I have two data sets data1 and data2 which have same variables but different cases. data1 has a variable x that has specific values. I want to capture cases in data2 variable x whose values match the data1 variable x values.
Is there any specific way to that?
I know we can select individual cases with individual values with each dataset but I am not aware of a method to do it across two data sets.
The way to do this is to create a new variable in data1 that is marked for values of X that exist in data2, and then you proceed as usual in data1:
dataset activate data2.
dataset declare xvals.
aggregate out=xvals /break = x/occursInData2=n.
dataset activate data1.
sort cases by X.
match files /file=*/table=xvals/by X.
At this point data1 will contain a new variable occursInData2 that only has a value in cases where the value of X occurs also in data2.
Related
I have two SPSS data sets that have the exact same variables. When I merge the data sets via "add cases", there are some cases in the merged data set that refer to the same person. The problem is that these cases are not perfect duplicates of each other. Say, for instance, there are two cases called 1 and 2 that refer to the same person, and two variables called A and B. 1 has a value for A, but its value for B is missing, where 2 has a value for B but its value for A is missing. Is there a way to merge 1 and 2 so that I end up with a single case that has a value for both A and B?
One thing you could do is aggregate by person and get the maximum of each value - which would combine the two cases of each person but get the existing values from both cases:
aggregate outfile=* /break=personID /A B=max(A B).
I want to add cases from many SPSS dataset to one SPSS dataset.
Here's my code:
DATASET ACTIVATE DataSet1.
ADD FILES /FILE=*
/FILE='Path\to\dataset.sav'.
EXECUTE.
But I get this error: Mismatched variable types on the input files.
I want SPSS to ignore the conflicting columns and add cases only from the columns where there is no conflict.
How do I do this?
This occurs because variables of the same name in the two different data sources have either different format types (STRING, NUMERIC, DATE ect) or either they are both STRINGS but of different length.
The latter, string variables of different lenghts, can be solved like this:
DATA LIST FREE / V(A1).
BEGIN DATA.
a b c
END DATA.
DATASET NAME DS1.
DATA LIST FREE / V(A2).
BEGIN DATA.
1 2 3
END DATA.
DATASET NAME DS2.
STATS ADJUST WIDTHS VARIABLES=ALL WIDTH=MAX /FILES DS1 DS2.
DATASET ACTIVATE DS1.
ADD FILES FILE=* /FILE=DS2.
However, if you have mismatch of different format types then that is a tad more complicated to solve due to many different permutations, so you would probably want to asses which variables are problematic and harmonize/delete them before merging files. Probably worth carrying out this exercise nonetheless as having same variable names with different format type could be signs of erroneous data.
If you know which variables conflict, you can use the KEEP subcommand to select the others, or you can use the RENAME command to assign new names and adjust the results afterwards.
If you need to harmonize the names and the issue is something like differing string lengths for variables that should be the same, the STATS ADJUST WIDTHS extension command can harmonize the widths before you merge.
I have a lot of movie data from IMDB and I'm in the middle of cleaning up the data and making it so that 1 row = 1 movie as the database often has multiple records for a single film.
I've restructured the data so that what was a single 'Country' variable with multiple cases for a single film, is now a set of 29 country columns. A single film may have up to 29 countries affiliated with it (most have just 1 or 2).
I plan to do some simple descriptive statistics and expected frequencies, perhaps look for correlations with other variables like genre etc.
Is it possible to have SPSS treat all 29 variables as a single variable? It doesn't matter which of the country variables a country is present in, just that it is present in one of them. For example I might want to find all Indian films, and ask SPSS to check for each row, whether 'India' is in any one of the country variables and return the row if it is present in any of them.
Is this possible, or do I just need to manually instruct SPSS with a list of OR commands whenever I run a query.
There are two types of multiple response sets: multiple dichotomy, which would be 29 yes/no variables as you describe, and multiple category, in which you have a list of arbitrary categories. See the MRSETS command for details.
Once defined, CTABLES can do all your statistical calculations on these, and these sets can also be used in graphics constructed in the Chart Builder or GGRAPH commands.
Don't confuse the sets created by MRSETS with the older MULTIPLE RESPONSE procedure, which is still available. MRSETS definitions persist with the data and are used with CTABLES and GGRAPH only.
With the ANY function, as Andy said above, you would use the individual variables, but you can use TO. So, for example, you could write
COMPUTE FILM7 = ANY(7, f1 to f29)
if you have MC variables. If using the MD structure, you would have to check, say, variable f7 in this example.
When setting up a for each loop to read products from an "objProduct" object variable, I got three options in "Enumerator Mode" pane as snapshot shows:
I know "Rows in the first table" is the right option for current case. However, I'm curious in which scenarios will the second and third options be used?
Seems that "ADO Object Source Variable" will contain multiple tables if 2nd/3rd is applied. That's confusing... shouldn't one variable be regarded as one table and thus, only the first option is needed?
P.S.
I did researches and only MSDN sheds some light as below, but not quite clear when they will be applied and for what purpose.
**Rows in all tables (ADO.NET dataset only)**
Select to enumerate rows in all tables. This option is available only if the objects to enumerate are all members of the same ADO.NET dataset.
**All tables (ADO.NET dataset only)**
Select to enumerate tables only.
Let's say that you execute the following SQL in an Execute SQL Task (using an ADO.NET connection) and you store the full result set in an SSIS Object variable.
select * from
(select 1 as id, 'test' as description) resultSet1
;
select * from
(select 2 as anotherId, 'test2' as description union
select 3 as anotherId, 'test3' as description) resultSet2
That object is actually a System.Data.DataSet, which can contain multiple result sets (accessible via the Tables property). Each of those result sets is a System.Data.DataTable object. Within each result set (or System.Data.DataTable) you have rows.
The Rows in all tables (ADO.NET dataset only) and All tables (ADO.NET dataset only) options can be used when you need to iterate through all the result sets (instead of just the first one). The difference between the two is what objects are being enumerated over.
Rows in all tables (ADO.NET dataset only) - take all the rows of data returned from the SQL above and go through them one by one, mapping the column values to variables specified in your Variable Mappings. For the example above, you would have 3 total iterations (3 total rows). This behavior in a Script Task would look something like this:
All tables (ADO.NET dataset only) - take all the result sets from the SQL above and go through them one by one, mapping the result set to the variable specified in Variable Mappings. For the example above, you would have 2 total iterations (2 total result sets). This behavior in a Script Task would look something like this:
I've never had the need to use either one of these options, so I can't provide any specific scenarios where I've used them.
Suppose we have a categorical variable X which can take on 10 values. There are counts inside each of these 10 categories. I want to see whether there are correlations between categories. How would I do this in SPSS? Is there a way to split X into 10 subvariables?
I go to Analyze ---> Correlate ---> Bivariate and can only find the variable X (not the 10 categories).
It sounds like you have a single variable with mutually exclusive categories. If this is the case then if the variable equals a particular category, then that means it does not equal any other category. Therefore, it makes no sense to correlate such a variable.
If you do not have mutually exclusive categories (i.e., you have what is sometimes called a multi-variable) then your 10 response options would be represented as 10 separate variables in SPSS. You could then potentially use Analyze - correlate - bivariate to examine relationships between category co-occurrence.