You can use it to discover patterns in the data,
to explain differences in one subset of data in terms of another subset
and to find out
whether certain beliefs about the data are justified.
This chapter does not attempt to introduce the theory behind the
statistical analysis,
You can use it to discover patterns in the data,
to explain differences in one subset of data in terms of another subset
and to find out
whether certain beliefs about the data are justified.
This chapter does not attempt to introduce the theory behind the
statistical analysis,
interactive mode from the command line.
However, the example commands can also be typed into a file and executed in
a post-hoc mode by typing @samp{pspp @var{filename}} at a shell prompt,
interactive mode from the command line.
However, the example commands can also be typed into a file and executed in
a post-hoc mode by typing @samp{pspp @var{filename}} at a shell prompt,
data with a string like @prompt{PSPP>} or @prompt{data>}.
In the examples of this chapter, whenever you see text like this, it
data with a string like @prompt{PSPP>} or @prompt{data>}.
In the examples of this chapter, whenever you see text like this, it
should type.
Throughout this chapter reference is made to a number of sample data files.
So that you can try the examples for yourself,
should type.
Throughout this chapter reference is made to a number of sample data files.
So that you can try the examples for yourself,
@footnote{These files contain purely fictitious data. They should not be used
for research purposes.}
@note{Normally these files are installed in the directory
@footnote{These files contain purely fictitious data. They should not be used
for research purposes.}
@note{Normally these files are installed in the directory
-Before analysis can commence, the data must be loaded into PSPP and
-arranged such that both PSPP and humans can understand what
+Before analysis can commence, the data must be loaded into @pspp{} and
+arranged such that both @pspp{} and humans can understand what
The word @samp{list} intentionally appears twice.
The first occurrence is part of the @cmd{DATA LIST} call,
whilst the second
The word @samp{list} intentionally appears twice.
The first occurrence is part of the @cmd{DATA LIST} call,
whilst the second
expecting a command.
However, when it's expecting data, the prompt changes to @prompt{data>}
so that you know to enter data and not a command.
@item
At the end of every command there is a terminating @samp{.} which tells
expecting a command.
However, when it's expecting data, the prompt changes to @prompt{data>}
so that you know to enter data and not a command.
@item
At the end of every command there is a terminating @samp{.} which tells
You should not enter @samp{.} when data is expected (@i{ie.} when
the @prompt{data>} prompt is current) since it is appropriate only for
terminating commands.
You should not enter @samp{.} when data is expected (@i{ie.} when
the @prompt{data>} prompt is current) since it is appropriate only for
terminating commands.
-When working with other PSPP users, or users of other software which
-uses the PSPP data format, you may be given the data in
-a pre-prepared PSPP file.
+When working with other @pspp{} users, or users of other software which
+uses the @pspp{} data format, you may be given the data in
+a pre-prepared @pspp{} file.
Such files contain not only the data, but the variable definitions,
along with their formats, labels and other meta-data.
Conventionally, these files (sometimes called ``system'' files)
Such files contain not only the data, but the variable definitions,
along with their formats, labels and other meta-data.
Conventionally, these files (sometimes called ``system'' files)
identify data which might be incorrect.
The @cmd{DESCRIPTIVES} command (@pxref{DESCRIPTIVES}) is used to generate
identify data which might be incorrect.
The @cmd{DESCRIPTIVES} command (@pxref{DESCRIPTIVES}) is used to generate
If possible, suspect data should be checked and re-measured.
However, this may not always be feasible, in which case the researcher may
decide to disregard these values.
If possible, suspect data should be checked and re-measured.
However, this may not always be feasible, in which case the researcher may
decide to disregard these values.
will be disregarded in future analysis. @xref{Missing Observations}.
You can set the two suspect values to the `SYSMIS' value using the @cmd{RECODE}
command.
@example
will be disregarded in future analysis. @xref{Missing Observations}.
You can set the two suspect values to the `SYSMIS' value using the @cmd{RECODE}
command.
@example
The sample file @file{hotel.sav} comprises data gathered from a
customer satisfaction survey of clients at a particular hotel.
In @ref{reliability}, this file is loaded for analysis.
The sample file @file{hotel.sav} comprises data gathered from a
customer satisfaction survey of clients at a particular hotel.
In @ref{reliability}, this file is loaded for analysis.
variables and associated data.
The output from this command has been omitted from the example for the sake of clarity, but
you will notice that each of the variables
variables and associated data.
The output from this command has been omitted from the example for the sake of clarity, but
you will notice that each of the variables
One would therefore expect the values of these variables (after recoding)
to closely follow one another, and we can test that with the @cmd{RELIABILITY}
command (@pxref{RELIABILITY}).
One would therefore expect the values of these variables (after recoding)
to closely follow one another, and we can test that with the @cmd{RELIABILITY}
command (@pxref{RELIABILITY}).
negatively scaled variables) requests reliability statistics for
@var{v1}, @var{v3} and @var{v5}.
negatively scaled variables) requests reliability statistics for
@var{v1}, @var{v3} and @var{v5}.
The researcher starts by making a @dfn{null hypothesis}.
Often this is a hypothesis which he suspects to be false.
The researcher starts by making a @dfn{null hypothesis}.
Often this is a hypothesis which he suspects to be false.
If the variances are equal, then a more powerful form of the T-test can be used.
However if it is unsafe to assume equal variances,
then an alternative calculation is necessary.
If the variances are equal, then a more powerful form of the T-test can be used.
However if it is unsafe to assume equal variances,
then an alternative calculation is necessary.
For the @var{height} variable, the output shows the significance of the
Levene test to be 0.33 which means there is a
For the @var{height} variable, the output shows the significance of the
Levene test to be 0.33 which means there is a