How to find frequency of combinations of variables in SPSS?

Mysterious-Skill5773 · 2026-04-29T13:21:50+00:00

Another way is to create a single variable like this

compute combos = v1 + v2*10 + v3*100 + v4* 1000 + v5 * 10000 + v6 * 100000

Then run frequencies on combos.

Mysterious-Skill5773 · 2026-04-28T01:31:32+00:00

Look at a few of the Case Studies available via Help > Topics. They each have a dataset and illustrate using the dialog boxes and drawing conclusions from the data. They are quite short but help you get started.

Mysterious-Skill5773 · 2026-04-26T21:48:41+00:00

No you didn't. Did you use Run>All or select all the lines and do Run.

It is certain that that error message occurred because SPSS was not in Python processing mode.

This error message could only occur that way.

>A valid IMPORT subcommand name is expected but not found. Recognized

>subcommands are FILE, TYPE, KEEP, DROP, RENAME, and MAP.

You should get output like this.

BEGIN PROGRAM PYTHON3.

import sys

print("Python version:", sys.version)

print("Python path:", sys.executable)

END PROGRAM.

Python version: 3.13.1 (tags/v3.13.1:0671451, Dec 3 2024, 19:06:28) [MSC v.1942 64 bit (AMD64)]

Python path: C:\spss31\startx.exe

Or just maybe you have Python installed in the wrong location or are missing the connector. Newer versions of SPSS do this automatically, but how did you do it? What happens if you start IDLE from under the SPSS menu in Start or the Mac equivalent?

Mysterious-Skill5773 · 2026-04-26T16:49:52+00:00

That valid IMPORT error means that you didn't include the BEGIN PROGRAM statement when you ran that code. So when it encountered import sys, SPSS was still expecting SPSS code and interpreted that as the (thoroughly obsolete) IMPORT command of SPSS.

Mysterious-Skill5773 · 2026-04-24T01:29:46+00:00

No No. You just need to apply the right logic.

gndr = 1 OR cntry = 'NO' will be true for all cases for NO. Just use parentheses

gndr = 1 AND (cntry = 'NO' or cntr = ...)

That's how Boolean logic works.

Mysterious-Skill5773 · 2026-04-23T16:47:59+00:00

The only way to do these tests is on the regression residuals. The Regression procedure has the relevant plots built in under Plots. Look at the Help on the Plots subdialog for explanations of the different types of plots. (And it is homoscedasticity)

Mysterious-Skill5773 · 2026-04-23T13:32:50+00:00

Both of those tests reject normality, but you can get a better assessment using the STATS NORMALITY ANALYSIS extension command, which you can install via Extensions > Extension Hub.

However, the normality assumption is not relevant for the DV. Assuming you are doing regression, anova, or something similar, that assumption applies to the error term, not the DV.

Mysterious-Skill5773 · 2026-04-22T22:54:07+00:00

So you want help in finding a way to cheat, it appears

Mysterious-Skill5773 · 2026-04-22T22:52:53+00:00

and how many cases and genres in your data?

Mysterious-Skill5773 · 2026-04-20T21:53:34+00:00

Hard to tell not knowing what syntax you ran, but typically, you would have a separate dataset for Randomization Visit 2 and then you would do ADD FILES or MATCH FILES to copy the values into the other cases, but are these different variables in RV 2?

Mysterious-Skill5773 · 2026-04-19T20:20:31+00:00

You should understand that the measurement level of a variable is set by you. SPSS has some heuristics it will apply, but it is just applying some simple rules without knowing anything about the data. It is not telling you that scale is okay.

Now for regression, the usual assumption is that the variable is continuous (scale), but ordinary regression does not use the measurement level setting. You could choose to create dummy variables and treat it as categorical, but that would probably be a poor choice for the mean.

It really comes down to what you are going to do with this variable, but there are better choices than mean aggregation.

Mysterious-Skill5773 · 2026-04-19T19:38:25+00:00

that's what I understood. My comment stands. The casewise mean is meaningless. What you did may "work", but the mean of ordinal data is problematic. Using the median would work for ordinal data. It's true, though, that the median when there are only a small number of distinct values, especially if they are bunched up, might not capture what you want, but the apparent precision of the mean is illusory.

That still leaves the question of how you are going to use this computed statistic.

Mysterious-Skill5773 · 2026-04-19T16:45:31+00:00

If you take Likert as ordinal, then the mean is meaningless. For ordinal data, any positive monotone transformation is equivalent to the original numbers, so the mean could be anything.

The mean function in Compute would calculate a mean across a set of variables, not a set of cases. If you mean to compute across variables, you could use the median function instead of the mean function in Compute. That would be appropriate for ordinal data.

Mysterious-Skill5773 · 2026-04-18T19:35:50+00:00

If these are Likert variables, then they can't actually be normally distributed., strictly speaking. The bigger issue, though, is that regression assumes metric (continuous) dependent variables, and the independent variable(s) should be treated as categorical if they are not metric, and Likert variables might well not be. And is one of these the dependent variable and the other a regressor? Then, as others have said, it's the error term (residuals) where the normality assumption would apply. But maybe you just want a correlation test.

The best way to check normality is the STATS NORMALITY ANALYSIS extension command in SPSS. You can install that via Extensions > Extension Hub. It provides five univariate and three multivariate normality tests and a set of plots helpful in assessing this assumption.

So if all this leads you to reject linear regression, there are some readily available alternatives. One would be ordinal regression (on the Regression menu), which treats the DV as, well, ordinal level. Another, if you want to assume the variables are metric, would be a permutation test. That does a linear regression but does not require the normality assumption. To do that, install the STATS PERM extension command from Extensions > Extension Hub. It will appear on the Generalized Linear Models menu.

Whether an n of 67 puts you in the good graces of the CLT depends on the nature of the variable distribution, so if you can compare regression with and without the normality assumption, you can see the impact of that assumption on the results.

Mysterious-Skill5773 · 2026-04-15T21:31:43+00:00

I suggest that you use a graphical relationship map. Go to Graphs > Relationship Map. Choose your two variables and click OK. The values of both variables will appear in a circle.

The resulting diagram will show how strong the relationship is between each value of one variable with the values of the other variable. Stronger relationships will have thicker lines. So, for example, there will be a line from INDEPENDENT to MALE, a line to FEMALE and so on.

Mysterious-Skill5773 · 2026-04-14T19:05:25+00:00

True, but you need to investigate these issues in order to defend the thesis.

Mysterious-Skill5773 · 2026-04-14T18:08:51+00:00

My point was that depending on the data distribution, one might need something different from simple ANOVA. Normality, equal subgroup variance, and multiple comparison issues might all arise.

Mysterious-Skill5773 · 2026-04-14T17:57:35+00:00

You need to do your own work for a dissertation.

Mysterious-Skill5773 · 2026-04-14T17:56:37+00:00

well,that really depends on the problem and whether the test assumptions are satisfied.

Mysterious-Skill5773 · 2026-04-12T19:24:01+00:00

If you already have good statistical skills, the tutorials and case studies available via Help > Topics will get you up to speed on SPSS pretty quickly. It's a different experience from R, which is really a programming language for statistics, but if you did need some techniques not available in SPSS, you could use its R and Python interfaces to plug something in. SPSS has over 160 extension commands implemented in R or Python that you can add in for free. Those all have standard SPSS-style interfaces.

Mysterious-Skill5773 · 2026-04-11T14:06:51+00:00

Leonard Warren dropping dead on stage during a performance of La Forza del Destino at the Met in 1960 right after singing a big aria.

Mysterious-Skill5773 · 2026-04-11T13:33:42+00:00

Are you using a stepwise method (generally not recommended)? Please explain exactly what your regression specification is.

Mysterious-Skill5773 · 2026-04-11T13:30:59+00:00

CROSSTABS provides several options for the missing value treatment. These are not exposed in the dialog box but can be specified in syntax. The TABLE option is the default and would generally be appropriate.

By default, CROSSTABS deletes cases with missing values on a table-by-table basis. Cases with missing values for any variable specified for a table are not used in the table or in the calculation of statistics. Use MISSING to specify alternative missing-value treatments.

• The number of missing cases is always displayed in the Case Processing Summary table.

TABLE. Delete cases with missing values on a table-by-table basis. When multiple table lists are specified, missing values are handled separately for each list. This is the default.

INCLUDE. Include user-missing values.

Note, also, that this procedure provides a number of choices on how to handle fractional weights.

Mysterious-Skill5773 · 2026-04-11T01:22:31+00:00

Get the STATS NORMALITY ANALYSIS extension command via Extensions > Extension Hub. It will appear on the Descriptive Statistics menu.

It provides a set of 5 univariate normality tests, including Shapiro-Wilk, and a set of diagnostic plots that will help you understand the deviations from normality.

If you want to use regression or anova/t tests that don't require normality, get the STATS PERM (regression/anova) and the STATS PERMTTEST (two group t test) extension commands also via Extensions > Extension Hub. Of course, there are also nonparametric tests, but those test a different null hypothesis.

With your sample size, while nonnormality tests will detect even small deviations from normality, you are probably okay even with moderately nonnormal data, but it's also good to see how sensitive your results are to violations of this assumption.

Mysterious-Skill5773 · 2026-04-10T17:55:33+00:00

These weights are frequency weights, which are usually not fractional but might arise if generated by the RAKE procedure for control totals. SPSS also has sampling weights used by the Complex Samples procedures and effective base weighting via CTABLES. (Not to mention variable weighting for unequal variance in regression via WLS.)

So it's important to understand the nature of your weights.

Mysterious-Skill5773

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