SPSS FILTER temporarily excludes a selection of cases

from all data analyses.
For excluding cases from data *editing*, use DO IF or IF instead.

- SPSS Filtering Basics
- Example 1 - Exclude Cases with Many Missing Values
- Example 2 - Filter on 2 Variables
- Example 3 - Filter without Filter Variable
- Tip - Commands with Built-In Filters
- Warning - Data Editing with Filter

## SPSS FILTER - Example Data

I'll use bank_clean.sav -partly shown below- for all examples in this tutorial. This file contains the data from a small bank employee survey. Feel free to download these data and rerun the examples yourself.

## SPSS Filtering Basics

Filtering in SPSS usually involves 4 steps:

**create**a filter variable;**activate**the filter variable;- run one or many
**analyses**-such as correlations, ANOVA or a chi-square test- with the filter variable in effect; **deactivate**the filter variable.

In theory, *any* variable can be used as a filter variable. After activating it, cases with

- zeroes,
- user missing values or
- system missing values

on the filter variable are **excluded** from all analyses until you deactivate the filter. For the sake of clarity, I recommend you
only use filter variables containing 0 or 1 for *each* case.
Enough theory. Let's put things into practice.

## Example 1 - Exclude Cases with Many Missing Values

At the end of our data, we find 9 rating scales: q1 to q9. Perhaps we'd like to run a factor analysis on them or use them as predictors in regression analysis. In any case, we may want to exclude cases having many missing values on these variables. We'll first just count them by running the syntax below.

***Create new variable holding number of missings over q1 to q9.**

compute mis_1 = nmiss(q1 to q9).

***Apply variable label.**

variable labels mis_1 'Number of missings on q1 to q9'.

***Check frequencies.**

frequencies mis_1.

## Result

Based on this frequency distribution, we decided to exclude the 8 cases having 3 or more missing values on q1 to q9. We'll create our filter variable with a simple RECODE as shown below.

***Recode mis_1 into filter variable.**

recode mis_1 (lo thru 2 = 1)(else = 0) into filt_1.

***Apply variable label.**

variable labels filt_1 'Filter out cases with 3 or more missings on q1 to q9'.

***Activate filter variable.**

filter by filt_1.

***Reinspect numbers of missings over q1 to q9.**

frequencies mis_1.

## Result

Note that SPSS now reports 456 instead of 464 cases. The 8 cases with 3 or more missing values are still in our data but they are excluded from all analyses. We can see why in data view as shown below.

Case 21 has 8 missing values on q1 to q9 and we recoded this into zero on our filter variable.

The strikethrough its $casenum shows that case 21 is currently filtered out.

The status bar confirms that a filter variable is in effect.
Finally, let's **deactivate** our filter by simply running
FILTER OFF.
We'll leave our filter variable filt_1 in the data. It won't bother us in any way.

## Example 2 - Filter on 2 Variables

For some other analysis, we'd like to use only female respondents working in sales or marketing. A good starting point is running a very simple contingency table as shown below.

***Show values and value labels in subsequent output tables.**

set tnumbers both.

***Show frequencies for job type per gender.**

crosstabs gender by jtype.

## Result

As our table shows, we've 181 female respondents working in either sales or marketing. We'll now create a new filter variable holding only zeroes. We'll then set it to 1 for our case selection with a simple IF command.

***Create filter variable holding only zeroes.**

compute filt_2 = 0.

***Set filter to 1 for females in job types 1 and 2.**

if(gender = 0 & jtype <= 2) filt_2 = 1.

***Apply variable label.**

variable labels filt_2 'Filter in females working in sales and marketing'.

***Activate filter.**

filter by filt_2.

***Confirm filter working properly.**

crosstabs gender by jtype.

Rerunning our contingency table (not shown) confirms that SPSS now reports only 181 female cases working in marketing or sales. Also note that we now have 2 filter variables in our data and that's just fine but only 1 filter variable can be active at any time. Ok. Let's deactive our new filter variable as well with FILTER OFF.

## Example 3 - Filter without Filter Variable

Experienced SPSS users may know that

- TEMPORARY can “undo” some data editing that follow it and
- SELECT IF permanently deletes cases from your data.

By combining them you can circumvent the need for creating a filter variable but for 1 analysis at the time only. The example below shows just that: the first CROSSTABS is limited to a selection of cases but also rolls back our case deletion. The second CROSSTABS therefore includes all cases again.

***Make following transformation(s) temporary.**

temporary.

***Delete cases unless gender = 1 & jtype = 3.**

select if (gender = 1 & jtype = 3).

***Crosstabs includes only males in IT and rolls back case selection.**

crosstabs gender by jtype.

***Crosstabs includes all cases again.**

crosstabs gender by jtype.

## Tip - Commands with Built-In Filters

Something else you may want to know is that some commands have a built-in filter. These are

- REGRESSION,
- LOGISTIC REGRESSION,
- FACTOR and
- DISCRIMINANT.

The dialog suggests you can filter cases -for this command only- based on just 1 variable. I suspect you can enter more complex conditions on the resulting /SELECT subcommand as well. I haven't tried it.

In any case, I think these **built-in filters can be very handy** and it kinda puzzles me they're only limited to the 4 aforementioned commands.

## Warning - Data Editing with Filter

Most data editing in SPSS is unaffected by filtering. For example, https://www.spss-tutorials.com/how-to-compute-means-in-spss/ **over variables** -as shown below- affects *all* cases, regardless of whatever filter is active. We therefore need DO IF or IF to restrict this transformation to a selection of cases. However,
an active filter *does* affect functions *over cases*.
Some examples that we'll demonstrate below are

- adding a case count with AGGREGATE;
- computing z-scores for one or many variables;
- adding ranks, or with RANK.

## SPSS Data Editing Affected by Filter Examples

***Reactivate female sales filter.**

filter by filt_2.

***Not affected by filter: add mean over q1 to q9 to data.**

compute mean_1 = mean(q1 to q9).

execute.

***Affected by filter: add case count to data.**

aggregate outfile * mode addvariables

/ofreq = n.

***Affected by filter: add z-scores salary to data..**

descriptives salary

/save.

***Affected by filter: add median groups salary to data.**

rank salary

/ntiles(2) into med_salary.

## Result

Right. So that's pretty much all about filtering in SPSS. I hope you found this tutorial helpful and

**Thanks for reading!**

## This tutorial has 5 comments

## By Ruben Geert van den Berg on May 7th, 2019

Hi Carolyn!

Create a filter variable based on more than 1 variable. You could use something like

IF(year = 2015 & ID = 117) filt_1 = 1.

It may help to first extract the years from the dates with XDATE.

If you find this difficult, we can also assist you personally and fix the data for you but we do have to charge for doing so.

## By Carolyn on May 7th, 2019

How do I select cases from a group. For example, I have medical records and want to select specific years of data within a group of records with the same ID number.

Thanks

## By Jon K Peck on May 1st, 2019

It's true that the COMPUTE can produce missing values, but I think it is helpful to know that the filter condition was undecidable for such cases rather than automatically excluding them.

To each his own.

## By Ruben Geert van den Berg on May 1st, 2019

Hi Jon!

I'm well aware of the direct COMPUTE command but it'll result in missing values if any of the "input" variables has a missing. Nowadays, I recommend my students to avoid missing values on the filter variable for the sake of clarity.

That's also why I chose "ELSE = 0" in the RECODEs in this tutorial.

P.s. bummer that so few procedures have a built-in filter...

## By Jon K Peck on April 30th, 2019

In procedures such as REGRESSION where you can specify a within-procedure filter, this can be handy for computing casewise statistics for excluded cases. For example, you might run a regression on one group but save predicted values for all cases so you can see predicted values for the excluded cases.

This is a shortcut for using the Scoring Wizard. The Wizard provides a more general scoring capability across many procedures and is connected with saving models as PMML.

Also, for filtering it is handy to recall that instead of COMPUTE filter = 0 and then an IF statement, you can just compute the logical expression. E.g.,

compute filter = gender = 0 and jtype = 2.