SPSS Information Table of Contents
SPSS Information Table of Contents _____________________________ 1
SPSS Information ____________________________________________ 2
General Tips for Using SPSS _________________________________
Instant help: ______________________________________________
Changing between windows: ________________________________
How to open a data file: ____________________________________
How to scan through data in the data window: ___________________
Getting information on the variables in a data file: ________________
Variable View in SPSS Data Editor: ___________________________
How to get to analysis methods and graphs: _____________________
Selecting variables for an analysis: ____________________________
Selecting cases for an analysis: _______________________________
Split file for analysis of each subgroup: ________________________
Scanning through output: ___________________________________
Making output that is hidden visible: __________________________
Editing output tables: ______________________________________
Removing scientific notation / changing # of decimals in tables: _____
Selecting output and pasting it into another application:____________
Editing graphs in SPSS: ____________________________________
Saving output from SPSS for later use: _________________________
Pasting SPSS syntax into a syntax window: _____________________
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Compute New Variables "How To": __________________________
Compute new variables by transformations: _____________________
Log transform: ___________________________________________
Polynomial transformations of variables: _______________________
How to center a variable: ___________________________________
How to standardize variables: ________________________________
Creating the square of a variable: _____________________________
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Dummy Variables "How To" ________________________________ 9
Example of coding dummy variables: __________________________ 9
Interactions "How To" ____________________________________ 10
Histogram "How To" ______________________________________ 11
Getting a histogram from the graphics menu: ___________________ 11
Getting a histogram and other descriptive statistics from the analysis
menu: _________________________________________________ 11
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Boxplot "How To" ________________________________________
Getting a boxplot from the graphs menu: ______________________
Getting side-by-side boxplots from the graphs menu:_____________
Getting a boxplot and descriptive statistics from the analysis menu: _
Getting a side-by-side boxplot and descriptive statistics from the
analysis menu: ___________________________________________
Modifying a Boxplot: _____________________________________
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Pie Chart "How To" ______________________________________ 14
Scatter Plot "How To" _____________________________________ 14
Linear, Quadratic, and Cubic Regression, and Loess Fit Options: ___ 15
Identify Points on a Scatter Plot: _____________________________ 15
Scatter Plot Matrix "How To" ______________________________ 15
Descriptive Statistics "How To" _____________________________ 15
Frequencies "How To" ____________________________________ 16
Correlation "How To" _____________________________________
Correlation Menu Items Selection: ___________________________
Correlation Options: ______________________________________
Correlation Missing values: ________________________________
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Linear Regression "How To" _______________________________
Statistics: _______________________________________________
Plots: __________________________________________________
Save: __________________________________________________
Options: ________________________________________________
How to do use stepwise or other selection methods for a model: ____
How to enter variables into a model in blocks: __________________
List of Variables That Can Be Saved From Regression in SPSS ____
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GLM "How To" __________________________________________ 20
SPSS Information
General Tips for Using SPSS
Instant help:
Instant help: Right-click any phrase in any dialogue box for a pop-up
explanation or description. For more in-depth help, click the Help button.
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Changing between windows:
You can switch between the data window and the output by clicking on the
tabs at the bottom of the screen, or by choosing the window you desire from
the Window Menu at the top of the screen.
How to open a data file:
Go to the File menu and Select Open...Data. Locate the a: drive at the top of
the dialogue box, then double-click on the data file you want to open.
How to scan through data in the data window:
Using the scroll bars at the right and across the bottom of the data, you can
peruse observations or variables. To examine information on any variable,
simply click on it's name. You will be taken to the "Variable View" window.
Information on individual variables can be obtained by clicking in various
portions of information about each variable. To get back into the data
window, click on the "Data View" tab.
Getting information on the variables in a data file:
Information on each variable is available by going to the Utilities menu and
selecting Variables...a Window will pop up with a list of all the variables.
You can click on any variable and get the variable Name, Label, Type,
Missing values, Measurement level and value labels. If you click in the
variable list and start typing the name of the variable, SPSS will pop you to
that variable in the list.
Information on all variables will be displayed in the Output Window if you
select Utilities...File Info.... This can be printed later to provide a dictionary
for the data set.
Variable View in SPSS Data Editor:
Information on all of the variables in a data set in spreadsheet format can be
gotten by going to the Data Editor and clicking on the Variable View tab at
the bottom. To get back to the data view, simply click on the Data View tab
at the bottom.
How to get to analysis methods and graphs:
For Regression, choose Analyze...Regression...Linear. Descriptive statistics
can be chosen by going to Analyze...Descriptive Statistics...Descriptives, or
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Frequencies. Graphs can be created by clicking on Graphs and then selecting
the type of graph you wish to make (e.g. Histogram, Scatter, Boxplot, etc).
Selecting variables for an analysis:
Variables can be selected one at a time, by clicking each one in the variable
list on the left and then clicking the arrow to move it to the "active" box on
the right. Lists of variables can be highlighted by holding down the Shift
key.
Selecting cases for an analysis:
Cases can be selected by going to the Data Editor Window and clicking on
Data...Select Cases. Select "If condition is satisfied" and click on "If...". In
the dialog box, type your selection criteria. For example: age < 20, will give
you cases with age less than 20.
Split file for analysis of each subgroup:
The data set can be split for analysis of subgroups by going to the Data
Editor Window and clicking on Data...Split File... Select "Organize output
by groups", highlight the variable that is to provide the groups (e.g. ORIGIN
for the Cars.sav data set) and move it into the "Groups Based On" box.
(Note: make sure the variable you use for Groups is a categorical variable
with a reasonable number of cases in each category). Click on "OK". All
future output will now be split into separate analyses for each country of
origin. To go back to analyzing all of the cases, go back to Data...Split File...
Select "Analyze all cases, do not create groups" and click on "OK". From
now on, all output will be for selected cases for the entire data set.
Scanning through output:
By clicking on any part of a procedure in the outline on the left of the output
window, you can "zoom" to that segment of the results.
Making output that is hidden visible:
Sometimes output will be partially hidden. You will see a red triangle at the
bottom of the output. Click once on the output, drag the bottom of the box
lower to display the remaining output.
Editing output tables:
Double-click on the portion of output you wish to edit. You can make
formatting or other changes. Then click on another part of the output to
discontinue edit mode.
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Removing scientific notation / changing # of decimals in tables:
Many users dislike the scientific notation used in SPSS output (e.g., 1.2E-03,
which is equal to .0012). To eliminate this notation, double-click on an
output table and then highlight cells containing the notation. Go to the
format menu at the top and select "Cell Properties". Choose a new format
(e.g. ###.##) and the number of decimals.
Selecting output and pasting it into another application:
Click once on the part of the output you wish to select. Go to Edit...Copy
Objects. Open the application (e.g.: Word). Hit return a few times, so you
are not at the beginning or end of the document. Click on Edit...Paste. You
can resize the output from SPSS in other applications, but it is not easy to
edit it.
Editing graphs in SPSS:
To edit a graph in SPSS, double click on the graph. It will appear in the
Chart Editor Window. Double-click on different features of the graph to
open a dialog box in which they can be changed. For example, to change the
x-axis on a scatter plot, double-click on it, and supply new values for the
axis minimum and maximum. Note: you may have to adjust the axis range
or major increments so the axis range is a multiple of the major increments.
To add a title to the graph, select Chart...Title.... After editing the graph,
click on the X box to close the chart editor window. The edited chart will
now be saved into your output window.
Saving output from SPSS for later use:
Go to File...Save...and save the SPSS output to a file. The output file will be
given the file extension .spo. SPSS output cannot be read directly by any
other program, but is able to be opened again in SPSS, where it can be
edited, copied into another program, etc.
Pasting SPSS syntax into a syntax window:
From any analysis window, make all of the desired selections, fill in any
dialog boxes and then click on "Paste" at the lower right corner. This will
paste the syntax (SPSS commands) into a Syntax Window. Open the syntax
window and select the portion of the syntax you wish to submit. Click on the
arrow key on the menu at the top. This will submit the commands.
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Note: commands can either be run at the time they are selected, or they can
be pasted. If you choose Paste, the commands will not be run until you select
and run them from the syntax window.
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Compute New Variables "How To":
New variables will often need to be created to be used in regression
analysis. First, be sure you are in the Data view of the SPSS Data Editor.
Click on Transform...Compute. You will be able to do create many new
types of variables in this window. We will discuss how to transform
variables and how to create dummy variables. New variables will be added
to the end of your data sheet. Be sure that you only use letters, numbers and
underscores in the names of new variables you compute, and that they have
8 or less characters in the name.
Compute new variables by transformations:
Transformations of variables are often helpful in regression analysis.
They may help to make the distribution of the dependent variable more
normal, or they may be used to help with non-homogeneous variances.
Log transform:
The log transformation is often very useful when the dependent
variable is highly skewed, or when there is heteroskedasticity in a regression
relationship. You can use log base 10 or natural log. To create the log base
10 of a value, type in the name of your new variable in the Target Variable
box, for example, the log of horsepower might be called loghorse. In the
Numeric Expression box, type in the formula for your new variable. There is
an extensive list of functions available, and also a numeric keypad. Type the
function or select it from the function list. To get the log base 10, use the
lg10 function, or type:
lg10(horse)
in the Numeric Expression box. To get the natural log, type ln(horse) or use
the ln function. Click on "OK" and the variable will be added to the end of
your data sheet. Note that the log function only works on variables that are
greater than zero. You may need to add a constant value to each observation
before taking the log.
An extensive choice of other transformations is available, by using
other functions from the list.
Polynomial transformations of variables:
If the relationship between the independent and dependent variables is not
linear, you may want to transform the independent variable, to create the
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square or cubic transformation. You may also want to center the predictor
before creating the polynomial transformations. To create
How to center a variable:
A variable can be centered by subtracting the mean from each observation.
To do this, first get descriptive statistics for the variable you wish to
transform, and then compute a new variable that subtracts the mean from
each value. Go to the Data Editor Window, and Select Transform...Compute.
In the Target Variable box, type the name of your new centered variable and
in the = Numeric Expression box, type the original variable – the mean of
the original variable. For example, in the htwt.sav data set, to center age for
those less than 20 years old, first get the mean value for age for those less
than 20 years old (16.3). Then type Centage in the Target Variable box. In
the = Numeric Expression box, type:
age – 16.3
and click on "OK". This saves centage as a new variable at the end of the
worksheet.
How to standardize variables:
You can choose to standardize a variable by going to the
Analyze...Descriptive statistics...Descriptives...window. Select the variables
you wish to standardize and place them into the Variable(s) list. Click on
"Save standardized values as variables". The standardized variables will be
added to the end of your data set with variable names that begin with "Z".
Note, the variable names may be modified, if the original variable names are
already 8 characters long, or if a standardized variable version for the same
variable already exists. You can use syntax to set up the variable names for
standardized variables, if you wish to have more control over their names.
Creating the square of a variable:
To create weight-squared, go to Transform...Compute in the Data Editor
Window. In the Target Variable box, type "Centage2" and in the Numeric
Expression box, type:
centage*centage
and click on "OK".
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Dummy Variables "How To"
Dummy variables can be created for categorical variables fairly easily
in SPSS. If you are creating dummies for a categorical variable to use in a
regression, you will need to have one fewer dummy variables than you have
categories. You can create a dummy variable for each level of your variable
and then use all but one of the variables in the model. The dummy variable
that you leave out of the regression is your reference category.
We will use the variable ORIGIN from the cars.sav data set to
illustrate.
Example of coding dummy variables:
ORIGIN, has 3 categories, you will need to use 2 dummy variables in
the regression model, but you can still create 3 dummy variables, one for
each category. Check out the coding of the variable ORIGIN by going to the
Utilities Menu and selecting Variables. Click on ORIGIN. You will see the
value labels that apply to this variable.
Value labels:
1 American
2 European
3 Japanese
American cars: Close the Variables window and go back to the Data
Editor Window, and select Transform...Compute. In the Target Variable box
type the name of your new variable, "American". In the Numeric Expression
window type:
origin=1
and click on "OK". This automatically computes a new variable called
AMERICAN that has a value of 1 for those vehicles whose country of origin
is American, and a value of 0 for all other origins.
European cars: In the Data Editor Window, select
Transform...Compute. In the Target Variable box type the name of your new
variable, "European". In the Numeric Expression window type:
origin=2
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and click on "OK". This automatically computes a new variable called
EUROPEAN that has a value of 1 for those vehicles whose country of origin
is European, and a value of 0 for all other origins.
Japanese cars: In the Data Editor Window, select
Transform...Compute. In the Target Variable box type the name of your new
variable, "JAPAN". In the Numeric Expression window type:
origin=3
and click on "OK". This automatically computes a new variable called
JAPAN that has a value of 1 for those vehicles whose country of origin is
Japan, and a value of 0 for all other origins.
Interactions "How To"
We have already seen how to create new variables. For a regression
model, it is necessary to create interaction variables before they can be
included in the model. In the next problem, we will be using interaction
terms between dummy variables for the number of cylinders and weight.
Compute dummy variables: We will be using dummy variables for
the variable, cylinder. First we need to create dummy variables for each
level of cylinder.
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Values
3 Cylinders
4 Cylinders
5 Cylinders
6 Cylinders
8 Cylinders
Dummy Variable
threecyl
fourcyl
fivecyl
sixcyl
eightcyl
Interaction Variable
threewt
fourwt
fivewt
sixwt
eightwt
Go to the Data Editor Window. Select Transform...Compute...In the
Target Variable window, type the name of the first dummy variable,
"threecyl". In the Numeric Expression window type:
cylinder=3
Click on "OK".
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Continue this process for each level of cylinder. You should have
created 5 new variables with the names shown above. Note that variable
names may not begin with a number.
Compute interaction variables: You will need one interaction per
dummy variable, so there will be 5 interaction variables.
Go to the Data Editor Window. Select Transform...Compute...In the
Target Variable window, type the name of the first interaction variable,
"threewt". In the Numeric Expression window type:
threecyl*weight
Click on "OK".
Repeat this process for each interaction variable.
Histogram "How To"
Getting a histogram from the graphics menu:
Histograms are useful to visualize the distributions of continuous
variables. They are found under Graphs...Histogram...Highlight the variable
that you wish to get the histogram for, and put it into the "Variable" box.
You may also select "Display normal curve" to superimpose a normal
density, with the same mean and variance as your variable, over the
distribution of your variable.
Getting a histogram and other descriptive statistics from the analysis
menu:
Go to Analyze...Descriptive Statistics...Explore. Click on the numeric
variable(s) that you wish to plot from the list on the left and put them into
the Dependent List box.
Choose the following Display options:
Statistics: to get summary information and suppress plots
Plots: to get histograms, stem and leaf plots and box plots
Both: to get both statistics and plots.
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Click on the Statistics button and choose Percentiles to display
percentiles.
Boxplot "How To"
Boxplots can be very helpful in visualizing the distribution of a
continuous variable. It shows the median and approximate 25 th and 75th
percentile, as well as giving an idea of skewness. Outliers are also displayed.
Boxplots are also extremely useful for visualizing the relationship between a
continuous variable and the categories of a categorical or ordinal variable.
Getting a boxplot from the graphs menu:
Go to the Graphs menu and select Boxplot..."Simple" and click on
"Summaries of Separate Variables". Then click "Define". In the dialog box
that appears, highlight the continuous variable that you wish to display, and
put it into the list "Boxes Represent". You can look at several variables in
one plot this way, but be cautious about different scales for the variables that
are selected. If the variables do have very different scales, they should
probably be put into separate graphs.
Getting side-by-side boxplots from the graphs menu:
Go to the Graphs menu and select Boxplot ..."Simple" and click on
"Summaries for Groups of Cases". Then click "Define". In the dialog box
that appeares, highlight the continuous variable that you wish to display (e.g.
mpg), and put it into the "Variable" box. Click on the Grouping variable
(e.g. Origin) and put it into the "Category Axis" box. Click on "Options" and
deselect "Display groups defined by missing values" (if you do not want a
separate box for the missing values) and click "Continue". Click on "OK".
Getting a boxplot and descriptive statistics from the analysis menu:
Go to Analyze...Descriptive Statistics...Explore. Click on the numeric
variable(s) that you wish to plot from the list on the left and put them into
the Dependent List box.
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Choose the following Display options:
Statistics: to get summary information and suppress plots
Plots: to get boxplots, stem-leaf plots, and histograms and
suppress stats
Both: to get both statistics and plots.
Click on the Statistics button and choose Percentiles to display
percentiles.
Getting a side-by-side boxplot and descriptive statistics from the analysis
menu:
Go to Analyze...Descriptive Statistics...Explore. Click on the numeric
variable(s) that you wish to plot from the list on the left and put them into
the Dependent List box.
Choose the following Display options:
Statistics: to get summary information and suppress plots
Plots: to get boxplots, stem-leaf plots, and histograms and
suppress stats
Both: to get both statistics and plots.
Click on the Statistics button and choose Percentiles to display percentiles.
Choose the categorical variable that you want to see included in the plot, and
add it to the Factor List. In this case, choose origin. Note that it is not
necessary to choose a Factor variable for this procedure.
You can get percentiles of the distribution by Clicking on the
"Statistics" button, and selecting "Percentiles".
Modifying a Boxplot:
Double-click on the boxplot and it will open in the Chart Editor Window.
Click on the Format menu item to change:
Fill pattern
Color
Marker
Line Style
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Bar Style
When done editing the boxplot, close the Chart Editor Window and
the modified graph will be saved in the output window.
Pie Chart "How To"
Pie charts can be used to display the number or percent of cases
within categories of a categorical variable. Go to the Graphs menu and select
Pie... "Summaries for groups of cases", then click on "Define". Click on
"Slices represent"...choose either "N of cases" or "% of cases". Highlight the
categorical variable you wish to display (e.g. Origin) and place it into the
"Define slices by" box. If you wish to exclude missing values as one of the
pie slices, click on the "Options" box and deselect "Display groups defined
by missing values", then select "Continue" and click on "OK". The pie chart
will now display in the output window.
Scatter Plot "How To"
Scatter plots can be very helpful in visualizing the relationship
between 2 continuous variables and in locating possible outliers or problem
observations. There are several options within scatter plots in SPSS that
make them especially useful.
Scatter plots are found under Graphs...Scatter. Click on Simple, then
Define. Select the variable you wish to have on the Y axis, and the variable
you wish to have on the X axis. If you want to see different groups of cases
using different colors, you can select a variable for "Set Markers by". Be
sure any variables you use for markers have only a limited number of
categories. Note that only numeric variables will show up in the list of
variables that can be selected for scatter plots.
Graphs can be edited in the Chart Editor Window. To get to this
window, double-click on the graph. The Chart Editor Window can be
maximized to see your work more easily. To change any aspect of the graph,
simply double-click on it, and then make changes.
Click on the Format menu item to change:
Fill pattern
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Color
Marker
Line Style
Bar Style
Linear, Quadratic, and Cubic Regression, and Loess Fit Options:
With the Scatter Plot open in the Chart Editor Window, click on
Chart...Options and you will have the option to look at different possible fits
for the scatter plot. Under Fit Line...you can select Total or Subgroups
(subgroups are only available if you previously selected "Set markers by").
Click on Fit Method...and you will be able to select Linear regression,
Quadratic regression, Cubic regression and Loess. You will also be able to
select Regression Prediction Lines to get confidence intervals on the mean
prediction and prediction limits for an individual observation. You can also
choose "Include constant in equation" (the default) and "Display R-square in
output". When done with these selections, click on "Continue".
Identify Points on a Scatter Plot:
With the Scatter Plot open in the Chart Editor Window, click on
Chart...Options...Case Labels... and choose "ON" from the drop-down menu
list. Then, select Case Number or ID as the source of Labels. Note: ID will
only show up in the graph if you chose an ID variable for your scatter plot
originally.
Close the Chart Editor Window and all the changes you have made will be
saved in the output window.
Scatter Plot Matrix "How To"
Go to the Graphs menu, select Scatter...Matrix, click on "Define". In the
window that pops up, highlight and select all of the numeric variables you
wish to have in the matrix. Click "OK".
Descriptive Statistics "How To"
Go to the Analyze menu and select "Descriptive
Statistics"...Descriptives...a list of all numeric variables in the data set will
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appear in a box on the left. Highlight the variables for which you wish to get
descriptive statistics and click on "OK". The default descriptive statistics are
n, min, max, mean and standard deviation. To get different or additional
descriptive statistics, click on the Options box and select the desired items.
Frequencies "How To"
Go to the Analyze menu and select "Descriptive
Statistics"...Frequencies...and select the variables for which you wish to have
frequencies displayed. You can also click on "Statistics" and get a wide
array of descriptive statistics. You can select Bar charts, Pie Charts or
Histograms. Note: frequencies will display values for variables that are
either numeric or character. Descriptive statistics are not available for
character variables.
Correlation "How To"
Correlation is found under Analyze...Correlate...Bivariate. Select the
continuous variables you wish to correlate and add them to the Variables
box.
Correlation Menu Items Selection:
Pearson Correlation Coefficients
Two-tailed
Flag significant correlations
Correlation Options:
Means and standard deviations
Correlation Missing values:
(Choose one. Not necessary if you have only 2 variables)
Exclude cases pairwise, or
Exclude cases listwise
Note: Exclude cases pairwise, includes for every pair of variables all cases
that are complete for both those variables. Exclude cases listwise only
includes those cases that are complete on all variables in the Variable list.
This option may decrease your sample size substantially if you have lots of
missing values, or lots of variables, each with a moderate number of missing
values.
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Linear Regression "How To"
Regression analysis is found under Analyze...Regression...Linear.
After entering the dependent variable (Y), enter the independent or predictor
variable(s) (X). The method should be Enter (the default). Note, both Y and
X must be in SPSS as numeric variables.
Next, use the buttons described below to specify the options and
settings from the choices listed in each. Variables that are specified from the
Save Window will be added to the end of your data file. They will be labeled
with a number indicating the number of the regression model that they came
from. If you want to save your data set with the new variables in it, go to
File...Save from the Data Editor Window.
Statistics:
Estimates
Confidence intervals
Model fit
Casewise diagnostics, all cases (optional)
Plots:
Histogram
Normal probability plot
Scatter, choose ZRESID as Y and ZPRED as X
Save:
(These are optional, others may be selected, see table on next page.
Note: once Save is selected, these variables will be saved until they
are de-selected from the Linear Regression Window.)
Predicted values:
Unstandardized
S.E. of mean predictions
Influence:
Leverage Values
Prediction Intervals:
Mean and Individual
Residuals:
Unstandardized
Studentized
Studentized deleted
Influence:
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DfBetas
Options:
Stepping method criteria:
Include constant in equation
Exclude cases listwise
How to do use stepwise or other selection methods for a model:
Click on Method in the regression window. The default is Enter (all
variables are entered. There is no selection). Other methods that can be
chosen include: Stepwise, Remove, Backward, and Forward. When using a
regression selection method, you will probably want to click on the
"Statistics" button and choose "R-square change".
How to enter variables into a model in blocks:
After filling in the dependent variable, highlight the variables for the
first block and put them into the Independent(s) list. Then, click on
Next...and highlight the next set of variables and put them into the
independent(s) list, and so on. Continue doing this until all blocks of
variables are entered. You should also click on the "Statistics" button and
choose "R-square change", so that the change in R-square for each
additional block of variables is produced as part of the output.
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List of Variables That Can Be Saved From Regression in SPSS
Variable Type
Variable Name
Description /Variable Label
Predicted Values
PRE_n
ZPR_n
ADJ_n
Unstandardized Predicted Value
Standardized Predicted Value
Adjusted Predicted Value
(Predicted Value if this obs were deleted)
Standard Error of Mean Predicted Value
Unstandardized Residual
Standardized Residual
Studentized Residual
Deleted Residual
Studentized Deleted Residual *
Mahalanobis Distance
Cook's Distance
Centered Leverage Value
DfBeta(s):
DFBETA for Intercept
DFBETA for Variable 1
DFBETA for Variable 2
Standardized DfBeta(s):
Standardized DFBETA for Intercept
Residuals
Distances
SER_n
RES_n
ZRE_n
SRE_n
DRE_n
SDR_n
MAH_n
COO_n
LEV_n
DFB0_n
DFB1_n
DFB2_n
Influence Statistics
SDFB0_n
SDFB1_n
SDFB2_n
DFF_n
SDF_n
COV_n
LMCI_n
Confidence/Prediction UMCI_n
Intervals
LICI_n
UICI_n
Standardized DFBETA for Variable 1
Standardized DFBETA for Variable 2
DFFIT
Standardized DFFIT
COVRATIO
Confidence Interval for Mean:
95% L CI for Dep Var mean
95% U CI for Dep Var mean
Prediction Interval for New Observation:
95% L CI for Dep Var individual
95% U CI for Dep Var individual
*Preferred Residuals to use in assessing model.
Note: Variable names end in a number (n) which indicates the regression
model from which they come. The first set of variables saved from a
regression would be labeled PRE_1, etc. Afternoon Lab Data Sets
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GLM "How To"
GLM (General Linear Model) is an Analysis of Variance program found in
Analyze...General Linear Model...Univariate. It will analyze the
relationships between a continous Dependent variable and categorical or
continuous Independent variable(s). The advantage of this program is that
dummy variables for categorical predictors do not have to be computed
before the model can be run.
After entering the dependent variable (Y), enter the Fixed Factor(s)
(Categorical variables). Enter the continuous variable(s) in the Covariates
Box. In order to get interactions, you must first click on Model... Custom.
Highlight the factor and the covariate and send them into the model box.
Then highlight both the fixed factor and covariate at the same time and click
on "Interaction". In the model with Horsepower as the dependent variable
and weight and categorical number of cylinders as predictors, choose Horse
as the Dependent variable, choose the factor to be cylinder and the covariate
to be weight, the Model dialog box would have in it:
cylinder
weight
cylinder*weight
(or equivalently, weight*cylinder)
GLM Options to Select:
Parameter estimates
Note that the parameter estimates are the same as from the regression
model. It is not necessary to create dummy variables for the categorical
variable, SPSS does this automatically. SPSS also chooses the highest
category of the factor variable to be the reference...so in this example 8
cylinders would be the reference category (by default). In addition, SPSS
gives an overall test for the effect of "Cylinder". It is an F-test with the
numerator degrees of freedom equal to the number of total parameters in the
model. It also gives us an overall test of all the interactions, again using an
F-test.
Note: the extensive diagnostics available from the regression model
are not part of GLM, although there are some diagnostics which can be
obtained through the Save option.
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