I have a SPSS file that has 600 variables and for each of them there can be 0 to 4 different missing values. I am trying to change the missing values according to this pattern:
996 -> -6
997 -> -7
998 -> -8
999 -> -9
Where 996 is "No object" , 997 is "Does not know" , 998 is "Refused", 999 is "Non declared". I would need the changed numbers to have the same labels.
There are other value labels in the variables therefore i can't simply delete the labels and add the new ones.
Is there a way to either delete specific value labels or change the value using a script?
If these labels are the same for all the variables, just use the ADD VALUE LABELS command specifying those four values. If these labels vary with the variable, somewhat more complicated code would be required, but let's not go there unless it is necessary.
This seems a duplicated question, which I answered myself sometime ago. Through conventional syntax I don't think it is still possible.
Well, nothing is impossible in the realms of computing. Raynald Levesque outlines a workaround solution here. And Ruben Geert van den Berg provides a python solution on his website also.
Related
I'm using flexlm_exporter to export my license usage to Prometheus and from Prometheus to custom service (Not Graphana).
As you know Prometheus hides missing values.
However, I need those missing values in my metric values, therefore I added to my prom query or vector(0)
For example:
flexlm_feature_used_users{app="vendor_lic-server01",name="Temp"} or vector(0)
This query adds a empty metric with zero values.
My question is if there's a way to merge the zero vector with each metric values?
Edit:
I need grouping, at least for a user and name labels, so vector(0) is probably not the best option here?
I tried multiple solutions in different StackOverflow threads, however, nothing works.
Please assist.
It would help if you used Absent with labels to convert the value from 1 to zero, use clamp_max
( Metrics{label=“a”} OR clamp_max(absent(notExists{label=“a”}),0))
+
( Metrics2{label=“a”} OR clamp_max(absent(notExists{label=“a”}),0)
Vector(0) has no label.
clamp_max(Absent(notExists{label=“a”},0) is 0 with label.
If you do sum(flexlm_feature_used_users{app="vendor_lic-server01",name="Temp"} or vector(0)) you should get what you're looking for, but you'll lose possibility to do group by, since vector(0) doesn't have any labels.
I needed a similar thing, and ended up flattening the options. What worked for me was something like:
(sum by xyz(flexlm_feature_used_users{app="vendor_lic-server01",name="Temp1"} + sum by xyz(flexlm_feature_used_users{app="vendor_lic-server01",name="Temp2"}) or
sum by xyz(flexlm_feature_used_users{app="vendor_lic-server01",name="Temp1"} or
sum by xyz(flexlm_feature_used_users{app="vendor_lic-server01",name="Temp2"}
There is no an easy generic way to fill gaps in returned time series with zeroes in Prometheus. But this can be easily done via default operator in VictoriaMetrics:
flexlm_feature_used_users{app="vendor_lic-server01",name="Temp"} default 0
The q default N fills gaps with the given default value N per each time series returned from q. See more details in MetricsQL docs.
Is there a way to display response options that have 0 responses in SPSS frequency output? The default is for SPSS to omit in the frequency table output any response option that is not selected by at least a single respondent. I looked for a syntax-driven option to no avail. Thank you in advance for any assistance!
It doesn't show because there is no one single case in the data is with that attribute. So, by forcing a row of zero you'll need to realize we're asking SPSS to do something incorrect.
Having said that, you can introduce a fake case with the missing category. E.g. if you have Orange, Apple, and Pear, but no one answered they like Pear, the add one fake case that says Pear.
Now, make a new weight variable that consists of only 1. But for the Pear case, make it very very small like 0.00001. Then, go to Data > Weight Cases > Weight cases by and put that new weight variable over. Click OK to apply. Now what happens is that SPSS will treat the "1" with a weight of 1 and the fake case with a weight that is 1/10000 of a normal case. If you rerun the frequency you should see the one with zero count shows up.
If you have purchased the Custom Table module you can also do that directly as well, as far as I can tell from their technical document. That module costs 637 to 3630 depending on license type, so probably only worth a try if your institute has it.
So, I'm a noob with SPSS, I (shame on me) have a cracked version of SPSS 22 and if I understood your question correctly, this is my solution:
double click the Frequency table in Output
right click table, select Table Properties
go to General and then uncheck the Hide empty rows and columns option
Hope this helps someone!
If your SPSS version has no Custom Tables installed and you haven't collected money for that module yet then use the following (run this syntax):
*Note: please use variable names up to 8 characters long.
set mxloops 1000. /*in case your list of values is longer than 40
matrix.
get vars /vari= V1 V2 /names= names /miss= omit. /*V1 V2 here is your categorical variable(s)
comp vals= {1,2,3,4,5,99}. /*let this be the list of possible values shared by the variables
comp freq= make(ncol(vals),ncol(vars),0).
loop i= 1 to ncol(vals).
comp freq(i,:)= csum(vars=vals(i)).
end loop.
comp names= {'vals',names}.
print {t(vals),freq} /cnames= names /title 'Frequency'. /*here you are - the frequencies
print {t(vals),freq/nrow(vars)*100} /cnames= names /format f8.2 /title 'Percent'. /*and percents
end matrix.
*If variables have missing values, they are deleted listwise. To include missings, use
get vars /vari= V1 V2 /names= names /miss= -999. /*or other value
*To exclude missings individually from each variable, analyze by separate variables.
I am working with SPSS 18.
I am using FREQUENCIES to calculate the 95th percentile of a variable.
FREQUENCIES SdrelPromSldDeu_Acr_5_0
/FORMAT=NOTABLE
/PERCENTILES 1,5,95,99.
The result is given in a table
Statistics
SdrelPromSldDeu_Acr_5_0
N Valid 8881
Missing 0
Percentiles 1 -1,001060644014
5 -1,000541440102
95 6619,140632636228
99 9223372,036854776000
But if I double-click the 9223372,036854776 to copy it, another number appears: 1.0757943411193715E7.
If I use MEANS to get the maximum value, the result is 2.4329524990388575E8, so the number that appears on the double-click seems possible.
I have seen 9223372,03 in other cases as well, as if it were some kind of upper limit SPSS is able to display.
Can anybody tell me if the 9223372,03 represents anything useful? Should I trust the bigger number?
Thanks!
It appears to be a bug in the display of SPSS.
The number you have shown is eerily similar to
9223372036854775807
which is the highest value possible if a variable is declared as a long integer.
see also:
https://en.wikipedia.org/wiki/9223372036854775807
Since your actual number is 11 degrees smaller, it should not reach this limit. Hence the conclusion that it must be a bug in the display software.
Do not trust it.
(the number behind may or may not be right, but the 9223372,03 is surely wrong)
I have a likert scale, (1-7), I want to group responses 2 to 7 together and leave out response 1. I'm using SPSS and getting a little confused.
I'll assume you wanted to convert a variable. Lets call a variable "var1", and it needs to be converted to a dummy variable (0 and 1). In this case, your original variable can be transformed to "var1r" by using the following code.
RECODE var1 (1=0) (2=1) (3=1) (4=1) (5=1) (6=1) (7=1) into var1r .
The previous codes, creates a new variable, in which higher responses (2-7) are denoted by 1, and lower numbers (1) are denoted by 0. If you cross this variables in a cross tab, by using the next syntax:
cross var1 by var1r .
It would show your different values correspondingly. The latter is a way to verify you have recode your variables correctly. You can use the similar syntax and change variables at will.
Good luck!
I think the best way is to clone the original variable and then RECODE it into the same variable. The advantages are that you'll keep your variables in the same order and you already have the variable label and value labels. So if the variable name is v1, clone it and run
RECODE v1 (7=2).
Now adjust the value labels with
ADD VALUE LABELS v1 2 "this value means ... or ...".
Here you give the meaning of values 2 and 7. For "leaving out" value 1, specify it as a missing value like so
MISSING VALUES v1 (1).
This presumes 1 is the only missing value. Now check all this by running
CROSSTABS v1 by copy_v1.
presuming your clone is called copy_v1. Following these steps is a short and safe road to a perfect result.
UPDATE
I think this syntax -although valid- may not be what the OP meant. Also, see comments below.
For recoding 1 into 0 and all other values into 1, an alternative is
compute v1 = v1 gt 1.
This looks like invalid syntax but it's explained in this tutorial. You can use this trick for a lot of other stuff too.
For staying on the safe side, first clone the original variable so you can cross it with the recoded version to ensure that the result is correct.
Since the last post is closed due to unclear expression, here is a edited one.
There are in total 20 items from 5 Likert-type scale questions from a questionnaire. I need to add the 20 items from 5 separate questions to create a total scale. I already got the data.
The question is just like the picture above. How can I run the command to add the 20 items from 5 separate questions? What is the command?
Is it something like Transform > Compute variable. Enter a variable name, specify which items to add up, and hey presto (e.g. "V1+V2+V3" etc)?
You can do exactly as you suggested, using the Transform -> Compute variable... function. Simply type in the name of your new scale in the Target variable box and the addition you want in the Numeric variable box.
You will see that the following SPSS syntax command is run:
COMPUTE total=v1 + v2 + v3 + v4.
EXECUTE.
If any of the variables has a missing value, the simply adding them will result in a missing value as well. If you don't want to impute for missing values, using the MEAN command in syntax works well. Also, if the variables are contiguous in the data file, you can make the syntax much more readable by using the TO modifier.
COMPUTE myscore=MEAN(variable1 TO variable5)*5.
The resulting value provides an efficient expected value.
However, it seems like the problem in this case is that the data entry process has dummy coded all of the items, producing 20 separate variables instead of 5, where each block of 4 variables has a value of 0 or 1 but represents the values 1 to 4. In this case, you can use the following syntax:
COMPUTE mycounter=1.
COMPUTE myscore=0.
EXECUTE.
DO REPEAT a=variable1 TO variable20.
COMPUTE myscore=myscore+mycounter*a.
COMPUTE mycounter=mycounter+1.
IF (mycounter=5) mycounter=1.
END REPEAT.
EXECUTE.
Note that the variables from variable1 to variable20 must have each set of dummy codes from the original items clustered together in ascending order.