 21:49:30 by KathyNordeen)

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