When running correlations in SPSS, we get the significance levels as well. In some cases, we don't want that: if our data hold an entire population, such p-values are actually nonsensical. For some stupid reason,
we can't get correlations *without* significance levels
from the correlations dialog. However, this tutorial shows 2 ways for getting them anyway. We'll use adolescents_clean.sav throughout.

## Option 1: FACTOR

A reasonable option is navigating to

as shown below.Next, we'll move iq through wellb into the variables box and follow the steps outlines in the next screenshot.

Clicking syntax below. It'll create a correlation matrix without significance levels or sample sizes. Note that FACTOR uses listwise deletion of missing values by default but we can easily change this to pairwise deletion. Also, we can **shorten the syntax** quite a bit in case we need more than one correlation matrix.

## Correlation Matrix from FACTOR Syntax

***Syntax pasted from Analyze - Dimension Reduction - Factor.**

FACTOR

/VARIABLES iq depr anxi soci wellb

/MISSING pairwise

**/* WATCH OUT HERE: DEFAULT IS LISTWISE! */**

/ANALYSIS iq depr anxi soci wellb

/PRINT CORRELATION EXTRACTION

/CRITERIA MINEIGEN(1) ITERATE(25)

/EXTRACTION PC

/ROTATION NOROTATE

/METHOD=CORRELATION.

***Can be shortened to...**

factor

/variables iq to wellb

/missing pairwise

/print correlation.

***...or even...**

factor

/variables iq to wellb

/print correlation.

***but this last version uses listwise deletion of missing values.**

## Result

When using pairwise deletion, we no longer see the sample sizes used for each correlation. We may not want those in our table but perhaps we'd like to say something about them in our table title.

More importantly, we've no idea which correlations are statistically significant and which aren't. Our second approach deals nicely with both issues.

## Option 2: Adjust Default Correlation Table

The fastest way to create correlations is simply running correlations iq to wellb. However, we sometimes want to have statistically significant correlations flagged. We'll do so by adding just one line.

***Create full correlation matrix and flag statistically signifcant correlations.**

correlations iq to wellb

/print nosig.

This results in a standard correlation matrix with all sample sizes and p-values. However, we'll now make everything except the actual correlations invisible.

## Adjusting Our Pivot Table Structure

We first right-click our correlation table and navigate to

as shown below.Select

from the menu.Drag and drop the Statistics (row) dimension into the LAYER area and close the pivot editor.

## Result

## Same Results Faster?

If you like the final result, you may wonder if there's a faster way to accomplish it. Well, there is: the Python syntax below makes the adjustment on **all pivot tables** in your output. So make sure there's only correlation tables in your output before running it. It may crash otherwise.

***Move last row dimension into layer for all tables in output window. This syntax requires the SPSS Python Essentials.**

begin program.

import SpssClient

SpssClient.StartClient()

oDoc = SpssClient.GetDesignatedOutputDoc()

oItems = oDoc.GetOutputItems()

for index in range(oItems.Size()):

oItem = oItems.GetItemAt(oItems.Size() - index - 1)

if oItem.GetType() == SpssClient.OutputItemType.PIVOT:

pTable = oItem.GetSpecificType()

pManager = pTable.PivotManager()

nRows = pManager.GetNumRowDimensions()

rDim = pManager.GetRowDimension(0)

rDim.MoveToLayer(0)

SpssClient.StopClient()

end program.

Well, that's it. Hope you liked this tutorial and my script -I actually run it from my toolbar pretty often.

Thanks for reading!

## This tutorial has 11 comments

## By SALIM M. SHEUNDA on March 14th, 2020

I am grateful for your input, At least I got input of spss data analysis and I cant wait to learn even more.