Transcript SPSS for Windows A brief tutorial This tutorial is a brief look at what SPSS for Windows is capable of doing.

SPSS for Windows
A brief tutorial
This tutorial is a brief look at what SPSS for Windows is
capable of doing. It is not our intention to teach you
about statistics in this tutorial. For that you should
rely on your classes in statistics and/or a good
textbook. If you're a novice this tutorial should give
you a feel for the program and how to navigate
through the many options. Beyond that, the SPSS
Help Files should be used as a resource. Further,
SPSS sells a number of very good manuals.
The Basics
SPSS for Windows has the same general look a feel of
most other programs for Windows. Virtually anything
statistic that you wish to perform can be
accomplished in combination with pointing and
clicking on the menus and various interactive dialog
boxes. You may have noted that the examples in the
Howell textbook are performed/analyzed via code.
That is, SPSS, like many other packages, can be
accessed by programming short scripts, instead of
pointing and clicking. We will not cover any
programming in this tutorial.
Presumeably, SPSS is already installed on
your computer. If you don't have a shortcut
on your desktop go to the [Start =>
Programs] menu and start the package by
clicking on the SPSS icon.
Before proceeding I should say a few words about a
very simple convention that will be used in this
tutorial. In this point and click environment one often
has to navigate through many layers of menu items
before encountering the required option. In the
above paragraph the prescribed task was to locate
the SPSS icon in the [Start] menu structure. To get
to that icon, one must first click on [Start] then
move the pointer to the [Programs] options, before
locating the SPSS icon. This sequence of events can
be conveyed by typing [Start => Programs] . That
is, one must move from the outer layer of the menu
structure to some inner layer in sequence....
Once you've clicked on the SPSS icon a new window will appear on
the screen. The appearance is that of a standard program for
windows with a spreadsheet-like interface.
As you can see, there are a number of menu options relating to statistics, on the
menu bar. There are also shortcut icons on the toolbar. These serve as quick
access to often used options. Holding your mouse over one of these icons for a
second or two will result in a short function description for that icon. The
current display is that of an empty data sheet. Clearly, data can either be
entered manually, or it can be read from an existing data file.
Browsing the file menu, below, reveals nothing too surprising - many of
the options are familiar. Although, the details are specific to SPSS. For
example, the [New] option is used to specify the type of window to open.
The various options, under the [New] heading are,
Data] Default window with a blank data sheet ready for
analyses
[Syntax] One can write scripts like those present in the Howell
text, instead of using the menus. See the SPSS manuals for help
on this topic.
[Output] Whenever a procedure is run, the out is directed to a
separate window. One can also have multiple [Output]
windows open to organize the various analyses that might be
conducted. Later, these results can be saved and/or printed.
[Script] This window provides the opportunity to write fullblown programs, in a BASIC-like language. These programs
have access to functions that make up SPSS. With such access
it is possible to write user-defined procedures - those not part of
SPSS - by taking advantage of the SPSS functions. Again, this is
beyond the scope of this tutorial.
Also present in the [File] menu are two
separate avenues for reading data from
existing files. The first is the [Open] option.
Like other application packages (e.g.,
WordPerfect, Excel, ....) SPSS also has it's
own format for saving data. In this case, the
accepted extension for any file saved using
the proprietary format is "sav". So, one can
have a datafile saved as "data1.sav".
Anyways, this format is not readable with a
text editor, it is a binary format. The benefits
are that all formatting changes are
maintained and the file can be read faster,
hence the [Open] option. It is specifically
meant for files saved in the SPSS format. The
second option, [Read ASCII Data], as the
name suggests is to read files that are saved
in ASCII format. As can be seen, there are
two choices - [Freefield] and [Fixed Columns].
Clicking on one of these options will produce
a dialog box. One must specify a number of
parameters before a file can be read
successfully.
Reading ASCII files requires that the user know something
about the format of the data file. Otherwise, one is likely get
stuck in the process of reading, or the result may be a costly
error. The more restrictive format is [Fixed Columns]. One
must know how many variables there are, whether a
variable is in numeric or string format, and the first and
last column of each variable. For example, think of the
following as an excerpt from an ASCII data file.
male 37 102
male 22 115
male 27 99
.... .. ...
female 48 107
female 21 103
female 28 122
....
.. ...
An examination of the data file provides several key pieces of
information,
1.There are 3 variables
2.Variable 1 is a string , Variable 2 and 3 are numeric
3.Variable 1: first column=1, last column=6
•Notice that none of the columns overlap. The longest case
for column one is the name "female", that spans from the
first column to the sixth - or, the letter e. As you can see,
one has to manually locate the first and last column, of
each variable.
4.Variable 2: first column=9, last column=10
5.Variable 3: first column=12, last column=14
One needs all of the above information, in addition to, name for
each of the three variables. It is a highly structured way of
setting up and describing the data. For such files I would
suggest becoming comfortable with a good text editor. Failing
that, you may wish to try Notepad or WordPad in Win95, but
ensure that you save as a textfile with WordPad. A fullfledged
word processor like Word or WordPerfect will also work
provided that you remember to save as a textfile. These same
editors will allow you to figure out the column locations for each
of the variables. The [Freefield] option is less restrictive.
Essentially, the columns can be ragged (i.e., overlapping). One
need only preserve the order of each variable across all of the
cases.
male 37 102
male 22 115
male 27 99
....
.. ...
female 48 107
female 21 103
female 28 122
....
.. ...
Experiment with creating datafiles and
reading them with this method. As for the
SPSS format, there are a large number of
sample datafiles included in your package.
Just click on [Open] and find the SPSS home
directory. Make sure the filetype in the dialog
box associated with [Open] is set to "*.sav"
- the default...
Before we move onto actual data,
click on [Statistics] . The menu
that appears reveals many classes
of statistics available for use.
Each class is further subdivided
into other options, as denoted by
the little arrow at the right size of
the menu selector. Explore what
is offered by moving your mouse
over the various procedures
listed.
Data
To begin the process of adding data, just
click on the first cell that is located in the
upper left corner of the datasheet. It's just
like a spreadsheet. You can enter your data
as shown. Enter each data-point then hit
[Enter]. Once you're done with one column of
data you can click on the first cell of the next
column.
If you're entering data for
the first time, like the
example, the variable
names will be
automatically generated
(e.g., var00001,
var00002,....).
They are not very informative. To change these names, click on
the variable name button. For example, double click on the
"var00001" button. Once you have done that, a dialog box will
appear. The simplest option is to change the name to something
meaningful. For instance, replace "var00001" in the textbox with
"RT" (see figure above).
In addition to changing the variable name one can make changes
specific to [Type], [Labels], [Missing Values], and [Column
Format].
• [Labels] Using the labels option can enhance the readability of the
output. A variable name is limited to a length of 8 characters,
however, by using a variable label the length can be as much as 256
characters. This provides the ability to have very descriptive labels
that will appear at the output.
• Often, there is a need to code categorical variables in numeric format.
For example, male and female can be coded as 1 and 2, respectively.
To reduce confusion, it is recommended that one uses value labels .
For the example of gender coding, Value:1 would have a correspoding
Value label: male. Similarly, Value:2 would be coded with Value
Label: female. (click on the [Labels] button to verify the above)
• [Missing Values] See the accompanying help. This option provides a
means to code for various types of missing values.
• [Column Format] The column format dialog provides control over
several features of each column (e.g., width of column).
The next image reflects the variable
name change.
Once data has been entered or modified, it is advisable
to save. In fact, save as often as possible [File =>
SaveAs].
SPSS offers a large number of possible
formats, including their own. A list of the
available formats can be viewed and
selected by clicking on the Save as type:
, on the SaveAs dialog box. If your
intention is to only work in SPSS, then
there may be some benefit to saving in
the SPSS(*.sav) format. I assume that this
format allows for faster reading and
writing of the data file. However, if your
data will be analyzed and looked by other
packages (e.g., a spreadsheet), it would
be adviseable to save in a more universal
format (e.g., Excel(*.xls), 1-2-3 Rel 3.0
(*.wk3).
Once the type of file has been selected, enter a filename, minus the
extension (e.g., sav, xls). You should also save the file in a meaningful
directory, on your hard drive or floppy. That is, for any given project a
separate directory should be created. You don't want your data to get
mixed-up.
The process of reading already saved data can be painless if the
saved format is in the SPSS or a spreadsheet format. All one has to
do is,
click on [File => New => Data]
click on [File => Open] : a dialog box will appear
navigate to desired directory using the Look in:
menu at the top of the dialog box
select file type in the Files of type menu
click on the filename that is needed.
The process of reading existing files is slightly more involved if the
format is ASCII/plain text (see the earlier description of
[Freefield] and [Fixed Columns]). As an example, the ASCII
data from table2.1 in the Howell text will be used. A file
containing the data should be included in the accompanying
disk for the text. [Note: It was not present in my disk, so I
downloaded the file from Howell's webpage.] I've placed the
files on my harddrive at c:\ascdat. In the case of this set of
data,there are four columns representing observation number,
reaction time, setsize, and the presence or absence of the
target stimulus. This information can be found in the
readme.txt file that is also on the disk. Typically, we are aware
of the contents of our own data files, however, it doesn't hurt to
keep a record of the contents of such files.
To make life easier the [File => Read ASCII
Data => Freefield] will be used.
The resulting dialog box requires that a File , a Name and a
Data Type be specified for each variable, or column of data.
The desired file is accessed by clicking on the [Browse]
button, and then navigating to the desired location. Since the
extension for the sought after file is dat there is no need to
change the Files of type: selection. However, if the extension
is something else (e.g., *.txt) then it would be necessary to
select All files(*.*) from the Files of type: menu. Since there
are 4 variables in this data set, 4 names with the corresponding
type information must be specified. To Add the first variable,
observations, to the list,
(Please explore the various options by clicking
on any accessible menu item.)
type "obs" in the Name box
the Data Type is set to Numeric by default. If "obs" was a string variable,
then one would have to click on String
click on the Add button to include this variable to the list.
repeat the above procedure with new names and data types for each of the
remaining variables. It is important that all variables be added to the list.
Otherwise, the data will be scrambled.
The resulting data files appears in the
data editor like the following.
Descriptive Statistics
We can replicate the frequency analyses that
are described in chapter 2 of the text, by
using the file that was just read into the data
editor - tab2-1.dat. These analyses were
conducted on the reaction time data. Recall,
that we have labelled this data as RT.
To begin, click on
[Statistics=>Summarize=>Frequencies]....
The result is a new dialog box that allows the user to select the variables
of interest. Also, note the other clickable buttons along the border of
the dialog box. The buttons labelled [Statistics...] and [Charts...]
are of particular importance. Since we're interested in the reaction
time data, click on rt followed by a mouse click on the arrow pointing
right. The consequence of this action is a transference of the rt
variable to the Variables list. At this point, clicking on the [OK]
button would spawn an output window with the Frequency
information for each of the reaction times. However, more
information can be gathered by exploring the options offered by the
[Statistics...] and [Charts...].
Statistics...] offers a number of summary statistics.
Any statistic that is selected will be summarized in the
output window.
As for the options under [Charts...] click on Bar
Charts to replicate the graph in the text.
Once the options have been selected, click on [OK] to run the procedure. The results are
then displayed in an output window. In this particular instance the window will include
summary statistics for the variable RT, the frequency distribution, and the frequency
distribution. You can see all of this by scrolling down the window. The results should also
be identical to those in the text.
You may have gathered from the above that calculating
summary statistics requires nothing more than
selecting variables, and then selecting the desired
statistics. The frequency example allowed us to
generate frequency information plus measures of
central tendencies and dispersion. These statistics
can be had by clicking directly on
[Statistics=>Summarize=>Descriptives]. Not
surprisingly, another dialog box is attached to this
procedure. To control the type of statistics produced,
click on the [Options...] button. Once again, the
options include the typical measures of central
tendency and dispersion.
Each time as statistical procedure is run, like [Frequencies...] and
[Descriptives...] the results are posted to an Output Window.
If several procedures are run during one session the results will
be appended to the same window. However, greater
organization can be reached by opening new Output windows
before running each procedure - [File=>New=>Output].
Further, the contents of each of these windows can be saved for
later review, or in the case of charts saved to be later included
in formattted documents. [Explore by left mouse clicking on any
of the output objects (e.g., a frequency table, a chart, ...)
followed by a right button click. The left left button click will
highlight the desired object, while the right button click will
popup a new menu. The next step is to click on the copy
option. This action will store the object on the clipboard so that
it can be pasted to Word for Windows document, for
example.....]
Chi-Square & T-Test
The computation of the Chi-Square statistic can be accomplished
by clicking on [Statistics => Summarize => Crosstabs...].
This particular procedure will be your first introduction to coding
of data, in the data editor. To this point data have been entered
in a column format. That is, one variable per column. However,
that method is not sufficient in a number of situations, including
the calculation of Chi-Square, Independent T-tests, and any
Factorial ANOVA design with between subjects factors. I'm sure
there are many other cases, but they will not be covered in this
tutorial. Essentially, the data have to be entered in a specific
format that makes the analysis possible. The format typcially
reflects the design of the study, as will be demonstrated in the
examples.
Essentially, the table - below - includes the observed
data and the expected data in parentheses.
Fault
Low
Guilty
153(127.559)
Not Guilty
24(49.441)
Total
177
High
Total
105(130.441)
258
76(50.559)
100
181
358
For the Chi-Square statistic, the table of data can be coded by indexing the column and row
of the observations. For example, the count for being guilty with Low fault is 153. This
specific cell can be indexed as coming from row=1 and column=1. Similarly, Not Guilty
with High fault is coded as row=2 and column=2. For each observation, four in this
instance, there is unique code for location on the table. These can be entered as follows,
Row
1
Column
1
Count
153
1
2
2
2
1
2
24
105
76
•So, 2 rows * 2 columns equals 4 observations. That should be clear.
•For each of the rows, there are 2 corresponding columns, that is reflected
in the Count column. The Count column represents the number of time
each unique combination Row and Column occurs.
The above presents the data in an unambiguous manner. Once entered, the
analysis is a matter of selecting the desired menu items, and perhaps selecting
additional options for that statistic.
To perform the analysis,
The first step is to inform SPSS that the
COUNT variable represents the frequency for
each unique coding of ROW and COLUMN, by
invoking the WEIGHT command. To do this,
click on [Data => Weight Cases]. In the
resultant dialog box, enable the Weight cases
by option, then move the COUNT variable into
the Frequency Variable box. If this step is
forgotten, the count for each cell will be 1 for
the table.
Now that the COUNT variable has been processed as a weighted variable, select
[Statistics => Summarize => Crosstabs...] to launch the controlling dialog
box.
At the bottom of the dialog box are three buttons, with the most important
being the [Statistics...] button. You must click on the [Statistics...] button
and then select the Chi-square option, otherwise the statistic will not be
calculated. Exploring this dialog box makes it clear that SPSS can be forced to
calcuate a number of other statistics in conjuction with Chi-square. For example,
one can select the various measures of association (e.g., contingency
coefficient, phi and cramer's v,...), among others.
Move the ROW variable into the Row(s): box, and the COLUMN variable into
the Column(s):, then click [OK] to perform the analysis. A subset of the
output looks like the following,
Although simple, the calculation of the Chi-square statistic is very
particular about all the required steps being followed. More generally, as
we enter hypothesis testing, the user should be very careful and should
make use of manuals for the program and textbooks for statistics.
T-tests
By now, you should know that there are two forms of
the t-test, one for dependent variables and one for
independent variables, or observations. To inform
SPSS, or any stats package for that matter, of the
type of design it is necessary to have to different
ways of laying out the data. For the dependent
design, the two variables in question must be entered
in two columns. For independent t-tests, the
observations for the two groups must be uniquely
coded with a Group variable. Like the calculation of
the Chi-square statistic, these calculations will
reinforce the practice of thinking about, and laying
out the data in the correct format.
Dependent T-Test
To calculate this statistic, one must select
[Statistics => Compare Means =>
Paired-Samples T Test...] after entering
the data.
Enter the data into a new
datafile. Your data should
look a bit like the
following. That is, the
two variables should
occupy separate
columns...
•Note that the variable names start with a letter
and are less than 8 characters long. This is a bit
constraining, however, one can use the variable
label option to label the variable with a longer
name. This more descriptive name will then be
reproduced in the output window.
•Mnths_6
•Mnths_24
•124
•114
•94
•88
•115
•102
•110
•2
•116
•2
•139
•2
•116
•2
•110
•2
•129
•2
•120
•2
•105
•2
•88
•2
•120
•2
•120
•2
•116
•2
•105
•2
•...
•...
•...
•...
•123
•132
To calculate the t statistic click on [Statistics =>
Compare Means => Paired-Samples T Test...],
then select the two variables of interest. To select
the two variables, hold the [Shift] key down while
using the mouse for selection. You will note that the
selection box requires that variables be selected two
at a time. Once the two variables have been
selected, move them to the Paired Variables: list.
This procedure can be repeated for each pair of
variables to be analyzed. In this case, select
MNTHS_6 and MNTHS_24 together, then move them
to the Paired Variables list. Finally, click the [OK]
button.
The critical result for the current analysis will
appear in the output window as follows,
•As you can see an exact t-value is provided along with an exact
p-value, and this p-value is greater that the expected value of
0.025, for a two-tailed assessment. Closer examination indicates
several other statistics are presented in output window.
Independent T-tests
When calculating an independent t-test, the
only difference involves the way the data are
formatted in the datasheet. The datasheet
must include both the raw data and group
coding, for each variable. For this example,
the data from table 7.5 will be used. As an
added bonus, the number of observations are
unequal for this example.
Take a look at the following
table to get a feel for how to
code the data.
From the data you can
see that we used the
"Group" variable to
code for the two
variables. The value
of 1 was used to code
for "LBWExperimental", while a
value of 2 was used
to code for "LBWControl". If you're
confused please study
the table, above.
Group
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
Exp_Con
96
127
127
119
109
143
...
...
106
109
114
88
104
104
91
96
...
...
114
132
To generate the t-statistic,
Clik on [Statistics => Compare Means => IndependentSamples T Test] to launch the appropriate dialog box.
Select "exp_con" - the dependent variable list - and move it to
the Test Variable(s): box.
Select "group" - the grouping variable list - and move it to the
Grouping Variable: box.
The final step requires that the groups be defined. That is, one
must specify that Group1 - the experimental group in this case is coded as 1, and Group2 - the control group in this case - is
coded as 2. To do this, click on the [Define Groups...] button.
Click on the [Continue] button to return to the controlling
dialog box.
Run the analysis by clicking on the [OK] button.
The output for the current analysis extracted
from the output window looks like the following.
The p-value of .004 is way lower than the cutoff of 0.025, and that
suggests that the means are significantly different. Further, a Levene's
Test is performed to ensure that the correct results are used. In this case
the variances are equal, however, the calculations for unequal variances
are also presented, among some other statistics - some not presented.
Correlations and Regression
This will be a brief tutorial, since there is very
little that is required to calculate correlations
and linear regressions. To calculate a simple
correlation matrix, one must use [Statistics
=> Correlate => Bivariate...], and
[Statistics => Regression => Linear] for
the calculation of a linear regression.
For this section, the analyses presented in the computer section of
the Correlation and Regression chapter will be replicated. To begin,
enter the data as follows
IQ
GPA
102
2.75
108
4.00
109
2.25
118
3.00
79
1.67
88
2.25
...
...
...
...
85
2.50
Simple Correlation
Click on [Statistics => Correlate =>
Bivariate...], then select and move "IQ" and
"GPA" to the Variables: list. [Explore the
options presented on this controlling dialog
box.]
Click on [OK] to generate the requested
statistics.
The results from output window should
look like the following,
As you can see, r=0.702, and p=.000. The results suggest that
the correlation is significant.
Correlation and Regression
Linear regression....it is possible to output the
regression coefficients necessary to predict one
variable from the other - that minimize error. To do
so, one must select the [Statistics => Regression
=> Linear...] option. Further, there is a need to
know which variable will be used as the dependent
variable and which will be used as the independent
variable(s). In our current example, GPA will be the
dependent variable, and IQ will act as the
independent variable.
Specifically,
Initiate the procedure by clicking on
[Statistics => Regression => Linear...]
Select and move GPA into the Dependent:
variable box
Select andmove IQ into the
Independent(s): variable box
Click on the [OK] to generate the statistics.
Some of the results of this
analysis are presented below,
The correlation is still 0.702, and the p value is still 0.000. The
additional statistics are "Constant", or a from the text, and
"Slope", or B from the text. If you recall, the dependent
variable is GPA, in this case. As such, one can predict GPA
with the following,
GPA = -1.777 + 0.0448*IQ
One-Way ANOVA
As in the independent t-test datasheet, the data
must be coded with a group variable. The
data that will be used for the first part of this
section is from Table 11.2, of Howell. There
are 5 groups of 10 observations each resulting in a total of 50 observations. The
group variable will be coded from 1 to 5, for
each group. Take a look at the following to
get an idea of the coding.
The coding scheme uniquely
identifies the origin of each
observation.
To complete the analysis,
Select [Statistics => Compare
Means => One-Way ANOVA...]
to launch the controlling dialog
box.
Select and move "Scores" into the
Dependent list:
Select and move "Groups" into the
Factor: list
Click on [OK]
Groups
1
1
1
...
1
2
2
2
...
...
...
5
5
...
5
Scores
9
8
6
...
7
7
9
6
...
...
...
10
19
...
11
The preceding is a complete specification of the design for this
one-way anova. The simple presentation of the results, as taken
from the output window, will look like the following,
The analysis that was just performed provides minimal details with regard
to the data. If you take a look at the controlling dialog box, you will find 3
additional buttons on the bottom half - [Contrasts...], [Post Hoc..], and
[Options...].
Selecting [Options...] you will find,
If Descriptive is enabled, then the descriptive statistics
for each condition will be generated. Making
Homogeneity-of-variance active forces a Levene's
test on the data. The statistics from both of these
analyses will be reproduced in the output window.
Selecting [Post Hoc] will launch the
following dialog box,
One can active one or multiple post hoc tests to be performed. The
results will then be placed in the output window. For example,
performing a R-E-G-W F statistic on the current data would
produce the following,
Finally, one can use the [Contrasts...] option to specify linear
and/or orthogonal sets of contrasts. One can also perform trend
analysis via this option. For example, we may wish to contrast the
third condition with the fifth,
For each contrast, the coefficients must be entered individually,
and in order. Once can also enter multiple contrasts, by using the
[Next] present in the dialog box. The result for the example
contrast would look like the following,
Further, one can use the Polynomial option to
test whether a specific trend in the data
exists.
Factorial ANOVA
To conduct a Factorial ANOVA one only
need extend the logic of the oneway
design. Table 13.2 presents the data for
a 2 by 5 factorial ANOVA. The first
factor, AGE, has two levels, and the
second factor, CONDITION, has five
levels.
So, once again each observation can be uniquely coded.
AGE
CONDITION
Old = 1
Counting = 1
Young = 2 Rhyming = 2
Adjective = 3
Imagery = 4
Intentional = 5
For each pairing of AGE and
CONDITION, there are 10
observations. That is, 2*5
conditions by 10 observations
per condition results in 100
observations, that can be
coded as follows. [Note, that
the names for the factors are
meaningful.]
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
CONDITIO
1
1
1
...
1
2
2
2
...
...
...
5
5
...
5
1
1
1
...
1
2
2
2
...
...
...
5
5
...
5
Scores
9
8
6
...
7
7
9
6
...
...
...
10
19
...
11
8
6
4
...
7
10
7
8
...
...
...
21
19
...
21
Examine the table carefully, until you understand how the coding
has been implemented. Note: one can enhance the readability of
the output by using Value Labels for the two factors.
To compute the relevant statistics - a simple
approach
Select [Statistics => General Linear Model => Simple Factorial...]
Select and move "Scores" into the Dependent: box
Select and move "Age" into the Factor(s): box.
Click on [Define Range...] to specify the range of coding for the Age factor. Recall
that 1 is used for Old and 2 is used for Young. So, the Minimum: value is <1>, and
the Maximum: value is 2. Click on [Continue].
Select and move "Conditio" into the Dependent: box
Click on [Define Range...] to specify the range of the Condition factor. In this case
the Minimum: value is 1 and the Maximum: value is 5.
By clicking on the [Options...] button one has the opportunity to select the Method
used. According to the online help,
"Method: Allows you to choose an alternate method for decomposing sums of squares.
Method selection controls how the effects are assessed."
For the our purposes, selecting the Hierarchical, or the Experimental method will
make available the option to output Means and counts. --- Note: I don't know the
details of these methods, however, they are probably documented.
Under [Options...] activate Hierarchical, or Experimental, then activate Means
and counts - Click [Continue]
Click on [OK] to generate the output.
As you can see the
use of the Means and
count option
produces a nice
summary table, with
all the Variable
Labels and Value
Labels that were
incorporated into the
datasheet. Again, the
use of those options
makes the output a
great deal more
readable.
The output is a complete source table with the
factors identified with Variable Labels
As noted earlier, the analysis that
was just conducted is the simplest
approach to performing a Factorial
ANOVA. If one uses [Statistics
=> General Linear Model =>
GLM - General Factorial...], then
more options become available.
The specification of the
Dependent and Independent
factors is the as the method used
for the Simple Factorial analysis.
Beyond that, the options include,
By selecting [Model...], one
can specify a Custom model.
The default is for a Fully
Factorial model, however,
with the Custom option one
can explicitly determine the
effects to look at.
The Contasts option allows
one "test the differences
among the levels of a factor"
(see the manual for greater
detail).
Various graphs can be
specified with the [Plots...]
option. For example, one can
plot "Conditio" on the
Horizontal Axis:, and "Age"
on Separate Lines:, to
generate a simple
"conditio*age" plot (see the
dialog box for [Plots...],
The standard post-hoc tests for each factor can be calculated by selecting
the desired options under [Post Hoc...]. All one has to do is select the
factors to analyze and the appropriate post-hoc(s).
The [Options...] dialog box provides a number of diagnostic and
descriptive features. One can generate descriptive statistics, estimates of
effect size, and tests for homogeneity of variance - among others. An
example source table using some of these options would look like the
following,
The use of the GLM - General Factorial procedure offers a great deal more than the Simple
Factorial. Depending on your needs, the former procedure may provide greater insight into your
data. Explore these options!
Higher order factorial designs are carried in the same manner as the two factor analysis presented
above. One need only code the factors appropriately, and enter the corresponding observations.