Spss 23 Free Download With Crack
Eleanora Parrot
I have developed a SQL Server database for a client that involves, as
one would guess, a whole handful of one-to-many relationships between
tables. At the heart of this database is the "Client". Basically
everything has a relationship to the "Client" table somehow (more or
less). The client that I have developed the database for has someone
working in partnership with them that wants to use SPSS to analyze the
data. Seems straightforward - or so I thought.The problem is that the person wanting to use SPSS is demanding that
all the data be in one row per "Client". So, for example, there is
the "Client" table and there is the "Children" table. Obviously,
there is a one-to-many relationship between the "Client" and
"Children" table. So a query/view incorporating both these tables
will necessarily have "repeats" in the "ClientID" field where that
"ClientID" has multiple entries in the "Children" table.The SPSS person claims that when she uses SPSS, the data she has used
has always fit this requirement of one row per primary data object.
In the example above, this means she would have data that, instead of
using a "Client" table a "Children" table, would have a fixed number
of columns, say 4. Each of these fields would store something like a
child's gender (for up to 4 children) for that one client. Clearly
terrible database design, but good for statistics, according to the
SPSS person.So has anyone come across this problem before?
I feel like a major stats package like SPSS should be able to handle
this situation. I just don't know anything about SPSS so I don't know
how it would deal with data that is in a one-to-many format.Is this a lack-of-training issue for the person using SPSS?Any thoughts are greatly appreciated.best,
jon
jlarosa at alumni dot brown dot edu
>
> The problem is that the person wanting to use SPSS is demanding that
> all the data be in one row per "Client". So, for example, there is
> the "Client" table and there is the "Children" table. Obviously,
> there is a one-to-many relationship between the "Client" and
> "Children" table. So a query/view incorporating both these tables
> will necessarily have "repeats" in the "ClientID" field where that
> "ClientID" has multiple entries in the "Children" table.
>
> The SPSS person claims that when she uses SPSS, the data she has used
> has always fit this requirement of one row per primary data object.
> In the example above, this means she would have data that, instead of
> using a "Client" table a "Children" table, would have a fixed number
> of columns, say 4. Each of these fields would store something like a
> child's gender (for up to 4 children) for that one client. Clearly
> terrible database design, but good for statistics, according to the
> SPSS person.
This is not an SPSS problem per se, but a problem in the way one is
thinking
about the data as a whole. There is no ONE WAY to analyze everything in
such
a situation, it all depends on what you are trying to do. E.g., if you
want
to report on how many children the clients have, then limiting the data to
a maximum of 4 children per client will give the wrong results. Instead,
one
can do a query that returns one record per child, as many records per
client
as needed, and then AGGREGATE (that's an SPSS operation) to one record per
client, counting the number of children for each client. Like this (in
syntax):sort cases by clientid. /* if not sorted yet.
aggregate outfile=* /presorted /break=clientid /nchild=N.That can then be followed by statements that display statistics on the
NCHILD
variable, e.g., means or frequencies.Other questions require other approaches. E.g., if you want to report on a
characteristic that is child-based, you need to keep it as one record per
child.Note that multiple queries on the database can be done, there is not need
to
try and do everything all at once. And whatever SQL queries you need to do
(including complex ones that relate multiple tables) can be done from SPSS
via ODBC, the SQL syntax is embedded in the SPSS ODBC query statement.- Moshe
You are right. I'm using SPSS to analyze data from a relational database
with no problem to cope with the structure your are describing. AGGREGATE
function can do its best with this type of data.!J.A.T.
Though agregates can solve some problems, there are other ways to do
things.We work with survey data (questionnaires) which, if put into a
relational structure, would take up many tables. I understand your
analist's problem in that for them analysis occurs per 'case' - this is
primarily a structure problem.For survey data (household rosters, basket of goods, etc.) flat files
work well.Structurally, you need to know what the maximum count per case is of
repeating data (e.g. maximum 4 children per respondent). In this case,
you would make four 'child' variables in which the data for that child
is stored (e.g. child0_age, child1_age, etc.). Now you must make the
four variables into a multiple response set (e.g. mr_children_age).
This enables you to use the new variable singly and holds great
analysis benefits, especially when things start to get complex...I used to run a lot of queries from spss, but ended up taking the time
to provide a single 'flat file' to analysts as this was easily
accessible to a wider audience.As a database user, I would appreciate spss giving an option of storing
data directly in a database and not in a file...Matty
B The left column lists all of the variables in your dataset. You can use this menu to add variables into a computation: either double-click on a variable to add it to the Numeric Expression field, or select the variable(s) that will be used in your computation and click the arrow to move them to the Numeric Expression text field (C).
C Numeric Expression: Specify how to compute the new variable by writing a numeric expression. This expression must include one or more variables from your dataset, and can use arithmetic or functions.
F Function group: You can also use the built-in functions in the Function group list on the right-hand side of the window. The function group contains many useful, common functions that may be used for calculating values for new variables (e.g., mean, logarithm). To find a specific function, simply click one of the function groups in the Function Group list. You will now see a list of functions that belong to that function group in the Functions and Special Variables area. If you click on a specific function, a description of that function will appear in the text field to the left.
2 The default specification is to Include all cases. To specify the conditions under which your computation should be applied, however, you will need to click Include if case satisfies condition. This will allow you to specify the conditions under which the computation will be applied to your data.
After you are finished defining the conditions under which your computation will be applied to the data, click Continue. Note that when you specify a condition in the Compute Variable: If Cases window, the computation will only be performed on the cases meeting the specified condition. If a case does not meet that condition, it will be assigned a missing value for the new variable.
You do not necessarily need to use the Compute Variables dialog window in order to compute variables or generate syntax. You can write your own syntax expressions to compute variables (and it is often faster and more convenient to do so!) Syntax expressions can be executed by opening a new Syntax file (File > New > Syntax), entering the commands in the Syntax window, and then pressing the Run button.
The first line gives the COMPUTE command, which specifies the name of the new variable on the left side of the equals sign, and its formula on the right side of the equals sign. The formula on the right side of the equals sign corresponds to what you would enter in the Numeric Expression field in the Compute Variables dialog window.
The EXECUTE command on the second line is what actually carries out the computation and adds the variable to the active dataset. (If you have tried to run COMPUTE syntax but do not see variables added to your dataset and do not also see error or warning messages in the Output Viewer, you may have forgotten to run the EXECUTE statement.)
It's also possible to use COMPUTE syntax to compute or transform string variables (i.e., variables containing characters other than numbers). To compute string variables, the general syntax is virtually identical. However, with string variables, you must first "declare" a new variable as a string variable before you can define it using a COMPUTE statement:
On the first line, STRING statement declares the new variable's name ( NewVariableName) and its format (A20) of a new string variable. Note that the format must be put inside parentheses. The format specification for strings will always start with the letter A, followed by a number giving the "width" of the string (the maximum number of characters that variable can contain). In this case, the new variable will have a width of 20, so data values can contain up to 20 characters. When declaring a new string variable, you should take care to set the width of the string to be wide enough so that your data values aren't accidentally cut short. On the second line, the COMPUTE statement gives the actual formula for the variable declared in the STRING statement. On the third line, the EXECUTE command tells SPSS to carry out the computation.
Now we will use what we have learned throughout this tutorial to demonstrate how to compute a new variable. In this example, we wish to compute BMI for the respondents in our sample. The height (in inches) and weight (in pounds) of the respondents were observed; so to compute BMI, we want to plug those values into the formula
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