Tutorial spss17

SPSS 17 – Analysing Quantitative Data 1

Analysing quantitative data using SPSS 17 for Windows
1) INTRODUCTION
1.1) Aims
¨ To illustrate computational analysis of data.
¨ To introduce the basics of quantitative data analysis.
¨ To provide familiarisation with the SPSS for Windows package so that users can
begin to assess its suitability for their own analysis.
1.2) About these notes
¨ During the class, work your way through exercises 1 to 16, excluding Exercise 3,
following the instructions as requested. The symbol usually means you
should undertake some work away from the computer or check that you have
already undertaken some tasks on the computer. The symbol usually
means that you should issue a command or series of commands to the
computer – this usually means pointing and clicking with the mouse's left
button.
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¨ Exercises 17 and 18 are designed to help you to analyse your own data in SPSS
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for Windows.
¨ During the class if you get stuck ask for help.
¨ Note: these notes assume the user is familiar with a Windows package such as
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Word for Windows or Excel.

1
SPSS is a registered trademark of SPSS Inc.
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Windows is a registered trademark of Microsoft Inc.
3
Word and Excel are registered trademarks of Microsoft Inc.

© Prof. Mark N.K. Saunders April 2009
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2) BACKGROUND
2.1) SPSS for Windows
SPSS for Windows is a powerful computer package providing statistical analyses
and data management. The SPSS suite of programs is the most widely used
statistical analysis package in the world.
2.2) Data types
Before data are analysed in SPSS it is necessary to understand what type of data
you are working with, as this will affect the analysis used.
¨ Categorical: categorical data consist of values which cannot be expressed
numerically but can be grouped into categories; for example gender which can
be grouped into male and female.
¨ Quantifiable: quantifiable data consist of values that can be expressed
numerically as quantities; for example year of birth.
Quantifiable data can further be sub‐divided into two groups:
¨ Discrete, where individual items of numeric data can have one of a finite
number of values within a specified range; such as spinal column point for the
variable salary scale. The value can usually be counted and it changes in
discrete units, in this case whole numbers. In some instances discrete data
may be rank data, for example the order a group of people finished in a race.
¨ Continuous, where numeric data are not restricted to specific values and are
usually measured on a continuous scale; such as journey to work distance (in
km).
Journey to work distance along here
0km 120km

With such data it is possible to tell the interval between the data values for
different cases; for example the interval between a journey to work of 15 miles
and another of 22 miles is 7 miles. NB Observed values of a continuous variable
always appear discrete due to limitations of the equipment used for
measurement (e.g. a car odometer).
One potentially confusing aspect of SPSS is that all data are usually coded
numerically (e.g. 1 = male). Although it appears less meaningful to code such
responses numerically, it is better from a data manipulation point of view since
SPSS allows only automatic recoding on codes which are numeric.

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2.3) The TEACH.SAV data file
This data file consists of data about 347 people recruited to work for a UK local
authority over a ten‐year period from the mid 1970s to mid 1980s and weas
obtained from personnel records. The data collection form is included as Appendix
2.
The vast majority of data relates to the time of their appointment and is taken
from a range of secondary data sources such as their application form and the
requisition orders to various meoutlets for vacancy advertisements. The data
refers predominantly to non‐manual employees, although there are a few manual
employees. The data have been anonymised in a variety of ways and all locational
data has been amended to preserve confidentiality. Permission was obtained from
the local authority to use these data in suitably anonymised format for teaching
purposes.
The data file can best be thought of as a large spreadsheet with each column
representing a variable for which data are available and each row representing
that data for an individual or case:
gender born marital educate profmemb
1 2 67 1 5 3
2 1 19 . 7 3
3 2 24 2 7 3

Thus, for the table above, row 1 represents a person who has gender code 2
(female), was born in 1967, has marital status code 1 (single), was educated up to
code 5 (O level/GCSE grade C or above), and professional membership code 3
(none). The data then continue to the right for further variables. The symbol "." is
the SPSS symbol for missing data, this is discussed in more detail in Help 17.4. A full
list of variables and their codes is given in Appendix 1.

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3) USING SPSS FOR WINDOWS
Exercise 1: To load SPSS
SPSS for Windows follows the conventions used in other Windows applications, making
use of a variety of menus and dialogue boxes. This means you rarely have to use the
keyboard other than for entering data, or for naming specific variables.
Power up your machine (switch it on!) and your normal screen will appear.
After clicking the button, SPSS will be located somewhere in the
Programs option as shown below:

Click to open SPSS. This will
take some time so be patient! You will see this
screen.
When you do, click at the bottom of
the dialogue box to remove it.
You should now have an Untitled SPSS Data Editor
screen.
End of Exercise 1

3.1) The SPSS Windows
When you load and run the SPSS package it opens up a menu bar and two views.
These are Data View (currently visible) and Variable View…
This sheet will
contain your data, each
column representing a
variable for which data are
available and each row
representing the data for
an individual or case. At
present this sheet should
be blank. As this sheet is
currently selected, its name
on the tab at the bottom is
in bold.
At present this sheet is not visible as the variable view sheet is not active.
Consequently, the name is not in bold. Do not bother to click on the tab and look at this
sheet yet, we will do that later.

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Menu Bar: This provides a selection of options (File, Edit, View, Data …) which allow you,
for example, to open files, edit data, generate graphs, create tables and perform
statistical analyses. Selecting from this menu bar will, like in other Windows packages,
provide further pull‐down menus and dialogue boxes.
The menu bar options are used as follows:
¨ File is used to access any files whether you want to Open an existing SPSS file or read
data from another application such as Excel or dBase, or start a New file. It is also
the menu option you choose to Save files.
¨ Edit can be used to alter data or text in the Data View or the Variable View.
¨ View can be used to alter the way your screen looks. Please leave this on the default
settings.
¨ Data is used to define variables and make changes to the data file you are using.
¨ Transform is used to make changes to selected variable(s) in the data file you are
using. This can include recode(ing) existing variables and compute(ing) new
variables.
¨ Analyze is used to undertake a variety of analyses such as producing Reports,
calculating Descriptive Statistics such as Frequencies and Crosstabs (crosstabulations)
and associated summary statistics, as well as various statistical procedures such as
Regression and Correlation.
¨ Graphs is used to create a variety of graphs and charts such as Bar, Line and Pie
charts.
¨ Utilities is for more general housekeeping such as changing display options and
fonts, displaying information on variables.
¨ Window operates in the same way as other Windows packages.
¨ Help is a context‐sensitive help feature which operates the same way as other
Windows packages.

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Exercise 2: To load a previously created SPSS for Windows data file
All versions of SPSS for Windows
will work with data files using a
filename of up to eight
characters and the file extension
.SAV, for example TEACH.SAV.
For the most recent versions
longer filenames can be used,
but it is better to be safe!
Make sure you have loaded
SPSS (see Exercise 1).

In the Menu Bar click File | Open | Data. The Open File dialogue box appears. Notice
that SPSS looks for data files in the most recently used sub‐directory. For example,
if you are going to load a file which is on a USB portable storage device you need
locate the appropriate drive.
Locate the TEACH.SAV file.
Open the TEACH.SAV by double‐clicking on it.
You will now see the data appear in the Data View window and the filename above the
menu bar change to TEACH.SAV. This may take some time so be patient!

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Alternatively, you can download the file TEACH.SAV from the web by following the
tutorial and datasets link: http://wps.pearsoned.co.uk/saunders (for the 5th Edn) and
then save it on your USB portable storage device.
End of Exercise 2
Exercise 3: To load an Excel spreadsheet data file
Do not undertake this exercise until you need to load your own data from an Excel
spreadsheet.
Make sure that your Excel spreadsheet file is set out with one column per variable and
one row for each individual (survey form). Note: the first row should be the variable
names. This is illustrated for the Excel equivalent of an extract from the teach.sav data file
below:
A B C D E
1 gender born marital educate profmemb
2 2 67 1 5 3
3 1 19 7 3
4 2 24 2 7 3

Make sure you have loaded SPSS (see Exercise 1).
In the Menu Bar, click File | Open | Data. The Open File dialogue box appears. Notice
that SPSS looks for data files in the most recently used sub‐directory. As you are
going to load an Excel file from a USB portable mass storage device you need to
insert this first.
Insert your USB portable mass storage device and click6in the Look in: box.
Click on the appropriate removable disk, for example: .
Click6in the Files of type: box and use the scroll arrows on the right of the dialogue
box to find Excel.
Click Excel (*.xls). You will see your Excel files displayed in the Open File dialogue
box.
Select the filename you want by clicking
on it and then click on the Open button.
The Opening Excel Data Source dialog box
appears.
Make sure there is a to the left of Read
variable names and click OK. You will see
the file appear in the Data View and the
filename above the menu bar change.
This will take some time so be patient!
End of Exercise 3
Because you are loading the file from Excel you will still need to add variable labels and
value labels within SPSS and save your data as an SPSS data file (*.sav).

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Exercise 4: To check how variables have been coded
To check what the column heading for each variable and the codes refer to:
Click sheet at the bottom of the screen. You will now see:

The first column contains the variable Name, in the case of the first row ‘gender’. This is
the column heading that appears in the .
The second column refers to the Type of data. Although gender is categorical data, it is
refered to as numeric because numeric code values have been used! The key to these
code values is given in the column headed Values.
The fifth column contains the variable’s Label. At present this is partially obscured by the
subsequent column. To see the full value label:
Move your mouse pointer in‐between the Label and the Values column headings
untill this, appears.
Click and drag the column width to the right until the
variable’s label can be read.
Note: if you wish to edit a variable’s label just retype the label in
the appropriate cell.
The sixth column contains the key to the codes used for each variable. These are known
as the Value labels.
To see the Value labels used:
Click on the cell containing the first value for the variable gender
Click on the to the right of this cell.

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The Value Labels dialogue box appears.
It shows the current value labels for this
variable.
Note: you can also use this option to
change each value label for the codes
or enter new value labels.

Click in the Value
Labels dialogue box to return to
the Variable View.
Use the ideas in this exercise to explore at least five other variables in the data set.
Check the codes with those that appear in Appendix 1, can you find any errors?
End of Exercise 4
Exercise 5: To undertake a frequency distribution

Return to .
Click Analyse | Descriptive
Statistics | Frequencies. The
Frequencies dialogue box
appears.
If the variables are arranged
alphabetically, use the
downward arrow on the
left‐hand box to scroll down
until Gender appears.
Highlight Gender in the left‐
hand box by clicking on it.

Click to move gender into the Variable(s) box.
Note the arrow button changes direction and the cursor moves to the Variable(s)
box. This is to allow you to reverse your decision if you wish.

Click .
You will see a series of tables displayed in the SPSS Viewer. Note that SPSS tells you if
there are missing cases. In this instance, there is one missing case.

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Use6and4to scroll to view the frequencies table. Note that SPSS lets you know if
there are any missing cases and calculates the valid percent appropriately.
Repeat this process using Analyze | Descriptive Statistics | Frequencies for at least
five other variables of your choice. You can do this by pointing and clicking on
the menu commands which are visible at the top of your screen.

While you are doing this, explore the effect of the
buttons on your output.
To remove the variables from the right Variable(s) box within the dialogue box

either click or highlight the variable in the right Variable(s) box and

click .

To quit this analysis (for example, if you make a mistake) click .
You may (or may not!) have noticed that each of the tasks you have performed in SPSS
has been automatically appended to the SPSS Output Viewer. You can see this by scrolling
through your output window using the up and down arrows on the right of the window.
You can edit the SPSS Viewer and save it, or parts of it, to a file which can subsequently be read
into a word processor. Alternatively you can print it out.

5.1: To delete output in the SPSS output viewer
To delete some output in the SPSS Output Viewer:
Click the area you want to delete, a line will
appear around it.
Press Delete.

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To delete all the output in the SPSS Output Viewer:
Ensure that the SPSS Output Viewer window is maximised.
In the SPSS Output Viewer click Edit | Select All.

Press Delete.

5.2: To save the contents of the SPSS output viewer to a file
Click File | Save As…
Type in the filename you
wish to save it to in the
File name box, making sure
the file type is *.spv.
Ensure that the file is
being saved to the correct
drive and directory (note:
please do not save output
from the TEACH.SAV file).

Click .
End of Exercise 5
Exercise 6: To calculate the arithmetic mean (average) and the standard deviation
Click Analyze | Descriptive Statistics | Descriptives.
Scroll down to and
select the Journey
variable (it’s at the
end of the variable
list), then click4to
put it in the
Variable(s) box.

Click .
Note how the results
are added to the end

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of your output in the
SPSS Viewer. Note:
you may need to
maximise the
window by clicking
[ ] to see all the
statistics.
We can therefore see that
the mean journey to work
is 11.45km.
End of Exercise 6
Calculating a mean makes sense, as we are working out the average distance. However
we have to be careful.
We could calculate the mean gender in the same way. SPSS would take the codes for
male (1) and female (2), add them all up and divide by the number of observations. It is
therefore important that you decide what statistic makes sense for the type of data
(Section 2.2).
Other statistics for the average are more appropriate in this case – the mode (the one
that occurs most often).
Exercise 7: To calculate the mode and other measures of central tendency
To calculate the mode for the variable gender:
Click Analyze | Descriptive Statistics | Frequencies.

Click to empty the Variable(s) box.
Select Gender, then
click4to put it in the
Variable(s) box.

Click on the
right

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Select Mode in the Central
Tendency dialogue box by
clicking on it.
A appears in the box when it is
selected (see right).

Click to return
to the Frequencies
dialogue box.
This time, we do not want
an output table so click
the box to the left of
to
remove the tick.

Click .

The following will be added to
the SPSS Output Viewer (don’t
forget to maximise the window
and scroll down).
We now know that the most
common gender is 2 – female.

Repeat this process by calculating the most appropriate average for the following
variables:
educate prevemp salary
seg class three others of your choice

Your choices for the most appropriate average are:
¨ Mean: normally known as the average of the data values.
¨ Median: the mid point once all the data values have been ranked.
¨ Mode: the data value that occurs most often.
End of Exercise 7
Exercise 8: To produce a bar chart
Click Analyze | Descriptive Statistics | Frequencies.
Deselect all variables by clicking the Reset button.

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Scroll down and select the variable (social class) Class.

Click .
Use the radio button to select Bar Charts | Continue.
At the Frequencies dialogue box click OK.
The SPSS Output
Viewer will contain
your bar chart.
Notice that missing
data are
automatically
excluded from the
chart. Notice also
that you are
presented with a
different menu bar
which allows you
to edit the current
chart and other
options.

To the left of your bar chart is a series of icons. These provide an index to your
output that is in the SPSS Output Viewer.

Click the icon on the left to see what happens.
Now practice your charting skills by creating another bar chart for the variable
(educational attainment) Educate but with the vertical axis displaying percentages
rather than frequencies.
You will need to:
¨ Deselect the variable
Class and select the
variable Educate.
¨ Select Percentages |
Continue.
This will give you a chart like
the one on the right.
End of Exercise 8

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Exercise 9: To create a table (Crosstab) of one variable against another
One of the most useful features of SPSS is its male female
ability to create crosstabulations of one variable postgrad plus
against another. In this exercise, you will create a up to degree
table of the variable Educate (Educational
up to HNC/D
attainment) by the variable Gender. You will want
up to A'level
your table to look like this.
up to O'level (GCSE C+)
To do this:
up to CSE (GCSE D‐)
Minimise the SPSS Output Viewer. No quals.
Click Analyze | Descriptive Statistics | Crosstabs.
This gives the Crosstabs
dialogue box.
Select the Row(s) variable
Educational Attainment and
the Column(s) variable Gender
using the same principles as
when selecting frequencies.
Once you have selected row
and column variables, you
will be able to click OK.
Your table will appear in the
SPSS Output Viewer.

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End of Exercise 9
Exercise 10: To calculate a Chic‐square statistic for a table
Minimise the SPSS Output Viewer.
Click Analyze | Descriptive Statistics |
Crosstabs.
Select the Row(s) variable Educational
Attainment and the Column(s) variable
Gender using the same principles as
when selecting frequencies.

Click , the Crosstab:
Statistics dialogue box appears.
Select Chi‐square option.
Click Continue | OK. The results will be
displayed in the SPSS Output Viewer.

The key elements of your output are in the row titled Pearson Chi‐Square and the
associated footnote.
The chi square statistic (the value for Pearson Chi‐Square), is, in this case, 52.529 with 6
degrees of freedom (df). This is highly significant .000. The footnote states that no cells
have an expected count of less than 5 and that the minimum expected frequency for
each cell in the table is 7.86. This means the assumptions of the Chi‐square test are
satisfied.

End of Exercise 10
Exercise 11: To add row and column percents to a table using crosstabs
Select two variables you wish to use to create a table as outlined in Exercise 9.

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Click at the top right of
the crosstabs dialogue box.
Click on Row and/or Column and/or
Total in the Percentages dialogue
box (see right) to obtain the desired
percentages.
Click Continue | OK.
Use SPSS to create further new
tables from pairs of variables of your
choice. Note: it would be sensible to
use variables that contain categorical
data rather that quantifiable data –
see Section 2.2.

End of Exercise 11
Exercise 12: To recode a variable's values into a new variable
In this exercise you are going to create a new variable educnew from the variable Educate
(Educational attainment) by recoding the values as follows:
Postgraduate study (1) 1 Up to A level (4) 4
Up to degree level (2) 1 Up to O level or equivalent (5) 4
Up to HNC/D or diploma (3) 1 Up to CSE or equivalent (6) 4
Not stated Missing
This will split educational attainment into those educated up to A level (code 4) and those
educated above A level (code 1).
Click Transform | Recode | Into Different Variables.
This gives the Recode into Different Variables dialogue box.
Click Educate in the variable list
on the left‐hand side, it will be
highlighted.

Click to transfer the
variable into the Numeric
Variable ‐> Output Variable box.
In the Output Variable area,
click in the Name: box and type
the new variable name
educnew and a new variable
label College Education? in the
Label box below.

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Click .
Notice that the new variable label appears in the Numeric Variable ‐> Output Variable
box.
Click

and the Recode into
Different Variables
dialogue box opens.
To recode the values 1, 2
and 3 into 1:
Click the Range radio
button in the Old
Value dialogue box
and type 1 in the first
box and 3 in the
second box.
Click the Value radio button in the New Value dialogue box and type 1 in the box to
the right.
Click Add.
Note that the recode has been added into the Old ‐‐> New dialogue box:
Revise this procedure to recode the values 4, 5 and 6 to 4.
Revise this procedure to recode the value 7 to a missing value, using the Value and
the System Missing radio buttons.

Check the Old ‐‐> New dialogue box looks like this:
Click Continue | OK.

The new variable will be created and you will be returned to the Data View.
Now use the procedures outlined in Exercise 5 to produce a frequency distribution
for your new variable.
End of Exercise 12
WARNING: it is possible to recode a variable into the same variable, however doing this will
DELETE the original values for the variable. If you decide to do this, make a security copy (save
onto a different disc) of your data first.
Exercise 13: To compute a new variable from existing variable(s)
In this exercise, you are going to create a new variable age from the variable born by
subtracting the variable born from the year, in this instance 95. Remember, year of birth
was only coded as the years in the last century and so we do not include the 19.
Age = 95 – born
Ensure the SPSS Output Viewer is minimised.

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Click Transform | Compute.
This gives the Compute
Variable dialogue box.
Type age in the Target
Variable: box.

Click
and label the variable
Age in years.
Click Continue.
Point and click the
number 9 followed by
5 on the number pad
in the dialogue box.
Point and click the
arithmetic operator

in the dialogue
box.
Click the variable born
(Year of birth) in the
list of variable names
on the list and click

.
Check that this
expression has
appeared in the
Numeric Expression
box.
If you make a complete mess of it, click Reset at the bottom and start again.
Click OK.
This is only a very simple compute and it is possible to do far more complex calculations. In
some cases it is better to write them down prior to typing them in!
End of Exercise 13

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Exercise 14: To undertake an analysis on part of the data set
In this exercise you are going to select a subset of your data: all female employees.
If necessary, minimise the SPSS Output Viewer.
Click Data | Select Cases.
Click on the If condition is
satisfied radio button.

Click button,
the following dialogue
box appears:
Click Gender in the
variable list.

variable into the box on
the right.

Click on the operator
in the dialogue box.
Click on the number 2 on
the number pad in the
dialogue box.
Check that the expression
gender = 2 has appeared in
the box.

Click .
Check that the Unselected
Cases Are: Filtered is
selected. Filtered means
that you will not be
deleting the rest of your
data, in this case all the
males!

Click OK.
Undertake an analysis of your choice, using just the data for females.
End of Exercise 14
Exercise 15: To return to the full data set after using a selection (select cases)
Make sure the SPSS Output Viewer is minimised.
Click Data | Select Cases.

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Click on the All cases radio button.
Click OK.
End of Exercise 15

Exercise 16: To exit SPSS
Click File | Exit.
SPSS will ask you if you want to save the contents of your Output Viewer and Data
Editor.
End of Exercise 16

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4) ANALYSING YOUR OWN DATA IN SPSS FOR WINDOWS
Exercise 17: To check that your data have been entered correctly
Make sure that you have loaded SPSS (Exercise 1).
Load your data file (Exercise 2, or Exercise 3 for an Excel file).
Construct a frequency distribution (frequencies) for each variable and check it in
the output window (Exercise 5). Provided you have labelled all valid value labels,
any values without labels will be errors! NB: if you have loaded an Excel file there
will be no value labels.
Construct tables (crosstabs) to discover if data have been entered for questions
where the respondent should have not responded (Exercise 9); perhaps due to a
skip generated by a filter question.
Compute new variables to make sure there are no foolish responses (Exercise 13)
such as employees aged over 65.
For each error note down the id number which corresponds to the survey form by
using:
¨ Select Cases (Exercise 14) to only select those cases which contain the error.

¨ Crosstabs (Exercise 9) to construct a table of the variable identifier by the
variable which contains the error.
Correct your data as described in Help 17.1 to 17.4.

Help 17.1: to replace a data value
Make sure that you have loaded SPSS and that the data file has already been opened.
Click the cell that contains the data value.
Enter the new value (this will replace the old value).

Press Return. The new value appears in the cell.

Help 17.2: To delete all data values for a variable (or case)
Note: before deleting an entire variable (or case), it is worth saving the data file (Exercise
10) in case you make a mistake.
Highlight all the values for that variable (or row) but not the variable name.

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Press Delete. Each value will be replaced by "." signifying a "missing value".

Note: if you make a mistake, you can rectify it immediately afterwards by clicking Edit |
Undo.

Help 17.3: To delete a variable (or case)
Click the variable name (or case number) to highlight the entire column (or case).

Press Delete. The variable (or case) will be deleted.

Note: if you make a mistake, you can rectify it immediately afterwards by clicking Edit |
Undo.

Help 17.4: To enter missing values
Missing values in a cell are signified by a "." as illustrated in Help 17.2.To enter a missing
value in a blank cell do not type "."
Click on cell in which to enter that the data is missing.

Press Tab to move one cell to the right.
Type in the next value.

Press Tab to move one cell to the right and so on.
To enter a missing value in a cell which already has a value it:
Click on cell which currently contains the data.

Press Delete.

Press Tab to move one cell to the right.
Exercise 18: To analyse data

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Note the research questions you wish to answer.
Choose the most appropriate statistical and charting techniques and SPSS
procedures.
Use the SPSS procedures to analyse the data (see Help 18.1 to 18.3 for additional
procedures).

Help 18.1: To test for a significant relationship between two variables (correlation)
Make sure you have loaded SPSS and that the data file has already been opened.
Click Analyze | Correlate | Bivariate.
Click on the first variable for
you wish to obtain a
correlation coefficient with
another variable.

variable into the Variables
box.
Repeat this procedure for the
other variable(s) you wish to
correlate with the first
variable.
Choose the most appropriate
Correlation Coefficient for your
data and make sure there is a
in the box.
Choose the most appropriate Test of Significance and click on the radio button.
Note: use a Two‐tailed test when the direction of the relationship, positive or negative,
cannot specified in advance. Use a One‐tailed test when it can be specified in advance.
Make sure there is a in the box Flag significant correlations. This will ensure the
significance level is displayed.
Click OK.
The results of the correlation will appear in the Output Window.

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In this output window,
the variables Salary and
Born from the
TEACH.SAV data set
have been correlated.
SPSS has produced a
correlation matrix.
Obviously there is a
perfect correlation
(1.0000) between the
variable initial annual
Salary and itself.
As the variable is correlated with itself it is impossible to calculate the significance (p = .
). There is no correlation (‐0.011) between the variable Salary and the variable Born and
this lack of correlation is significant at the 0.841 (p = 0.841) level.

Help 18.2: To test for a significant causal relationship between one dependent and one
or more independent variables (linear regression)
Make sure you have loaded SPSS and that the data file has already been opened.
Check that your data are appropriate for regression analysis.
Click Analyze | Regression | Linear.
Click on the
dependent
variable
which you
wish to
predict
using
another
variable or
variables.

Click to
transfer the
variable into
the
Dependent
box.

Repeat this procedure to transfer the independent variable(s) you wish to use
to predict the dependent variable into the Independent(s) box.
Click OK.

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Warning: interpreting the regression output is comparatively complicated. You need to
understand the regression coefficient (r2) and the regression equation (y = a + bx). The
SPSS manual (Norusis, 1992) explains these in some detail. A simpler explanation of
regression with one independent variable is provided in Section 2, Unit 18 of Saunders
and Cooper (1993).

Help 18.3: To use other statistical tests
Given the introductory nature of this hand out, and the need for a reasonable statistical
knowledge to make informed decisions about the use of statistical tests, the procedures
for other statistical tests are not discussed.

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5) FURTHER READING
The most useful book on SPSS in my opinion is:
q Norusis, M.J. (2007). SPSS 15 Guide to Data Analysis. London: Prentice Hall.
Unlike many computer manuals this is both readable and easy to use! It also contains
advice regarding when to use different statistical tests. However, at the time of writing,
the update of the book for version 10 has yet to be written.
Two good books on SPSS for beginners, which also clearly explain the statistics are:
q Field, A. (2009). Discovering Statistics Using SPSS (3rd Edn). London: Sage.
q Pallant, J. (2007). SPSS Survival Manual: A Step by Step Guide to Data Analysis Using
SPSS for Windows (Version 15). Buckingham: OUP.
These books offer a clear, non‐technical approach to using SPSS. They assume little
familiarity with the data analysis software and cover both inputting data and how to
generate and interpret a wide range of tables, diagrams and statistics.
A reasonably straightforward book on collecting your data and preparing it for
quantitative analysis is:
q Saunders, M.N.K., Lewis, P. and Thornhill, A. (2009). Research Methods for Business
Students (5th Edn). London: Financial Times Prentice Hall, Chapters 10 and 11.
If you need a statistics book that assumes virtually no statistical knowledge focussing
upon which test or graph, when to use it and why. It is written for people who are
fearful and anxious about statistics and do not think they can understand numbers then
you may find the following helpful:
q Berman Brown, R. and Saunders, M. (2008). Dealing with Statistics: What you need to
know. Maidenhead: McGraw Hill Open University Press.

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6) APPENDIX 1: LIST OF VARIABLES 1) Local Government
AND THEIR CODES FOR DATA SET 2) Outside Local Government
TEACH.SAV 3) Student
Variable names are in capitals with the 4) Unemployed
variable label on the same line. Codes and 5) Self‐employed
value labels appear on subsequent lines.
6) Youth Training Scheme
GENDER
7) Retired
Gender of Employee
PREVEAST
1) Male
Town of previous employment Eastings
2) Female
PREVNOR
BORN
Town of previous employment Northings
Year of Birth
APPLEAST
19 – year
Home Town when applied Eastings
MARITAL
APPLNOR
Marital Status
Home Town when Applied Northings
1) Single
OCCUPAT
2) Married
Occupation (OPCS 1980 Classification)
3) Widowed
1.00 Solicitor
4) Divorced
2.10 Auditor
EDUCATE
2.20 Accountant
Educational Attainment
2.50 Valuer
1) Postgraduate Study
3.10 Personnel Officer
2) Up to Degree Level
3.20 Work Study Officer
3) Up to HNC/D or Diploma
4.20 Computer Programmer
4) Up to A level
5.20 Advertising Executive
5) Up to O level
6.10 EH Officer
6) Up to CSE
6.20 Building Inspector
7) No educational qualifications stated
8.00 Admin Executive
PROFMEMB
9.50 Legal Executive
Professional Body Membership
9.80 Curator (Museum)
1) Member of Professional Body
13.10 Warden (OAP)
2) Not a member of a Professional Body
13.20 Play Group Leader
PREVEMP
13.30 Welfare Occupations n.e.c.
Nature of Previous Employment
18.20 Vet

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22.20 Projectionist 62.30 Art Gallary Attendant
25.00 Municipal Engineer 63.30 Supervisor Bar
31.10 Architect/Town Planner 63.40 Supervisor Catering
31.20 Quantity Surveyor 65.20 Bar person
31.30 Building Surveyor 66.10 Counter Hand
33.10 Architect/Town Planner Technician 71.10 Supervisor Caretakers
33.20 Building/Engineering Technician 71.40 Supervisor Car Parks
33.40 Works Manager 72.10 Caretaker
35.10 Maintenance Supervisor 72.20 Cleaner
35.20 Clerk of Works 75.20 Car Park Attendant
36.20 Transport Manager 75.60 Service Worker n.e.c.
36.30 Stores Controller 76.30 Foreman Gardeners
37.20 Office Manager 78.10 Horticulture Workers
39.50 Entertainments/Sports Manager 78.20 Gardener
44.10 Caravan Site Manager 83.00 Dog Warden
44.40 Managers n.e.c. 100.30 Print Machine Operator
45.20 Supervisor Stores Clerks 105.10 Carpenter
45.30 Supervisor Drawing Assistants 109.30 Black Smith
45.40 Supervisor Clerks 111.00 Management Trainee
45.50 Supervisor Cashiers 114.50 Foreman Fitters
46.10 Clerk Stores 118.10 Fitter
46.20 Tracer Assistant 125.00 Plumber
46.30 Clerk Non‐retail 133.40 Painter and Decorator
47.00 Cashier Retail 139.10 Foreman Bricklayers
48.20 Supervisor Machine Operators
139.11 Foreman Sewage Workers
49.10 Receptionist 139.12 Foreman Construction Workers
49.20 Typist 139.80 Highways Inspector
50.00 Punch Card Operator 140.10 Bricklayer
51.10 Telephone Receptionist 140.50 Building Worker
51.20 Switch Board Operator 142.10 Seage Plant Attendant
56.00 Meals on Wheels Operator 152.20 Refuge Vehicle Driver
60.40 Estate Ranger 152.30 Road Sweeper Driver
60.60 Supervisor Security 156.10 Foreman Storekeeper
62.10 Park Keeper 156.40 Foreman Refuse Collection

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157.10 Store Keeper EMPLEAST
157.40 Refuse Collector Town of Employment Eastings
159.80 Foreman Labourers EMPLNOR

160.80 Labourer n.e.c. Town of Employment (Northings)
EMPSTAT HOMEEAST

Employment Status on application Home Town at start of Employment
(Eastings)
1) Manager
HOMENOR
2) Foreman/Supervisor
Home Town at start of Employment
3) Apprentice/Trainee (Northings)
4) Employee n.e.c. NOTIFY
SEG 1) Notification Outlet 1
Socio‐economic Group 2) Internal
1.2 Managers in Government and
3) Word of Mouth
Industry
4) Letter of Enquiry
4.0 Professional Employees
5) Circular to adjacent Local Authorities
5.1 Ancillary Workers
6) Job Centre
5.2 Foremen/Supervisors (non manual)
6.0 Junior Non manual 7) PER (Professional Executive Register)

7.0 Personal Services 8) Employment Agency (Private Sector)

8.0 Foremen/Supervisors (manual) 9) Careers Office (local)
DAILY LOCAL NEWSPAPERS
9.0 Skilled Manual
10.0 Semi skilled Manual 10) Evening Post
WEEKLY LOCAL NEWSPAPERS
11.0 Unskilled Manual
11) Two County Courier
15.0 Agricultural Workers
12) Local Town Chronicle
CLASS
Social Class 13) County Messenger

1) Professional 14) County Express

2) Intermediate Non‐manual 15) Local Town News

3) Junior Non‐manual 16) East County Gazette
FREE WEEKLY LOCAL NEWSPAPERS
4) Skilled Manual
17) Local Town News in Focus
5) Semi‐skilled Manual
18) News in Focus
6) Unskilled Manual
19) Local Town Times
SALARY
Initial Annual Salary (Spinal Column Point) 20) Local Town Times and Gazette
NATIONAL DAILY NEWSPAPERS

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21) Daily Mail 50) Municipal Journal*
22) Daily Telegraph 51) Public Service and Local
23) The Times Government*

24) The Guardian 52) Municipal Engineer*

NATIONAL SUNDAY NEWSPAPERS 53) Baths Service Circular

25) Sunday Times 54) Caterer and Hotel Supervisor

JOURNALS AND MAGAZINES (*PUBLIC 55) Accountancy Age
SERVICE) 56) Management (Work Study) Services
26) Commercial Motor 57) Quantity Surveyors Weekly
27) Horticulture and Amenities Weekly 58) Surveyor
28) Army Appointments Service 59) Architects Journal
Magazine
60) The Planner
29) Church Times
61) Building
30) Parks and Recreation
62) Planning
31) Golf Illustrated
63) Law Society Gazette
32) Groundsman
64) Meat Trades Journal
33) Health and Social Services Journal
65) Protection
34) Lady Magazine
66) Institute of Personnel Managers
35) Public Finance and Accounting* Digest
36) Institute of Management Science 67) New Civel Engineer
Journal
68) Veterinary Record
37) UK Press Gazette
69) Current Vacancies
38) Building Trades Journal
70) The Stage
39) Gardeners Chronicle
71) Regional Arts Magazine
40) Solicitors Journal
72) Health and Safety at Work
41) Nursing Times
NOTIFY2
42) Nurising Mirror
Notification Outlet as for NOTIFY1
43) Policy Holder Insurance Journal
NOTIFY3
44) Opportunities*
Notification Outlet as for NOTIFY1
45) Estates Gazette
NOTIFY4
46) Clerk of Works Weekly Notification Outlet as for NOTIFY1
47) Computing NOTIFY5
48) Association of Recreation Managers Notification Outlet as for NOTIFY1
Appointments
NOTIFY6
49) Local Government Chronicle*

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Notification Outlet as for NOTIFY1 10) Death
NOTIFY7 11) Illness
Notification Outlet as for NOTIFY1 12) Marriage
NOTIFY8 13) Family Commitments
Notification Outlet as for NOTIFY1 14) First Job Commitments
NOTIFY9 15) Leaving the Country
Notification Outlet as for NOTIFY1 16) Travel Costs
NOTIFY10 17) Fixed Term Contract
Notification Outlet as for NOTIFY1 18) Other
YEAREMP FULLPART
Year of Employment Full or Part‐time Employment
19‐‐ Year of Employment 1) Part Time
ASSISTAC
2) Part Time (second job)
Assistance by Accommodation Provision 3) Full time
1) Permanent Accommodation Provided HEARDN1
2) Temporary Accommodation Provided NOTIFY1 is how first heard of vacancy
3) No assistance given 1) Yes
ASSISTMO
2) No
Assistance with Moving Expenses The following additional variables have been
1) Financial Assistance Given created using SPSS

2) No Assistance Given INTERNAL

TERMYEAR Notified internally
Year of Termination of Employment 1) used
19 – Year of Termination 2) not used
TERMWHY CIRCULAR

1) Reason for Termination Notified by circulating other Local
Authorities
2) New Job
1) used
3) Spouse's New Job
2) not used
4) Going to College
JOBCENTR
5) Having a Baby
Notified at Job Centre
6) Early Retirement
1) used
7) Retirement
2) not used
8) Redundancy
LOCAL
9) Dismissed
Notified in Local Press

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1) used 1) used
2) not used 2) not used
REGIONAL DISTMIG
Notified in Regional Media Distance Migrated (km)
1) used ‐‐ straightline distance migrated
2) not used JTOWORK
NATIONAL Journey to work (km) – new job
Notified in National Media – straightline distance of journey

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page:

Full time/part time
Reason for termination (9)
Year of termination
Assistance (8)
Year of Employment
DATA COLLECTION SHEET FOR PERSONNEL RECORDS

Notification outlet(s) (7)
Home town – new (4)

April 2009
New Employment – location (4)
Spinal Column point (6)
Occupation (5)
Home town – applied (4)
Previous Employment –location (4)
Previous Employment ‐nature (3)
Professional membership (2)
Education (1)
Marital status
YoB

© Prof. Mark N.K. Saunders
Name of organisation:

Gender
ID

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Name of organisation:

7) NOTES ON DATA COLLECTION
1) Recorded as highest qualification achieved.
2) Recorded on a yes/no basis.
3) Coded at collection (1: Local Government, 3: Outside Local Government, 4: Student,
5: Unemployed, 6: SelfeEmployed; additional codes added as necessary during
collection).
4) Recorded as place name, subsequently coded as a grid reference (Eastings and
Northings).
5) Recorded as actual job; subsequently coded into occupation, employment status,
socio‐economic group and social class using existing UK government classification
(see pages 333–4 of Research Methods for Business Students, 3rd Edn for details of
UK government classifications).
6) Recorded as spinal column point to overcome the impact of inflation on salaries.
7) All outlets recorded, where outlet actually heard about job known, indicated by *.

8) Provision of accommodation and/or moving expenses recorded.
9) Recorded as actual reason, subsequently coded.

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